forecasting system gfs: Topics by Science.gov

Sample records for forecasting system gfs

Regional Model Nesting Within GFS Daily Forecasts Over West Africa

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

2010-01-01

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.
GFS Products

Science.gov Websites

Inventory Image of horizontal rule Global Products Updated: 7/28/2017 Global Forecast System (GFS) Model Global Data Assimilation System (GDAS) Model * Information about the GFS * Information about the GFS Name GFS GFS - Global longitude-latitude grid WCOSS File Name Inventory 0.25 degree resolution
Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS)

DTIC Science & Technology

2016-09-01

Laboratory Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS) by JL Cogan...analysis. As expected, accuracy generally tended to decline as the large-scale data aged , but appeared to improve slightly as the age of the large...19 Table 7 Minimum and maximum mean RMDs for each WRF time (or GFS data age ) category. Minimum and
NCEP Data Products

Science.gov Websites

Image of NCEP Logo WHERE AMERICA'S CLIMATE AND WEATHER SERVICES BEGIN Inventory of Data Products on Generated Products Image of horizontal rule Global Forecast System (GFS) GFS Ensemble Forecast System (GEFS of horizontal rule External Products Image of horizontal rule Canadian Ensemble Forecast System
A Preliminary Evaluation of the GFS Physics in the Navy Global Environmental Model

NASA Astrophysics Data System (ADS)

Liu, M.; Langland, R.; Martini, M.; Viner, K.

2017-12-01

Global extended long-range weather forecast is a goal in the near future at Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). In an effort to improve the performance of the Navy Global Environmental Model (NAVGEM) operated at FNMOC, and to gain more understanding of the impact of atmospheric physics in the long-range forecast, the physics package of the Global Forecast System (GFS) of the National Centers for Environmental Prediction is being evaluated in the framework of NAVGEM. That is GFS physics being transported by NAVGEM Semi-Lagrangian Semi-Implicit advection, and update-cycled by the 4D-variational data assimilation along with the assimilated land surface data of NASA's Land Information System. The output of free long runs of 10-day GFS physics forecast in a summer and a winter season are evaluated through the comparisons with the output of NAVGEM physics long forecast, and through the validations with observations and with the European Center's analyses data. It is found that the GFS physics is able to effectively reduce some of the modeling biases of NAVGEM, especially wind speed of the troposphere and land surface temperature that is an important surface boundary condition. The bias corrections increase with forecast leads, reaching maximum at 240 hours. To further understand the relative roles of physics and dynamics in extended long-range forecast, the tendencies of physics components and advection are also calculated and analyzed to compare their forces of magnitudes in the integration of winds, temperature, and moisture. The comparisons reveal the strength and limitation of GFS physics in the overall improvement of NAVGEM prediction system.
Prediction of Winter Storm Tracks and Intensities Using the GFDL fvGFS Model

NASA Astrophysics Data System (ADS)

Rees, S.; Boaggio, K.; Marchok, T.; Morin, M.; Lin, S. J.

2017-12-01

The GFDL Finite-Volume Cubed-Sphere Dynamical core (FV3) is coupled to a modified version of the Global Forecast System (GFS) physics and initial conditions, to form the fvGFS model. This model is similar to the one being implemented as the next-generation operational weather model for the NWS, which is also FV3-powered. Much work has been done to verify fvGFS tropical cyclone prediction, but little has been done to verify winter storm prediction. These costly and dangerous storms impact parts of the U.S. every year. To verify winter storms we ran the NCEP operational cyclone tracker, developed at GFDL, on semi-real-time 13 km horizontal resolution fvGFS forecasts. We have found that fvGFS compares well to the operational GFS in storm track and intensity, though often predicts slightly higher intensities. This presentation will show the track and intensity verification from the past two winter seasons and explore possible reasons for bias.
Using Analysis Increments (AI) to Estimate and Correct Systematic Errors in the Global Forecast System (GFS) Online

NASA Astrophysics Data System (ADS)

Bhargava, K.; Kalnay, E.; Carton, J.; Yang, F.

2017-12-01

Systematic forecast errors, arising from model deficiencies, form a significant portion of the total forecast error in weather prediction models like the Global Forecast System (GFS). While much effort has been expended to improve models, substantial model error remains. The aim here is to (i) estimate the model deficiencies in the GFS that lead to systematic forecast errors, (ii) implement an online correction (i.e., within the model) scheme to correct GFS following the methodology of Danforth et al. [2007] and Danforth and Kalnay [2008, GRL]. Analysis Increments represent the corrections that new observations make on, in this case, the 6-hr forecast in the analysis cycle. Model bias corrections are estimated from the time average of the analysis increments divided by 6-hr, assuming that initial model errors grow linearly and first ignoring the impact of observation bias. During 2012-2016, seasonal means of the 6-hr model bias are generally robust despite changes in model resolution and data assimilation systems, and their broad continental scales explain their insensitivity to model resolution. The daily bias dominates the sub-monthly analysis increments and consists primarily of diurnal and semidiurnal components, also requiring a low dimensional correction. Analysis increments in 2015 and 2016 are reduced over oceans, which is attributed to improvements in the specification of the SSTs. These results encourage application of online correction, as suggested by Danforth and Kalnay, for mean, seasonal and diurnal and semidiurnal model biases in GFS to reduce both systematic and random errors. As the error growth in the short-term is still linear, estimated model bias corrections can be added as a forcing term in the model tendency equation to correct online. Preliminary experiments with GFS, correcting temperature and specific humidity online show reduction in model bias in 6-hr forecast. This approach can then be used to guide and optimize the design of sub-grid scale physical parameterizations, more accurate discretization of the model dynamics, boundary conditions, radiative transfer codes, and other potential model improvements which can then replace the empirical correction scheme. The analysis increments also provide guidance in testing new physical parameterizations.
Improving Subtropical Boundary Layer Cloudiness in the 2011 NCEP GFS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fletcher, J. K.; Bretherton, Christopher S.; Xiao, Heng

2014-09-23

The current operational version of National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) shows significant low cloud bias. These biases also appear in the Coupled Forecast System (CFS), which is developed from the GFS. These low cloud biases degrade seasonal and longer climate forecasts, particularly of short-wave cloud radiative forcing, and affect predicted sea surface temperature. Reducing this bias in the GFS will aid the development of future CFS versions and contributes to NCEP's goal of unified weather and climate modelling. Changes are made to the shallow convection and planetary boundary layer parameterisations to make them more consistentmore » with current knowledge of these processes and to reduce the low cloud bias. These changes are tested in a single-column version of GFS and in global simulations with GFS coupled to a dynamical ocean model. In the single-column model, we focus on changing parameters that set the following: the strength of shallow cumulus lateral entrainment, the conversion of updraught liquid water to precipitation and grid-scale condensate, shallow cumulus cloud top, and the effect of shallow convection in stratocumulus environments. Results show that these changes improve the single-column simulations when compared to large eddy simulations, in particular through decreasing the precipitation efficiency of boundary layer clouds. These changes, combined with a few other model improvements, also reduce boundary layer cloud and albedo biases in global coupled simulations.« less
A study comparison of two system model performance in estimated lifted index over Indonesia.

NASA Astrophysics Data System (ADS)

lestari, Juliana tri; Wandala, Agie

2018-05-01

Lifted index (LI) is one of atmospheric stability indices that used for thunderstorm forecasting. Numerical weather Prediction Models are essential for accurate weather forecast these day. This study has completed the attempt to compare the two NWP models these are Weather Research Forecasting (WRF) model and Global Forecasting System (GFS) model in estimates LI at 20 locations over Indonesia and verified the result with observation. Taylor diagram was used to comparing the models skill with shown the value of standard deviation, coefficient correlation and Root mean square error (RMSE). This study using the dataset on 00.00 UTC and 12.00 UTC during mid-March to Mid-April 2017. From the sample of LI distributions, both models have a tendency to overestimated LI value in almost all region in Indonesia while the WRF models has the better ability to catch the LI pattern distribution with observation than GFS model has. The verification result shows how both WRF and GFS model have such a weak relationship with observation except Eltari meteorologi station that its coefficient correlation reach almost 0.6 with the low RMSE value. Mean while WRF model have a better performance than GFS model. This study suggest that estimated LI of WRF model can provide the good performance for Thunderstorm forecasting over Indonesia in the future. However unsufficient relation between output models and observation in the certain location need a further investigation.
Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.
The Implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for Global Dust Forecasting at NOAA NCEP

NASA Technical Reports Server (NTRS)

Lu, Cheng-Hsuan; Da Silva, Arlindo M.; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S.; Chen, Shen-Po; Chuang, Hui-Ya;

2016-01-01

The NOAA National Centers for Environmental Prediction (NCEP) implemented the NOAA Environmental Modeling System (NEMS) Global Forecast System (GFS) Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5-day dust forecasts at 1deg x 1deg resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders, as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.

Thirty Years of Improving the NCEP Global Forecast System

NASA Astrophysics Data System (ADS)

White, G. H.; Manikin, G.; Yang, F.

2014-12-01

Current eight day forecasts by the NCEP Global Forecast System are as accurate as five day forecasts 30 years ago. This revolution in weather forecasting reflects increases in computer power, improvements in the assimilation of observations, especially satellite data, improvements in model physics, improvements in observations and international cooperation and competition. One important component has been and is the diagnosis, evaluation and reduction of systematic errors. The effect of proposed improvements in the GFS on systematic errors is one component of the thorough testing of such improvements by the Global Climate and Weather Modeling Branch. Examples of reductions in systematic errors in zonal mean temperatures and winds and other fields will be presented. One challenge in evaluating systematic errors is uncertainty in what reality is. Model initial states can be regarded as the best overall depiction of the atmosphere, but can be misleading in areas of few observations or for fields not well observed such as humidity or precipitation over the oceans. Verification of model physics is particularly difficult. The Environmental Modeling Center emphasizes the evaluation of systematic biases against observations. Recently EMC has placed greater emphasis on synoptic evaluation and on precipitation, 2-meter temperatures and dew points and 10 meter winds. A weekly EMC map discussion reviews the performance of many models over the United States and has helped diagnose and alleviate significant systematic errors in the GFS, including a near surface summertime evening cold wet bias over the eastern US and a multi-week period when the GFS persistently developed bogus tropical storms off Central America. The GFS exhibits a wet bias for light rain and a dry bias for moderate to heavy rain over the continental United States. Significant changes to the GFS are scheduled to be implemented in the fall of 2014. These include higher resolution, improved physics and improvements to the assimilation. These changes significantly improve the tropospheric flow and reduce a tropical upper tropospheric warm bias. One important error remaining is the failure of the GFS to maintain deep convection over Indonesia and in the tropical west Pacific. This and other current systematic errors will be presented.
Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

NASA Astrophysics Data System (ADS)

Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

2012-04-01

Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture and other land observations into GFS will also be discussed.
Testing a Coupled Global-limited-area Data Assimilation System using Observations from the 2004 Pacific Typhoon Season

NASA Astrophysics Data System (ADS)

Holt, C. R.; Szunyogh, I.; Gyarmati, G.; Hoffman, R. N.; Leidner, M.

2011-12-01

Tropical cyclone (TC) track and intensity forecasts have improved in recent years due to increased model resolution, improved data assimilation, and the rapid increase in the number of routinely assimilated observations over oceans. The data assimilation approach that has received the most attention in recent years is Ensemble Kalman Filtering (EnKF). The most attractive feature of the EnKF is that it uses a fully flow-dependent estimate of the error statistics, which can have important benefits for the analysis of rapidly developing TCs. We implement the Local Ensemble Transform Kalman Filter algorithm, a vari- ation of the EnKF, on a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the NCEP Regional Spectral Model (RSM) to build a coupled global-limited area anal- ysis/forecast system. This is the first time, to our knowledge, that such a system is used for the analysis and forecast of tropical cyclones. We use data from summer 2004 to study eight tropical cyclones in the Northwest Pacific. The benchmark data sets that we use to assess the performance of our system are the NCEP Reanalysis and the NCEP Operational GFS analyses from 2004. These benchmark analyses were both obtained by the Statistical Spectral Interpolation, which was the operational data assimilation system of NCEP in 2004. The GFS Operational analysis assimilated a large number of satellite radiance observations in addition to the observations assimilated in our system. All analyses are verified against the Joint Typhoon Warning Center Best Track data set. The errors are calculated for the position and intensity of the TCs. The global component of the ensemble-based system shows improvement in po- sition analysis over the NCEP Reanalysis, but shows no significant difference from the NCEP operational analysis for most of the storm tracks. The regional com- ponent of our system improves position analysis over all the global analyses. The intensity analyses, measured by the minimum sea level pressure, are of similar quality in all of the analyses. Regional deterministic forecasts started from our analyses are generally not significantly different from those started from the GFS operational analysis. On average, the regional experiments performed better for longer than 48 h sea level pressure forecasts, while the global forecast performed better in predicting the position for longer than 48 h.
The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP

PubMed Central

Lu, Cheng-Hsuan; da Silva, Arlindo; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S.; Chen, Shen-Po; Chuang, Hui-Ya; Juang, Hann-Ming Henry; McQueen, Jeffery; Iredell, Mark

2018-01-01

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5 day dust forecasts at 1°×1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered. PMID:29652411
The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP.

PubMed

Lu, Cheng-Hsuan; da Silva, Arlindo; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S; Chen, Shen-Po; Chuang, Hui-Ya; Juang, Hann-Ming Henry; McQueen, Jeffery; Iredell, Mark

2016-01-01

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5 day dust forecasts at 1°×1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.
Simulation of the Summer Monsoon Rainfall over East Asia using the NCEP GFS Cumulus Parameterization at Different Horizontal Resolutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Kyo-Sun; Hong, Song You; Yoon, Jin-Ho

2014-10-01

The most recent version of Simplified Arakawa-Schubert (SAS) cumulus scheme in National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) (GFS SAS) has been implemented into the Weather and Research Forecasting (WRF) model with a modification of triggering condition and convective mass flux to become depending on model’s horizontal grid spacing. East Asian Summer Monsoon of 2006 from June to August is selected to evaluate the performance of the modified GFS SAS scheme. Simulated monsoon rainfall with the modified GFS SAS scheme shows better agreement with observation compared to the original GFS SAS scheme. The original GFS SAS schememore » simulates the similar ratio of subgrid-scale precipitation, which is calculated from a cumulus scheme, against total precipitation regardless of model’s horizontal grid spacing. This is counter-intuitive because the portion of resolved clouds in a grid box should be increased as the model grid spacing decreases. This counter-intuitive behavior of the original GFS SAS scheme is alleviated by the modified GFS SAS scheme. Further, three different cumulus schemes (Grell and Freitas, Kain and Fritsch, and Betts-Miller-Janjic) are chosen to investigate the role of a horizontal resolution on simulated monsoon rainfall. The performance of high-resolution modeling is not always enhanced as the spatial resolution becomes higher. Even though improvement of probability density function of rain rate and long wave fluxes by the higher-resolution simulation is robust regardless of a choice of cumulus parameterization scheme, the overall skill score of surface rainfall is not monotonically increasing with spatial resolution.« less
National Centers for Environmental Prediction

Science.gov Websites

Modeling Center continuously monitors its NWP model performance against different performance measures, and AIRCFT GFS SSI and forecast fits to RAOBS for last 7 days spatial bias maps for different regions different regions GFS SSI and forecast fits to RAOBS for calendar months (time series, spatial and vertical
MODEL CHANGES SINCE 1991

Science.gov Websites

, effects of balloon drift in time and space included Forecast and post processing: Improved orography minor changes: Observations and analysis: Higher resolution sea ice mask Forecast and post processing . 12/04/07 12Z: Use of Unified Post Processor in GFS 12/04/07 12Z: GFS Ensemble (NAEFS/TIGGE) UPGRADE
Tropical-Cyclone Formation: Theory and Idealized Modelling

DTIC Science & Technology

2010-11-01

to saturation at the sea-surface temperature and the positive entropy flux from the ocean surface...and Atmospheric Administration; IFEX = Intensity Forecasting Experiment. 15GFS = NOAA Global Forecasting System ; NOGAPS = Navy Operational Global... Atmospheric Prediction System ; UKMET = United Kingdom Meteorological Office. 16 http://www.met.nps.edu/~mtmontgo/storms2010.html 18 overcomes

Evaluation of Causes of Large 96-H and 120-H Track Errors in the Western North Pacific

DTIC Science & Technology

2006-06-01

Interpolated GFS (A) forecast track for 11W ( Mawar ) for the 0600 UTC 22 August 2005 E-DCI-m case study. The solid sections of the forecast tracks...fields for 11W ( Mawar ) predicted by GFS for taus (a) 54 and (c) 66 for 0600 UTC 22 August 2005 and the corresponding verifying 00-h NOGAPS analyses...pressure (mb) fields for 11W ( Mawar ) predicted by GFS for taus (a) 90 and (c) 114 for 0600 UTC 22 August 2005 and the corresponding verifying 00-h
Potential Technologies for Assessing Risk Associated with a Mesoscale Forecast

DTIC Science & Technology

2015-10-01

American GFS models, and informally applied on the Weather Research and Forecasting ( WRF ) model. The current CI equation is as follows...Reen B, Penc R. Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) model using a Geographic Information System (GIS). J...Forecast model ( WRF -ARW) with extensions that might include finer terrain resolutions and more detailed representations of the underlying atmospheric
Evaluation of snow modeling with Noah and Noah-MP land surface models in NCEP GFS/CFS system

NASA Astrophysics Data System (ADS)

Dong, J.; Ek, M. B.; Wei, H.; Meng, J.

2017-12-01

Land surface serves as lower boundary forcing in global forecast system (GFS) and climate forecast system (CFS), simulating interactions between land and the atmosphere. Understanding the underlying land model physics is a key to improving weather and seasonal prediction skills. With the upgrades in land model physics (e.g., release of newer versions of a land model), different land initializations, changes in parameterization schemes used in the land model (e.g., land physical parametrization options), and how the land impact is handled (e.g., physics ensemble approach), it always prompts the necessity that climate prediction experiments need to be re-conducted to examine its impact. The current NASA LIS (version 7) integrates NOAA operational land surface and hydrological models (NCEP's Noah, versions from 2.7.1 to 3.6 and the future Noah-MP), high-resolution satellite and observational data, and land DA tools. The newer versions of the Noah LSM used in operational models have a variety of enhancements compared to older versions, where the Noah-MP allows for different physics parameterization options and the choice could have large impact on physical processes underlying seasonal predictions. These impacts need to be reexamined before implemented into NCEP operational systems. A set of offline numerical experiments driven by the GFS forecast forcing have been conducted to evaluate the impact of snow modeling with daily Global Historical Climatology Network (GHCN).
Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

2018-01-01

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.
Global Turbulence Decision Support for Aviation

NASA Astrophysics Data System (ADS)

Williams, J.; Sharman, R.; Kessinger, C.; Feltz, W.; Wimmers, A.

2009-09-01

Turbulence is widely recognized as the leading cause of injuries to flight attendants and passengers on commercial air carriers, yet legacy decision support products such as SIGMETs and SIGWX charts provide relatively low spatial- and temporal-resolution assessments and forecasts of turbulence, with limited usefulness for strategic planning and tactical turbulence avoidance. A new effort is underway to develop an automated, rapid-update, gridded global turbulence diagnosis and forecast system that addresses upper-level clear-air turbulence, mountain-wave turbulence, and convectively-induced turbulence. This NASA-funded effort, modeled on the U.S. Federal Aviation Administration's Graphical Turbulence Guidance (GTG) and GTG Nowcast systems, employs NCEP Global Forecast System (GFS) model output and data from NASA and operational satellites to produce quantitative turbulence nowcasts and forecasts. A convective nowcast element based on GFS forecasts and satellite data provides a basis for diagnosing convective turbulence. An operational prototype "Global GTG” system has been running in real-time at the U.S. National Center for Atmospheric Research since the spring of 2009. Initial verification based on data from TRMM, Cloudsat and MODIS (for the convection nowcasting) and AIREPs and AMDAR data (for turbulence) are presented. This product aims to provide the "single authoritative source” for global turbulence information for the U.S. Next Generation Air Transportation System.
Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation

NASA Technical Reports Server (NTRS)

Lien, Guo-Yuan; Kalnay, Eugenia; Miyoshi, Takemasa; Huffman, George J.

2016-01-01

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFSTMPA precipitation assimilation experiments presented in the companion paper.The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially andor temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.
National Centers for Environmental Prediction

Science.gov Websites

---------------------------------------------------------------------------------------------------------------------------------------------------- IITM CFS v2 Forecast for 2017 monsoon : link Experimental short-range GEFS ensemble forecast : link Experimental short-range GFS-T1534(upto 8days) forecast : link
Real-time Extremely Heavy Rainfall Forecast and Warning over Rajasthan During the Monsoon Season (2016)

NASA Astrophysics Data System (ADS)

Srivastava, Kuldeep; Pradhan, D.

2018-01-01

Two events of extremely heavy rainfall occurred over Rajasthan during 7-9 August 2016 and 19-21 August 2016. Due to these events, flooding occurred over east Rajasthan and affected the normal life of people. A low-pressure area lying over northwest Madhya Pradesh on 7 August 2016 moved north-westward and lay near east Rajasthan and adjoining northwest Madhya Pradesh on 8 and 9 August 2016. Under the influence of this low-pressure system, Chittorgarh district and adjoining areas of Rajasthan received extremely heavy rainfall of 23 cm till 0300 UTC of 8 August 2016 and 34 cm on 0300 UTC of 9 August 2016. A deep depression lying over extreme south Uttar Pradesh and adjoining northeast Madhya Pradesh on 19 August 2016 moved westward and gradually weakened into a depression on 20 August 2016. It further weakened into a low-pressure area and lay over east Rajasthan on 21 and 22 August 2016. Under the influence of this deep depression, Jhalawar received 31 cm and Baran received 25 cm on 19 August. On 20 August 2016, extremely heavy rainfall (EHR) occurred over Banswara (23.5 cm), Pratapgarh (23.2 cm) and Chittorgarh (22.7 cm) districts. In this paper, the performance of the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) model for real-time forecast and warning of heavy to very heavy/EHR that occurred over Rajasthan during 7-9 August 2016 and 19-21 August 2016 has been examined. The NCEP GFS forecast rainfall (Day 1, Day 2 and Day 3) was compared with the corresponding observed gridded rainfall. Based on the predictions given by the NCEP GFS model for heavy rainfall and with their application in real-time rainfall forecast and warnings issued by the Regional Weather Forecasting Center in New Delhi, it is concluded that the model has predicted the wind pattern and EHR event associated with the low-pressure system very accurately on day 1 and day 2 forecasts and with small errors in intensity and space for day 3.
A Prototype Nonhydrostatic Regional-to-Global Nested-Grid Atmosphere Model for Medium-range Weather Forecasting

NASA Astrophysics Data System (ADS)

Harris, L.; Lin, S. J.; Zhou, L.; Chen, J. H.; Benson, R.; Rees, S.

2016-12-01

Limited-area convection-permitting models have proven useful for short-range NWP, but are unable to interact with the larger scales needed for longer lead-time skill. A new global forecast model, fvGFS, has been designed combining a modern nonhydrostatic dynamical core, the GFDL Finite-Volume Cubed-Sphere dynamical core (FV3) with operational GFS physics and initial conditions, and has been shown to provide excellent global skill while improving representation of small-scale phenomena. The nested-grid capability of FV3 allows us to build a regional-to-global variable-resolution model to efficiently refine to 3-km grid spacing over the Continental US. The use of two-way grid nesting allows us to reach these resolutions very efficiently, with the operational requirement easily attainable on current supercomputing systems.Even without a boundary-layer or advanced microphysical scheme appropriate for convection-perrmitting resolutions, the effectiveness of fvGFS can be demonstrated for a variety of weather events. We demonstrate successful proof-of-concept simulations of a variety of phenomena. We show the capability to develop intense hurricanes with realistic fine-scale eyewalls and rainbands. The new model also produces skillful predictions of severe weather outbreaks and of organized mesoscale convective systems. Fine-scale orographic and boundary-layer phenomena are also simulated with excellent fidelity by fvGFS. Further expected improvements are discussed, including the introduction of more sophisticated microphysics and of scale-aware convection schemes.
Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

NASA Astrophysics Data System (ADS)

Brown, James D.; Wu, Limin; He, Minxue; Regonda, Satish; Lee, Haksu; Seo, Dong-Jun

2014-11-01

Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised ;raw; precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date (;resampled climatology;). In both cases, the forecast horizon was 1-14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November-April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus appropriately correct for, conditions that are undetected by the GFS. The calibration of the MEFP to provide reliable and skillful forecasts of a range of precipitation amounts (not only large amounts) is a secondary factor responsible for these Type-II conditional biases. Interpretation of the verification results leads to guidance on the expected performance and limitations of the MEFP, together with recommendations on future enhancements.
Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs.

NASA Astrophysics Data System (ADS)

Krueger, S. K.; Belochitski, A.; Moorthi, S.; Bogenschutz, P.; Pincus, R.

2015-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation and cloudiness. Unlike other similar methods, only one new prognostic variable, turbulent kinetic energy (TKE), needs to be intoduced, making the technique computationally efficient.SHOC code was adopted for a global model environment from its origins in a cloud resolving model, and incorporated into NCEP GFS. SHOC was first tested in a non-interactive mode, a configuration where SHOC receives inputs from the host model, but its outputs are not returned to the GFS. In this configuration: a) SGS TKE values produced by GFS SHOC are consistent with those produced by SHOC in a CRM, b) SGS TKE in GFS SHOC exhibits a well defined diurnal cycle, c) there's enhanced boundary layer turbulence in the subtropical stratocumulus and tropical transition-to-cumulus areas d) buoyancy flux diagnosed from the assumed PDF is consistent with independently calculated Brunt-Vaisala frequency in identifying stable and unstable regions.Next, SHOC was coupled to GFS, namely turbulent diffusion coefficients computed by SHOC are now used in place of those currently produced by the GFS boundary layer and shallow convection schemes (Han and Pan, 2011), as well as condensation and cloud fraction diagnosed from the SGS PDF replace those calculated in the current large-scale cloudines scheme (Zhao and Carr, 1997). Ongoing activities consist of debugging the fully coupled GFS/SHOC.Future work will consist of evaluating model performance and tuning the physics if necessary, by performing medium-range NWP forecasts with prescribed initial conditions, and AMIP-type climate tests with prescribed SSTs. Depending on the results, the model will be tuned or parameterizations modified. Next, SHOC will be implemented in the NCEP CFS, and tuned and evaluated for climate applications - seasonal prediction and long coupled climate runs. Impact of new physics on ENSO, MJO, ISO, monsoon variability, etc will be examined.
EMC: Verification

Science.gov Websites

, GFS, RAP, HRRR, HIRESW, SREF mean, International Global Models, HPC analysis Precipitation Skill Scores : 1995-Present NAM, GFS, NAM CONUS nest, International Models EMC Forecast Verfication Stats: NAM ) Real Time Verification of NCEP Operational Models against observations Real Time Verification of NCEP
Integrating Unified Gravity Wave Physics into the NOAA Next Generation Global Prediction System

NASA Astrophysics Data System (ADS)

Alpert, J. C.; Yudin, V.; Fuller-Rowell, T. J.; Akmaev, R. A.

2017-12-01

The Unified Gravity Wave Physics (UGWP) project for the Next Generation Global Prediction System (NGGPS) is a NOAA collaborative effort between the National Centers for Environmental Prediction (NCEP), Environemntal Modeling Center (EMC) and the University of Colorado, Cooperative Institute for Research in Environmental Sciences (CU-CIRES) to support upgrades and improvements of GW dynamics (resolved scales) and physics (sub-grid scales) in the NOAA Environmental Modeling System (NEMS)†. As envisioned the global climate, weather and space weather models of NEMS will substantially improve their predictions and forecasts with the resolution-sensitive (scale-aware) formulations planned under the UGWP framework for both orographic and non-stationary waves. In particular, the planned improvements for the Global Forecast System (GFS) model of NEMS are: calibration of model physics for higher vertical and horizontal resolution and an extended vertical range of simulations, upgrades to GW schemes, including the turbulent heating and eddy mixing due to wave dissipation and breaking, and representation of the internally-generated QBO. The main priority of the UGWP project is unified parameterization of orographic and non-orographic GW effects including momentum deposition in the middle atmosphere and turbulent heating and eddies due to wave dissipation and breaking. The latter effects are not currently represented in NOAA atmosphere models. The team has tested and evaluated four candidate GW solvers integrating the selected GW schemes into the NGGPS model. Our current work and planned activity is to implement the UGWP schemes in the first available GFS/FV3 (open FV3) configuration including adapted GFDL modification for sub-grid orography in GFS. Initial global model results will be shown for the operational and research GFS configuration for spectral and FV3 dynamical cores. †http://www.emc.ncep.noaa.gov/index.php?branch=NEMS
Validation of the vertical profiles of three meteorological models using radiosondes from Antofagasta, Paranal and Llano de Chajnantor

NASA Astrophysics Data System (ADS)

Cortés, L.; Curé, M.

2011-11-01

This research presents an evaluation of three meteorological models, the Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the mesoscale model WRF (Weather Research and Forecasting) for three sites located in north of Chile. Cerro Moreno Airport, the Paranal Observatory and Llano de Chajnantor are located at 25, 130 and 283 km from the city of Antofagasta, respectively. Results for the three sites show that the lowest correlation and the highest errors occur at the surface. ECMWF model presents the best results at these levels for the two hours analyzed. This could be due to the fact that the ECMWF model has 91 vertical levels, compared to the 64 and 27 vertical levels of GFS and WRF models, respectively. Therefore, it can represent better the processes in the Planetary Boundary Layer (PBL). In relation to the middle and upper troposphere, the three models show good agreement.
Evaluation and Improvement of Polar WRF simulations using the observed atmospheric profiles in the Arctic seasonal ice zone

NASA Astrophysics Data System (ADS)

Liu, Z.; Schweiger, A. J. B.

2016-12-01

We use the Polar Weather Research and Forecasting (WRF) model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) over the Beaufort Sea in the summer since 2013. With the 119 SIZRS dropsondes in the18 cross sections along the 150W and 140W longitude lines, we evaluate the performance of WRF simulations and two forcing data sets, the ERA-Interim reanalysis and the Global Forecast System (GFS) analysis, and explore the improvement of the Polar WRF performance when the dropsonde data are assimilated using observation nudging. Polar WRF, ERA-Interim, and GFS can reproduce the general features of the observed mean atmospheric profiles, such as low-level temperature inversion, low-level jet (LLJ) and specific humidity inversion. The Polar WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing, which is likely related to the lower values of the boundary layer diffusion in WRF than in the global models such as ECMWF and GFS. The Polar WRF simulated relative humidity closely resembles the forcing datasets while having large biases compared to observations. This suggests that the performance of Polar WRF and its forecasts in this region are limited by the quality of the forcing dataset and that the assimilation of more and better-calibrated observations, such as humidity data, is critical for their improvement. We investigate the potential of assimilating the SIZRS dropsonde dataset in improving the weather forecast over the Beaufort Sea. A simple local nudging approach is adopted. Along SIZRS flight cross sections, a set of Polar WRF simulations are performed with varying number of variables and dropsonde profiles assimilated. Different model physics are tested to examine the sensitivity of different aspects of model physics, such as boundary layer schemes, cloud microphysics, and radiation parameterization, to data assimilation. The comparison of the Polar WRF runs with assimilation and the runs without assimilation demonstrates the importance of SIZRS dropsonde data to the improvement of atmospheric analysis and reanalysis such as GFS and ERA-Interim, and consequently to the improvement of weather forecast in this region.
Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Bradley; Jedlovec, Gary; Wick, Gary; Neiman, Paul

2013-01-01

Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.
Climate and Weather Analysis of Afghanistan Thunderstorms

DTIC Science & Technology

2011-09-01

dry, continental polar (cP) air. The subtropical jet (STJ) and Extratropical storm track tend to lie south of Kabul. Mean high SFC temperatures...March-April-May (MAM). Note that AFG lies to the east of a broad trough centered over southern Europe and to the west of broad ridge centered over... Extratropical Cyclone FAR False Alarm Rate FOB Forward Operating Base FRN Forecaster Reference Notebook GFS Global Forecast System GoA
Using High Resolution Model Data to Improve Lightning Forecasts across Southern California

NASA Astrophysics Data System (ADS)

Capps, S. B.; Rolinski, T.

2014-12-01

Dry lightning often results in a significant amount of fire starts in areas where the vegetation is dry and continuous. Meteorologists from the USDA Forest Service Predictive Services' program in Riverside, California are tasked to provide southern and central California's fire agencies with fire potential outlooks. Logistic regression equations were developed by these meteorologists several years ago, which forecast probabilities of lightning as well as lightning amounts, out to seven days across southern California. These regression equations were developed using ten years of historical gridded data from the Global Forecast System (GFS) model on a coarse scale (0.5 degree resolution), correlated with historical lightning strike data. These equations do a reasonably good job of capturing a lightning episode (3-5 consecutive days or greater of lightning), but perform poorly regarding more detailed information such as exact location and amounts. It is postulated that the inadequacies in resolving the finer details of episodic lightning events is due to the coarse resolution of the GFS data, along with limited predictors. Stability parameters, such as the Lifted Index (LI), the Total Totals index (TT), Convective Available Potential Energy (CAPE), along with Precipitable Water (PW) are the only parameters being considered as predictors. It is hypothesized that the statistical forecasts will benefit from higher resolution data both in training and implementing the statistical model. We have dynamically downscaled NCEP FNL (Final) reanalysis data using the Weather Research and Forecasting model (WRF) to 3km spatial and hourly temporal resolution across a decade. This dataset will be used to evaluate the contribution to the success of the statistical model of additional predictors in higher vertical, spatial and temporal resolution. If successful, we will implement an operational dynamically downscaled GFS forecast product to generate predictors for the resulting statistical lightning model. This data will help fire agencies be better prepared to pre-deploy resources in advance of these events. Specific information regarding duration, amount, and location will be especially valuable.
National Centers for Environmental Prediction

Science.gov Websites

Organization Search Enter text Search Navigation Bar End Cap Search EMC Go Branches Global Climate and Weather / VISION | About EMC EMC > GLOBAL BRANCH > GFS > HOME Home Implementations Documentation References Products Model Guidance Performance Developers VLab GLOBAL FORECAST SYSTEM Global Data
National Centers for Environmental Prediction

Science.gov Websites

available at IMD Click here to go to the Special Report page Aug 2016 - IITM started experimental real-time Experimental version of GFS 10.0.0 ported to IITM & NCMRWF - February 2012 EnKF Hybrid GSI update - Spring diagnostics *Experimental* Climate Prediction Center (CPC) links... African Desk: SWFDP GFS forecasts South

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

NASA Technical Reports Server (NTRS)

Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

2014-01-01

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.
National Centers for Environmental Prediction

Science.gov Websites

Organization Search Enter text Search Navigation Bar End Cap Search EMC Go Branches Global Climate and Weather Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Products People GLOBAL CLIMATE & WEATHER MODELING Global Forecast System (GFS) products - Please see
Special cases of AIRS v4.0.x retrievals: missing forecast surface pressure and regression-only retrieval

NASA Technical Reports Server (NTRS)

Hearty, Thomas; Manning, Evan

2005-01-01

This memo examines the differences that can be expected when performing two special cases of retrievals with the v.4.0.x PGE: (1) retrivals without the surface pressure from the NOAA Global Forecast System (GFS) and (2) regression only retrievals. An understanding of these differences is important for users who may want to give up some accuracy in the retrieval in exchange for a rapid solution.
Operational Impact of Data Collected from the Global Hawk Unmanned Aircraft During SHOUT

NASA Astrophysics Data System (ADS)

Wick, G. A.; Dunion, J. P.; Sippel, J.; Cucurull, L.; Aksoy, A.; Kren, A.; Christophersen, H.; Black, P.

2017-12-01

The primary scientific goal of the Sensing Hazards with Operational Unmanned Technology (SHOUT) Project was to determine the potential utility of observations from high-altitude, long-endurance unmanned aircraft systems such as the Global Hawk (GH) aircraft to improve operational forecasts of high-impact weather events or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. Hurricanes and tropical cyclones are among the most potentially destructive high-impact weather events and pose a major forecasting challenge to NOAA. Major winter storms over the Pacific Ocean, including atmospheric river events, which make landfall and bring strong winds and extreme precipitation to the West Coast and Alaska are also important to forecast accurately because of their societal impact in those parts of the country. In response, the SHOUT project supported three field campaigns with the GH aircraft and dedicated data impact studies exploring the potential for the real-time data from the aircraft to improve the forecasting of both tropical cyclones and landfalling Pacific storms. Dropsonde observations from the GH aircraft were assimilated into the operational Hurricane Weather Research and Forecasting (HWRF) and Global Forecast System (GFS) models. The results from several diverse but complementary studies consistently demonstrated significant positive forecast benefits spanning the regional and global models. Forecast skill improvements within HWRF reached up to about 9% for track and 14% for intensity. Within GFS, track skill improvements for multi-storm averages exceeded 10% and improvements for individual storms reached over 20% depending on forecast lead time. Forecasted precipitation was also improved. Impacts for Pacific winter storms were smaller but still positive. The results are highly encouraging and support the potential for operational utilization of data from a platform like the GH. This presentation summarizes the observations collected and highlights the multiple impact studies completed.
Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites

NASA Astrophysics Data System (ADS)

Marín, Julio C.; Pozo, Diana; Curé, Michel

2015-01-01

In this work, we describe a method to estimate the precipitable water vapor (PWV) from Geostationary Observational Environmental Satellite (GOES) data at high altitude sites. The method was applied at Atacama Pathfinder Experiment (APEX) and Cerro Toco sites, located above 5000 m altitude in the Chajnantor plateau, in the north of Chile. It was validated using GOES-12 satellite data over the range 0-1.2 mm since submillimeter/millimeter astronomical observations are only useful within this PWV range. The PWV estimated from GOES and the Final Analyses (FNL) at APEX for 2007 and 2009 show root mean square error values of 0.23 mm and 0.36 mm over the ranges 0-0.4 mm and 0.4-1.2 mm, respectively. However, absolute relative errors of 51% and 33% were shown over these PWV ranges, respectively. We recommend using high-resolution thermodynamic profiles from the Global Forecast System (GFS) model to estimate the PWV from GOES data since they are available every three hours and at an earlier time than the FNL data. The estimated PWV from GOES/GFS agrees better with the observed PWV at both sites during night time. The largest errors are shown during daytime. Short-term PWV forecasts were implemented at both sites, applying a simple persistence method to the PWV estimated from GOES/GFS. The 12 h and 24 h PWV forecasts evaluated from August to October 2009 indicates that 25% of them show a very good agreement with observations whereas 50% of them show reasonably good agreement with observations. Transmission uncertainties calculated for PWV estimations and forecasts over the studied sites are larger over the range 0-0.4 mm than over the range 0.4-1.2 mm. Thus, the method can be used over the latter interval with more confidence.
AIRS associated accomplishments at the JCSDA: First use of full spatial resolution hyperspectral data show significant improvements in global forecasts

NASA Astrophysics Data System (ADS)

Le Marshall, J.; Jung, J.; Lord, S. J.; Derber, J. C.; Treadon, R.; Joiner, J.; Goldberg, M.; Wolf, W.; Liu, H. C.

2005-08-01

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Department of Defense (DoD), Joint Center for Satellite Data Assimilation (JCSDA) was established in 2000/2001. The goal of the JCSDA is to accelerate the use of observations from earth-orbiting satellites into operational numerical environmental analysis and prediction systems for the purpose of improving weather and oceanic forecasts, seasonal climate forecasts and the accuracy of climate data sets. As a result, a series of data assimilation experiments were undertaken at the JCSDA as part of the preparations for the operational assimilation of AIRS data by its partner organizations1,2. Here, for the first time full spatial resolution radiance data, available in real-time from the AIRS instrument, were used at the JCSDA in data assimilation studies over the globe utilizing the operational NCEP Global Forecast System (GFS). The radiance data from each channel of the instrument were carefully screened for cloud effects and those radiances which were deemed to be clear of cloud effects were used by the GFS forecast system. The result of these assimilation trials has been a first demonstration of significant improvements in forecast skill over both the Northern and Southern Hemisphere compared to the operational system without AIRS data. The experimental system was designed in a way that rendered it feasible for operational application, and that constraint involved using the subset of AIRS channels chosen for operational distribution and an analysis methodology close to the current analysis practice, with particular consideration given to time limitations. As a result, operational application of these AIRS data was enabled by the recent NCEP operational upgrade. In addition, because of the improved impact resulting from use of this enhanced data set compared to that used operationally to date, provision of a realtime "warmest field" of view data set has been established for use by international NWP Centers.
Climate Prediction Center - Forecasts & Outlook Maps, Graphs and Tables

Science.gov Websites

moisture, and a forecast for daily ultraviolet (UV) radiation index. Many of the outlook maps have an acute short-term threats due to severe weather events. Another of the many products available is the GFS
Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.
Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

2016-12-01

In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.
Potential Vorticity Analysis of Low Level Thunderstorm Dynamics in an Idealized Supercell Simulation

DTIC Science & Technology

2009-03-01

Severe Weather, Supercell, Weather Research and Forecasting Model , Advanced WRF 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...27 A. ADVANCED RESEARCH WRF MODEL .................................................27 1. Data, Model Setup, and Methodology...03/11/2006 GFS model run. Top row: 11/12Z initialization. Middle row: 12 hour forecast valid at 12/00Z. Bottom row: 24 hour forecast valid at
A CPT for Improving Turbulence and Cloud Processes in the NCEP Global Models

NASA Astrophysics Data System (ADS)

Krueger, S. K.; Moorthi, S.; Randall, D. A.; Pincus, R.; Bogenschutz, P.; Belochitski, A.; Chikira, M.; Dazlich, D. A.; Swales, D. J.; Thakur, P. K.; Yang, F.; Cheng, A.

2016-12-01

Our Climate Process Team (CPT) is based on the premise that the NCEP (National Centers for Environmental Prediction) global models can be improved by installing an integrated, self-consistent description of turbulence, clouds, deep convection, and the interactions between clouds and radiative and microphysical processes. The goal of our CPT is to unify the representation of turbulence and subgrid-scale (SGS) cloud processes and to unify the representation of SGS deep convective precipitation and grid-scale precipitation as the horizontal resolution decreases. We aim to improve the representation of small-scale phenomena by implementing a PDF-based SGS turbulence and cloudiness scheme that replaces the boundary layer turbulence scheme, the shallow convection scheme, and the cloud fraction schemes in the GFS (Global Forecast System) and CFS (Climate Forecast System) global models. We intend to improve the treatment of deep convection by introducing a unified parameterization that scales continuously between the simulation of individual clouds when and where the grid spacing is sufficiently fine and the behavior of a conventional parameterization of deep convection when and where the grid spacing is coarse. We will endeavor to improve the representation of the interactions of clouds, radiation, and microphysics in the GFS/CFS by using the additional information provided by the PDF-based SGS cloud scheme. The team is evaluating the impacts of the model upgrades with metrics used by the NCEP short-range and seasonal forecast operations.
Using Ground Measurements to Examine the Surface Layer Parameterization Scheme in NCEP GFS

NASA Astrophysics Data System (ADS)

Zheng, W.; Ek, M. B.; Mitchell, K.

2017-12-01

Understanding the behavior and the limitation of the surface layer parameneterization scheme is important for parameterization of surface-atmosphere exchange processes in atmospheric models, accurate prediction of near-surface temperature and identifying the role of different physical processes in contributing to errors. In this study, we examine the surface layer paramerization scheme in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) using the ground flux measurements including the FLUXNET data. The model simulated surface fluxes, surface temperature and vertical profiles of temperature and wind speed are compared against the observations. The limits of applicability of the Monin-Obukhov similarity theory (MOST), which describes the vertical behavior of nondimensionalized mean flow and turbulence properties within the surface layer, are quantified in daytime and nighttime using the data. Results from unstable regimes and stable regimes are discussed.
Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

NASA Astrophysics Data System (ADS)

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.
Predictability of short-range forecasting: a multimodel approach

NASA Astrophysics Data System (ADS)

García-Moya, Jose-Antonio; Callado, Alfons; Escribà, Pau; Santos, Carlos; Santos-Muñoz, Daniel; Simarro, Juan

2011-05-01

Numerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly different model runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the Spanish Meteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).
Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.
Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.
Handling Input and Output for COAMPS

NASA Technical Reports Server (NTRS)

Fitzpatrick, Patrick; Tran, Nam; Li, Yongzuo; Anantharaj, Valentine

2007-01-01

Two suites of software have been developed to handle the input and output of the Coupled Ocean Atmosphere Prediction System (COAMPS), which is a regional atmospheric model developed by the Navy for simulating and predicting weather. Typically, the initial and boundary conditions for COAMPS are provided by a flat-file representation of the Navy s global model. Additional algorithms are needed for running the COAMPS software using global models. One of the present suites satisfies this need for running COAMPS using the Global Forecast System (GFS) model of the National Oceanic and Atmospheric Administration. The first step in running COAMPS downloading of GFS data from an Internet file-transfer-protocol (FTP) server computer of the National Centers for Environmental Prediction (NCEP) is performed by one of the programs (SSC-00273) in this suite. The GFS data, which are in gridded binary (GRIB) format, are then changed to a COAMPS-compatible format by another program in the suite (SSC-00278). Once a forecast is complete, still another program in the suite (SSC-00274) sends the output data to a different server computer. The second suite of software (SSC- 00275) addresses the need to ingest up-to-date land-use-and-land-cover (LULC) data into COAMPS for use in specifying typical climatological values of such surface parameters as albedo, aerodynamic roughness, and ground wetness. This suite includes (1) a program to process LULC data derived from observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Terra and Aqua satellites, (2) programs to derive new climatological parameters for the 17-land-use-category MODIS data; and (3) a modified version of a FORTRAN subroutine to be used by COAMPS. The MODIS data files are processed to reformat them into a compressed American Standard Code for Information Interchange (ASCII) format used by COAMPS for efficient processing.
Spin-up simulation behaviors in a climate model to build a basement of long-time simulation

NASA Astrophysics Data System (ADS)

Lee, J.; Xue, Y.; De Sales, F.

2015-12-01

It is essential to develop start-up information when conducting long-time climate simulation. In case that the initial condition is already available from the previous simulation of same type model this does not necessary; however, if not, model needs spin-up simulation to have adjusted and balanced initial condition with the model climatology. Otherwise, a severe spin may take several years. Some of model variables such as deep soil temperature fields and temperature in ocean deep layers in initial fields would affect model's further long-time simulation due to their long residual memories. To investigate the important factor for spin-up simulation in producing an atmospheric initial condition, we had conducted two different spin-up simulations when no atmospheric condition is available from exist datasets. One simulation employed atmospheric global circulation model (AGCM), namely Global Forecast System (GFS) of National Center for Environmental Prediction (NCEP), while the other employed atmosphere-ocean coupled global circulation model (CGCM), namely Climate Forecast System (CFS) of NCEP. Both models share the atmospheric modeling part and only difference is in applying of ocean model coupling, which is conducted by Modular Ocean Model version 4 (MOM4) of Geophysical Fluid Dynamics Laboratory (GFDL) in CFS. During a decade of spin-up simulation, prescribed sea-surface temperature (SST) fields of target year is forced to the GFS daily basis, while CFS digested only first time step ocean condition and freely iterated for the rest of the period. Both models were forced by CO2 condition and solar constant given from the target year. Our analyses of spin-up simulation results indicate that freely conducted interaction between the ocean and the atmosphere is more helpful to produce the initial condition for the target year rather than produced by fixed SST forcing. Since the GFS used prescribed forcing exactly given from the target year, this result is unexpected. The detail analysis will be discussed in this presentation.
Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.
The Good, the Bad, and the Ugly: Numerical Prediction for Hurricane Juan (2003)

NASA Astrophysics Data System (ADS)

Gyakum, J.; McTaggart-Cowan, R.

2004-05-01

The range of accuracy of the numerical weather prediction (NWP) guidance for the landfall of Hurricane Juan (2003), from nearly perfect to nearly useless, motivates a study of the NWP forecast errors on 28-29 September 2003 in the eastern North Atlantic. Although the forecasts issued over the period were of very high quality, this is primarily because of the diligence of the forecasters, and not related to the reliability of the numerical predictions provided to them by the North American operational centers and the research community. A bifurcation in the forecast fields from various centers and institutes occurred beginning with the 0000 UTC run of 28 September, and continuing until landfall just after 0000 UTC on 29 September. The GFS (NCEP), Eta (NCEP), GEM (Canadian Meteorological Centre; CMC), and MC2 (McGill) forecast models all showed an extremely weak (minimum SLP above 1000 hPa) remnant vortex moving north-northwestward into the Gulf of Maine and merging with a diabatically-developed surface low offshore. The GFS uses a vortex-relocation scheme, the Eta a vortex bogus, and the GEM and MC2 are run on CMC analyses that contain no enhanced vortex. The UK Met Office operational, the GFDL, and the NOGAPS (US Navy) forecast models all ran a small-scale hurricane-like vortex directly into Nova Scotia and verified very well for this case. The UKMO model uses synthetic observations to enhance structures in poorly-forecasted areas during the analysis cycle and both the GFDL and NOGAPS model use advanced idealized vortex bogusing in their initial conditions. The quality of the McGill MC2 forecast is found to be significantly enhanced using a bogusing technique similar to that used in the initialization of the successful forecast models. A verification of the improved forecast is presented along with a discussion of the need for operational quality control of the background fields in the analysis cycle and for proper representation of strong, small-scale tropical vortices.

Implementing Journaling in a Linux Shared Disk File System

NASA Technical Reports Server (NTRS)

Preslan, Kenneth W.; Barry, Andrew; Brassow, Jonathan; Cattelan, Russell; Manthei, Adam; Nygaard, Erling; VanOort, Seth; Teigland, David; Tilstra, Mike; O'Keefe, Matthew;

2000-01-01

In computer systems today, speed and responsiveness is often determined by network and storage subsystem performance. Faster, more scalable networking interfaces like Fibre Channel and Gigabit Ethernet provide the scaffolding from which higher performance computer systems implementations may be constructed, but new thinking is required about how machines interact with network-enabled storage devices. In this paper we describe how we implemented journaling in the Global File System (GFS), a shared-disk, cluster file system for Linux. Our previous three papers on GFS at the Mass Storage Symposium discussed our first three GFS implementations, their performance, and the lessons learned. Our fourth paper describes, appropriately enough, the evolution of GFS version 3 to version 4, which supports journaling and recovery from client failures. In addition, GFS scalability tests extending to 8 machines accessing 8 4-disk enclosures were conducted: these tests showed good scaling. We describe the GFS cluster infrastructure, which is necessary for proper recovery from machine and disk failures in a collection of machines sharing disks using GFS. Finally, we discuss the suitability of Linux for handling the big data requirements of supercomputing centers.

Application and Evaluation of an Explicit Prognostic Cloud-Cover Scheme in GRAPES Global Forecast System

NASA Astrophysics Data System (ADS)

Ma, Zhanshan; Liu, Qijun; Zhao, Chuanfeng; Shen, Xueshun; Wang, Yuan; Jiang, Jonathan H.; Li, Zhe; Yung, Yuk

2018-03-01

An explicit prognostic cloud-cover scheme (PROGCS) is implemented into the Global/Regional Assimilation and Prediction System (GRAPES) for global middle-range numerical weather predication system (GRAPES_GFS) to improve the model performance in simulating cloud cover and radiation. Unlike the previous diagnostic cloud-cover scheme (DIAGCS), PROGCS considers the formation and dissipation of cloud cover by physically connecting it to the cumulus convection and large-scale stratiform condensation processes. Our simulation results show that clouds in mid-high latitudes arise mainly from large-scale stratiform condensation processes, while cumulus convection and large-scale condensation processes jointly determine cloud cover in low latitudes. Compared with DIAGCS, PROGCS captures more consistent vertical distributions of cloud cover with the observations from Atmospheric Radiation Measurements (ARM) program at the Southern Great Plains (SGP) site and simulates more realistic diurnal cycle of marine stratocumulus with the ERA-Interim reanalysis data. The low, high, and total cloud covers that are determined via PROGCS appear to be more realistic than those simulated via DIAGCS when both are compared with satellite retrievals though the former maintains slight negative biases. In addition, the simulations of outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) from PROGCS runs have been considerably improved as well, resulting in less biases in radiative heating rates at heights below 850 hPa and above 400 hPa of GRAPES_GFS. Our results indicate that a prognostic method of cloud-cover calculation has significant advantage over the conventional diagnostic one, and it should be adopted in both weather and climate simulation and forecast.
SW radiative effect of aerosol in GRAPES_GFS

NASA Astrophysics Data System (ADS)

Chen, Qiying

2017-04-01

The aerosol particles can scatter and absorb solar radiation, and so change the shortwave radiation absorbed by the atmosphere, reached the surface and that reflected back to outer space at TOA. Since this process doesn't interact with other processes, it is called direct radiation effect. The clear sky downward SW and net SW fluxes at the surface in GRAPES_GFS of China Meteorological Administration are overestimated in Northern multitudes and Tropics. The main source of these errors is the absence of aerosol SW effect in GRAPES_GFS. The climatic aerosol mass concentration data, which include 13 kinds of aerosol and their 14 SW bands optical properties are considered in GRAPES_GFS. The calculated total optical depth, single scatter albedo and asymmetry factor are used as the input to radiation scheme. Compared with the satellite observation from MISER, the calculated total optical depth is in good consistent. The seasonal experiments show that, the summer averaged clear sky radiation fluxes at the surface are improved after including the SW effect of aerosol. The biases in the clear sky downward SW and net SW fluxes at the surface in Northern multitudes and Tropic reduced obviously. Furthermore, the weather forecast experiments also show that the skill scores in Northern hemisphere and East Asia also become better.
Climate Prediction - NOAA's National Weather Service

Science.gov Websites

Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Climate > Predictions Climate Prediction Long range forecasts across the U.S. Climate Prediction Web Sites Climate Prediction
Continuous Estimates of Precipitable Water Vapor Within and Around Hurricane Systems

NASA Astrophysics Data System (ADS)

Braun, J. J.; Iwabuchi, T.; van Hove, T.

2008-12-01

This study investigates how estimates of precipitable water vapor (PW) from Global Positioning System (GPS) stations can be used to quantify how atmospheric moisture influences the intensity of tropical storms and hurricanes. The motivation for this study is based on the fact that hurricanes derive their strength through water vapor that is both evaporated from warm ocean surfaces and the existing moisture in the surrounding atmospheric environment. Observationally, there are relatively few instruments that can accurately measure water vapor in the presence of clouds and rain. Retrievals of PW using GPS stations may be the most reliable way to continuously monitor column integrated water vapor. Using storm information from the National Hurricane Center (www.nhc.noaa.gov), we have compared storm intensity to PW estimates for all tropical storms and hurricanes making landfall within 100-km of a GPS station between 2003 and 2008. We find that PW is inversely correlated (r**2 < -0.7) to the drop in surface pressure observed at that station. We have also begun to relate atmospheric PW at a station to the local sea surface temperature (SST). This comparison can be used to measure how strongly atmospheric water vapor and SST are coupled. It can also be used to measure how quickly the atmosphere responds to changes in SST. Finally we have compared the estimated PW to the Global Forecast System (GFS) analysis fields that are used to initialize numerical weather prediction models. This comparison indicates that the GFS analysis fields have significantly larger errors in atmospheric moisture in the Caribbean and Gulf of Mexico when compared to differences over the continental United States. These results illustrate that estimates of PW are an important data set for atmospheric scientists and forecasters attempting to improve the prediction of hurricane intensity.
GFS-10/10/2007-12Z

Science.gov Websites

Mountainous Coasts: A change to the GFS post codes will remove a persistent, spurious high pressure system ENVIRONMENTAL PREDICTION /NCEP/ WILL UPGRADE THE GFS POST PROCESSOR. THE PRIMARY EFFORT BEHIND THIS UPGRADE WILL BE TO UNIFY THE POST PROCESSING CODE FOR THE NORTH AMERICAN MESO SCALE /NAM/ MODEL AND THE GFS INTO
Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

NASA Astrophysics Data System (ADS)

Pattanayak, Sujata; Mohanty, U. C.

2018-06-01

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April-3 May 2008), Aila (23-26 May 2009) and Jal (4-8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.
A non-parametric postprocessor for bias-correcting multi-model ensemble forecasts of hydrometeorological and hydrologic variables

NASA Astrophysics Data System (ADS)

Brown, James; Seo, Dong-Jun

2010-05-01

Operational forecasts of hydrometeorological and hydrologic variables often contain large uncertainties, for which ensemble techniques are increasingly used. However, the utility of ensemble forecasts depends on the unbiasedness of the forecast probabilities. We describe a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables, intended for use in operational forecasting. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast from one or several forecasting systems (multi-model ensembles). The technique is based on Bayesian optimal linear estimation of indicator variables, and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator expectation at each threshold, it effectively minimizes the Continuous Ranked Probability Score (CRPS) when infinitely many thresholds are employed. However, the ensemble members used as predictors in ICK, and other bias-correction techniques, are often highly cross-correlated, both within and between models. Thus, we propose an orthogonal transform of the predictors used in ICK, which is analogous to using their principal components in the linear system of equations. This leads to a well-posed problem in which a minimum number of predictors are used to provide maximum information content in terms of the total variance explained. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS), for which independent validation results are presented. Extension to multimodel ensembles from the NCEP GFS and Short Range Ensemble Forecast (SREF) systems is also proposed.
Precipitation forecasts for rainfall runoff predictions. A case study in poorly gauged Ribb and Gumara catchments, upper Blue Nile, Ethiopia

NASA Astrophysics Data System (ADS)

Seyoum, Mesgana; van Andel, Schalk Jan; Xuan, Yunqing; Amare, Kibreab

Flow forecasting in poorly gauged, flood-prone Ribb and Gumara sub-catchments of the Blue Nile was studied with the aim of testing the performance of Quantitative Precipitation Forecasts (QPFs). Four types of QPFs namely MM5 forecasts with a spatial resolution of 2 km; the Maximum, Mean and Minimum members (MaxEPS, MeanEPS and MinEPS where EPS stands for Ensemble Prediction System) of the fixed, low resolution (2.5 by 2.5 degrees) National Oceanic and Atmospheric Administration Global Forecast System (NOAA GFS) ensemble forecasts were used. Both the MM5 and the EPS were not calibrated (bias correction, downscaling (for EPS), etc.). In addition, zero forecasts assuming no rainfall in the coming days, and monthly average forecasts assuming average monthly rainfall in the coming days, were used. These rainfall forecasts were then used to drive the Hydrologic Engineering Center’s-Hydrologic Modeling System, HEC-HMS, hydrologic model for flow predictions. The results show that flow predictions using MaxEPS and MM5 precipitation forecasts over-predicted the peak flow for most of the seven events analyzed, whereas under-predicted peak flow was found using zero- and monthly average rainfall. The comparison of observed and predicted flow hydrographs shows that MM5, MaxEPS and MeanEPS precipitation forecasts were able to capture the rainfall signal that caused peak flows. Flow predictions based on MaxEPS and MeanEPS gave results that were quantitatively close to the observed flow for most events, whereas flow predictions based on MM5 resulted in large overestimations for some events. In follow-up research for this particular case study, calibration of the MM5 model will be performed. The overall analysis shows that freely available atmospheric forecasting products can provide additional information on upcoming rainfall and peak flow events in areas where only base-line forecasts such as no-rainfall or climatology are available.
P161 Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley T.; Chou, Shih-Hung; Jedlovec, Gary J.

2012-01-01

For over 6 years, AIRS radiances have been assimilated operationally into National (e.g. Environmental Modeling Center (EMC)) and International (e.g. European Centre for Medium-Range Weather Forecasts (ECMWF)), operational centers; assimilated in the North American Mesoscale (NAM) since 2008. Due partly to data latency and operational constraints, hyperspectral radiance assimilation has had less impact on the Gridpoint Statistical Interpolation (GSI) system used in the NAM and GFS. Objective of this project is to use AIRS retrieved profiles as a proxy for the AIRS radiances in situations where AIRS radiances are unable to be assimilated in the current operational system by evaluating location and magnitude of analysis increments.
A Cause and A Solution for the Underprediction of Extreme Wave Events in the Northeast Pacific

NASA Astrophysics Data System (ADS)

Ellenson, A. N.; Ozkan-Haller, H. T.; Thomson, J.; Brown, A. C.; Haller, M. C.

2016-12-01

Along the coastlines of Washington and Oregon, at least one 10 m wave height event occurs every year, and the strongest storms produce wave heights of 14-15 m. Extremely high wave heights can cause severe damage to coastal infrastructure and pose hazards to stakeholders along the coast. A system which can accurately predict such sea states is important for quantifying risk and aiding in preparation for extreme wave events. This study explores how to optimize forecast model performance for extreme wave events by utilizing different physics packages or wind input in four model configurations. The different wind input products consist of a reanalyzed Global Forecasting System (GFS) wind input and a Climate Forecast System Reanalysis (CFSR) from the National Center of Environmental Prediction (NCEP). The physics packages are the Tolman-Chalikov (1996) ST2 physics package and the Ardhuin et al (2009) ST4 physics package associated with version 4.18 of WaveWatch III. A hindcast was previously performed to assess the wave character along the Pacific Northwest Coastline for wave energy applications. Inspection of hindcast model results showed that the operational model, which consisted of ST2 physics and GFS wind, underpredicted events where wave height exceeded six meters.The under-prediction is most severe for cases with the combined conditions of a distant cyclone and a strong coastal jet. Three such cases were re-analyzed with the four model configurations. Model output is compared with observations at NDBC buoy 46050, offshore of Newport, OR. The model configuration consisting of ST4 physics package and CFSR wind input performs best as compared with the original model, reducing significant wave height underprediction from 1.25 m to approximately 0.67 m and mean wave direction error from 30 degrees to 17 degrees for wave heights greater than 6 m. Spectral analysis shows that the ST4-CFSR model configuration best resolves southerly wave energy, and all model configurations tend to overestimate northerly wave energy. This directional distinction is important when attempting to identify which atmospheric feature has induced the extreme wave energy.
Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Pincus, R.

2016-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation and cloudiness. Unlike other similar methods, only one new prognostic variable, turbulent kinetic energy (TKE), needs to be intoduced, making the technique computationally efficient.SHOC is now incorporated into a version of GFS, as well as into the next generation of the NCEP global model - NOAA Environmental Modeling System (NEMS). Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these variables. Radiative transfer parameterization uses cloudiness computed by SHOC.Outstanding problems include high level tropical cloud fraction being too high in SHOC runs, possibly related to the interaction of SHOC with condensate detrained from deep convection.Future work will consist of evaluating model performance and tuning the physics if necessary, by performing medium-range NWP forecasts with prescribed initial conditions, and AMIP-type climate tests with prescribed SSTs. Depending on the results, the model will be tuned or parameterizations modified. Next, SHOC will be implemented in the NCEP CFS, and tuned and evaluated for climate applications - seasonal prediction and long coupled climate runs. Impact of new physics on ENSO, MJO, ISO, monsoon variability, etc will be examined.
An Overview of the National Weather Service National Water Model

NASA Astrophysics Data System (ADS)

Cosgrove, B.; Gochis, D.; Clark, E. P.; Cui, Z.; Dugger, A. L.; Feng, X.; Karsten, L. R.; Khan, S.; Kitzmiller, D.; Lee, H. S.; Liu, Y.; McCreight, J. L.; Newman, A. J.; Oubeidillah, A.; Pan, L.; Pham, C.; Salas, F.; Sampson, K. M.; Sood, G.; Wood, A.; Yates, D. N.; Yu, W.

2016-12-01

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research (NCAR) and the NWS National Centers for Environmental Prediction (NCEP) recently implemented version 1.0 of the National Water Model (NWM) into operations. This model is an hourly cycling uncoupled analysis and forecast system that provides streamflow for 2.7 million river reaches and other hydrologic information on 1km and 250m grids. It will provide complementary hydrologic guidance at current NWS river forecast locations and significantly expand guidance coverage and type in underserved locations. The core of this system is the NCAR-supported community Weather Research and Forecasting (WRF)-Hydro hydrologic model. It ingests forcing from a variety of sources including Multi-Sensor Multi-Radar (MRMS) radar-gauge observed precipitation data and High Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Global Forecast System (GFS) and Climate Forecast System (CFS) forecast data. WRF-Hydro is configured to use the Noah-Multi Parameterization (Noah-MP) Land Surface Model (LSM) to simulate land surface processes. Separate water routing modules perform diffusive wave surface routing and saturated subsurface flow routing on a 250m grid, and Muskingum-Cunge channel routing down National Hydrogaphy Dataset Plus V2 (NHDPlusV2) stream reaches. River analyses and forecasts are provided across a domain encompassing the Continental United States (CONUS) and hydrologically contributing areas, while land surface output is available on a larger domain that extends beyond the CONUS into Canada and Mexico (roughly from latitude 19N to 58N). The system includes an analysis and assimilation configuration along with three forecast configurations. These include a short-range 15 hour deterministic forecast, a medium-Range 10 day deterministic forecast and a long-range 30 day 16-member ensemble forecast. United Sates Geologic Survey (USGS) streamflow observations are assimilated into the analysis and assimilation configuration, and all four configurations benefit from the inclusion of 1,260 reservoirs. An overview of the National Water Model will be given, along with information on ongoing evaluation activities and plans for future NWM enhancements.
Extended Range Prediction of Indian Summer Monsoon: Current status

NASA Astrophysics Data System (ADS)

Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.

2014-12-01

The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further added value to both deterministic and probability forecast compared to raw SME's and this better skill is probably flows from large spread and improved spread-error relationship. CGMME system is currently capable of generating ER prediction in real time and successfully delivering its experimental operational ER forecast of ISM for the last few years.
Intraseasonal Variability of the Indian Monsoon as Simulated by a Global Model

NASA Astrophysics Data System (ADS)

Joshi, Sneh; Kar, S. C.

2018-01-01

This study uses the global forecast system (GFS) model at T126 horizontal resolution to carry out seasonal simulations with prescribed sea-surface temperatures. Main objectives of the study are to evaluate the simulated Indian monsoon variability in intraseasonal timescales. The GFS model has been integrated for 29 monsoon seasons with 15 member ensembles forced with observed sea-surface temperatures (SSTs) and additional 16-member ensemble runs have been carried out using climatological SSTs. Northward propagation of intraseasonal rainfall anomalies over the Indian region from the model simulations has been examined. It is found that the model is unable to simulate the observed moisture pattern when the active zone of convection is over central India. However, the model simulates the observed pattern of specific humidity during the life cycle of northward propagation on day - 10 and day + 10 of maximum convection over central India. The space-time spectral analysis of the simulated equatorial waves shows that the ensemble members have varying amount of power in each band of wavenumbers and frequencies. However, variations among ensemble members are more in the antisymmetric component of westward moving waves and maximum difference in power is seen in the 8-20 day mode among ensemble members.
Detecting Non-Gaussian and Lognormal Characteristics of Temperature and Water Vapor Mixing Ratio

NASA Astrophysics Data System (ADS)

Kliewer, A.; Fletcher, S. J.; Jones, A. S.; Forsythe, J. M.

2017-12-01

Many operational data assimilation and retrieval systems assume that the errors and variables come from a Gaussian distribution. This study builds upon previous results that shows that positive definite variables, specifically water vapor mixing ratio and temperature, can follow a non-Gaussian distribution and moreover a lognormal distribution. Previously, statistical testing procedures which included the Jarque-Bera test, the Shapiro-Wilk test, the Chi-squared goodness-of-fit test, and a composite test which incorporated the results of the former tests were employed to determine locations and time spans where atmospheric variables assume a non-Gaussian distribution. These tests are now investigated in a "sliding window" fashion in order to extend the testing procedure to near real-time. The analyzed 1-degree resolution data comes from the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS) six hour forecast from the 0Z analysis. These results indicate the necessity of a Data Assimilation (DA) system to be able to properly use the lognormally-distributed variables in an appropriate Bayesian analysis that does not assume the variables are Gaussian.
Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.
Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers. During launch operations, the payload/launch team sometimes asks the LWOs if they expect the upper-level winds to change during the countdown. The LWOs used numerical weather prediction model point forecasts to provide the information, but did not have the capability to quickly retrieve or adequately display the upper-level observations and compare them directly in the same display to the model point forecasts to help them determine which model performed the best. The LWOs requested the Applied Meteorology Unit (AMU) develop a graphical user interface (GUI) that will plot upper-level wind speed and direction observations from the Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Profiling System (AMPS) rawinsondes with point forecast wind profiles from the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM), Rapid Refresh (RAP) and Global Forecast System (GFS) models to assess the performance of these models. The AMU suggested adding observations from the NASA 50 MHz wind profiler and one of the US Air Force 915 MHz wind profilers, both located near the Kennedy Space Center (KSC) Shuttle Landing Facility, to supplement the AMPS observations with more frequent upper-level profiles. Figure 1 shows a map of KSC/CCAFS with the locations of the observation sites and the model point forecasts.
Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.
4D Hybrid Ensemble-Variational Data Assimilation for the NCEP GFS: Outer Loops and Variable Transforms

NASA Astrophysics Data System (ADS)

Kleist, D. T.; Ide, K.; Mahajan, R.; Thomas, C.

2014-12-01

The use of hybrid error covariance models has become quite popular for numerical weather prediction (NWP). One such method for incorporating localized covariances from an ensemble within the variational framework utilizes an augmented control variable (EnVar), and has been implemented in the operational NCEP data assimilation system (GSI). By taking the existing 3D EnVar algorithm in GSI and allowing for four-dimensional ensemble perturbations, coupled with the 4DVAR infrastructure already in place, a 4D EnVar capability has been developed. The 4D EnVar algorithm has a few attractive qualities relative to 4DVAR, including the lack of need for tangent-linear and adjoint model as well as reduced computational cost. Preliminary results using real observations have been encouraging, showing forecast improvements nearly as large as were found in moving from 3DVAR to hybrid 3D EnVar. 4D EnVar is the method of choice for the next generation assimilation system for use with the operational NCEP global model, the global forecast system (GFS). The use of an outer-loop has long been the method of choice for 4DVar data assimilation to help address nonlinearity. An outer loop involves the re-running of the (deterministic) background forecast from the updated initial condition at the beginning of the assimilation window, and proceeding with another inner loop minimization. Within 4D EnVar, a similar procedure can be adopted since the solver evaluates a 4D analysis increment throughout the window, consistent with the valid times of the 4D ensemble perturbations. In this procedure, the ensemble perturbations are kept fixed and centered about the updated background state. This is analogous to the quasi-outer loop idea developed for the EnKF. Here, we present results for both toy model and real NWP systems demonstrating the impact from incorporating outer loops to address nonlinearity within the 4D EnVar context. The appropriate amplitudes for observation and background error covariances in subsequent outer loops will be explored. Lastly, variable transformations on the ensemble perturbations will be utilized to help address issues of non-Gaussianity. This may be particularly important for variables that clearly have non-Gaussian error characteristics such as water vapor and cloud condensate.

Benefits of an ultra large and multiresolution ensemble for estimating available wind power

NASA Astrophysics Data System (ADS)

Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

2016-04-01

In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.
An operational global ocean forecast system and its applications

NASA Astrophysics Data System (ADS)

Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

2012-12-01

A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.
The FALL3D Ash Cloud Dispersion Model and its Implementation at the Buenos Aires VAAC

NASA Astrophysics Data System (ADS)

Folch, A.; Suaya, M.; Costa, A.; Viramonte, J.

2009-12-01

Airborne volcanic ash and aerosols threat aerial navigation and affect the quality of air at medium to large distances downwind from the volcano. Airplane re-routing and airport disruption carry important socioeconomic consequences at regional and national levels. Models to forecast volcanic ash clouds constitute, together with satellite imagery, a valuable predictive tool during a crisis. FALL3D is an Eulerian ash cloud dispersion model based on the advection-diffusion-sedimentation equation. The model runs at any scale, from regional to global. The dispersion model is off-line coupled with global (e.g. GFS, NMM-b) and mesoscalar (e.g. NMM-b, WRF, ETA) meteorological models and with re-analysis datasets. FALL3D has been recently installed at the Buenos Aires VAAC, depending on the Argentinean National Meteorological Service (SMN). In this presentation we summarize the characteristics of the model and its implementation at the VAAC, including the different domains, the meteorological forecast inputs (ETA or GFS) and the scenarios assumed for some critical volcanoes (Chaitén, Llaima, Lascar, etc.). Pre-defined scenarios are necessary to give an early first order prediction when data is poor or unavailable. This is particularly critical in Central Andes, were most active volcanoes are located in remote areas with poor or inexistent monitoring.
Monitoring the Global Soil Moisture Climatology Using GLDAS/LIS

NASA Astrophysics Data System (ADS)

Meng, J.; Mitchell, K.; Wei, H.; Gottschalck, J.

2006-05-01

Soil moisture plays a crucial role in the terrestrial water cycle through governing the process of partitioning precipitation among infiltration, runoff and evaporation. Accurate assessment of soil moisture and other land states, namely, soil temperature, snowpack, and vegetation, is critical in numerical environmental prediction systems because of their regulation of surface water and energy fluxes between the surface and atmosphere over a variety of spatial and temporal scales. The Global Land Data Assimilation System (GLDAS) is developed, jointly by NASA Goddard Space Flight Center (GSFC) and NOAA National Centers for Environmental Prediction (NCEP), to perform high-quality global land surface simulation using state-of-art land surface models and further minimizing the errors of simulation by constraining the models with observation- based precipitation, and satellite land data assimilation techniques. The GLDAS-based Land Information System (LIS) infrastructure has been installed on the NCEP supercomputer that serves the operational weather and climate prediction systems. In this experiment, the Noah land surface model is offline executed within the GLDAS/LIS infrastructure, driven by the NCEP Global Reanalysis-2 (GR2) and the CPC Merged Analysis of Precipitation (CMAP). We use the same Noah code that is coupled to the operational NCEP Global Forecast System (GFS) for weather prediction and test bed versions of the NCEP Climate Forecast System (CFS) for seasonal prediction. For assessment, it is crucial that this uncoupled GLDAS/Noah uses exactly the same Noah code (and soil and vegetation parameters therein), and executes with the same horizontal grid, landmask, terrain field, soil and vegetation types, seasonal cycle of green vegetation fraction and surface albedo as in the coupled GFS/Noah and CFS/Noah. This execution is for the 25-year period of 1980-2005, starting with a pre-execution 10-year spin-up. This 25-year GLDAS/Noah global land climatology will be used for both climate variability assessment and as a source of land initial conditions for ensemble CFS/Noah seasonal hindcast experiments. Finally, this GLDAS/Noah climatology will serve as the foundation for a global drought/flood monitoring system that includes near realtime daily updates of the global land states.
Preparation and in vivo evaluation of cationic elastic liposomes comprising highly skin-permeable growth factors combined with hyaluronic acid for enhanced diabetic wound-healing therapy.

PubMed

Choi, Jeong Uk; Lee, Seong Wook; Pangeni, Rudra; Byun, Youngro; Yoon, In-Soo; Park, Jin Woo

2017-07-15

To enhance the therapeutic effects of exogenous administration of growth factors (GFs) in the treatment of chronic wounds, we constructed GF combinations of highly skin-permeable epidermal growth factor (EGF), insulin-like growth factor-I (IGF-I), and platelet-derived growth factor-A (PDGF-A). We genetically conjugated a low-molecular-weight protamine (LMWP) to the N-termini of these GFs to form LMWP-EGF, LMWP-IGF-I, and LMWP-PDGF-A. Subsequently, these molecules were complexed with hyaluronic acid (HA). Combinations of native or LMWP-fused GFs significantly promoted fibroblast proliferation and the synthesis of procollagen, with a magnification of these results observed after the GFs were complexed with HA. The optimal proportions of LMWP-EGF, LMWP-IGF-I, LMWP-PDGF-A, and HA were 1, 1, 0.02, and 200, respectively. After confirming the presence of a synergistic effect, we incorporated the LMWP-fused GFs-HA complex into cationic elastic liposomes (ELs) of 107±0.757nm in diameter and a zeta potential of 56.5±1.13mV. The LMWP-fused GFs had significantly improved skin permeation compared with native GFs. The in vitro wound recovery rate of the LMWP-fused GFs-HA complex was 23% higher than that of cationic ELs composed of LMWP-fused GFs alone. Moreover, the cationic ELs containing the LMWP-fused GFs-HA complex significantly accelerated the wound closure rate in a diabetic mouse model and the wound size was maximally decreased by 65% and 58% compared to cationic ELs loaded with vehicle or native GFs-HA complex, respectively. Thus, topical treatment with cationic ELs loaded with the LMWP-fused GFs-HA complex synergistically enhanced the healing of chronic wounds, exerting both rapid and prolonged effects. We believe that our study makes a significant contribution to the literature, because it demonstrated the potential application of cationic elastic liposomes as topical delivery systems for growth factors (GFs) that have certain limitations in their therapeutic effects (e.g., low percutaneous absorption of GFs at the lesion site and the requirement for various GFs at different healing stages). Topical treatment with cationic elastic liposomes loaded with highly skin-permeable low-molecular-weight protamine (LMWP)-fused GFs-hyaluronic acid (HA) complex synergistically enhanced the healing of diabetic wounds, exerting both rapid and prolonged effects. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.
A near real time regional JPSS and GOES-R data assimilation system for high impact weather research and applications

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Schmit, T. J.

2014-12-01

JPSS and GOES-R observations play important role in numerical weather prediction (NWP). However, how to best represent the information from satellite observations and how to get value added information from these satellite data into regional NWP models, including both radiance and derived products, still need investigations. In order to enhance the applications of JPSS and GOES-R data in regional NWP for high impact weather forecasts, scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have recently developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system consists of the community Gridpoint Statistical Interpolation (GSI) assimilation system and the advanced Weather Research Forecast (WRF) model. In addition to assimilate GOES, AMSUA/AMSUB, HIRS, MHS, ATMS (Suomi-NPP), AIRS and IASI radiances, the SDAT is also able to assimilate satellite-derived products such as hyperspectral IR retrieved temperature and moisture profiles, total precipitable water (TPW), GOES Sounder (and future GOES-R) layer precipitable water (LPW) and GOES Imager atmospheric motion vector (AMV) products into the system. Real time forecasted GOES infrared (IR) images simulated from SDAT output have also been part of the SDAT system for applications and forecast evaluations. To set up the system parameters, a series of experiments have been carried out to test the impacts of different initialization schemes, including different background error matrix, different NCEP global model date sets, and different WRF model horizontal resolutions. Using SDAT as a research testbed, researches have been conducted for different satellite data impacts study, as well as different techniques for handling clouds in radiance assimilation. Since the fall of 2013, the SDAT system has been running in near real time. The results from historical cases and 2014 hurricane season cases will be compared with the operational GFS and HWRF, and presented at the meeting.
A Preliminary Examination of the Second Generation CMORPH Real-time Production

NASA Astrophysics Data System (ADS)

Joyce, R.; Xie, P.; Wu, S.

2017-12-01

The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05olat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and precipitation simulations from the NCEP operational global forecast system (GFS). Inputs from the various sources are first inter-calibrated to ensure quantitative consistencies in representing precipitation events of different intensities through PDF calibration against a common reference standard. The inter-calibrated PMW retrievals and IR-based precipitation estimates are then propagated from their respective observation times to the target analysis time along the motion vectors of the precipitating clouds. Motion vectors are first derived separately from the satellite IR based precipitation estimates and the GFS precipitation fields. These individually derived motion vectors are then combined through a 2D-VAR technique to form an analyzed field of cloud motion vectors over the entire globe. The propagated PMW and IR based precipitation estimates are finally integrated into a single field of global precipitation through the Kalman Filter framework. A set of procedures have been established to examine the performance of the CMORPH2 real-time production. CMORPH2 satellite precipitation estimates are compared against the CPC daily gauge analysis, Stage IV radar precipitation over the CONUS, and numerical model forecasts to discover potential shortcomings and quantify improvements against the first generation CMORPH. Special attention has been focused on the CMORPH behavior over high-latitude areas beyond the coverage of the first generation CMORPH. Detailed results will be reported at the AGU.
Effects of Planetary Boundary Layer Parameterizations on CWRF Regional Climate Simulation

NASA Astrophysics Data System (ADS)

Liu, S.; Liang, X.

2011-12-01

Planetary Boundary Layer (PBL) parameterizations incorporated in CWRF (Climate extension of the Weather Research and Forecasting model) are first evaluated by comparing simulated PBL heights with observations. Among the 10 evaluated PBL schemes, 2 (CAM, UW) are new in CWRF while the other 8 are original WRF schemes. MYJ, QNSE and UW determine the PBL heights based on turbulent kinetic energy (TKE) profiles, while others (YSU, ACM, GFS, CAM, TEMF) are from bulk Richardson criteria. All TKE-based schemes (MYJ, MYNN, QNSE, UW, Boulac) substantially underestimate convective or residual PBL heights from noon toward evening, while others (ACM, CAM, YSU) well capture the observed diurnal cycle except for the GFS with systematic overestimation. These differences among the schemes are representative over most areas of the simulation domain, suggesting systematic behaviors of the parameterizations. Lower PBL heights simulated by the QNSE and MYJ are consistent with their smaller Bowen ratios and heavier rainfalls, while higher PBL tops by the GFS correspond to warmer surface temperatures. Effects of PBL parameterizations on CWRF regional climate simulation are then compared. The QNSE PBL scheme yields systematically heavier rainfall almost everywhere and throughout the year; this is identified with a much greater surface Bowen ratio (smaller sensible versus larger latent heating) and wetter soil moisture than other PBL schemes. Its predecessor MYJ scheme shares the same deficiency to a lesser degree. For temperature, the performance of the QNSE and MYJ schemes remains poor, having substantially larger rms errors in all seasons. GFS PBL scheme also produces large warm biases. Pronounced sensitivities are also found to the PBL schemes in winter and spring over most areas except the southern U.S. (Southeast, Gulf States, NAM); excluding the outliers (QNSE, MYJ, GFS) that cause extreme biases of -6 to +3°C, the differences among the schemes are still visible (±2°C), where the CAM is generally more realistic. QNSE, MYJ, GFS and BouLac PBL parameterizations are identified as obvious outliers of overall performance in representing precipitation, surface air temperature or PBL height variations. Their poor performance may result from deficiencies in physical formulations, dependences on applicable scales, or trouble numerical implementations, requiring future detailed investigation to isolate the actual cause.
Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Brad; Blackwell, William

2014-01-01

Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. This paper will describe the bias correction technique and results from forecasts evaluated by validation against a Total Precipitable Water (TPW) product from CIRA and against Global Forecast System (GFS) analyses.
Autonomous water sampling for long-term monitoring of trace metals in remote environments.

PubMed

Kim, Hyojin; Bishop, James K B; Wood, Todd J; Fung, Inez Y

2012-10-16

A remotely controlled autonomous method for long-term high-frequency sampling of environmental waters in remote locations is described. The method which preserves sample integrity of dissolved trace metals and major ions for month-long periods employs a gravitational filtration system (GFS) that separates dissolved and particulate phases as samples are collected. The key elements of GFS are (1) a modified "air-outlet" filter holder to maximize filtration rate and thus minimize filtration artifacts; and (2) the direct delivery of filtrate to dedicated bottle sets for specific analytes. Depth and screen filter types were evaluated with depth filters showing best performance. GFS performance is validated using ground, stream, and estuary waters. Over 30 days of storage, samples with GFS treatment had average recoveries of 95 ± 19% and 105 ± 7% of Fe and Mn, respectively; without GFS treatment, average recoveries were only 16% and 18%. Dissolved major cations K, Mg, and Na were stable independent of collection methodology, whereas Ca in some groundwater samples decreased up to 42% without GFS due to CaCO(3) precipitation. In-field performance of GFS equipped autosamplers is demonstrated using ground and streamwater samples collected at the Angelo Coast Range Reserve, California from October 3 to November 4 2011.
Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: Impact of microphysical parameterization schemes

NASA Astrophysics Data System (ADS)

Singh, K. S.; Bonthu, Subbareddy; Purvaja, R.; Robin, R. S.; Kannan, B. A. M.; Ramesh, R.

2018-04-01

This study attempts to investigate the real-time prediction of a heavy rainfall event over the Chennai Metropolitan City, Tamil Nadu, India that occurred on 01 December 2015 using Advanced Research Weather Research and Forecasting (WRF-ARW) model. The study evaluates the impact of six microphysical (Lin, WSM6, Goddard, Thompson, Morrison and WDM6) parameterization schemes of the model on prediction of heavy rainfall event. In addition, model sensitivity has also been evaluated with six Planetary Boundary Layer (PBL) and two Land Surface Model (LSM) schemes. Model forecast was carried out using nested domain and the impact of model horizontal grid resolutions were assessed at 9 km, 6 km and 3 km. Analysis of the synoptic features using National Center for Environmental Prediction Global Forecast System (NCEP-GFS) analysis data revealed strong upper-level divergence and high moisture content at lower level were favorable for the occurrence of heavy rainfall event over the northeast coast of Tamil Nadu. The study signified that forecasted rainfall was more sensitive to the microphysics and PBL schemes compared to the LSM schemes. The model provided better forecast of the heavy rainfall event using the logical combination of Goddard microphysics, YSU PBL and Noah LSM schemes, and it was mostly attributed to timely initiation and development of the convective system. The forecast with different horizontal resolutions using cumulus parameterization indicated that the rainfall prediction was not well represented at 9 km and 6 km. The forecast with 3 km horizontal resolution provided better prediction in terms of timely initiation and development of the event. The study highlights that forecast of heavy rainfall events using a high-resolution mesoscale model with suitable representations of physical parameterization schemes are useful for disaster management and planning to minimize the potential loss of life and property.
A similarity retrieval approach for weighted track and ambient field of tropical cyclones

NASA Astrophysics Data System (ADS)

Li, Ying; Xu, Luan; Hu, Bo; Li, Yuejun

2018-03-01

Retrieving historical tropical cyclones (TC) which have similar position and hazard intensity to the objective TC is an important means in TC track forecast and TC disaster assessment. A new similarity retrieval scheme is put forward based on historical TC track data and ambient field data, including ERA-Interim reanalysis and GFS and EC-fine forecast. It takes account of both TC track similarity and ambient field similarity, and optimal weight combination is explored subsequently. Result shows that both the distance and direction errors of TC track forecast at 24-hour timescale follow an approximately U-shape distribution. They tend to be large when the weight assigned to track similarity is close to 0 or 1.0, while relatively small when track similarity weight is from 0.2˜0.7 for distance error and 0.3˜0.6 for direction error.
Real-Time Ocean Prediction System for the East Coast of India

NASA Astrophysics Data System (ADS)

Warrior, H. V.

2016-02-01

The primary objective of the research work reported in this abstract was to develop a Realtime Environmental model for Ocean Dispersion and Impact (as part of an already in-place Decision Support System) for the purpose of radiological safety for the area along Kalpakkam (East Indian) coast. This system involves combining real-time ocean observations with numerical models of ocean processes to provide hindcasts, nowcasts and forecasts of currents, tides and waves. In this work we present the development of an Automated Coupled Atmospheric - Ocean Model (we call it IIT-CAOM) used to forecast the sea surface currents, sea surface temperature (SST) and salinity etc of the Bay of Bengal region under the influence of transient and unsteady atmospheric conditions. This method uses a coupling of Atmosphere and Ocean model. The models used here are the WRF for atmospheric simulations and POM for the ocean counterpart. It has a 3 km X 3 km resolution. This Coupled Model uses GFS (Global Forecast System) Data or FNL (Final Analyses) Data as initial conditions for jump-starting the atmospheric model. The Atmospheric model is run first thus extracting air temperature, wind speed and relative humidity. The heat flux subroutine computes the net heat flux, using above mentioned parameters data. The net heat flux feeds to the ocean model by simply adding net heat flux subroutine to the ocean model code without changing the model original structure. The online forecast of the IIT-CAOM is currently available in the web. The whole system has been automized and runs without any more manual support. The IIT-CAOM simulations have been carried out for Kalpakkam region, which is located on the East coast of India, about 70 km south of Chennai in Tamilnadu State and a three day forecast of sea surface currents, sea surface temperature (SST) and salinity, etc have been obtained.
Rate-programming of nano-particulate delivery systems for smart bioactive scaffolds in tissue engineering.

PubMed

Izadifar, Mohammad; Haddadi, Azita; Chen, Xiongbiao; Kelly, Michael E

2015-01-09

Development of smart bioactive scaffolds is of importance in tissue engineering, where cell proliferation, differentiation and migration within scaffolds can be regulated by the interactions between cells and scaffold through the use of growth factors (GFs) and extra cellular matrix peptides. One challenge in this area is to spatiotemporally control the dose, sequence and profile of release of GFs so as to regulate cellular fates during tissue regeneration. This challenge would be addressed by rate-programming of nano-particulate delivery systems, where the release of GFs via polymeric nanoparticles is controlled by means of the methods of, such as externally-controlled and physicochemically/architecturally-modulated so as to mimic the profile of physiological GFs. Identifying and understanding such factors as the desired release profiles, mechanisms of release, physicochemical characteristics of polymeric nanoparticles, and externally-triggering stimuli are essential for designing and optimizing such delivery systems. This review surveys the recent studies on the desired release profiles of GFs in various tissue engineering applications, elucidates the major release mechanisms and critical factors affecting release profiles, and overviews the role played by the mathematical models for optimizing nano-particulate delivery systems. Potentials of stimuli responsive nanoparticles for spatiotemporal control of GF release are also presented, along with the recent advances in strategies for spatiotemporal control of GF delivery within tissue engineered scaffolds. The recommendation for the future studies to overcome challenges for developing sophisticated particulate delivery systems in tissue engineering is discussed prior to the presentation of conclusions drawn from this paper.
Next Generation Community Based Unified Global Modeling System Development and Operational Implementation Strategies at NCEP

NASA Astrophysics Data System (ADS)

Tallapragada, V.

2017-12-01

NOAA's Next Generation Global Prediction System (NGGPS) has provided the unique opportunity to develop and implement a non-hydrostatic global model based on Geophysical Fluid Dynamics Laboratory (GFDL) Finite Volume Cubed Sphere (FV3) Dynamic Core at National Centers for Environmental Prediction (NCEP), making a leap-step advancement in seamless prediction capabilities across all spatial and temporal scales. Model development efforts are centralized with unified model development in the NOAA Environmental Modeling System (NEMS) infrastructure based on Earth System Modeling Framework (ESMF). A more sophisticated coupling among various earth system components is being enabled within NEMS following National Unified Operational Prediction Capability (NUOPC) standards. The eventual goal of unifying global and regional models will enable operational global models operating at convective resolving scales. Apart from the advanced non-hydrostatic dynamic core and coupling to various earth system components, advanced physics and data assimilation techniques are essential for improved forecast skill. NGGPS is spearheading ambitious physics and data assimilation strategies, concentrating on creation of a Common Community Physics Package (CCPP) and Joint Effort for Data Assimilation Integration (JEDI). Both initiatives are expected to be community developed, with emphasis on research transitioning to operations (R2O). The unified modeling system is being built to support the needs of both operations and research. Different layers of community partners are also established with specific roles/responsibilities for researchers, core development partners, trusted super-users, and operations. Stakeholders are engaged at all stages to help drive the direction of development, resources allocations and prioritization. This talk presents the current and future plans of unified model development at NCEP for weather, sub-seasonal, and seasonal climate prediction applications with special emphasis on implementation of NCEP FV3 Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) into operations by 2019.
The Global File System

NASA Technical Reports Server (NTRS)

Soltis, Steven R.; Ruwart, Thomas M.; OKeefe, Matthew T.

1996-01-01

The global file system (GFS) is a prototype design for a distributed file system in which cluster nodes physically share storage devices connected via a network-like fiber channel. Networks and network-attached storage devices have advanced to a level of performance and extensibility so that the previous disadvantages of shared disk architectures are no longer valid. This shared storage architecture attempts to exploit the sophistication of storage device technologies whereas a server architecture diminishes a device's role to that of a simple component. GFS distributes the file system responsibilities across processing nodes, storage across the devices, and file system resources across the entire storage pool. GFS caches data on the storage devices instead of the main memories of the machines. Consistency is established by using a locking mechanism maintained by the storage devices to facilitate atomic read-modify-write operations. The locking mechanism is being prototyped in the Silicon Graphics IRIX operating system and is accessed using standard Unix commands and modules.
Assessing the Impact of Observations on the Prediction of Effective Atmospheric Angular Momentum from NAVGEM

NASA Astrophysics Data System (ADS)

Baker, N. L.; Langland, R.

2016-12-01

Variations in Earth rotation are measured by comparing a time based on Earth's variable rotation rate about its axis to a time standard based on an internationally coordinated ensemble of atomic clocks that provide a uniform time scale. The variability of Earth's rotation is partly due to the changes in angular momentum that occur in the atmosphere and ocean as weather patterns and ocean features develop, propagate, and dissipate. The NAVGEM Effective Atmospheric Angular Momentum Functions (EAAMF) and their predictions are computed following Barnes et al. (1983), and provided to the U.S. Naval Observatory daily. These along with similar data from the NOAA GFS model are used to calculate and predict the Earth orientation parameters (Stamatakos et al., 2016). The Navy's high-resolution global weather prediction system consists of the Navy Global Environmental Model (NAVGEM; Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (4DVar) (Kuhl et al., 2013). An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Langland and Baker, 2004). The FSOI allows for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. For this study, along with the FSOI based on the global moist energy error norm, we computed the FSOI using an error norm based on the Effective Angular Momentum Functions. This modification allowed us to assess which observations were most beneficial in reducing the 24-hr forecast error for the atmospheric angular momentum.
Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste; Iredell, Lena; Rosenberg, Robert

2013-01-01

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.
Tropospheric dry layers in the tropical western Pacific: comparisons of GPS radio occultation with multiple data sets

NASA Astrophysics Data System (ADS)

Rieckh, Therese; Anthes, Richard; Randel, William; Ho, Shu-Peng; Foelsche, Ulrich

2017-03-01

We use GPS radio occultation (RO) data to investigate the structure and temporal behavior of extremely dry, high-ozone tropospheric air in the tropical western Pacific during the 6-week period of the CONTRAST (CONvective TRansport of Active Species in the Tropics) experiment (January and February 2014). Our analyses are aimed at testing whether the RO method is capable of detecting these extremely dry layers and evaluating comparisons with in situ measurements, satellite observations, and model analyses. We use multiple data sources as comparisons, including CONTRAST research aircraft profiles, radiosonde profiles, AIRS (Atmospheric Infrared Sounder) satellite retrievals, and profiles extracted from the ERA (ERA-Interim reanalysis) and the GFS (US National Weather Service Global Forecast System) analyses, as well as MTSAT-2 satellite images. The independent and complementary radiosonde, aircraft, and RO data provide high vertical resolution observations of the dry layers. However, they all have limitations. The coverage of the radiosonde data is limited by having only a single station in this oceanic region; the aircraft data are limited in their temporal and spatial coverage; and the RO data are limited in their number and horizontal resolution over this period. However, nearby observations from the three types of data are highly consistent with each other and with the lower-vertical-resolution AIRS profiles. They are also consistent with the ERA and GFS data. We show that the RO data, used here for the first time to study this phenomenon, contribute significant information on the water vapor content and are capable of detecting layers in the tropics and subtropics with extremely low humidity (less than 10 %), independent of the retrieval used to extract moisture information. Our results also verify the quality of the ERA and GFS data sets, giving confidence to the reanalyses and their use in diagnosing the full four-dimensional structure of the dry layers.

Simulation of low clouds in the Southeast Pacific by the NCEP GFS: sensitivity to vertical mixing

NASA Astrophysics Data System (ADS)

Sun, R.; Moorthi, S.; Xiao, H.; Mechoso, C. R.

2010-12-01

The NCEP Global Forecast System (GFS) model has an important systematic error shared by many other models: stratocumuli are missed over the subtropical eastern oceans. It is shown that this error can be alleviated in the GFS by introducing a consideration of the low-level inversion and making two modifications in the model's representation of vertical mixing. The modifications consist of (a) the elimination of background vertical diffusion above the inversion and (b) the incorporation of a stability parameter based on the cloud-top entrainment instability (CTEI) criterion, which limits the strength of shallow convective mixing across the inversion. A control simulation and three experiments are performed in order to examine both the individual and combined effects of modifications on the generation of the stratocumulus clouds. Individually, both modifications result in enhanced cloudiness in the Southeast Pacific (SEP) region, although the cloudiness is still low compared to the ISCCP climatology. If the modifications are applied together, however, the total cloudiness produced in the southeast Pacific has realistic values. This nonlinearity arises as the effects of both modifications reinforce each other in reducing the leakage of moisture across the inversion. Increased moisture trapped below the inversion than in the control run without modifications leads to an increase in cloud amount and cloud-top radiative cooling. Then a positive feedback due to enhanced turbulent mixing in the planetary boundary layer by cloud-top radiative cooling leads to and maintains the stratocumulus cover. Although the amount of total cloudiness obtained with both modifications has realistic values, the relative contributions of low, middle, and high layers tend to differ from the observations. These results demonstrate that it is possible to simulate realistic marine boundary clouds in large-scale models by implementing direct and physically based improvements in the model parameterizations.
Simulation of low clouds in the Southeast Pacific by the NCEP GFS: sensitivity to vertical mixing

NASA Astrophysics Data System (ADS)

Sun, R.; Moorthi, S.; Xiao, H.; Mechoso, C.-R.

2010-08-01

The NCEP Global Forecast System (GFS) model has an important systematic error shared by many other models: stratocumuli are missed over the subtropical eastern oceans. It is shown that this error can be alleviated in the GFS by introducing a consideration of the low-level inversion and making two modifications in the model's representation of vertical mixing. The modifications consist of (a) the elimination of background vertical diffusion above the inversion and (b) the incorporation of a stability parameter based on the cloud-top entrainment instability (CTEI) criterion, which limits the strength of shallow convective mixing across the inversion. A control simulation and three experiments are performed in order to examine both the individual and combined effects of modifications on the generation of the stratocumulus clouds. Individually, both modifications result in enhanced cloudiness in the Southeast Pacific (SEP) region, although the cloudiness is still low compared to the ISCCP climatology. If the modifications are applied together, however, the total cloudiness produced in the southeast Pacific has realistic values. This nonlinearity arises as the effects of both modifications reinforce each other in reducing the leakage of moisture across the inversion. Increased moisture trapped below the inversion than in the control run without modifications leads to an increase in cloud amount and cloud-top radiative cooling. Then a positive feedback due to enhanced turbulent mixing in the planetary boundary layer by cloud-top radiative cooling leads to and maintains the stratocumulus cover. Although the amount of total cloudiness obtained with both modifications has realistic values, the relative contributions of low, middle, and high layers tend to differ from the observations. These results demonstrate that it is possible to simulate realistic marine boundary clouds in large-scale models by implementing direct and physically based improvements in the model parameterizations.
Ensemble-sensitivity Analysis Based Observation Targeting for Mesoscale Convection Forecasts and Factors Influencing Observation-Impact Prediction

NASA Astrophysics Data System (ADS)

Hill, A.; Weiss, C.; Ancell, B. C.

2017-12-01

The basic premise of observation targeting is that additional observations, when gathered and assimilated with a numerical weather prediction (NWP) model, will produce a more accurate forecast related to a specific phenomenon. Ensemble-sensitivity analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008) is a tool capable of accurately estimating the proper location of targeted observations in areas that have initial model uncertainty and large error growth, as well as predicting the reduction of forecast variance due to the assimilated observation. ESA relates an ensemble of NWP model forecasts, specifically an ensemble of scalar forecast metrics, linearly to earlier model states. A thorough investigation is presented to determine how different factors of the forecast process are impacting our ability to successfully target new observations for mesoscale convection forecasts. Our primary goals for this work are to determine: (1) If targeted observations hold more positive impact over non-targeted (i.e. randomly chosen) observations; (2) If there are lead-time constraints to targeting for convection; (3) How inflation, localization, and the assimilation filter influence impact prediction and realized results; (4) If there exist differences between targeted observations at the surface versus aloft; and (5) how physics errors and nonlinearity may augment observation impacts.Ten cases of dryline-initiated convection between 2011 to 2013 are simulated within a simplified OSSE framework and presented here. Ensemble simulations are produced from a cycling system that utilizes the Weather Research and Forecasting (WRF) model v3.8.1 within the Data Assimilation Research Testbed (DART). A "truth" (nature) simulation is produced by supplying a 3-km WRF run with GFS analyses and integrating the model forward 90 hours, from the beginning of ensemble initialization through the end of the forecast. Target locations for surface and radiosonde observations are computed 6, 12, and 18 hours into the forecast based on a chosen scalar forecast response metric (e.g., maximum reflectivity at convection initiation). A variety of experiments are designed to achieve the aforementioned goals and will be presented, along with their results, detailing the feasibility of targeting for mesoscale convection forecasts.
High resolution probabilistic precipitation forecast over Spain combining the statistical downscaling tool PROMETEO and the AEMET short range EPS system (AEMET/SREPS)

NASA Astrophysics Data System (ADS)

Cofino, A. S.; Santos, C.; Garcia-Moya, J. A.; Gutierrez, J. M.; Orfila, B.

2009-04-01

The Short-Range Ensemble Prediction System (SREPS) is a multi-LAM (UM, HIRLAM, MM5, LM and HRM) multi analysis/boundary conditions (ECMWF, UKMetOffice, DWD and GFS) run twice a day by AEMET (72 hours lead time) over a European domain, with a total of 5 (LAMs) x 4 (GCMs) = 20 members. One of the main goals of this project is analyzing the impact of models and boundary conditions in the short-range high-resolution forecasted precipitation. A previous validation of this method has been done considering a set of climate networks in Spain, France and Germany, by interpolating the prediction to the gauge locations (SREPS, 2008). In this work we compare these results with those obtained by using a statistical downscaling method to post-process the global predictions, obtaining an "advanced interpolation" for the local precipitation using climate network precipitation observations. In particular, we apply the PROMETEO downscaling system based on analogs and compare the SREPS ensemble of 20 members with the PROMETEO statistical ensemble of 5 (analog ensemble) x 4 (GCMs) = 20 members. Moreover, we will also compare the performance of a combined approach post-processing the SREPS outputs using the PROMETEO system. References: SREPS 2008. 2008 EWGLAM-SRNWP Meeting (http://www.aemet.es/documentos/va/divulgacion/conferencias/prediccion/Ewglam/PRED_CSantos.pdf)
Joint Meteorological Statistics of Observing Sites for the Event Horizon Telescope

NASA Astrophysics Data System (ADS)

Lope Córdova Rosado, Rodrigo Eduardo; Doeleman, Sheperd; Paine, Scott; Johnson, Michael; Event Horizon Telescope (EHT)

2018-01-01

The Event Horizon Telescope (EHT) aims to resolve the general relativistic shadow of Sgr A*, the supermassive black hole at the center of our galaxy, via Very Long Baseline Interferometry (VLBI) measurements with a multinational array of radio observatories. In order to optimize the scheduling of future observations, we have developed tools to model the atmospheric opacity at each EHT site using the past 10 years of Global Forecast System (GFS) data describing the atmospheric state. These tools allow us to determine the ideal observing windows for EHT observations and to assess the suitability and impact of new EHT sites. We describe our modeling framework, compare our models to in-situ measurements at EHT sites, and discuss the implications of weather limitations for planned extensions of the EHT to higher frequencies, as well as additional sites and observation windows.
Guide to GFS History File Change on May 1, 2007

Science.gov Websites

Guide to GFS History File Change on May 1, 2007 On May 1, 2007 12Z, the GFS had a major change. The change caused the internal binary GFS history file to change formats. The file is still in spectral space but now pressure is calculated in a different way. Sometime in the future, the GFS history file may be
Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day-of-launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program and NASA's Ground Systems Development and Operations Program. They currently do not have the capability to display and overlay profiles of upper-level observations and numerical weather prediction model forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a tool in the form of a graphical user interface (GUI) that will allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center (KSC) 50 MHz tropospheric wind profiling radar, KSC Shuttle Landing Facility 915 MHz boundary layer wind profiling radar and Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Processing System (AMPS) radiosondes, and then overlay forecast wind profiles from the model point data including the North American Mesoscale (NAM) model, Rapid Refresh (RAP) model and Global Forecast System (GFS) model to assess the performance of these models. The AMU developed an Excel-based tool that provides an objective method for the LWOs to compare the model-forecast upper-level winds to the KSC wind profiling radars and CCAFS AMPS observations to assess the model potential to accurately forecast changes in the upperlevel profile through the launch count. The AMU wrote Excel Visual Basic for Applications (VBA) scripts to automatically retrieve model point data for CCAFS (XMR) from the Iowa State University Archive Data Server (http://mtarchive.qeol.iastate.edu) and the 50 MHz, 915 MHz and AMPS observations from the NASA/KSC Spaceport Weather Data Archive web site (http://trmm.ksc.nasa.gov). The AMU then developed code in Excel VBA to automatically ingest and format the observations and model point data in Excel to ready the data for generating Excel charts for the LWO's. The resulting charts allow the LWOs to independently initialize the three models 0-hour forecasts against the observations to determine which is the best performing model and then overlay the model forecasts on time-matched observations during the launch countdown to further assess the model performance and forecasts. This paper will demonstrate integration of observed and predicted atmospheric conditions into a decision support tool and demonstrate how the GUI is implemented in operations.
The POLIMI forecasting chain for real time flood and drought predictions

NASA Astrophysics Data System (ADS)

Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Mancini, Marco

2016-04-01

Nowadays coupling meteorological and hydrological models is recognized by scientific community as a necessary way to forecast extreme hydrological phenomena, in order to activate useful mitigation measurements and alert systems in advance. The development and implementation of a real-time forecasting chain with a hydro-meteorological operational alert procedure for flood and drought events is presented in this study. Different weather models are used to build the POLIMI operative chain: the probabilistic COSMO-LEPS model with 16 ensembles developed by ARPA-Emilia Romagna, the deterministic Bolam and Moloch models, developed by the Italian ISAC-CNR, and nine further simulations obtained by different runs of the WRF-ARW (3), WRF-NMM (2), ETA2012 (1) and the GFS (3), provided by the private Epson Meteo Center and Terraria companies. All the meteorological runs are then implemented with the rainfall-runoff physically-based distributed FEST-WB model, developed at Politecnico di Milano to obtain a multi-model approach system with hydrological ensemble forecasts in different areas of study over the Italian country. As far as concerning drought predictions, three test-beds are monitored: two in maize fields, one in the Puglia region (South of Italy), and another in the Po Valley area, (northern Italy), and one in a golf course in Milan city. The hydrological model was here calibrated and validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station, TDR probes and remote sensing images. Regarding flood forecasts, two test-sites are chosen: the first one is the urban area northern Milan where three catchments (the Seveso, Olona, and Lambro River basins) are used to show how early warning systems are an effective complement to structural measures for flood control in Milan city which flooded frequently in the last 25 years, while the second test-site is the Idro Lake, located between the Lombardy and Trentino region where the POLIMI hydro-meteorological chain is performed to forecast the hydrometric lake level for a better management of the upstream and downstream basin. The same hydrological model has been here calibrated and validated with observed data coming from local bodies: ARPA Lombardy, Meteonetwork and Meteo Trentino. Reliability of the forecasting system and its benefits are assessed with skill scores on some cases-study occurred in the recent years and through the real-time visualization of the implemented dashboards.
Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.
Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

2012-12-01

Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate forecasts are bias corrected, downscaled and used as inputs to the VIC LSM as well as forecasts based on ESP and CPC official seasonal outlook. For Africa, data from a combination of remote sensing (TMPA-based precipitation, land cover characteristics) and GFS analysis fields (temperature and wind) are used to monitor drought using our soil moisture drought index as well as 1, 3 and and 6-month SPI. River discharge is also estimated at over 900 locations. Seasonal forecasts have been developed using CFSv2 climate forecasts following the approaches used over CONUS. We will discuss the performance of the system to evaluate the depiction of drought over various scales, from regional to the African continent, and over a number of years to capture multiple drought events. Furthermore, the hindcasts from the seasonal drought forecast system are analyzed to assess the ability of seasonal climate models to detect drought on-set and its recovery. Finally, we will discuss whether our ADM provides a pathway to a Global Drought Information System, a goal of the WCRP Drought Task Force.
Understanding the formation mechanism of graphene frameworks synthesized by solvothermal and rapid pyrolytic processes based on an alcohol-sodium hydroxide system.

PubMed

Cui, Huijuan; Zheng, Jianfeng; Yang, Pengju; Zhu, Yanyan; Wang, Zhijian; Zhu, Zhenping

2015-06-03

The determination of ways to facilitate the 2D-oriented assembly of carbons into graphene instead of other carbon structures while restraining the π-π stacking interaction is a challenge for the controllable bulk synthesis of graphene, which is vital both scientifically and technically. In this study, graphene frameworks (GFs) are synthesized by solvothermal and rapid pyrolytic processes based on an alcohol-sodium hydroxide system. The evolution mechanism of GFs is investigated systematically. Under sodium catalysis, the abundant carbon atoms produced by the fast decomposition of solvothermal intermediate self-assembled to graphene. The existence of abundant ether bonds may be favorable for 3D graphene formation. More importantly, GFs were successfully obtained using acetic acid as the carbon source in the synthetic process, suggesting the reasonability of analyzing the formation mechanism. It is quite possible to determine more favorable routes to synthesize graphene under this cognition. The electrochemical energy storage capacity of GFs was also studied, which revealed a high supercapacitor performance with a specific capacitance of 310.7 F/g at the current density of 0.2 A/g.
SP-100 GES/NAT radiation shielding systems design and development testing

NASA Astrophysics Data System (ADS)

Disney, Richard K.; Kulikowski, Henry D.; McGinnis, Cynthia A.; Reese, James C.; Thomas, Kevin; Wiltshire, Frank

1991-01-01

Advanced Energy Systems (AES) of Westinghouse Electric Corporation is under subcontract to the General Electric Company to supply nuclear radiation shielding components for the SP-100 Ground Engineering System (GES) Nuclear Assembly Test to be conducted at Westinghouse Hanford Company at Richland, Washington. The radiation shielding components are integral to the Nuclear Assembly Test (NAT) assembly and include prototypic and non-prototypic radiation shielding components which provide prototypic test conditions for the SP-100 reactor subsystem and reactor control subsystem components during the GES/NAT operations. W-AES is designing three radiation shield components for the NAT assembly; a prototypic Generic Flight System (GFS) shield, the Lower Internal Facility Shield (LIFS), and the Upper Internal Facility Shield (UIFS). This paper describes the design approach and development testing to support the design, fabrication, and assembly of these three shield components for use within the vacuum vessel of the GES/NAT. The GES/NAT shields must be designed to operate in a high vacuum which simulates space operations. The GFS shield and LIFS must provide prototypic radiation/thermal environments and mechanical interfaces for reactor system components. The NAT shields, in combination with the test facility shielding, must provide adequate radiation attenuation for overall test operations. Special design considerations account for the ground test facility effects on the prototypic GFS shield. Validation of the GFS shield design and performance will be based on detailed Monte Carlo analyses and developmental testing of design features. Full scale prototype testing of the shield subsystems is not planned.
Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.
A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.
Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades

NASA Astrophysics Data System (ADS)

Pawar, Prashant M.; Ganguli, Ranjan

2007-07-01

A structural health monitoring (SHM) methodology is developed for composite rotor blades. An aeroelastic analysis of composite rotor blades based on the finite element method in space and time and with implanted matrix cracking and debonding/delamination damage is used to obtain measurable system parameters such as blade response, loads and strains. A rotor blade with a two-cell airfoil section and [0/±45/90]s family of laminates is used for numerical simulations. The model based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems (GFS) are developed for global online damage detection using displacement and force-based measurement deviations between damaged and undamaged conditions and for local online damage detection using strains. It is observed that the success rate of the GFS depends on number of measurements, type of measurements and training and testing noise level. The GFS work quite well with noisy data and is recommended for online SHM of composite helicopter rotor blades.
An Assessment of the SST Simulation Using the Climate Forecast System Coupled to the SSiB Surface Model

NASA Astrophysics Data System (ADS)

Wang, Y.; Xue, Y.; Huang, B.; Lee, J.; De Sales, F.

2016-12-01

A long term simulation has been conducted using the Climate Forecast System (CFSv2) coupled to the SSiB-2 land model, which consists of the Global Forecast System atmospheric model (GFS) and the Modular Ocean model - version 4 (MOM4) as the ocean component. This study evaluates the model's performance in simulating sea surface temperature (SST) mean state, trend, and inter-annual and decadal variabilities. The model is able to produce the reasonable spatial distribution of the SST climatology; however, it has prominent large scale biases. In the middle latitude of the Northern Hemisphere, major cold biases is close to the warm side of the large SST gradients, which may be associated with the weaker Kuroshio and Gulf Stream extensions that diffuse the SST gradient. IN addition, warm biases extend along the west coast of the North America continent to the high latitude, which may be related with excessive Ekman down-welling and solar radiation fluxes reaching to the surface due to the lack of cloud there. Warm biases also exist over the tropical cold tough areas in the Pacific and Atlantic. The global SST trend and interannual variations are well captured except for that in the south Hemisphere after year 2000, which is mainly contributed by the bias from the southern Pacific Ocean. Although the model fails to accurately produce ENSO events in proper years, it does reproduce the ENSO frequency well; they are skewed toward more warm events after 1990. The model also shows ability in SST decadal variation, such as the so-called inter-decadal Pacific oscillation (IPO); however, its phases seem to go reversely compared with the observation.
Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization

NASA Astrophysics Data System (ADS)

Lee, Y. J.; Bonfanti, C. E.; Trailovic, L.; Etherton, B.; Govett, M.; Stewart, J.

2017-12-01

At present, a fraction of all satellite observations are ultimately used for model assimilation. The satellite data assimilation process is computationally expensive and data are often reduced in resolution to allow timely incorporation into the forecast. This problem is only exacerbated by the recent launch of Geostationary Operational Environmental Satellite (GOES)-16 satellite and future satellites providing several order of magnitude increase in data volume. At the NOAA Earth System Research Laboratory (ESRL) we are researching the use of machine learning the improve the initial selection of satellite data to be used in the model assimilation process. In particular, we are investigating the use of deep learning. Deep learning is being applied to many image processing and computer vision problems with great success. Through our research, we are using convolutional neural network to find and mark regions of interest (ROI) to lead to intelligent extraction of observations from satellite observation systems. These targeted observations will be used to improve the quality of data selected for model assimilation and ultimately improve the impact of satellite data on weather forecasts. Our preliminary efforts to identify the ROI's are focused in two areas: applying and comparing state-of-art convolutional neural network models using the analysis data from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) weather model, and using these results as a starting point to optimize convolution neural network model for pattern recognition on the higher resolution water vapor data from GOES-WEST and other satellite. This presentation will provide an introduction to our convolutional neural network model to identify and process these ROI's, along with the challenges of data preparation, training the model, and parameter optimization.
An Initial Assessment of the Impact of CYGNSS Ocean Surface Wind Assimilation on Navy Global and Mesoscale Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Baker, N. L.; Tsu, J.; Swadley, S. D.

2017-12-01

We assess the impact of assimilation of CYclone Global Navigation Satellite System (CYGNSS) ocean surface winds observations into the NAVGEM[i] global and COAMPS®[ii] mesoscale numerical weather prediction (NWP) systems. Both NAVGEM and COAMPS® used the NRL 4DVar assimilation system NAVDAS-AR[iii]. Long term monitoring of the NAVGEM Forecast Sensitivity Observation Impact (FSOI) indicates that the forecast error reduction for ocean surface wind vectors (ASCAT and WindSat) are significantly larger than for SSMIS wind speed observations. These differences are larger than can be explained by simply two pieces of information (for wind vectors) versus one (wind speed). To help understand these results, we conducted a series of Observing System Experiments (OSEs) to compare the assimilation of ASCAT wind vectors with the equivalent (computed) ASCAT wind speed observations. We found that wind vector assimilation was typically 3 times more effective at reducing the NAVGEM forecast error, with a higher percentage of beneficial observations. These results suggested that 4DVar, in the absence of an additional nonlinear outer loop, has limited ability to modify the analysis wind direction. We examined several strategies for assimilating CYGNSS ocean surface wind speed observations. In the first approach, we assimilated CYGNSS as wind speed observations, following the same methodology used for SSMIS winds. The next two approaches converted CYGNSS wind speed to wind vectors, using NAVGEM sea level pressure fields (following Holton, 1979), and using NAVGEM 10-m wind fields with the AER Variational Analysis Method. Finally, we compared these methods to CYGNSS wind speed assimilation using multiple outer loops with NAVGEM Hybrid 4DVar. Results support the earlier studies suggesting that NAVDAS-AR wind speed assimilation is sub-optimal. We present detailed results from multi-month NAVGEM assimilation runs along with case studies using COAMPS®. Comparisons include the fit of analyses and forecasts with in-situ observations and analyses from other NWP centers (e.g. ECMWF and GFS). [i] NAVy Global Environmental Model [ii] COAMPS® is a registered trademark of the Naval Research Laboratory for the Navy's Coupled Ocean Atmosphere Mesoscale Prediction System. [iii] NRL Atmospheric Variational Data Assimilation System
Operational flood forecasting: further lessons learned form a recent inundation in Tuscany, Italy

NASA Astrophysics Data System (ADS)

Caparrini, F.; Castelli, F.; di Carlo, E.

2010-09-01

After a few years of experimental setup, model refinement and parameters calibration, a distributed flood forecasting system for the Tuscany region was promoted to operational use in early 2008. The hydrologic core of the system, MOBIDIC, is a fully distributed soil moisture accounting model, with sequential assimilation of hydrometric data. The model is forced by the real-time dense hydrometeorological network of the Regional Hydrologic Service as well from the QPF products of a number of different limited area meteorological models (LAMI, WRF+ECMWF, WRF+GFS). Given the relatively short response time of the Tuscany basins, the river flow forecasts based on ground measured precipitation are operationally used mainly as a monitoring tool, while the true usable predictions are necessarily based on the QPF input. The first severe flooding event the system had to face occurred in late December 2009, when a failure of the right levee of the Serchio river caused an extensive inundation (on December 25th). In the days following the levee breaking, intensive monitoring and forecast was needed (another flood peak occurred on the night between December 29th and January 1st 2010) as a support for decisions regarding the management of the increased vulnerability of the area and the planning of emergency reparation works at the river banks. The operational use of the system during such a complex event, when both the meteorological and the hydrological components may be said to have performed well form a strict modeling point of view, brought to attention a number of additional issues about the system as a whole. The main of these issues may be phrased in terms of additional system requirements, namely: the ranking of different QPF products in terms of some likelihood measure; the rapid redefinition of alarm thresholds due to sudden changes in the river flow capacity; the supervised prediction for evaluating the consequences of different management scenarios for reservoirs, regulated floodplains, levees, etc. In order to quantitatively address these issues, a multivariate sensitivity hindcast of the above event is presented here, where variation of model predictions and subsequent likely decision making are measured against QPF accuracy, other possible levees failures, different reservoir releases.
Using Enabling Technologies to Facilitate the Comparison of Satellite Observations with the Model Forecasts for Hurricane Study

NASA Astrophysics Data System (ADS)

Li, P.; Knosp, B.; Hristova-Veleva, S. M.; Niamsuwan, N.; Johnson, M. P.; Shen, T. P. J.; Tanelli, S.; Turk, J.; Vu, Q. A.

2014-12-01

Due to their complexity and volume, the satellite data are underutilized in today's hurricane research and operations. To better utilize these data, we developed the JPL Tropical Cyclone Information System (TCIS) - an Interactive Data Portal providing fusion between Near-Real-Time satellite observations and model forecasts to facilitate model evaluation and improvement. We have collected satellite observations and model forecasts in the Atlantic Basin and the East Pacific for the hurricane seasons since 2010 and supported the NASA Airborne Campaigns for Hurricane Study such as the Genesis and Rapid Intensification Processes (GRIP) in 2010 and the Hurricane and Severe Storm Sentinel (HS3) from 2012 to 2014. To enable the direct inter-comparisons of the satellite observations and the model forecasts, the TCIS was integrated with the NASA Earth Observing System Simulator Suite (NEOS3) to produce synthetic observations (e.g. simulated passive microwave brightness temperatures) from a number of operational hurricane forecast models (HWRF and GFS). An automated process was developed to trigger NEOS3 simulations via web services given the location and time of satellite observations, monitor the progress of the NEOS3 simulations, display the synthetic observation and ingest them into the TCIS database when they are done. In addition, three analysis tools, the joint PDF analysis of the brightness temperatures, ARCHER for finding the storm-center and the storm organization and the Wave Number Analysis tool for storm asymmetry and morphology analysis were integrated into TCIS to provide statistical and structural analysis on both observed and synthetic data. Interactive tools were built in the TCIS visualization system to allow the spatial and temporal selections of the datasets, the invocation of the tools with user specified parameters, and the display and the delivery of the results. In this presentation, we will describe the key enabling technologies behind the design of the TCIS interactive data portal and analysis tools, including the spatial database technology for the representation and query of the level 2 satellite data, the automatic process flow using web services, the interactive user interface using the Google Earth API, and a common and expandable Python wrapper to invoke the analysis tools.

Near-Field Tsunami Models with Rapid Earthquake Source Inversions from Land and Ocean-Based Observations: The Potential for Forecast and Warning

NASA Astrophysics Data System (ADS)

Melgar, D.; Bock, Y.; Crowell, B. W.; Haase, J. S.

2013-12-01

Computation of predicted tsunami wave heights and runup in the regions adjacent to large earthquakes immediately after rupture initiation remains a challenging problem. Limitations of traditional seismological instrumentation in the near field which cannot be objectively employed for real-time inversions and the non-unique source inversion results are a major concern for tsunami modelers. Employing near-field seismic, GPS and wave gauge data from the Mw 9.0 2011 Tohoku-oki earthquake, we test the capacity of static finite fault slip models obtained from newly developed algorithms to produce reliable tsunami forecasts. First we demonstrate the ability of seismogeodetic source models determined from combined land-based GPS and strong motion seismometers to forecast near-source tsunamis in ~3 minutes after earthquake origin time (OT). We show that these models, based on land-borne sensors only tend to underestimate the tsunami but are good enough to provide a realistic first warning. We then demonstrate that rapid ingestion of offshore shallow water (100 - 1000 m) wave gauge data significantly improves the model forecasts and possible warnings. We ingest data from 2 near-source ocean-bottom pressure sensors and 6 GPS buoys into the earthquake source inversion process. Tsunami Green functions (tGFs) are generated using the GeoClaw package, a benchmarked finite volume code with adaptive mesh refinement. These tGFs are used for a joint inversion with the land-based data and substantially improve the earthquake source and tsunami forecast. Model skill is assessed by detailed comparisons of the simulation output to 2000+ tsunami runup survey measurements collected after the event. We update the source model and tsunami forecast and warning at 10 min intervals. We show that by 20 min after OT the tsunami is well-predicted with a high variance reduction to the survey data and by ~30 minutes a model that can be considered final, since little changed is observed afterwards, is achieved. This is an indirect approach to tsunami warning, it relies on automatic determination of the earthquake source prior to tsunami simulation. It is more robust than ad-hoc approaches because it relies on computation of a finite-extent centroid moment tensor to objectively determine the style of faulting and the fault plane geometry on which to launch the heterogeneous static slip inversion. Operator interaction and physical assumptions are minimal. Thus, the approach can provide the initial conditions for tsunami simulation (seafloor motion) irrespective of the type of earthquake source and relies heavily on oceanic wave gauge measurements for source determination. It reliably distinguishes among strike-slip, normal and thrust faulting events, all of which have been observed recently to occur in subduction zones and pose distinct tsunami hazards.
SP-100 GES/NAT radiation shielding systems design and development testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Disney, R.K.; Kulikowski, H.D.; McGinnis, C.A.

1991-01-10

Advanced Energy Systems (AES) of Westinghouse Electric Corporation is under subcontract to the General Electric Company to supply nuclear radiation shielding components for the SP-100 Ground Engineering System (GES) Nuclear Assembly Test to be conducted at Westinghouse Hanford Company at Richland, Washington. The radiation shielding components are integral to the Nuclear Assembly Test (NAT) assembly and include prototypic and non-prototypic radiation shielding components which provide prototypic test conditions for the SP-100 reactor subsystem and reactor control subsystem components during the GES/NAT operations. W-AES is designing three radiation shield components for the NAT assembly; a prototypic Generic Flight System (GFS) shield,more » the Lower Internal Facility Shield (LIFS), and the Upper Internal Facility Shield (UIFS). This paper describes the design approach and development testing to support the design, fabrication, and assembly of these three shield components for use within the vacuum vessel of the GES/NAT. The GES/NAT shields must be designed to operate in a high vacuum which simulates space operations. The GFS shield and LIFS must provide prototypic radiation/thermal environments and mechanical interfaces for reactor system components. The NAT shields, in combination with the test facility shielding, must provide adequate radiation attenuation for overall test operations. Special design considerations account for the ground test facility effects on the prototypic GFS shield. Validation of the GFS shield design and performance will be based on detailed Monte Carlo analyses and developmental testing of design features. Full scale prototype testing of the shield subsystems is not planned.« less
Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.
Automated turbulence forecasts for aviation hazards

NASA Astrophysics Data System (ADS)

Sharman, R.; Frehlich, R.; Vandenberghe, F.

2010-09-01

An operational turbulence forecast system for commercial and aviation use is described that is based on an ensemble of turbulence diagnostics derived from standard NWP model outputs. In the U. S. this forecast product is named GTG (Graphical Turbulence Guidance) and has been described in detail in Sharman et al., WAF 2006. Since turbulence has many sources in the atmosphere, the ensemble approach of combining diagnostics has been shown to provide greater statistical accuracy than the use of a single diagnostic, or of a subgrid tke parameterization. GTG is sponsored by the FAA, and has undergone rigorous accuracy, safety, and usability evaluations. The GTG product is now hosted on NOAA's Aviation Data Service (ADDS), web site (http://aviationweather.gov/), for access by pilots, air traffic controllers, and dispatchers. During this talk the various turbulence diagnostics, their statistical properties, and their relative performance (based on comparisons to observations) will be presented. Importantly, the model output is ɛ1/3 (where ɛ is the eddy dissipation rate), so is aircraft independent. The diagnostics are individually and collectively calibrated so that their PDFs satisfy the expected log normal distribution of ɛ^1/3. Some of the diagnostics try to take into account the role of gravity waves and inertia-gravity waves in the turbulence generation process. Although the current GTG product is based on the RUC forecast model running over the CONUS, it is transitioning to a WRF based product, and in fact WRF-based versions are currently running operationally over Taiwan and has also been implemented for use by the French Navy in climatological studies. Yet another version has been developed which uses GFS model output to provide global turbulence forecasts. Thus the forecast product is available as a postprocessing program for WRF or other model output and provides 3D maps of turbulence likelihood of any region where NWP model data is available. Although the current GTG has been used mainly for large commercial aircraft, since the output is aircraft independent it could readily be scaled to smaller aircraft such as UAVs. Further, the ensemble technique allows the diagnostics to be used to form probabilistic forecasts, in a manner similar to ensemble NWP forecasts.
Improved Survival of Full-Thickness Skin Graft With Low-Molecular Weight Heparin-Protamine Micro/Nanoparticles Including Platelet-Rich Plasma.

PubMed

Takabayashi, Yuki; Ishihara, Masayuki; Kuwabara, Masahiro; Takikawa, Makoto; Nakamura, Shingo; Hattori, Hidemi; Kiyosawa, Tomoharu

2017-05-01

Activated platelet-rich plasma secrets many growth factors (GFs), and low-molecular weight heparin-protamine micro/nanoparticles (LMWH-P M/NPs) significantly interact with, enhance, and stabilize the secreted GFs. The purpose of this study was to evaluate the effects of LMWH-P M/NPs and GFs (from platelet-rich plasma) on full-thickness skin graft (FTSG). A total of 96 inbred male rats were anesthetized and 4-cm full-thickness skin wound were created on dorsal skin of rats. LMWH-P M/NPs and GFs, LMWH-P M/NPs, GFs and saline (control) were then injected evenly into cutaneous muscles at the wound. The next day, the rats underwent FTSG. On the indicated days after FTSG, blood flow of FTSG site (wound bed and FTSG) was examined by 2-dimensional laser Doppler blood flowmeter. On 10 days, pictures of FTSG site were taken and FTSG survival rate was evaluated. Histologic analyses of skin samples were performed on 4, 7, and 10 days. Treatment of full-thickness skin wound with LMWH-P M/NPs and GFs effectively promoted survival rate of FTSG and blood flow of FTSG site compared with those treated with GFs, LMWH-P M/NPs, and control. LMWH-P M/NPs and GFs also promoted new vessel formation at FTSG site. The prior injection of LMWH-P M/NPs and GFs into wound bed increases FTSG survival rate, and promotes blood flow and angiogenesis at FTSG site.
Investigating NWP initialization sensitivities in heavy precipitation events

NASA Astrophysics Data System (ADS)

Frediani, M. E. B.; Anagnostou, E. N.; Papadopoulos, A.

2010-09-01

This study aims to investigate the effect of different types of model initialization applied to extreme storms simulations. Storms with extreme precipitation can usually produce flash floods that cause several damages to the society. Lives and property are destroyed from the landslides when they could be speared if forecasted a few hours in advance. The forecasts depend on several factors; among them the initialization fields play an important role. These fields are the starting point for the simulation and therefore it controls the quality of the forecast. This study evaluates the sensitivities of WRF to the initialization from two perspectives, (1) resolution and (2) initial atmospheric fields. Two storms that lead to flash flood are simulated. The first one happened in Northeast Italy in 04/09/2009 (NI), and the second in Germany, in 02/06/2008 (GE). These storms present contrasting characteristics, NI was a maritime originated storm enhanced by local orography while GE was a typical summer convection. Three different sources of atmospheric fields defining the initial conditions are applied: (a) ECMWF operational analysis at resolution of 0.25 deg, (b) GFS operational analysis at 0.5deg and (c) LAPS analysis at ~15km, produced operationally at HCMR. The rainfall forecasted is compared against in situ ground radar and surface rain gauges observations through a set of quantitative precipitation forecast scores.
TOWARDS OPERATIONAL FORECASTING OF LOWER ATMOSPHERE EFFECTS ON THE UPPER ATMOSPHERE AND IONOSPHERE: INTEGRATED DYNAMICS IN EARTH’S ATMOSPHERE (IDEA)

NASA Astrophysics Data System (ADS)

Akmaev, R. A.; Fuller-Rowell, T. J.; Wu, F.; Wang, H.; Juang, H.; Moorthi, S.; Iredell, M.

2009-12-01

The upper atmosphere and ionosphere exhibit variability and phenomena that have been associated with planetary and tidal waves originating in the lower atmosphere. To study and be able to predict the effects of these global-scale dynamical perturbations on the coupled thermosphere-ionosphere-electrodynamics system a new coupled model is being developed under the IDEA project. To efficiently cross the infamous R2O “death valley”, from the outset the IDEA project leverages the natural synergy between NOAA’s National Weather Service’s (NWS) Space Weather Prediction and Environmental Modeling Centers and a NOAA-University of Colorado cooperative institute (CIRES). IDEA interactively couples a Whole Atmosphere Model (WAM) with ionosphere-plasmasphere and electrodynamics models. WAM is a 150-layer general circulation model (GCM) based on NWS’s operational weather prediction Global Forecast System (GFS) extended from its nominal top altitude of 62 km to over 600 km. It incorporates relevant physical processes including those responsible for the generation of tidal and planetary waves in the troposphere and stratosphere. Long-term simulations reveal realistic seasonal variability of tidal waves with a substantial contribution from non-migrating tidal modes, recently implicated in the observed morphology of the ionosphere. Such phenomena as the thermospheric Midnight Temperature Maximum (MTM), previously associated with the tides, are also realistically simulated for the first time.
The Global Fleet Station Concept: Meeting Strategic Level Requirements

DTIC Science & Technology

2008-06-13

combined, and interagency assets to establish a persistent presence with a minimal footprint ashore in the Caribbean Basin and Central America in...the GFS concept and its 2007 deployment to Central America to determine if GFS meets defined strategic requirements. A qualitative analysis was...review the GFS concept and its 2007 deployment to Central America to determine if GFS meets defined strategic requirements. A qualitative analysis was
Resolution analysis of finite fault source inversion using one- and three-dimensional Green's functions 1. Strong motions

USGS Publications Warehouse

Graves, R.W.; Wald, D.J.

2001-01-01

We develop a methodology to perform finite fault source inversions from strong motion data using Green's functions (GFs) calculated for a three-dimensional (3-D) velocity structure. The 3-D GFs are calculated numerically by inserting body forces at each of the strong motion sites and then recording the resulting strains along the target fault surface. Using reciprocity, these GFs can be recombined to represent the ground motion at each site for any (heterogeneous) slip distribution on the fault. The reciprocal formulation significantly reduces the required number of 3-D finite difference computations to at most 3NS, where NS is the number of strong motion sites used in the inversion. Using controlled numerical resolution tests, we have examined the relative importance of accurate GFs for finite fault source inversions which rely on near-source ground motions. These experiments use both 1-D and 3-D GFs in inversions for hypothetical rupture models in order (1) to analyze the ability of the 3-D methodology to resolve trade-offs between complex source phenomena and 3-D path effects, (2) to address the sensitivity of the inversion results to uncertainties in the 3-D velocity structure, and (3) to test the adequacy of the 1-D GF method when propagation effects are known to be three-dimensional. We find that given "data" from a prescribed 3-D Earth structure, the use of well-calibrated 3-D GFs in the inversion provides very good resolution of the assumed slip distribution, thus adequately separating source and 3-D propagation effects. In contrast, using a set of inexact 3-D GFs or a set of hybrid 1-D GFs allows only partial recovery of the slip distribution. These findings suggest that in regions of complex geology the use of well-calibrated 3-D GFs has the potential for increased resolution of the rupture process relative to 1-D GFs. However, realizing this full potential requires that the 3-D velocity model and associated GFs should be carefully validated against the true 3-D Earth structure before performing the inverse problem with actual data. Copyright 2001 by the American Geophysical Union.
Contribution of land use changes to meteorological parameters in Greater Jakarta: Case 17 January 2014

NASA Astrophysics Data System (ADS)

Nuryanto, D. E.; Pawitan, H.; Hidayat, R.; Aldrian, E.

2018-05-01

The impact of land use changes on meteorological parameters during a heavy rainfall event on 17 January 2014 in Greater Jakarta (GJ) was examined using the Weather Research and Forecasting (WRF) model. This study performed two experimental simulation methods. The first WRF simulation uses default land use (CTL). The second simulation applies the experiment by changing the size of urban and built-up land use (SCE). The Global Forecast System (GFS) data is applied to provide more realistic initial and boundary conditions for the nested model domains (3 km, 1 km). The simulations were initiated at 00:00 UTC January 13, 2014 and the period of modeling was equal to six days. The air temperature and the precipitation pattern in GJ shows a good agreement between the observed and simulated data. The results show a consistent significant contribution of urban development and accompany land use changes in air temperature and precipitation. According to the model simulation, urban and built-up land contributed about 6% of heavy rainfall and about 0.2 degrees of air temperatures in the morning. Simulations indicate that new urban developments led to an intensification and expansion of the rain area. The results can support the decision-making of flooding and watershed management.
Microanalysis and preliminary pharmacokinetic studies of a sulfated polysaccharide from Laminaria japonica

NASA Astrophysics Data System (ADS)

Zhang, Wenjing; Sun, Delin; Zhao, Xia; Jin, Weihua; Wang, Jing; Zhang, Quanbin

2016-01-01

A rapid, sensitive and reproducible high performance liquid chromatography (HPLC) method with post-column fluorescence derivatization has been developed to determine the amount of low-molecular-weight sulfated polysaccharide (GFS) in vivo. The metabolism of GFS has been shown to fit a two component model following its administration by intravenous injection, and its pharmacokinetic parameters were determined to be as follows: half-time of distribution phase ( t 1/2α)=11.24±2.93 min, half-time of elimination phase ( t 1/2β)=98.20±25.78 min, maximum concentration ( C max)=110.53 μg/mL and peak time ( T max)=5 min. The pharmacokinetic behavior of GFS was also investigated following intragastric administration. However, the concentration of GFS found in serum was too low for detection, and GFS could only be detected for up to 2 h after intragastric administration (200 mg/kg body weight). Thus, the bioavailability of GFS was low following intragastric administration because of the metabolism of GFS. In conclusion, HPLC with postcolumn derivatization could be used for quantitative microanalysis and pharmacokinetic studies to determine the presence of polysaccharides in the serum following intravenous injection.
Analysis of the nexus between population, water resources and Global Food Security highlights significance of governance and research investments and policy priorities.

PubMed

Yunusa, Isa A M; Zerihun, Ayalsew; Gibberd, Mark R

2018-05-10

Analyses of sensitivity of Global Food Security (GFS) score to a key set of supply or demand factors often suggest population and water supply as being the most critical and on which policies tend to focus. To explore other policy options, we characterised the nexus between GFS and a set of supply or demand factors including defining including population, agricultural and industrial water-use, agricultural publications (as a surrogate for investment in agricultural research and development [R&D]), and corruption perception index (CPI), to reveal opportunities for attaining enduring GFS. We found that despite being the primary driver of demand for food, population showed no significant correlation with GFS scores. Similarly agricultural water-use was poorly correlated with GFS scores, except in countries where evaporation exceeds precipitation and irrigation is significant. However, GFS had a strong positive association with industrial water-use as a surrogate for overall industrialisation. Recent expansions in cultivated land area failed to yield concomitant improvements in GFS score since such expansions have been mostly into marginal lands with low productivity and also barely compensated for lands retired from cropping in several developed economies. However, GFS was positively associated with agricultural R&D investments, as it was with the CPI scores. The apparent and relative strengths of these drivers on GFS outcome amongst countries were in the order: industrial water-use ≈ publication rate ≈ corruption perception > agricultural water-use > population. We concluded by suggesting that to enshrine enduring food security, policies should prioritise (1) increased R&D investments that address farmer needs, and (2) governance mechanisms that promote accountability in both research and production value chains. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
BIOMECHANICAL DIFFERENCES IN BRAZILIAN JIU-JITSU ATHLETES: THE ROLE OF COMBAT STYLE.

PubMed

Lima, Pedro Olavo de Paula; Lima, Alane Almeida; Coelho, Anita Camila Sampaio; Lima, Yuri Lopes; Almeida, Gabriel Peixoto Leão; Bezerra, Márcio Almeida; de Oliveira, Rodrigo Ribeiro

2017-02-01

Brazilian Jiu-Jitsu (BJJ) athletes can be divided into two combat styles: pass fighters (PFs) and guard fighters (GFs). Flexibility of the posterior chain muscles is highly necessary in these athletes, especially in GFs. On the other hand, isometric strength of the trunk extensors is required in PFs. Handgrip strength is important in holding the kimono of the opponent, and symmetrical lower-limb strength is important for the prevention of injuries due to the overload caused by training. The aim of this study was to compare the biomechanical profiles of BJJ athletes with different combat styles using the following outcome measures: flexibility, trunk extensor isometric endurance, postural balance, handgrip isometric endurance and lower-limb muscle strength. A cross-sectional study was conducted using 19 GFs and 19 PFs. The sit-and-reach test was used to evaluate the flexibility of the posterior chain muscles. The Biodex Balance System® was used to evaluate balance. A handgrip dynamometer and a dorsal dynamometer were used to evaluate handgrip and trunk extensor endurance, respectively. Quadriceps and hamstring strength were evaluated with an isokinetic dynamometer at 60 °/s. No differences were observed between groups in terms of flexibility, balance, handgrip isometric endurance or quadriceps and hamstring strength; however, PFs (81.33) showed more isometric trunk extension endurance than GFs (68.85) ( p = 0.02). Both groups had low values for hamstring/quadriceps ratio. No significant biomechanical differences were observed between PFs and GFs. 2b.
Nanoparticle-mediated growth factor delivery systems: A new way to treat Alzheimer's disease.

PubMed

Lauzon, Marc-Antoine; Daviau, Alex; Marcos, Bernard; Faucheux, Nathalie

2015-05-28

The number of people diagnosed with Alzheimer's disease (AD) is increasing steadily as the world population ages, thus creating a huge socio-economic burden. Current treatments have only transient effects and concentrate on a single aspect of AD. There is much evidence suggesting that growth factors (GFs) have a great therapeutic potential and can play on all AD hallmarks. Because GFs are prone to denaturation and clearance, a delivery system is required to ensure protection and a sustainable delivery. This review provides information about the latest advances in the development of GF delivery systems (GFDS) targeting the brain in terms of in vitro and in vivo effects in the context of AD and discusses new strategies designed to increase the availability and the specificity of GFs to the brain. This paper also discusses, on a mechanistic level, the different delivery hurdles encountered by the carrier or the GF itself from its injection site up to the brain tissue. The major mass transport phenomena influencing the delivery systems targeting the brain are addressed and insights are given about how mechanistic mathematical frameworks can be developed to use and optimize them. Copyright © 2015. Published by Elsevier B.V.
a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

NASA Astrophysics Data System (ADS)

Paramygin, V.; Davis, J. R.; Sheng, Y.

2012-12-01

A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and East, which continues north to the Florida/Georgia border. The system has a data acquisition and processing module that is used to collect data for model runs (e.g. wind, river flow, precipitation). Depending on the domain, forecasts runs can take ~1-18 hours to complete on a single CPU (8-core) system (1-2 hrs for 2D setup and up to 18 hrs for a 3D setup) with 4 forecasts generated per day. All data is archived / catalogued and model forecast skill is continuously being evaluated. In addition to the baseline forecasts, additional forecasts are being perform using various options for wind forcing (GFS, GFDL, WRF, and parametric hurricane models), model configurations (2D/ 3D), and open boundary conditions by coupling with large scale models (ROMS, NCOM, HYCOM), as well as incorporating real-time and forecast river flow and precipitation data to better understand how to improve model skill. In addition, new forecast products (e.g. more informative inundation maps) are being developed to targeted stakeholders. To support modern data standards, CH3D-SSMS results are available online via a THREDDS server in CF-Compliant NetCDF format as well as other stakeholder-friendly (e.g. GIS) formats. The SECOORA website provides visualization of the model via GODIVA-THREDDS interface.
High-resolution visibility and air quality forecasting using multi-layer urban canopy model for highly urbanized Hong Kong and the Pearl River Delta

NASA Astrophysics Data System (ADS)

Piu NG, Chak; HAO, Song; Fat LAM, Yun

2015-04-01

Visibility is a universally critical element which affects the public in many aspects, including economic activities, health of local citizens and safety of marine transportation and aviation. The Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility equation, an empirical equation developed by USEPA, has been modified by various studies to fit into the application upon the Asian continent including Hong Kong and China. Often these studies focused on the improvement of the existing IMPROVE equation by modifying its particulate speciation using local observation data. In this study, we developed an Integrated Forecast System (IFS) to predict the next-day air quality and visibility using Weather Research and Forecasting model with Building Energy Parameterization and Building Energy Model (WRF-BEP+BEM) and Community Multi-scale Air Quality Model (CMAQ). Unlike the other studies, the core of this study is to include detailed urbanization impacts with calibrated "IMPROVE equation for PRD" into the modeling system for Hong Kong's environs. The ultra-high resolution land cover information (~1km x 1km) from Google images, was digitized into the Geographic Information System (GIS) for preparing the model-ready input for IFS. The NCEP FNL (Final) Operation Global Analysis (FNL) and the Global Forecasting System (GFS) datasets were tested for both hind-cast and forecast cases, in order to calibrate the input of urban parameters in the WRF-BEP+BEM model. The evaluation of model performance with sensitivity cases was performed on sea surface temperature (SST), surface temperature (T), wind speed/direction with the major pollutants (i.e., PM10, PM2.5, NOx, SO2 and O3) using local observation and will be presented/discussed in this paper. References: 1. Y. L. Lee, R. Sequeira, Visibility degradation across Hong Kong its components and their relative contribution. Atmospheric Environment 2001, 35, 5861-5872. doi:10.1016/S1352-2310(01)00395-8 2. R. Zhang, Q. Bian, J. C. H. Fung, A. K. H. Lau, Mathematical modeling of seasonal variations in visibility in Hong Kong and the Pearl River Delta region. Atmospheric Environment 2013, 77, 803-816. http://dx.doi.org/10.1016/j.atmosenv.2013.05.048
Inhibition of the Differentiation of Monocyte-Derived Dendritic Cells by Human Gingival Fibroblasts

PubMed Central

Séguier, Sylvie; Tartour, Eric; Guérin, Coralie; Couty, Ludovic; Lemitre, Mathilde; Lallement, Laetitia; Folliguet, Marysette; Naderi, Samah El; Terme, Magali; Badoual, Cécile; Lafont, Antoine; Coulomb, Bernard

2013-01-01

We investigated whether gingival fibroblasts (GFs) can modulate the differentiation and/or maturation of monocyte-derived dendritic cells (DCs) and analyzed soluble factors that may be involved in this immune modulation. Experiments were performed using human monocytes in co-culture with human GFs in Transwell® chambers or using monocyte cultures treated with conditioned media (CM) from GFs of four donors. The four CM and supernatants from cell culture were assayed by ELISA for cytokines involved in the differentiation of dendritic cells, such as IL-6, VEGF, TGFβ1, IL-13 and IL-10. The maturation of monocyte-derived DCs induced by LPS in presence of CM was also studied. Cell surface phenotype markers were analyzed by flow cytometry. In co-cultures, GFs inhibited the differentiation of monocyte-derived DCs and the strength of this blockade correlated with the GF/monocyte ratio. Conditioned media from GFs showed similar effects, suggesting the involvement of soluble factors produced by GFs. This inhibition was associated with a lower stimulatory activity in MLR of DCs generated with GFs or its CM. Neutralizing antibodies against IL-6 and VEGF significantly (P<0.05) inhibited the inhibitory effect of CM on the differentiation of monocytes-derived DCs and in a dose dependent manner. Our data suggest that IL-6 is the main factor responsible for the inhibition of DCs differentiation mediated by GFs but that VEGF is also involved and constitutes an additional mechanism. PMID:23936476
Data Assimilation Cycling for Weather Analysis

NASA Technical Reports Server (NTRS)

Tran, Nam; Li, Yongzuo; Fitzpatrick, Patrick

2008-01-01

This software package runs the atmospheric model MM5 in data assimilation cycling mode to produce an optimized weather analysis, including the ability to insert or adjust a hurricane vortex. The program runs MM5 through a cycle of short forecasts every three hours where the vortex is adjusted to match the observed hurricane location and storm intensity. This technique adjusts the surrounding environment so that the proper steering current and environmental shear are achieved. MM5cycle uses a Cressman analysis to blend observation into model fields to get a more accurate weather analysis. Quality control of observations is also done in every cycle to remove bad data that may contaminate the analysis. This technique can assimilate and propagate data in time from intermittent and infrequent observations while maintaining the atmospheric field in a dynamically balanced state. The software consists of a C-shell script (MM5cycle.driver) and three FORTRAN programs (splitMM5files.F, comRegrid.F, and insert_vortex.F), and are contained in the pre-processor component of MM5 called "Regridder." The model is first initialized with data from a global model such as the Global Forecast System (GFS), which also provides lateral boundary conditions. These data are separated into single-time files using splitMM5.F. The hurricane vortex is then bogussed in the correct location and with the correct wind field using insert_vortex.F. The modified initial and boundary conditions are then recombined into the model fields using comRegrid.F. The model then makes a three-hour forecast. The three-hour forecast data from MM5 now become the analysis for the next short forecast run, where the vortex will again be adjusted. The process repeats itself until the desired time of analysis is achieved. This code can also assimilate observations if desired.
Innovations in gene and growth factor delivery systems for diabetic wound healing

PubMed Central

Laiva, Ashang Luwang; O'Brien, Fergal J.

2017-01-01

Abstract The rise in lower extremity amputations due to nonhealing of foot ulcers in diabetic patients calls for rapid improvement in effective treatment regimens. Administration of growth factors (GFs) are thought to offer an off‐the‐shelf treatment; however, the dose‐ and time‐dependent efficacy of the GFs together with the hostile environment of diabetic wound beds impose a major hindrance in the selection of an ideal route for GF delivery. As an alternative, the delivery of therapeutic genes using viral and nonviral vectors, capable of transiently expressing the genes until the recovery of the wounded tissue offers promise. The development of implantable biomaterial dressings capable of modulating the release of either single or combinatorial GFs/genes may offer solutions to this overgrowing problem. This article reviews the state of the art on gene and protein delivery and the strategic optimization of clinically adopted delivery strategies for the healing of diabetic wounds. PMID:28482114
Novel drug delivery systems for releasing growth factors to the CNS: focus on Alzheimer's and Parkinson's diseases.

PubMed

Herran, E; Igartua, M; Pedraz, J L; Hernandez, R M

2014-01-01

Alzheimer's disease (AD) and Parkinson's disease (PD) represent the most common neurodegenerative disorders and affect more than 35 million people. Due to the limited effectiveness of available treatments in halting the neurodegenerative process, new therapies, such therapies based on growth factors (GFs), have been investigated. Nevertheless, the efficacies of these new treatments depend not only on the application of neurotrophins but also on the approaches used to deliver these proteins such that they can reach the brain. This review summarises the most widely used drug delivery systems (DDSs) for releasing GFs as possible treatments for AD and PD.

BIOMECHANICAL DIFFERENCES IN BRAZILIAN JIU-JITSU ATHLETES: THE ROLE OF COMBAT STYLE

PubMed Central

Lima, Alane Almeida; Coelho, Anita Camila Sampaio; Lima, Yuri Lopes; Almeida, Gabriel Peixoto Leão; Bezerra, Márcio Almeida; de Oliveira, Rodrigo Ribeiro

2017-01-01

Background Brazilian Jiu-Jitsu (BJJ) athletes can be divided into two combat styles: pass fighters (PFs) and guard fighters (GFs). Flexibility of the posterior chain muscles is highly necessary in these athletes, especially in GFs. On the other hand, isometric strength of the trunk extensors is required in PFs. Handgrip strength is important in holding the kimono of the opponent, and symmetrical lower-limb strength is important for the prevention of injuries due to the overload caused by training. Purpose The aim of this study was to compare the biomechanical profiles of BJJ athletes with different combat styles using the following outcome measures: flexibility, trunk extensor isometric endurance, postural balance, handgrip isometric endurance and lower-limb muscle strength. Methods A cross-sectional study was conducted using 19 GFs and 19 PFs. The sit-and-reach test was used to evaluate the flexibility of the posterior chain muscles. The Biodex Balance System® was used to evaluate balance. A handgrip dynamometer and a dorsal dynamometer were used to evaluate handgrip and trunk extensor endurance, respectively. Quadriceps and hamstring strength were evaluated with an isokinetic dynamometer at 60 °/s. Results No differences were observed between groups in terms of flexibility, balance, handgrip isometric endurance or quadriceps and hamstring strength; however, PFs (81.33) showed more isometric trunk extension endurance than GFs (68.85) (p = 0.02). Both groups had low values for hamstring/quadriceps ratio. Conclusion No significant biomechanical differences were observed between PFs and GFs. Level of Evidence 2b PMID:28217417
Developing of operational hydro-meteorological simulating and displaying system

NASA Astrophysics Data System (ADS)

Wang, Y.; Shih, D.; Chen, C.

2010-12-01

Hydrological hazards, which often occur in conjunction with extreme precipitation events, are the most frequent type of natural disaster in Taiwan. Hence, the researchers at the Taiwan Typhoon and Flood Research Institute (TTFRI) are devoted to analyzing and gaining a better understanding of the causes and effects of natural disasters, and in particular, typhoons and floods. The long-term goal of the TTFRI is to develop a unified weather-hydrological-oceanic model suitable for simulations with local parameterizations in Taiwan. The development of a fully coupled weather-hydrology interaction model is not yet completed but some operational hydro-meteorological simulations are presented as a step in the direction of completing a full model. The predicted rainfall data from Weather Research Forecasting (WRF) are used as our meteorological forcing on watershed modeling. The hydrology and hydraulic modeling are conducted by WASH123D numerical model. And the WRF/WASH123D coupled system is applied to simulate floods during the typhoon landfall periods. The daily operational runs start at 04UTC, 10UTC, 16UTC and 22UTC, about 4 hours after data downloaded from NCEP GFS. This system will execute 72-hr weather forecasts. The simulation of WASH123D will sequentially trigger after receiving WRF rainfall data. This study presents the preliminary framework of establishing this system, and our goal is to build this earlier warning system to alert the public form dangerous. The simulation results are further display by a 3D GIS web service system. This system is established following the Open Geospatial Consortium (OGC) standardization process for GIS web service, such as Web Map Service (WMS) and Web Feature Service (WFS). The traditional 2D GIS data, such as high resolution aerial photomaps and satellite images are integrated into 3D landscape model. The simulated flooding and inundation area can be dynamically mapped on Wed 3D world. The final goal of this system is to real-time forecast flood and the results can be visually displayed on the virtual catchment. The policymaker can easily and real-time gain visual information for decision making at any site through internet.
High-performance wearable supercapacitors fabricated with surface activated continuous filament graphite fibers

NASA Astrophysics Data System (ADS)

Jia, Dedong; Yu, Xin; Chen, Tinghan; Wang, Shu; Tan, Hua; Liu, Hong; Wang, Zhong Lin; Li, Linlin

2017-08-01

Generally, carbon or graphite fibers (GFs) are used as the supporting materials for the preparation of flexible supercapacitors (SCs) by assembling various electrochemically active nanomaterials on them. A facile and rapid electrochemical oxidation method with a voltage of 3 V in a mixed H2SO4-HNO3 solution for 2-15 min is proposed to active continuous filament GFs. Detailed structural characterization, SEM, TEM, XRD, Raman and XPS demonstrate that the GFs-8 (oxidized for 8 min) possessing high specific surface area which provided numerous electrochemical sites and a large number of oxygen-containing functional groups producing pseudocapacitance. Cyclic voltammetric (CV), galvanostatic charge-discharge measurements and electrochemical impedance spectroscopy (EIS) are conducted to test the capacitive of GFs and activated GFs. The capacitance of GFs-8 reaches as high as 570 mF cm-1 at the current density of 1 mA cm-1 in LiCl electrolyte, a 1965-fold enhancement with respect to the pristine GFs (0.29 mF cm-1). The fabricated fiber solid-state supercapacitors (SSCs) provide high energy density of 0.68 mWh cm-3 at the power density 3.3 W cm-3 and have excellent durability with 90% capacitance retention after 10000 cycles. In addition, such fiber SSCs features flexibility and mechanical stability, which may have wide applications in wearable electronic devices.
Observed Structure and Characteristics of Cold Pools over Tropical Oceans using Vector Wind Retrievals and WRF simulations

NASA Astrophysics Data System (ADS)

Garg, P.; Nesbitt, S. W.; Lang, T. J.; Chronis, T.; Thayer, J. D.; Hence, D. A.

2017-12-01

Cold pools generated in the wake of convective activity can enhance the surface sensible heat flux, latent heat flux, and also changes in evaporation out of, and fresh water flux into, the ocean. Recent studies have shown that over the open ocean, cold pool outflow boundaries and their intersections can organize and initiate a spectrum of deep convective clouds, which is a key driver of shallow and deep convection over conditionally-unstable tropical oceans. The primary goal of this study is to understand the structure and characteristics of cold pools over the tropical oceans using observations. With the idea that cold pools will have strong wind gradients at their boundaries, we use ASCAT vector wind retrievals. We identify regions of steep gradients in wind vectors as gradient features (GFs), akin to cold pools. Corresponding to these GFs, sensible and latent heat fluxes were calculated using the observed winds and background temperatures from MERRA-2 reanalysis. To evaluate the proposed technique, cold pools were observed using S-PolKa radar from the DYNAMO/AMIE field campaign in the Indian Ocean for the period of 1 October 2011 to 31 March 2012 and were compared with ASCAT GFs. To relate the thermodynamic and kinematic characteristics of observed and simulated cold pools, simulations were carried out on WRF on a 3-km domain explicitly. The areas of cold pools were identified in the models using virtual temperature (Tv), which is a direct measure of air density, while GFs were identified using model simulated winds. Quantitative measures indicate that GFs are highly correspondent with model-simulated cold pools. In global measurements of cold pools from 2007-2015, it is possible to examine the characteristics of GFs across all tropical ocean basins, and relate them to meteorological conditions, as well as the characteristics of the parent precipitation systems. Our results indicate that while there is a general relationship between the amount of precipitation and the number of cold pools, the largest cold pools exist over the Eastern Pacific basin, where the most stratiform rain is produced from oceanic MCSs. It is anticipated that improved understanding of cold pools, which are a primary triggering mechanism of oceanic shallow and deep convection, will improve prediction of this important component of the climate system.
Increased phase synchronization during continuous face integration measured simultaneously with EEG and fMRI.

PubMed

Kottlow, Mara; Jann, Kay; Dierks, Thomas; Koenig, Thomas

2012-08-01

Gamma zero-lag phase synchronization has been measured in the animal brain during visual binding. Human scalp EEG studies used a phase locking factor (trial-to-trial phase-shift consistency) or gamma amplitude to measure binding but did not analyze common-phase signals so far. This study introduces a method to identify networks oscillating with near zero-lag phase synchronization in human subjects. We presented unpredictably moving face parts (NOFACE) which - during some periods - produced a complete schematic face (FACE). The amount of zero-lag phase synchronization was measured using global field synchronization (GFS). GFS provides global information on the amount of instantaneous coincidences in specific frequencies throughout the brain. Gamma GFS was increased during the FACE condition. To localize the underlying areas, we correlated gamma GFS with simultaneously recorded BOLD responses. Positive correlates comprised the bilateral middle fusiform gyrus and the left precuneus. These areas may form a network of areas transiently synchronized during face integration, including face-specific as well as binding-specific regions and regions for visual processing in general. Thus, the amount of zero-lag phase synchronization between remote regions of the human visual system can be measured with simultaneously acquired EEG/fMRI. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Growth factors delivery from hybrid PCL-starch scaffolds processed using supercritical fluid technology.

PubMed

Diaz-Gomez, Luis; Concheiro, Angel; Alvarez-Lorenzo, Carmen; García-González, Carlos A

2016-05-20

Synthetic polymeric scaffolds to be used as surrogates of autologous bone grafts should not only have suitable physicochemical and mechanical properties, but also contain bioactive agents such as growth factors (GFs) to facilitate the tissue growth. For this purpose, cost-effective and autologous GFs sources are preferred to avoid some post-surgery complications after implantation, like immunogenicity or disease transmission, and the scaffolds should be processed using methods able to preserve GFs activity. In this work, poly(ɛ-caprolactone) (PCL) scaffolds incorporating GFs were processed using a green foaming process based on supercritical fluid technology. Preparation rich in growth factors (PRGF), a natural and highly available cocktail of GFs obtained from platelet rich plasma (PRP), was used as GF source. PCL:starch:PRGF (85:10:5 weight ratio) porous solid scaffolds were obtained by a supercritical CO2-assisted foaming process at 100 bar and 37 °C with no need of post-processing steps. Bioactivity of GFs after processing and scaffold cytocompatibility were confirmed using mesenchymal stem cells. The performance of starch as GF control release component was shown to be dependent on starch pre-gelification conditions. Copyright © 2016 Elsevier Ltd. All rights reserved.
Three-Dimensional Printed Graphene Foams.

PubMed

Sha, Junwei; Li, Yilun; Villegas Salvatierra, Rodrigo; Wang, Tuo; Dong, Pei; Ji, Yongsung; Lee, Seoung-Ki; Zhang, Chenhao; Zhang, Jibo; Smith, Robert H; Ajayan, Pulickel M; Lou, Jun; Zhao, Naiqin; Tour, James M

2017-07-25

An automated metal powder three-dimensional (3D) printing method for in situ synthesis of free-standing 3D graphene foams (GFs) was successfully modeled by manually placing a mixture of Ni and sucrose onto a platform and then using a commercial CO 2 laser to convert the Ni/sucrose mixture into 3D GFs. The sucrose acted as the solid carbon source for graphene, and the sintered Ni metal acted as the catalyst and template for graphene growth. This simple and efficient method combines powder metallurgy templating with 3D printing techniques and enables direct in situ 3D printing of GFs with no high-temperature furnace or lengthy growth process required. The 3D printed GFs show high-porosity (∼99.3%), low-density (∼0.015g cm -3 ), high-quality, and multilayered graphene features. The GFs have an electrical conductivity of ∼8.7 S cm -1 , a remarkable storage modulus of ∼11 kPa, and a high damping capacity of ∼0.06. These excellent physical properties of 3D printed GFs indicate potential applications in fields requiring rapid design and manufacturing of 3D carbon materials, for example, energy storage devices, damping materials, and sound absorption.
Added value of non-calibrated and BMA calibrated AEMET-SREPS probabilistic forecasts: the 24 January 2009 extreme wind event over Catalonia

NASA Astrophysics Data System (ADS)

Escriba, P. A.; Callado, A.; Santos, D.; Santos, C.; Simarro, J.; García-Moya, J. A.

2009-09-01

At 00 UTC 24 January 2009 an explosive ciclogenesis originated over the Atlantic Ocean reached its maximum intensity with observed surface pressures lower than 970 hPa on its center and placed at Gulf of Vizcaya. During its path through southern France this low caused strong westerly and north-westerly winds over the Iberian Peninsula higher than 150 km/h at some places. These extreme winds leaved 10 casualties in Spain, 8 of them in Catalonia. The aim of this work is to show whether exists an added value in the short range prediction of the 24 January 2009 strong winds when using the Short Range Ensemble Prediction System (SREPS) of the Spanish Meteorological Agency (AEMET), with respect to the operational forecasting tools. This study emphasizes two aspects of probabilistic forecasting: the ability of a 3-day forecast of warn an extreme windy event and the ability of quantifying the predictability of the event so that giving value to deterministic forecast. Two type of probabilistic forecasts of wind are carried out, a non-calibrated and a calibrated one using Bayesian Model Averaging (BMA). AEMET runs daily experimentally SREPS twice a day (00 and 12 UTC). This system consists of 20 members that are constructed by integrating 5 local area models, COSMO (COSMO), HIRLAM (HIRLAM Consortium), HRM (DWD), MM5 (NOAA) and UM (UKMO), at 25 km of horizontal resolution. Each model uses 4 different initial and boundary conditions, the global models GFS (NCEP), GME (DWD), IFS (ECMWF) and UM. By this way it is obtained a probabilistic forecast that takes into account the initial, the contour and the model errors. BMA is a statistical tool for combining predictive probability functions from different sources. The BMA predictive probability density function (PDF) is a weighted average of PDFs centered on the individual bias-corrected forecasts. The weights are equal to posterior probabilities of the models generating the forecasts and reflect the skill of the ensemble members. Here BMA is applied to provide probabilistic forecasts of wind speed. In this work several forecasts for different time ranges (H+72, H+48 and H+24) of 10 meters wind speed over Catalonia are verified subjectively at one of the instants of maximum intensity, 12 UTC 24 January 2009. On one hand, three probabilistic forecasts are compared, ECMWF EPS, non-calibrated SREPS and calibrated SREPS. On the other hand, the relationship between predictability and skill of deterministic forecast is studied by looking at HIRLAM 0.16 deterministic forecasts of the event. Verification is focused on location and intensity of 10 meters wind speed and 10-minutal measures from AEMET automatic ground stations are used as observations. The results indicate that SREPS is able to forecast three days ahead mean winds higher than 36 km/h and that correctly localizes them with a significant probability of ocurrence in the affected area. The probability is higher after BMA calibration of the ensemble. The fact that probability of strong winds is high allows us to state that the predictability of the event is also high and, as a consequence, deterministic forecasts are more reliable. This is confirmed when verifying HIRLAM deterministic forecasts against observed values.
National Centers for Environmental Prediction

Science.gov Websites

OPERATIONAL 00Z, .... 12Z ... EXPERIMENTAL Daily Comparisons between GFS/GEFS control & ECMWF/ECMWF control 00Z T382/38km GFS, 00Z T190/70km GEFS control 12Z T1279/16km ECMWF, 12Z T639/30km ECMWF ensemble control Daily Values of 500 hPa Height AC, RMS, Talagrand & Outliers Mean of 14 GFS, 10 ECMWF and 16
Multidimensional Generalized Functions in Aeroacoustics and Fluid Mechanics. Part 1; Basic Concepts and Operations

NASA Technical Reports Server (NTRS)

Farassat, Fereidoun; Myers, Michael K.

2011-01-01

This paper is the first part of a three part tutorial on multidimensional generalized functions (GFs) and their applications in aeroacoustics and fluid mechanics. The subject is highly fascinating and essential in many areas of science and, in particular, wave propagation problems. In this tutorial, we strive to present rigorously and clearly the basic concepts and the tools that are needed to use GFs in applications effectively and with ease. We give many examples to help the readers in understanding the mathematical ideas presented here. The first part of the tutorial is on the basic concepts of GFs. Here we define GFs, their properties and some common operations on them. We define the important concept of generalized differentiation and then give some interesting elementary and advanced examples on Green's functions and wave propagation problems. Here, the analytic power of GFs in applications is demonstrated with ease and elegance. Part 2 of this tutorial is on the diverse applications of generalized derivatives (GDs). Part 3 is on generalized Fourier transformations and some more advanced topics. One goal of writing this tutorial is to convince readers that, because of their powerful operational properties, GFs are absolutely essential and useful in engineering and physics, particularly in aeroacoustics and fluid mechanics.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Bin; Gu, Meng; Nie, Zimin

Graphite felts (GFs), as typical electrode materials for all vanadium redox flow batteries (VRBs), limit the cell operation to low current density because of their poor kinetic reversibility and electrochemical activity. Here, in order to address this issue we report an electrocatalyst, Nb2O5, decorating the surface of GFs to reduce the activation barrier for redox conversion. Nb2O5 nanofibers with monoclinic phases are synthesized by hydrothermal method and deposited on GFs, which is confirmed to have catalytic effects towards redox couples of V(II)/V(III) at the negative side and V(IV)/V(V) at the positive side, and thus applied in both electrodes of VRBmore » cells. Due to the low conductivity of Nb2O5, the performance of electrodes heavily depends on the nano size and uniform distribution of catalysts on GFs surfaces. The addition of the water-soluble compounds containing W element into the precursor solutions facilitates the precipitation of nanofibers on the GFs. Accordingly, an optimal amount of W-doped Nb2O5 nanofibers with weaker agglomeration and better distribution on GFs surfaces are obtained, leading to significant improvement of the electrochemical performances of VRB cells particularly under the high power operation. The corresponding energy efficiency is enhanced by 10.7 % under the operation of high charge/discharge current density (150 mA•cm-2) owing to faster charge transfer as compared with that without catalysts. These results suggest that Nb2O5 based nanofibers-decorating GFs hold great promise as high-performance electrodes for VRB applications.« less
Simulating the Effects of Irrigation over the U.S. in a Land Surface Model Based on Satellite Derived Agricultural Data

NASA Technical Reports Server (NTRS)

Ozdogan, Mutlu; Rodell, Matthew; Beaudoing, Hiroko Kato; Toll, David L.

2009-01-01

A novel method is introduced for integrating satellite derived irrigation data and high-resolution crop type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land-atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here we show that application of the new irrigation scheme over the continental US significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In our experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental US during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W/m from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W/m(sup 2), 20 W/m(sup 2), 5 mm/day, 0.3 mm/day, and 100 mm, respectively. These results are highly relevant to continental- to global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. Based on the results presented here, we expect that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA's Global Forecast System (GFS).
High Density Polyethylene Composites Reinforced with Hybrid Inorganic Fillers: Morphology, Mechanical and Thermal Expansion Performance

PubMed Central

Huang, Runzhou; Xu, Xinwu; Lee, Sunyoung; Zhang, Yang; Kim, Birm-June; Wu, Qinglin

2013-01-01

The effect of individual and combined talc and glass fibers (GFs) on mechanical and thermal expansion performance of the filled high density polyethylene (HDPE) composites was studied. Several published models were adapted to fit the measured tensile modulus and strength of various composite systems. It was shown that the use of silane-modified GFs had a much larger effect in improving mechanical properties and in reducing linear coefficient of thermal expansion (LCTE) values of filled composites, compared with the use of un-modified talc particles due to enhanced bonding to the matrix, larger aspect ratio, and fiber alignment for GFs. Mechanical properties and LCTE values of composites with combined talc and GF fillers varied with talc and GF ratio at a given total filler loading level. The use of a larger portion of GFs in the mix can lead to better composite performance, while the use of talc can help lower the composite costs and increase its recyclability. The use of 30 wt % combined filler seems necessary to control LCTE values of filled HDPE in the data value range generally reported for commercial wood plastic composites. Tensile modulus for talc-filled composite can be predicted with rule of mixture, while a PPA-based model can be used to predict the modulus and strength of GF-filled composites. PMID:28788322
Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs.

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Cheng, A.

2017-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity, and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation, and cloudiness. Unlike other similar methods, comparatively few new prognostic variables needs to be introduced, making the technique computationally efficient. In the base version of SHOC it is SGS turbulent kinetic energy (TKE), and in the developmental version — SGS TKE, and variances of total water and moist static energy (MSE). SHOC is now incorporated into a version of GFS that will become a part of the NOAA Next Generation Global Prediction System based around NOAA GFDL's FV3 dynamical core, NOAA Environmental Modeling System (NEMS) coupled modeling infrastructure software, and a set novel physical parameterizations. Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these quantities. Radiative transfer parameterization uses cloudiness computed by SHOC. An outstanding problem with implementation of SHOC in the NCEP global models is excessively large high level tropical cloudiness. Comparison of the moments of the SGS PDF diagnosed by SHOC to the moments calculated in a GigaLES simulation of tropical deep convection case (GATE), shows that SHOC diagnoses too narrow PDF distributions of total cloud water and MSE in the areas of deep convective detrainment. A subsequent sensitivity study of SHOC's diagnosed cloud fraction (CF) to higher order input moments of the SGS PDF demonstrated that CF is improved if SHOC is provided with correct variances of total water and MSE. Consequently, SHOC was modified to include two new prognostic equations for variances of total water and MSE, and coupled with the Chikira-Sugiyama parameterization of deep convection to include effects of detrainment on the prognostic variances.
Multiple Sensitivity Testing for Regional Air Quality Model in summer 2014

NASA Astrophysics Data System (ADS)

Tang, Y.; Lee, P.; Pan, L.; Tong, D.; Kim, H. C.; Huang, M.; Wang, J.; McQueen, J.; Lu, C. H.; Artz, R. S.

2015-12-01

The NOAA Air Resources laboratory leads to improve the performance of the U.S. Air Quality Forecasting Capability (NAQFC). It is operational in NOAA National Centers for Environmental Prediction (NCEP) which focuses on predicting surface ozone and PM2.5. In order to improve its performance, we tested several approaches, including NOAA Environmental Modeling System Global Aerosol Component (NGAC) simulation derived ozone and aerosol lateral boundary conditions (LBC), bi-direction NH3 emission and HMS(Hazard Mapping System)-BlueSky emission with the latest U.S. EPA Community Multi-scale Air Quality model (CMAQ) version and the U.S EPA National Emission Inventory (NEI)-2011 anthropogenic emissions. The operational NAQFC uses static profiles for its lateral boundary condition (LBC), which does not impose severe issue for near-surface air quality prediction. However, its degraded performance for the upper layer (e.g. above 3km) is evident when comparing with aircraft measured ozone. NCEP's Global Forecast System (GFS) has tracer O3 prediction treated as 3-D prognostic variable (Moorthi and Iredell, 1998) after being initialized with Solar Backscatter Ultra Violet-2 (SBUV-2) satellite data. We applied that ozone LBC to the CMAQ's upper layers and yield more reasonable O3 prediction than that with static LBC comparing with the aircraft data in Discover-AQ Colorado campaign. NGAC's aerosol LBC also improved the PM2.5 prediction with more realistic background aerosols. The bi-direction NH3 emission used in CMAQ also help reduce the NH3 and nitrate under-prediction issue. During summer 2014, strong wildfires occurred in northwestern USA, and we used the US Forest Service's BlueSky fire emission with HMS fire counts to drive CMAQ and tested the difference of day-1 and day-2 fire emission estimation. Other related issues were also discussed.
Size of graphene sheets determines the structural and mechanical properties of 3D graphene foams

NASA Astrophysics Data System (ADS)

Shen, Zhiqiang; Ye, Huilin; Zhou, Chi; Kröger, Martin; Li, Ying

2018-03-01

Graphene is recognized as an emerging 2D nanomaterial for many applications. Assembly of graphene sheets into 3D structures is an attractive way to enable their macroscopic applications and to preserve the exceptional mechanical and physical properties of their constituents. In this study, we develop a coarse-grained (CG) model for 3D graphene foams (GFs) based on the CG model for a 2D graphene sheet by Ruiz et al (2015 Carbon 82 103-15). We find that the size of graphene sheets plays an important role in both the structural and mechanical properties of 3D GFs. When their size is smaller than 10 nm, the graphene sheets can easily stack together under the influence of van der Waals interactions (vdW). These stacks behave like building blocks and are tightly packed together within 3D GFs, leading to high density, small pore radii, and a large Young’s modulus. However, if the sheet sizes exceed 10 nm, they are staggered together with a significant amount of deformation (bending). Therefore, the density of 3D GFs has been dramatically reduced due to the loosely packed graphene sheets, accompanied by large pore radii and a small Young’s modulus. Under uniaxial compression, rubber-like stress-strain curves are observed for all 3D GFs. This material characteristic is dominated by the vdW interactions between different graphene layers and slightly affected by the out-of-plane deformation of the graphene sheets. We find a simple scaling law E˜ {ρ }4.2 between the density ρ and Young’s modulus E for a model of 3D GFs. The simulation results reveal structure-property relations of 3D GFs, which can be applied to guide the design of 3D graphene assemblies with exceptional properties.
A Numerical Simulation (Study) of a Strong West Coast December 2014 Winter Storm

NASA Astrophysics Data System (ADS)

Smelser, I.; Xu, L.; Amerault, C. M.; Baker, N. L.; Satterfield, E.; Chua, B.

2016-12-01

From December 10 through December 13, 2014, a powerful winter storm swept across the western US coastal states bringing widespread power outages, numerous downed trees and power lines, heavy rains, flooding and even a tornado in the Los Angeles basin. This windstorm was the strongest since October 2009, and was similar to classic wind storms such as the 1962 Columbus Day Storm (Read, 2015).The storm started developing over the Pacific Ocean north of Hawaii on Nov. 30, and formed an atmospheric river that eventually stretched from Hawaii to the west coast. The storm initially hit the Pacific Northwest on Dec. 9th and then split. The highest precipitation amounts started in British Colombia and moved south along the coast. By the Dec. 11th, the highest precipitation amounts were near San Francisco (CA). The peak wind gust (14.4 ms-1) for Monterey (CA) occurred at 1116Z on Dec. 11th while the heaviest 6-hr precipitation (42.9 mm) occurred between 18Z on Dec. 11th to 00Z on Dec. 12th. By Dec. 12th, the storm was centered over Southern California.This storm was poorly forecast by many operational NWP models even 2-3 days in advance (Mass, 2014). The NCEP Global Forecast System (GFS) showed considerably variability between successive model runs, and significant differences existed between Environment Canada, UK Met Office and ECMWF model forecasts. To study this extreme weather event, we used the Navy global (NAVGEM) and mesoscale (COAMPS®) NWP models, and compared the resulting forecasts to observations, satellite imagery and ECMWF (TIGGE) forecasts. NAVGEM, with Hybrid 4DVar, was run with a resolution of 31 km, and generated the boundary conditions for COAMPS® 4DVar and forecasts, that were run with triple-nested grids of 27, 9, and 3 km. The MesoWest data from the University of Utah were used for forecast verification, and to locate the times of highest precipitation and wind speed for different points along the coast. Both the online API and the python module were used to access and pull information from the data base. Overall, both NAVGEM and COAMPS® predicted the storm well. NAVGEM predicted the storm to be slower and more powerful than the analyses. The NAVGEM analysis and corresponding 5-day forecast accumulated 6-hr precipitation (Fig. 1) for Dec. 12th at 00Z agree well with the observed precipitation (4.29 cm) for Monterey (KMRY).
Patient-Derived Human Induced Pluripotent Stem Cells From Gingival Fibroblasts Composited With Defined Nanohydroxyapatite/Chitosan/Gelatin Porous Scaffolds as Potential Bone Graft Substitutes.

PubMed

Ji, Jun; Tong, Xin; Huang, Xiaofeng; Zhang, Junfeng; Qin, Haiyan; Hu, Qingang

2016-01-01

Human embryonic stem cells and adult stem cells have always been the cell source for bone tissue engineering. However, their limitations are obvious, including ethical concerns and/or a short lifespan. The use of human induced pluripotent stem cells (hiPSCs) could avoid these problems. Nanohydroxyapatite (nHA) is an important component of natural bone and bone tissue engineering scaffolds. However, its regulation on osteogenic differentiation with hiPSCs from human gingival fibroblasts (hGFs) is unknown. The purpose of the present study was to investigate the osteogenic differentiation of hiPSCs from patient-derived hGFs regulated by nHA/chitosan/gelatin (HCG) scaffolds with different nHA ratios, such as HCG-111 (1 wt/vol% nHA) and HCG-311 (3 wt/vol% nHA). First, hGFs were reprogrammed into hiPSCs, which have enhanced osteogenic differentiation capability. Second, HCG-111 and HCG-311 scaffolds were successfully synthesized. Finally, hiPSC/HCG complexes were cultured in vitro or subcutaneously transplanted into immunocompromised mice in vivo. The osteogenic differentiation effects of two types of HCG scaffolds on hiPSCs were assessed for up to 12 weeks. The results showed that HCG-311 increased osteogenic-related gene expression of hiPSCs in vitro proved by quantitative real-time polymerase chain reaction, and hiPSC/HCG-311 complexes formed much bone-like tissue in vivo, indicated by cone-beam computed tomography imaging, H&E staining, Masson staining, and RUNX-2, OCN immunohistochemistry staining. In conclusion, our study has shown that osteogenic differentiation of hiPSCs from hGFs was improved by HCG-311. The mechanism might be that the nHA addition stimulates osteogenic marker expression of hiPSCs from hGFs. Our work has provided an innovative autologous cell-based bone tissue engineering approach with soft tissues such as clinically abundant gingiva. The present study focused on patient-personalized bone tissue engineering. Human induced pluripotent stem cells (hiPSCs) were established from clinically easily derived human gingival fibroblasts (hGFs) and defined nanohydroxyapatite/chitosan/gelatin (HCG) scaffolds. hiPSCs derived from hGFs had better osteogenesis capability than that of hGFs. More interestingly, osteogenic differentiation of hiPSCs from hGFs was elevated significantly when composited with HCG-311 scaffolds in vitro and in vivo. The present study has uncovered the important role of different nHA ratios in HCG scaffolds in osteogenesis induction of hiPSCs derived from hGFs. This technique could serve as a potential innovative approach for bone tissue engineering, especially large bone regeneration clinically. ©AlphaMed Press.
Patient-Derived Human Induced Pluripotent Stem Cells From Gingival Fibroblasts Composited With Defined Nanohydroxyapatite/Chitosan/Gelatin Porous Scaffolds as Potential Bone Graft Substitutes

PubMed Central

Ji, Jun; Tong, Xin; Huang, Xiaofeng; Zhang, Junfeng

2016-01-01

Human embryonic stem cells and adult stem cells have always been the cell source for bone tissue engineering. However, their limitations are obvious, including ethical concerns and/or a short lifespan. The use of human induced pluripotent stem cells (hiPSCs) could avoid these problems. Nanohydroxyapatite (nHA) is an important component of natural bone and bone tissue engineering scaffolds. However, its regulation on osteogenic differentiation with hiPSCs from human gingival fibroblasts (hGFs) is unknown. The purpose of the present study was to investigate the osteogenic differentiation of hiPSCs from patient-derived hGFs regulated by nHA/chitosan/gelatin (HCG) scaffolds with different nHA ratios, such as HCG-111 (1 wt/vol% nHA) and HCG-311 (3 wt/vol% nHA). First, hGFs were reprogrammed into hiPSCs, which have enhanced osteogenic differentiation capability. Second, HCG-111 and HCG-311 scaffolds were successfully synthesized. Finally, hiPSC/HCG complexes were cultured in vitro or subcutaneously transplanted into immunocompromised mice in vivo. The osteogenic differentiation effects of two types of HCG scaffolds on hiPSCs were assessed for up to 12 weeks. The results showed that HCG-311 increased osteogenic-related gene expression of hiPSCs in vitro proved by quantitative real-time polymerase chain reaction, and hiPSC/HCG-311 complexes formed much bone-like tissue in vivo, indicated by cone-beam computed tomography imaging, H&E staining, Masson staining, and RUNX-2, OCN immunohistochemistry staining. In conclusion, our study has shown that osteogenic differentiation of hiPSCs from hGFs was improved by HCG-311. The mechanism might be that the nHA addition stimulates osteogenic marker expression of hiPSCs from hGFs. Our work has provided an innovative autologous cell-based bone tissue engineering approach with soft tissues such as clinically abundant gingiva. Significance The present study focused on patient-personalized bone tissue engineering. Human induced pluripotent stem cells (hiPSCs) were established from clinically easily derived human gingival fibroblasts (hGFs) and defined nanohydroxyapatite/chitosan/gelatin (HCG) scaffolds. hiPSCs derived from hGFs had better osteogenesis capability than that of hGFs. More interestingly, osteogenic differentiation of hiPSCs from hGFs was elevated significantly when composited with HCG-311 scaffolds in vitro and in vivo. The present study has uncovered the important role of different nHA ratios in HCG scaffolds in osteogenesis induction of hiPSCs derived from hGFs. This technique could serve as a potential innovative approach for bone tissue engineering, especially large bone regeneration clinically. PMID:26586776
It Takes Two: NASA and NOAA's Shared Path of Hurricane Science Flights with the Global Hawk. Time for the Research To Operations (R2O) Transition?

NASA Astrophysics Data System (ADS)

Emory, A. E.; Wick, G. A.; Dunion, J. P.; McLinden, M.; Schreier, M. M.; Black, P.; Hood, R. E.; Sippel, J.; Tallapragada, V.

2017-12-01

The impacts of Harvey, Irma, and Maria during the 2017 Atlantic hurricane season re-emphasized the critical need for accurate operational forecasts. The combined NASA East Pacific Origins and Characteristics of Hurricanes (EPOCH) and NOAA UAS field campaign during August 2017 was the fourth campaign in a series of dual agency partnerships between NASA and NOAA to improve forecasting accuracy in tropical cyclogenesis and rapid intensification. A brief history of Global Hawk (GH) hurricane field campaigns, including GRIP (2010), HS3 (2012-2014), NOAA-SHOUT (2015-2016) and EPOCH (2017), will show the incremental steps taken over the last eight years to bring the GH from a research platform to a candidate for operational hurricane reconnaissance. GH dropsondes were assimilated into the ECMWF and HWRF forecast models during the 2015-2016 NOAA SHOUT campaigns. EPOCH marked the first time that GH dropsondes were assimilated in real-time into NOAA's GFS forecast model. Early results show that assimilating dropsonde data significantly increases skill in predicting intensity change, which is game changing since the National Hurricane Center intensity error trend has remained virtually unchanged, particularly at 24 hours, over the last 25 years. The results from the past few years suggest that a paradigm shift of sampling the environment with a high-altitude, long-duration UAS like the GH that is capable of deploying up to 90 dropsondes ahead of and over the top of a developing or strengthening tropical cyclone could produce the best return on hurricane forecast predictions in subsequent years. Recommendations for the future, including lessons learned and the potential for R2O transition will be discussed.

--No Title--

Science.gov Websites

/ARW1 : IC/BC from 6-h old GFS Alaska ARW2 : IC from current NAM, BC from 6-h old NAM CONUS NMMB/ARW1 : IC from current RAP, BC from 6-h old GFS CONUS ARW2 ; IC from current NAM, BC from 6-h old NAM Hawaii NMMB/ARW1: IC/BC from 6-h old GFS Hawaii ARW2 : IC from current NAM, BC from 6-h old NAM Puerto Rico
Acoustically-Responsive Scaffolds: Control of Growth Factor Release for Tissue Regeneration Using Ultrasound

NASA Astrophysics Data System (ADS)

Moncion, Alexander

Administration of exogenous growth factors (GFs) is a proposed method of stimulating tissue regeneration. Conventional administration routes, such as at-site or systemic injections, have yielded problems with efficacy and/or safety, thus hindering the translation of GF-based regenerative techniques. Hydrogel scaffolds are commonly used as biocompatible delivery vehicles for GFs. Yet hydrogels do not afford spatial or temporal control of GF release - two critical parameters for tissue regeneration. Controlled delivery of GFs is critical for angiogenesis, which is a crucial process in tissue engineering that provides oxygen and nutrients to cells within an implanted hydrogel scaffold. Angiogenesis requires multiple GFs that are presented with distinct spatial and temporal profiles. Thus, controlled release of GFs with spatiotemporal modulation would significantly improve tissue regeneration by recapitulating endogenous GF presentation. In order to achieve this goal, we have developed acoustically-responsive scaffolds (ARSs), which are fibrin hydrogels doped with sonosensitive perfluorocarbon (PFC) emulsions capable of encapsulating various payloads. Focused, mega-Hertz range, ultrasound (US) can modulate the release of a payload non-invasively and in an on-demand manner from ARSs via physical mechanisms termed acoustic droplet vaporization (ADV) and inertial cavitation (IC). This work presents the relationship between the ADV/IC thresholds and various US and hydrogel parameters. These physical mechanisms were used for the controlled release of fluorescent dextran in vitro and in vivo to determine the ARS and US parameters that yielded optimal payload release. The optimal ARS and US parameters were used to demonstrate the controlled release of basic fibroblast growth factor from an in vivo subcutaneous implant model - leading to enhanced angiogenesis and perfusion. Additionally, different acoustic parameters and PFCs were tested and optimized to demonstrate the controlled release of two encapsulated payloads within an ARS. Overall, ARSs are a promising platform for GF delivery in tissue regeneration applications.
Metal-Organic Frameworks as Highly Active Electrocatalysts for High-Energy Density, Aqueous Zinc-Polyiodide Redox Flow Batteries.

PubMed

Li, Bin; Liu, Jian; Nie, Zimin; Wang, Wei; Reed, David; Liu, Jun; McGrail, Pete; Sprenkle, Vincent

2016-07-13

The new aqueous zinc-polyiodide redox flow battery (RFB) system with highly soluble active materials as well as ambipolar and bifunctional designs demonstrated significantly enhanced energy density, which shows great potential to reduce RFB cost. However, the poor kinetic reversibility and electrochemical activity of the redox reaction of I3(-)/I(-) couples on graphite felts (GFs) electrode can result in low energy efficiency. Two nanoporous metal-organic frameworks (MOFs), MIL-125-NH2 and UiO-66-CH3, that have high surface areas when introduced to GF surfaces accelerated the I3(-)/I(-) redox reaction. The flow cell with MOF-modified GFs serving as a positive electrode showed higher energy efficiency than the pristine GFs; increases of about 6.4% and 2.7% occurred at the current density of 30 mA/cm(2) for MIL-125-NH2 and UiO-66-CH3, respectively. Moreover, UiO-66-CH3 is more promising due to its excellent chemical stability in the weakly acidic electrolyte. This letter highlights a way for MOFs to be used in the field of RFBs.
Theoretical Considerations of the Prigogine-Defay Ration with Regard to the Glass-Forming Ability of Drugs from Undercooled Melts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wyttenbach, Nicole; Kirchmeyer, Wiebke; Alsenz, Jochem

Drug behavior in undercooled melts is highly important for pharmaceutics with regard to amorphous solid dispersions, and therefore, categories were recently introduced that differentiate glass formers (GFs) from other drugs that are nonglass formers (nGFs). The present study is based on the assumption that molecular properties relevant for the so-called Prigogine–Defay (PD) ratio would be indicative of a drug’s glass-forming ability. The PD ratio depends in theory on the entropy of fusion and molar volume. Experimental data were gathered from a broad set of pharmaceutical compounds (n = 54) using differential scanning calorimetry. The obtained entropy of fusion and molarmore » volume were indeed found to significantly discriminate GFs from nGFs. In a next step, the entropy of fusion was predicted by different in silico methods. A first group contribution method provided rather unreliable estimates for the entropy of fusion, while an alternative in silico approach seemed more promising for drug categorization. Thus, a significant discrimination model employed molar volume, a so-called effective hydrogen bond number, and effective number of torsional bonds (or torsional units) to categorize GFs and nGFs (p ≤ 0.0000). The results led to new insights into drug vitrification and to practical rules of thumb. The latter may serve as guidance in pharmaceutical profiling and early formulation development with respect to amorphous drug formulations.« less
Gingival Fibroblasts as Autologous Feeders for Induced Pluripotent Stem Cells.

PubMed

Yu, G; Okawa, H; Okita, K; Kamano, Y; Wang, F; Saeki, M; Yatani, H; Egusa, H

2016-01-01

Human gingival fibroblasts (hGFs) present an attractive source of induced pluripotent stem cells (iPSCs), which are expected to be a powerful tool for regenerative dentistry. However, problems to be addressed prior to clinical application include the use of animal-derived feeder cells for cultures. The aim of this study was to establish an autologous hGF-derived iPSC (hGF-iPSC) culture system by evaluating the feeder ability of hGFs. In both serum-containing and serum-free media, hGFs showed higher proliferation than human dermal fibroblasts (hDFs). Three hGF strains were isolated under serum-free conditions, although 2 showed impaired proliferation. When hGF-iPSCs were transferred onto mitomycin C-inactivated hGFs, hDFs, or mouse-derived SNL feeders, hGF and SNL feeders were clearly hGF-iPSC supportive for more than 50 passages, whereas hDF feeders were only able to maintain undifferentiated hGF-iPSC growth for a few passages. After 20 passages on hGF feeders, embryonic stem cell marker expression and CpG methylation at the NANOG and OCT3/4 promoters were similar for hGF-iPSCs cultured on hGF and SNL feeder cells. Long-term cultures of hGF-iPSCs on hGF feeders sustained their normal karyotype and pluripotency. On hGF feeders, hGF-iPSC colonies were surrounded by many colony-derived fibroblast-like cells, and the size of intact colonies at 7 d after passage was significantly larger than that on SNL feeders. Allogeneic hGF strains also maintained hGF-iPSCs for 10 passages. Compared with hDFs, hGFs showed a higher production of laminin-332, laminin α5 chain, and insulin-like growth factor-II, which have been reported to sustain the long-term self-renewal of pluripotent stem cells. These results suggest that hGFs possess an excellent feeder capability and thus can be used as alternatives to conventional mouse-derived SNL and hDF feeders. In addition, our findings suggest that hGF feeders are promising candidates for animal component-free ex vivo expansion of autologous hGF-iPSCs, thus providing an important step toward the future therapeutic application of hGF-iPSCs. © International & American Associations for Dental Research 2015.
Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

NASA Astrophysics Data System (ADS)

Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, G.; Patil, D. J.; Hunt, Brian R.; Kalnay, Eugenia; Ott, Edward; Yorke, James A.

2005-08-01

The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. The performance of the data assimilation system is assessed for different configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs. The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles. Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.
Preparing for Operational Use of High Priority Products from the Joint Polar Satellite System (JPSS) in Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Nandi, S.; Layns, A. L.; Goldberg, M.; Gambacorta, A.; Ling, Y.; Collard, A.; Grumbine, R. W.; Sapper, J.; Ignatov, A.; Yoe, J. G.

2017-12-01

This work describes end to end operational implementation of high priority products from National Oceanic and Atmospheric Administration's (NOAA) operational polar-orbiting satellite constellation, to include Suomi National Polar-orbiting Partnership (S-NPP) and the Joint Polar Satellite System series initial satellite (JPSS-1), into numerical weather prediction and earth systems models. Development and evaluation needed for the initial implementations of VIIRS Environmental Data Records (EDR) for Sea Surface Temperature ingestion in the Real-Time Global Sea Surface Temperature Analysis (RTG) and Polar Winds assimilated in the National Weather Service (NWS) Global Forecast System (GFS) is presented. These implementations ensure continuity of data in these models in the event of loss of legacy sensor data. Also discussed is accelerated operational implementation of Advanced Technology Microwave Sounder (ATMS) Temperature Data Records (TDR) and Cross-track Infrared Sounder (CrIS) Sensor Data Records, identified as Key Performance Parameters by the National Weather Service. Operational use of SNPP after 28 October, 2011 launch took more than one year due to the learning curve and development needed for full exploitation of new remote sensing capabilities. Today, ATMS and CrIS data positively impact weather forecast accuracy. For NOAA's JPSS initial satellite (JPSS-1), scheduled for launch in late 2017, we identify scope and timelines for pre-launch and post-launch activities needed to efficiently transition these capabilities into operations. As part of these alignment efforts, operational readiness for KPPs will be possible as soon as 90 days after launch. The schedule acceleration is possible because of the experience with S-NPP. NOAA operational polar-orbiting satellite constellation provides continuity and enhancement of earth systems observations out to 2036. Program best practices and lessons learned will inform future implementation for follow-on JPSS-3 and -4 missions ensuring benefits and enhancements during the system's design life.
Human lung fibroblast-derived matrix facilitates vascular morphogenesis in 3D environment and enhances skin wound healing.

PubMed

Du, Ping; Suhaeri, Muhammad; Ha, Sang Su; Oh, Seung Ja; Kim, Sang-Heon; Park, Kwideok

2017-05-01

Extracellular matrix (ECM) is crucial to many aspects of vascular morphogenesis and maintenance of vasculature function. Currently the recapitulation of angiogenic ECM microenvironment is still challenging, due mainly to its diverse components and complex organization. Here we investigate the angiogenic potential of human lung fibroblast-derived matrix (hFDM) in creating a three-dimensional (3D) vascular construct. hFDM was obtained via decellularization of in vitro cultured human lung fibroblasts and analyzed via immunofluorescence staining and ELISA, which detect multiple ECM macromolecules and angiogenic growth factors (GFs). Human umbilical vein endothelial cells (HUVECs) morphology was more elongated and better proliferative on hFDM than on gelatin-coated substrate. To prepare 3D construct, hFDM is collected, quantitatively analyzed, and incorporated in collagen hydrogel (Col) with HUVECs. Capillary-like structure (CLS) formation at 7day was significantly better with the groups containing higher doses of hFDM compared to the Col group (control). Moreover, the group (Col/hFDM/GFs) with both hFDM and angiogenic GFs (VEGF, bFGF, SDF-1) showed the synergistic activity on CLS formation and found much larger capillary lumen diameters with time. Further analysis of hFDM via angiogenesis antibody array kit reveals abundant biochemical cues, such as angiogenesis-related cytokines, GFs, and proteolytic enzymes. Significantly up-regulated expression of VE-cadherin and ECM-specific integrin subunits was also noticed in Col/hFDM/GFs. In addition, transplantation of Col/hFMD/GFs with HUVECs in skin wound model presents more effective re-epithelialization, many regenerated hair follicles, better transplanted cells viability, and advanced neovascularization. We believe that current system is a very promising platform for 3D vasculature construction in vitro and for cell delivery toward therapeutic applications in vivo. Functional 3D vasculature construction in vitro is still challenging due to the difficulty of recapitulating the complex angiogenic extracellular matrix (ECM) environment. Herein, we present a simple and practical method to create an angiogenic 3D environment via incorporation of human lung fibroblast-derived matrix (hFDM) into collagen hydrogel. We found that hFDM offers a significantly improved angiogenic microenvironment for HUVECs on 2D substrates and in 3D construct. A synergistic effect of hFDM and angiogenic growth factors has been well confirmed in 3D condition. The prevascularized 3D collagen constructs also facilitate skin wound healing. We believe that current system should be a convenient and powerful platform in engineering 3D vasculature in vitro, and in delivering cells for therapeutic purposes in vivo. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Biomaterial delivery of morphogens to mimic the natural healing cascade in bone

PubMed Central

Mehta, Manav; Schmidt-Bleek, Katharina; Duda, Georg N; Mooney, David J

2012-01-01

Complications in treatment of large bone defects using bone grafting still remain. Our understanding of the endogenous bone regeneration cascade has inspired the exploration of a wide variety of growth factors (GFs) in an effort to mimic the natural signaling that controls bone healing. Biomaterial-based delivery of single exogenous GFs has shown therapeutic efficacy, and this likely relates to its ability to recruit and promote replication of cells involved in tissue development and the healing process. However, as the natural bone healing cascade involves the action of multiple factors, each acting in a specific spatiotemporal pattern, strategies aiming to mimic the critical aspects of this process will likely benefit from the usage of multiple therapeutic agents. This article reviews the current status of approaches to deliver single GFs, as well as ongoing efforts to develop sophisticated delivery platforms to deliver multiple lineage-directing morphogens (multiple GFs) during bone healing. PMID:22626978
Synthesis and characterization of Acacia gum-Fe0Np-silica nanocomposite: an efficient Fenton-like catalyst for the degradation of Remazol Brilliant Violet dye

NASA Astrophysics Data System (ADS)

Singh, Vandana; Singh, Jadveer; Srivastava, Preeti

2018-04-01

Acacia gum-Fe0Np-silica nanocomposite (GFS1) has been crafted through sol-gel technique using a two-step process that involved the reduction of iron salt to zerovalent iron nanoparticles (Fe0Nps) followed by their impregnation within Acacia gum-silica matrix. GFS1 was characterized using Fourier transform infrared spectroscopy (FTIR), high-resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray analysis (EDX), field emission scanning electron microscopy (FE-SEM), vibrating sample magnetometry (VSM), and X-ray photoelectron spectroscopy (XPS) techniques. GFS1 is decorated with Fe0Nps of 5 nm average size. The VSM study revealed that GFS1 has ferromagnetic nature. GFS1 was used as a heterogeneous Fenton-like catalyst for the degradation of azo dyes using Remazol Brilliant Violet (RBV) dye as a model dye. In first 5 min of operation, > 86% dye degradation was achieved and 94% dye (from 100 mg L-1 dye solution) was successfully degraded in 50 min. The dye degradation followed pseudo-first-order kinetics. The GFS1 performed efficiently well over the wide range of dye concentrations (25-200 mg L-1). The catalyst was reused for eight repeated cycles where 12.5% dye degradation was possible even in the eighth cycle. The catalyst behaved fairly well for the degradation of Metanil Yellow (MY) and Orange G (OG) dyes also. Under the optimum conditions of RBV dye degradation, Metanil Yellow (MY) and Orange G (OG) dyes were degraded to the extent of 97 and 26.3%, respectively.
Global field synchronization in gamma range of the sleep EEG tracks sleep depth: Artifact introduced by a rectangular analysis window.

PubMed

Rusterholz, Thomas; Achermann, Peter; Dürr, Roland; Koenig, Thomas; Tarokh, Leila

2017-06-01

Investigating functional connectivity between brain networks has become an area of interest in neuroscience. Several methods for investigating connectivity have recently been developed, however, these techniques need to be applied with care. We demonstrate that global field synchronization (GFS), a global measure of phase alignment in the EEG as a function of frequency, must be applied considering signal processing principles in order to yield valid results. Multichannel EEG (27 derivations) was analyzed for GFS based on the complex spectrum derived by the fast Fourier transform (FFT). We examined the effect of window functions on GFS, in particular of non-rectangular windows. Applying a rectangular window when calculating the FFT revealed high GFS values for high frequencies (>15Hz) that were highly correlated (r=0.9) with spectral power in the lower frequency range (0.75-4.5Hz) and tracked the depth of sleep. This turned out to be spurious synchronization. With a non-rectangular window (Tukey or Hanning window) these high frequency synchronization vanished. Both, GFS and power density spectra significantly differed for rectangular and non-rectangular windows. Previous papers using GFS typically did not specify the applied window and may have used a rectangular window function. However, the demonstrated impact of the window function raises the question of the validity of some previous findings at higher frequencies. We demonstrated that it is crucial to apply an appropriate window function for determining synchronization measures based on a spectral approach to avoid spurious synchronization in the beta/gamma range. Copyright © 2017 Elsevier B.V. All rights reserved.
Chitinolytic and chitosanolytic activities from crude cellulase extract produced by A. niger grown on apple pomace through Koji fermentation.

PubMed

Dhillon, Gurpreet Singh; Brar, Satinder Kaur; Kaur, Surinder; Valero, Jose R; Verma, Mausam

2011-12-01

Enzyme extracts of cellulase [filter paper cellulase (FPase) and carboxymethyl cellulase (CMCase)], chitinase, and chitosanase produced by Aspergillus niger NRRL-567 were evaluated. The interactive effects of initial moisture and different inducers for FP cellulase and CMCase production were optimized using response surface methodology. Higher enzyme activities [FPase 79.24+/- 4.22 IU/gram fermented substrate (gfs) and CMCase 124.04+/-7.78 IU/gfs] were achieved after 48 h fermentation in solid-state medium containing apple pomace supplemented with rice husk [1% (w/w)] under optimized conditions [pH 4.5, moisture 55% (v/w), and inducers veratryl alcohol (2 mM/kg), copper sulfate (1.5 mM/kg), and lactose 2% (w/w)] (p<0.05). Koji fermentation in trays was carried out and higher enzyme activities (FPase 96.67+/-4.18 IU/gfs and CMCase 146.50+/-11.92 IU/gfs) were achieved. The nonspecific chitinase and chitosanase activities of cellulase enzyme extract were analyzed using chitin and chitosan substrates with different physicochemical characteristics, such as degree of deacetylation, molecular weight, and viscosity. Higher chitinase and chitosanase activities of 70.28+/-3.34 IU/gfs and 60.18+/-3.82 to 64.20+/-4.12 IU/gfs, respectively, were achieved. Moreover, the enzyme was stable and retained 92-94% activity even after one month. Cellulase enzyme extract obtained from A. niger with chitinolytic and chitosanolytic activities could be potentially used for making low-molecular-weight chitin and chitosan oligomers, having promising applications in biomedicine, pharmaceuticals, food, and agricultural industries, and in biocontrol formulations.
SP-100 initial startup and restart control strategy

NASA Astrophysics Data System (ADS)

Halfen, Frank J.; Wong, Kwok K.; Switick, Dennis M.; Shukla, Jaikaran N.

Startup control strategies for SP-100 are described. Revised control and operating strategies are discussed which have been developed and tested using the SP-100 dynamic simulation model Aries-GFS (Generic Flight System).
Inventory of GFS Files on NOMADS

Science.gov Websites

Inventory of GFS Files on NOMADS GRIB Filter options Description Filename Cycles Available 0.25 .fFFF 00,06,12,18 UTC OPeNDAP options Description Filename Cycles Available 0.25 Degree (3 hourly to 240
Platelet-Rich Plasma-Loaded Poly(d,l-lactide)-Poly(ethylene glycol)-Poly(d,l-lactide) Hydrogel Dressing Promotes Full-Thickness Skin Wound Healing in a Rodent Model

PubMed Central

Qiu, Manle; Chen, Daoyun; Shen, Chaoyong; Shen, Ji; Zhao, Huakun; He, Yaohua

2016-01-01

Traditional therapeutic methods for skin wounds have many disadvantages, and new wound dressings that can facilitate the healing process are thus urgently needed. Platelet-rich plasma (PRP) contains multiple growth factors (GFs) and shows a significant capacity to heal soft tissue wounds. However, these GFs have a short half-life and deactivate rapidly; we therefore need a sustained delivery system to overcome this shortcoming. In this study, poly(d,l-lactide)-poly(ethylene glycol)-poly(d,l-lactide) (PDLLA-PEG-PDLLA: PLEL) hydrogel was successfully created as delivery vehicle for PRP GFs and was evaluated systematically. PLEL hydrogel was injectable at room temperature and exhibited a smart thermosensitive in situ gel-formation behavior at body temperature. In vitro cell culture showed PRP-loaded PLEL hydrogel (PRP/PLEL) had little cytotoxicity, and promoted EaHy926 proliferation, migration and tube formation; the factor release assay additionally indicated that PLEL realized the controlled release of PRP GFs for as long as 14 days. When employed to treat rodents’ full-thickness skin defects, PRP/PLEL showed a significantly better ability to raise the number of both newly formed and mature blood vessels compared to the control, PLEL and PRP groups. Furthermore, the PRP/PLEL-treated group displayed faster wound closure, better reepithelialization and collagen formation. Taken together, PRP/PLEL provides a promising strategy for promoting angiogenesis and skin wound healing, which extends the potential of this dressing for clinical application. PMID:27347938
Ameliorative effect of black grape juice on systemic alterations and mandibular osteoradionecrosis induced by whole brain irradiation in rats.

PubMed

Freitas, Robson B; González, Paquita; Martins, Nara Maria B; Andrade, Edson R; Cesteros Morante, María Jesús; Conles Picos, Iban; Costilla García, Serafín; Bauermann, Liliane F; Barrio, Juan Pablo

2017-02-01

Whole brain irradiation (WBI) causes a variety of secondary side-effects including anorexia and bone necrosis. We evaluated the radiomodifying effect of black grape juice (BGJ) on WBI alterations in rats measuring food and water intake, body weight, hemogram, and morphological and histological mandibular parameters. Forty male rats (200-250 g) were exposed to eight sessions of cranial X-ray irradiation. The total dose absorbed was 32 Gy delivered over 2 weeks. Four groups were defined: (i) NG: non-irradiated, glucose and fructose solution-supplemented (GFS); (ii) NJ: non-irradiated, BGJ-supplemented; (iii) RG: irradiated, GFS-supplemented; and (iv) RJ: irradiated, BGJ-supplemented. Rats received daily BGJ or GFS dosing by gavage starting 4 days before, continuing during, and ending 4 days after WBI. RJ rats ingested more food and water and showed less body weight loss than RG rats during the irradiation period. Forty days after WBI, irradiated animals started losing weight again compared with controls as a consequence of masticatory hypofunction by mandibular osteoradionecrosis (ORN). Osteoclastic activity and inflammation were apparent in RG rat mandibles. BGJ was able to attenuate the severity of ORN as well as to improve white and red blood cell counts. Fractionated whole brain irradiation induces mandibular changes that interfere with normal feeding. BGJ can be used to mitigate systemic side-effects of brain irradiation and ORN.
A Weather Analysis and Forecasting System for Baja California, Mexico

NASA Astrophysics Data System (ADS)

Farfan, L. M.

2006-05-01

The weather of the Baja California Peninsula, part of northwestern Mexico, is mild and dry most of the year. However, during the summer, humid air masses associated with tropical cyclones move northward in the eastern Pacific Ocean. Added features that create a unique meteorological situation include mountain ranges along the spine of the peninsula, warm water in the Gulf of California, and the cold California Current in the Pacific. These features interact with the environmental flow to induce conditions that play a role in the occurrence of localized, convective systems during the approach of tropical cyclones. Most of these events occur late in the summer, generating heavy precipitation, strong winds, lightning, and are associated with significant property damage to the local populations. Our goal is to provide information on the characteristics of these weather systems by performing an analysis of observations derived from a regional network. This includes imagery from radar and geostationary satellite, and data from surface stations. A set of real-time products are generated in our research center and are made available to a broad audience (researchers, students, and business employees) by using an internet site. Graphical products are updated anywhere from one to 24 hours and includes predictions from numerical models. Forecasts are derived from an operational model (GFS) and locally generated simulations based on a mesoscale model (MM5). Our analysis and forecasting system has been in operation since the summer of 2005 and was used as a reference for a set of discussions during the development of eastern Pacific tropical cyclones. This basin had 15 named storms and none of them made landfall on the west coast of Mexico; however, four systems were within 800 km from the area of interest, resulting in some convective activity. During the whole season, a group of 30 users from our institution, government offices, and local businesses received daily information on storm location, expected track, and potential impact on weather conditions over Baja California. From late June through October, a set of more than 50 messages were issued daily and distributed to these users. This presentation focuses on providing an overview of the lessons learned from this experience, feedback from users, and our plans for the upcoming 2006 season.
Seasonal prediction of typhoon genesis frequency and track patterns in the North West Pacific area

NASA Astrophysics Data System (ADS)

Hyoun, Yoosun; Kang, Kiryong; Shin, Do-Shick

2014-05-01

This study is to investigate the performance of the typhoon seasonal predictability using a dynamical model. The check items are the monthly statistics for total number of typhoon genesis in Western North Pacific (WNP) area and possible threat to Korean peninsula among them, and the probability of each categorized track pattern. As the dynamical model the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) was used, and it uses five ensemble members including control run are generated using time-lagged methods and the resolution of T126L27 (a Gaussian grid spacing of 0.94º). The model initial conditions are obtained from the National Center for Enviromental Prediction Global Forecast System (NCEP GFS) and the SST from Climate Forecast System with bias correction was used for ocean surface boundary condition. The summer (Jun-Jul-Aug) season prediction is made one month prior to target season. The detection of tropical cyclone used in this system is based on six criteria. First, the isolated vortex type minimum sea level pressure should be below 1008hPa. Second, the maximum wind speed is larger than 17m s-1. Third, the magnitude of the maximum relative vorticity at 850hPa exceeds 3.5x10-5s-1. Fourth, the average temperature difference from the area mean of surrounding region at 300hPa, 500hPa, 700hPa exceeds 2.5K. Fifth, the maximum wind speed at 850hPa is larger than that at 300hPa. Sixth, this identified vortex should last more than two days. These criteria were chosen after close examination from model-observation comparison. In this study, we will focus on performance of the system typhoon frequency and track pattern in the WNP area during 2004-2013.
Simulation of boreal Summer Monsoon Rainfall using CFSV2_SSiB model: sensitivity to Land Use Land Cover (LULC)

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.

2016-12-01

The land surface play a vital role in determining the surface energy budget, accurate representation of land use and land cover (LULC) is necessary to improve forecast. In this study, we have investigated the influence of surface vegetation maps with different LULC on simulating the boreal summer monsoon rainfall. Using a National Centres for Environmental Prediction (NCEP) Coupled Forecast System version 2(CFSv2) model coupled with Simplified Simple Biosphere (SSiB) model, two experiments were conducted: one with old vegetation map and one with new vegetation map. The significant differences between new and old vegetation map were in semi-arid and arid areas. For example, in old map Tibetan plateau classified as desert, which is not appropriate, while in new map it was classified as grasslands or shrubs with bare soil. Old map classified the Sahara desert as a bare soil and shrubs with bare soil, whereas in new map it was classified as bare ground. In addition to central Asia and the Sahara desert, in new vegetation map, Europe had more cropped area and India's vegetation cover was changed from crops and forests to wooded grassland and small areas of grassland and shrubs. The simulated surface air temperature with new map shows a significant improvement over Asia, South Africa, and northern America by some 1 to 2ºC and 2 to 3ºC over north east China and these are consistent with the reduced rainfall biases over Africa, near Somali coast, north east India, Bangladesh, east China sea, eastern Pacific and northern USA. Over Indian continent and bay of Bengal dry rainfall anomalies that is the only area showing large dry rainfall bias, however, they were unchanged with new map simulation. Overall the CFSv2(coupled with SSiB) model with new vegetation map show a promising result in improving the monsoon forecast by improving the Land -Atmosphere interactions. To compare with the LULC forcing, experiment was conducted using the Global Forecast System (GFS) simulations forced with different observed Sea Surface Temperatures (SST) for the same period: one is from NCEP reanalysis and one from Hadley Center. They have substantial difference in Indian Ocean. Preliminary analysis shows that, the impact of these two SST data sets on Indian summer monsoon rainfall has no significant impact.
Ternary metal complexes of guaifenesin drug: Synthesis, spectroscopic characterization and in vitro anticancer activity of the metal complexes.

PubMed

Mahmoud, W H; Mahmoud, N F; Mohamed, G G; El-Sonbati, A Z; El-Bindary, A A

2015-01-01

The coordination behavior of a series of transition metal ions named Cr(III), Fe(III), Mn(II), Co(II), Ni(II), Cu(II), Zn(II) and Cd(II) with a mono negative tridentate guaifenesin ligand (GFS) (OOO donation sites) and 1,10-phenanthroline (Phen) is reported. The metal complexes are characterized based on elemental analyses, IR, (1)H NMR, solid reflectance, magnetic moment, molar conductance, UV-vis spectral studies, mass spectroscopy, ESR, XRD and thermal analysis (TG and DTG). The ternary metal complexes were found to have the formulae of [M(GFS)(Phen)Cl]Cl·nH2O (M=Cr(III) (n=1) and Fe(III) (n=0)), [M(GFS)(Phen)Cl]·nH2O (M=Mn(II) (n=0), Zn(II) (n=0) and Cu(II) (n=3)) and [M(GFS)(Phen)(H2O)]Cl·nH2O (M=Co(II) (n=0), Ni(II) (n=0) and Cd(II) (n=4)). All the chelates are found to have octahedral geometrical structures. The ligand and its ternary chelates are subjected to thermal analyses (TG and DTG). The GFS ligand, in comparison to its ternary metal complexes also was screened for their antibacterial activity on gram positive bacteria (Bacillus subtilis and Staphylococcus aureus), gram negative bacteria (Escherichia coli and Neisseria gonorrhoeae) and for in vitro antifungal activity against (Candida albicans). The activity data show that the metal complexes have antibacterial and antifungal activity more than the parent GFS ligand. The complexes were also screened for its in vitro anticancer activity against the Breast cell line (MFC7) and the results obtained show that they exhibit a considerable anticancer activity. Copyright © 2015 Elsevier B.V. All rights reserved.

Seasonal variation in xylem pressure of walnut trees: root and stem pressures.

PubMed

Ewers, F W; Améglio, T; Cochard, H; Beaujard, F; Martignac, M; Vandame, M; Bodet, C; Cruiziat, P

2001-09-01

Measurements of air and soil temperatures and xylem pressure were made on 17-year-old orchard trees and on 5-year-old potted trees of walnut (Juglans regia L.). Cooling chambers were used to determine the relationships between temperature and sugar concentration ([glucose] + [fructose] + [sucrose], GFS) and seasonal changes in xylem pressure development. Pressure transducers were attached to twigs of intact plants, root stumps and excised shoots while the potted trees were subjected to various temperature regimes in autumn, winter and spring. Osmolarity and GFS of the xylem sap (apoplast) were measured before and after cooling or warming treatments. In autumn and spring, xylem pressures of up to 160 kPa were closely correlated with soil temperature but were not correlated with GFS in xylem sap. High root pressures were associated with uptake of mineral nutrients from soil, especially nitrate. In autumn and spring, xylem pressures were detected in root stumps as well as in intact plants, but not in excised stems. In contrast, in winter, 83% of the xylem sap osmolarity in both excised stems and intact plants could be accounted for by GFS, and both GFS and osmolarity were inversely proportional to temperature. Plants kept at 1.5 degrees C developed positive xylem pressures up to 35 kPa, xylem sap osmolarities up to 260 mosmol l(-1) and GFS concentrations up to 70 g l(-1). Autumn and spring xylem pressures, which appeared to be of root origin, were about 55% of the theoretical pressures predicted by osmolarity of the xylem sap. In contrast, winter pressures appeared to be of stem origin and were only 7% of the theoretical pressures, perhaps because of a lower stem water content during winter.
Generation and periodontal differentiation of human gingival fibroblasts-derived integration-free induced pluripotent stem cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yin, Xiaohui; Peking University Stem Cell Research Center and Department of Cell Biology, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191; Li, Yang

Induced pluripotent stem cells (iPSCs) have been recognized as a promising cell source for periodontal tissue regeneration. However, the conventional virus-based reprogramming approach is associated with a high risk of genetic mutation and limits their therapeutic utility. Here, we successfully generated iPSCs from readily accessible human gingival fibroblasts (hGFs) through an integration-free and feeder-free approach via delivery of reprogramming factors of Oct4, Sox2, Klf4, L-myc, Lin28 and TP53 shRNA with episomal plasmid vectors. The iPSCs presented similar morphology and proliferation characteristics as embryonic stem cells (ESCs), and expressed pluripotent markers including Oct4, Tra181, Nanog and SSEA-4. Additionally, these cells maintainedmore » a normal karyotype and showed decreased CpG methylation ratio in the promoter regions of Oct4 and Nanog. In vivo teratoma formation assay revealed the development of tissues representative of three germ layers, confirming the acquisition of pluripotency. Furthermore, treatment of the iPSCs in vitro with enamel matrix derivative (EMD) or growth/differentiation factor-5 (GDF-5) significantly up-regulated the expression of periodontal tissue markers associated with bone, periodontal ligament and cementum respectively. Taken together, our data demonstrate that hGFs are a valuable cell source for generating integration-free iPSCs, which could be sequentially induced toward periodontal cells under the treatment of EMD and GDF-5. - Highlights: • Integration-free iPSCs are successfully generated from hGFs via an episomal approach. • EMD promotes differentiation of the hGFs-derived iPSCs toward periodontal cells. • GDF-5 promotes differentiation of the hGFs-derived iPSCs toward periodontal cells. • hGFs-derived iPSCs could be a promising cell source for periodontal regeneration.« less
Aggregation of Environmental Model Data for Decision Support

NASA Astrophysics Data System (ADS)

Alpert, J. C.

2013-12-01

Weather forecasts and warnings must be prepared and then delivered so as to reach their intended audience in good time to enable effective decision-making. An effort to mitigate these difficulties was studied at a Workshop, 'Sustaining National Meteorological Services - Strengthening WMO Regional and Global Centers' convened, June , 2013, by the World Bank, WMO and the US National Weather Service (NWS). The skill and accuracy of atmospheric forecasts from deterministic models have increased and there are now ensembles of such models that improve decisions to protect life, property and commerce. The NWS production of numerical weather prediction products result in model output from global and high resolution regional ensemble forecasts. Ensembles are constructed by changing the initial conditions to make a 'cloud' of forecasts that attempt to span the space of possible atmospheric realizations which can quantify not only the most likely forecast, but also the uncertainty. This has led to an unprecedented increase in data production and information content from higher resolution, multi-model output and secondary calculations. One difficulty is to obtain the needed subset of data required to estimate the probability of events, and report the information. The calibration required to reliably estimate the probability of events, and honing of threshold adjustments to reduce false alarms for decision makers is also needed. To meet the future needs of the ever-broadening user community and address these issues on a national and international basis, the weather service implemented the NOAA Operational Model Archive and Distribution System (NOMADS). NOMADS provides real-time and retrospective format independent access to climate, ocean and weather model data and delivers high availability content services as part of NOAA's official real time data dissemination at its new NCWCP web operations center. An important aspect of the server's abilities is to aggregate the matrix of model output offering access to probability and calibrating information for real time decision making. The aggregation content server reports over ensemble component and forecast time in addition to the other data dimensions of vertical layer and position for each variable. The unpacking, organization and reading of many binary packed files is accomplished most efficiently on the server while weather element event probability calculations, the thresholds for more accurate decision support, or display remain for the client. Our goal is to reduce uncertainty for variables of interest, e.g, agricultural importance. The weather service operational GFS model ensemble and short range ensemble forecasts can make skillful probability forecasts to alert users if and when their selected weather events will occur. A description of how this framework operates and how it can be implemented using existing NOMADS content services and applications is described.
Superconductivity in Cage Compounds LaTr2Al20 with Tr = Ti, V, Nb, and Ta

NASA Astrophysics Data System (ADS)

Yamada, Akira; Higashinaka, Ryuji; Matsuda, Tatsuma D.; Aoki, Yuji

2018-03-01

Electrical resistivity, magnetic susceptibility, and specific heat measurements on single crystals of LaTr2Al20 (Tr = Ti, V, Nb, and Ta) revealed that these four compounds exhibit weak-coupling superconductivity with transition temperatures Tc = 0.46, 0.15, 1.05, and 1.03 K, respectively. LaTi2Al20 is most probably a type-I superconductor, which is quite rare among intermetallic compounds. Single-crystal X-ray diffraction suggests "rattling" anharmonic large-amplitude oscillations of Al ions (16c site) on the Al16 cage, while no such feature is suggested for the cage-center La ion. Using a parameter dGFS quantifying the "guest free space" of the cage-center ion, we demonstrate that nonmagnetic RTr2Al20 superconductors are classified into two groups, i.e., (A) dGFS ≠ 0 and Tc correlates with dGFS, and (B) dGFS ≃ 0 and Tc seems to be governed by other factors.
Three-dimensional graphene foam as a biocompatible and conductive scaffold for neural stem cells

PubMed Central

Li, Ning; Zhang, Qi; Gao, Song; Song, Qin; Huang, Rong; Wang, Long; Liu, Liwei; Dai, Jianwu; Tang, Mingliang; Cheng, Guosheng

2013-01-01

Neural stem cell (NSC) based therapy provides a promising approach for neural regeneration. For the success of NSC clinical application, a scaffold is required to provide three-dimensional (3D) cell growth microenvironments and appropriate synergistic cell guidance cues. Here, we report the first utilization of graphene foam, a 3D porous structure, as a novel scaffold for NSCs in vitro. It was found that three-dimensional graphene foams (3D-GFs) can not only support NSC growth, but also keep cell at an active proliferation state with upregulation of Ki67 expression than that of two-dimensional graphene films. Meanwhile, phenotypic analysis indicated that 3D-GFs can enhance the NSC differentiation towards astrocytes and especially neurons. Furthermore, a good electrical coupling of 3D-GFs with differentiated NSCs for efficient electrical stimulation was observed. Our findings implicate 3D-GFs could offer a powerful platform for NSC research, neural tissue engineering and neural prostheses. PMID:23549373
Biomaterial delivery of morphogens to mimic the natural healing cascade in bone.

PubMed

Mehta, Manav; Schmidt-Bleek, Katharina; Duda, Georg N; Mooney, David J

2012-09-01

Complications in treatment of large bone defects using bone grafting still remain. Our understanding of the endogenous bone regeneration cascade has inspired the exploration of a wide variety of growth factors (GFs) in an effort to mimic the natural signaling that controls bone healing. Biomaterial-based delivery of single exogenous GFs has shown therapeutic efficacy, and this likely relates to its ability to recruit and promote replication of cells involved in tissue development and the healing process. However, as the natural bone healing cascade involves the action of multiple factors, each acting in a specific spatiotemporal pattern, strategies aiming to mimic the critical aspects of this process will likely benefit from the usage of multiple therapeutic agents. This article reviews the current status of approaches to deliver single GFs, as well as ongoing efforts to develop sophisticated delivery platforms to deliver multiple lineage-directing morphogens (multiple GFs) during bone healing. Copyright © 2012 Elsevier B.V. All rights reserved.
A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.
Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

2017-12-01

The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to Quebrada de Ramón's time of concentration) and the temperatures during the event. This is a fundamental input to a hydrological model that could forecast flash floods. Finally, WSM-6Class microphysics was chosen as the one with best performance, but a geostatistical approach to countervail WRF forecasts' shortcomings over Andean piedmont is required.
A novel approach to Lagrangian sampling of marine boundary layer cloud and aerosol in the northeast Pacific: case studies from CSET

NASA Astrophysics Data System (ADS)

Mohrmann, J.; Albrecht, B. A.; Bretherton, C. S.; Ghate, V. P.; Zuidema, P.; Wood, R.

2015-12-01

The Cloud System Evolution in the Trades (CSET) field campaign took place during July/August 2015 with the purpose of characterizing the cloud, aerosol and thermodynamic properties of the northeast Pacific marine boundary layer. One major science goal of the campaign was to observe a Lagrangian transition from thin stratocumulus (Sc) upwind near California to trade cumulus (Cu) nearer to Hawaii. Cloud properties were observed from the NSF/NCAR Gulfstream V research plane (GV) using the HIAPER Cloud Radar (HCR) and the HIAPER Spectral Resolution Lidar (HSRL), among other instrumentation. Aircraft observations were complemented by a suite of satellite-derived products. To observe a the evolution of airmasses over the course of two days, upwind regions were sampled on an outbound flight to from Sacramento, CA, to Kona, HI. The sampled airmasses were then tracked using HYSPLIT trajectories based on GFS model forecasts, and the return flight to California was planned to intercept those airmasses, using satellite observation to track cloud evolution in the interim. This approach required that trajectories were reasonably stable up to 3 days prior to final sampling, and also that forecast trajectories were in agreement with post-flight analysis and visual cloud feature tracking. The extent to which this was realised, and hence the validity of this new approach to Lagrangian airmass observation, is assessed here. We also present results showing that a Sc-Cu airmass transition was consistently observed during the CSET study using measurements from research flights and satellite.
Mode entanglement of Gaussian fermionic states

NASA Astrophysics Data System (ADS)

Spee, C.; Schwaiger, K.; Giedke, G.; Kraus, B.

2018-04-01

We investigate the entanglement of n -mode n -partite Gaussian fermionic states (GFS). First, we identify a reasonable definition of separability for GFS and derive a standard form for mixed states, to which any state can be mapped via Gaussian local unitaries (GLU). As the standard form is unique, two GFS are equivalent under GLU if and only if their standard forms coincide. Then, we investigate the important class of local operations assisted by classical communication (LOCC). These are central in entanglement theory as they allow one to partially order the entanglement contained in states. We show, however, that there are no nontrivial Gaussian LOCC (GLOCC) among pure n -partite (fully entangled) states. That is, any such GLOCC transformation can also be accomplished via GLU. To obtain further insight into the entanglement properties of such GFS, we investigate the richer class of Gaussian stochastic local operations assisted by classical communication (SLOCC). We characterize Gaussian SLOCC classes of pure n -mode n -partite states and derive them explicitly for few-mode states. Furthermore, we consider certain fermionic LOCC and show how to identify the maximally entangled set of pure n -mode n -partite GFS, i.e., the minimal set of states having the property that any other state can be obtained from one state inside this set via fermionic LOCC. We generalize these findings also to the pure m -mode n -partite (for m >n ) case.
Therapeutic Potential of Gingival Fibroblasts for Cutaneous Radiation Syndrome: Comparison to Bone Marrow-Mesenchymal Stem Cell Grafts

PubMed Central

Tissedre, Frederique; Busson, Elodie; Holler, Valerie; Leclerc, Thomas; Strup-Perrot, Carine; Couty, Ludovic; L'homme, Bruno; Benderitter, Marc; Lafont, Antoine; Lataillade, Jean Jacques; Coulomb, Bernard

2015-01-01

Mesenchymal stem cell (MSC) therapy has recently been investigated as a potential treatment for cutaneous radiation burns. We tested the hypothesis that injection of local gingival fibroblasts (GFs) would promote healing of radiation burn lesions and compared results with those for MSC transplantation. Human clinical- grade GFs or bone marrow-derived MSCs were intradermally injected into mice 21 days after local leg irradiation. Immunostaining and real-time PCR analysis were used to assess the effects of each treatment on extracellular matrix remodeling and inflammation in skin on days 28 and 50 postirradiation. GFs induced the early development of thick, fully regenerated epidermis, skin appendages, and hair follicles, earlier than MSCs did. The acceleration of wound healing by GFs involved rearrangement of the deposited collagen, modification of the Col/MMP/TIMP balance, and modulation of the expression and localization of tenascin-C and of the expression of growth factors (VEGF, EGF, and FGF7). As MSC treatment did, GF injection decreased the irradiation-induced inflammatory response and switched the differentiation of macrophages toward an M2-like phenotype, characterized by CD163+ macrophage infiltration and strong expression of arginase-1. These findings indicate that GFs are an attractive target for regenerative medicine, for easier to collect, can grow in culture, and promote cutaneous wound healing in irradiation burn lesions. PMID:25584741
Micro-precise spatiotemporal delivery system embedded in 3D printing for complex tissue regeneration.

PubMed

Tarafder, Solaiman; Koch, Alia; Jun, Yena; Chou, Conrad; Awadallah, Mary R; Lee, Chang H

2016-04-25

Three dimensional (3D) printing has emerged as an efficient tool for tissue engineering and regenerative medicine, given its advantages for constructing custom-designed scaffolds with tunable microstructure/physical properties. Here we developed a micro-precise spatiotemporal delivery system embedded in 3D printed scaffolds. PLGA microspheres (μS) were encapsulated with growth factors (GFs) and then embedded inside PCL microfibers that constitute custom-designed 3D scaffolds. Given the substantial difference in the melting points between PLGA and PCL and their low heat conductivity, μS were able to maintain its original structure while protecting GF's bioactivities. Micro-precise spatial control of multiple GFs was achieved by interchanging dispensing cartridges during a single printing process. Spatially controlled delivery of GFs, with a prolonged release, guided formation of multi-tissue interfaces from bone marrow derived mesenchymal stem/progenitor cells (MSCs). To investigate efficacy of the micro-precise delivery system embedded in 3D printed scaffold, temporomandibular joint (TMJ) disc scaffolds were fabricated with micro-precise spatiotemporal delivery of CTGF and TGFβ3, mimicking native-like multiphase fibrocartilage. In vitro, TMJ disc scaffolds spatially embedded with CTGF/TGFβ3-μS resulted in formation of multiphase fibrocartilaginous tissues from MSCs. In vivo, TMJ disc perforation was performed in rabbits, followed by implantation of CTGF/TGFβ3-μS-embedded scaffolds. After 4 wks, CTGF/TGFβ3-μS embedded scaffolds significantly improved healing of the perforated TMJ disc as compared to the degenerated TMJ disc in the control group with scaffold embedded with empty μS. In addition, CTGF/TGFβ3-μS embedded scaffolds significantly prevented arthritic changes on TMJ condyles. In conclusion, our micro-precise spatiotemporal delivery system embedded in 3D printing may serve as an efficient tool to regenerate complex and inhomogeneous tissues.
National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Documentation, experiments, web content Nicole McKee Makefiles, scripts, launcher Edward Colon NEMSIO, post Yang GFS post Hui-ya Chuang NAM development Tom Black Dusan Jovic Jim Abeles GFS development S Moorthi
The hourly updated US High-Resolution Rapid Refresh (HRRR) storm-scale forecast model

NASA Astrophysics Data System (ADS)

Alexander, Curtis; Dowell, David; Benjamin, Stan; Weygandt, Stephen; Olson, Joseph; Kenyon, Jaymes; Grell, Georg; Smirnova, Tanya; Ladwig, Terra; Brown, John; James, Eric; Hu, Ming

2016-04-01

The 3-km convective-allowing High-Resolution Rapid Refresh (HRRR) is a US NOAA hourly updating weather forecast model that use a specially configured version of the Advanced Research WRF (ARW) model and assimilate many novel and most conventional observation types on an hourly basis using Gridpoint Statistical Interpolation (GSI). Included in this assimilation is a procedure for initializing ongoing precipitation systems from observed radar reflectivity data (and proxy reflectivity from lightning and satellite data), a cloud analysis to initialize stable layer clouds from METAR and satellite observations, and special techniques to enhance retention of surface observation information. The HRRR is run hourly out to 15 forecast hours over a domain covering the entire conterminous United States using initial and boundary conditions from the hourly-cycled 13km Rapid Refresh (RAP, using similar physics and data assimilation) covering North America and a significant part of the Northern Hemisphere. The HRRR is continually developed and refined at NOAA's Earth System Research Laboratory, and an initial version was implemented into the operational NOAA/NCEP production suite in September 2014. Ongoing experimental RAP and HRRR model development throughout 2014 and 2015 has culminated in a set of data assimilation and model enhancements that will be incorporated into the first simultaneous upgrade of both the operational RAP and HRRR that is scheduled for spring 2016 at NCEP. This presentation will discuss the operational RAP and HRRR changes contained in this upgrade. The RAP domain is being expanded to encompass the NAM domain and the forecast lengths of both the RAP and HRRR are being extended. RAP and HRRR assimilation enhancements have focused on (1) extending surface data assimilation to include mesonet observations and improved use of all surface observations through better background estimates of 2-m temperature and dewpoint including projection of 2-m temperature observations through the model boundary layer and (2) extending the use of radar observations to include both radial velocity and 3-D retrieval of rain hydrometeors from observed radar reflectivities in the warm-season. The RAP hybrid EnKF 3D-variational data assimilation will increase weighting of GFS ensemble-based background error covariance estimation and introduce this hybrid data assimilation configuration in the HRRR. Enhancement of RAP and HRRR model physics include improved land surface and boundary layer prediction using the updated Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization scheme, Grell-Freitas-Olson (GFO) shallow and deep convective parameterization, aerosol-aware Thompson microphysics and upgraded Rapid Update Cycle (RUC) land-surface model. The presentation will highlight improvements in the RAP and HRRR model physics to reduce certain systematic forecast biases including a warm and dry daytime bias over the central and eastern CONUS during the warm season along with improved convective forecasts in more weakly-forced diurnally-driven events. Examples of RAP and HRRR forecast improvements will be demonstrated through both retrospective and real-time verification statistics and case-study examples.
Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

NASA Astrophysics Data System (ADS)

Rieckh, Therese; Anthes, Richard; Randel, William; Ho, Shu-Peng; Foelsche, Ulrich

2018-05-01

While water vapor is the most important tropospheric greenhouse gas, it is also highly variable in both space and time, and water vapor concentrations range over 3 orders of magnitude in the troposphere. These properties challenge all observing systems to accurately measure and resolve the vertical structure and variability of tropospheric humidity. In this study we characterize the humidity measurements of various observing techniques, including four separate Global Positioning System (GPS) radio occultation (RO) humidity retrievals (University Corporation for Atmospheric Research (UCAR) direct, UCAR one-dimensional variational retrieval (1D-Var), Wegener Center for Climate and Global Change (WEGC) 1D-Var, Jet Propulsion Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and NCEP Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we analyzed time-height cross sections over four radiosonde stations in the tropical and subtropical western Pacific for the year 2007. We found that the accuracy of RO humidity is comparable to or better than both radiosonde and AIRS humidity over 800 to 400 hPa, as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000-400 hPa layer, and differences are most pronounced above 600 hPa.
Evaluation of the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA) for use in Wind Energy Applications in the Great Basin of Nevada

NASA Astrophysics Data System (ADS)

King, Kristien C.

In order to further assess the wind energy potential for Nevada, the accuracy of a computational meteorological model, the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA), was evaluated by comparing simulation results with data collected from a wind monitoring tower near Tonopah, NV. The state of Nevada is characterized by high mountains and low-lying valleys, therefore, in order to determine the wind potential for the state, meteorological models that predict the wind must be able to accurately represent and account for terrain features and simulate topographic forcing with accuracy. Topographic forcing has a dominant role in the development and modification of mesoscale flows in regions of complex terrain, like Tonopah, especially at the level of wind turbine blade heights (~80 m). Additionally, model factors such as horizontal resolution, terrain database resolution, model physics, time of model initialization, stability regime, and source of initial conditions may each affect the ability of a mesoscale model to forecast winds correctly. The observational tower used for comparison was located at Stone Cabin, Nevada. The tower had both sonic anemometers and cup anemometers installed at heights of 40 m, 60 m, and 80 m above the surface. During a previous experiment, tower data were collected for the period February 9 through March 10, 2007 and compared to model simulations using the MM5 and WRF models at a number of varying horizontal resolutions. In this previous research, neither the MM5 nor the WRF showed a significant improvement in ability to forecast wind speed with increasing horizontal grid resolution. The present research evaluated the ability of OMEGA to reproduce point winds as compared to the observational data from the Stone Cabin Tower at heights of 40 m, 60 m, and 80 m. Unlike other mesoscale atmospheric models, OMEGA incorporates an unstructured triangular adaptive grid which allows for increased flexibility and accuracy in characterizing areas of complex terrain. Model sensitivity to horizontal grid resolution, initial conditions, and time of initialization were tested. OMEGA was run over three different horizontal grid resolutions with minimum horizontal edge lengths of: 18 km, 6 km, and 2 km. For each resolution, the model was initialized using both the Global Forecasting System (GFS) and North American Regional Reanalysis (NARR) to determine model sensitivity to initial conditions. For both the NARR and GFS initializations, the model was started at both 0000 UTC and 1200 UTC to determine the effect of start time and stability regime on the performance of the model. An additional intensive study into the model's performance was also conducted by a detailed evaluation of model results during two separate 24-hour periods, the first a period where the model performed well and the second a period where the model performed poorly, to determine which atmospheric factors most affect the predictive ability of the OMEGA model. The statistical results were then compared with the results from the MM5 and WRF simulations to determine the most appropriate model for wind energy potential studies in complex terrain.
Alterations of Growth Factors in Autism and Attention-Deficit/Hyperactivity Disorder

PubMed Central

Galvez-Contreras, Alma Y.; Campos-Ordonez, Tania; Gonzalez-Castaneda, Rocio E.; Gonzalez-Perez, Oscar

2017-01-01

Growth factors (GFs) are cytokines that regulate the neural development. Recent evidence indicates that alterations in the expression level of GFs during embryogenesis are linked to the pathophysiology and clinical manifestations of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD). In this concise review, we summarize the current evidence that supports the role of brain-derived neurotrophic factor, insulin-like growth factor 2, hepatocyte growth factor (HGF), glial-derived neurotrophic factor, nerve growth factor, neurotrophins 3 and 4, and epidermal growth factor in the pathogenesis of ADHD and ASD. We also highlight the potential use of these GFs as clinical markers for diagnosis and prognosis of these neurodevelopmental disorders. PMID:28751869
Ultrastrong Graphene-Copper Core-Shell Wires for High-Performance Electrical Cables.

PubMed

Kim, Sang Jin; Shin, Dong Heon; Choi, Yong Seok; Rho, Hokyun; Park, Min; Moon, Byung Joon; Kim, Youngsoo; Lee, Seuoung-Ki; Lee, Dong Su; Kim, Tae-Wook; Lee, Sang Hyun; Kim, Keun Soo; Hong, Byung Hee; Bae, Sukang

2018-03-27

Recent development in mobile electronic devices and electric vehicles requires electrical wires with reduced weight as well as enhanced stability. In addition, since electric energy is mostly generated from power plants located far from its consuming places, mechanically stronger and higher electric power transmission cables are strongly demanded. However, there has been no alternative materials that can practically replace copper materials. Here, we report a method to prepare ultrastrong graphene fibers (GFs)-Cu core-shell wires with significantly enhanced electrical and mechanical properties. The core GFs are synthesized by chemical vapor deposition, followed by electroplating of Cu shells, where the large surface area of GFs in contact with Cu maximizes the mechanical toughness of the core-shell wires. At the same time, the unique electrical and thermal characteristics of graphene allow a ∼10 times higher current density limit, providing more efficient and reliable delivery of electrical energies through the GFs-Cu wires. We believe that our results would be useful to overcome the current limit in electrical wires and cables for lightweight, energy-saving, and high-power applications.
Aeromedical evacuation of injured hikers in Hong Kong

PubMed Central

Wong, Tai Wai; Lau, Ping Fat; Lau, Chor Chiu

2010-01-01

BACKGROUND: Hiking is a very popular sport in Hong Kong. Serious injuries can sometimes occur in the remote areas not accessible to roads. Aeromedical evacuation service is run by the Government Flying Service (GFS) with emergency physicians and nurses as volunteers in Hong Kong. In this paper we describe the profile and outcome of injured hikers rescued by the GFS. METHODS: In this retrospective review, nature of the complaints, medical team composition, vital signs, clinical assessment and diagnosis on site were collected from the GFS medical record. Demographic data, final diagnoses and outcomes of the patients were retrieved from emergency department (ED) and hospital discharge records. RESULTS: A total of 275 cases were recruited for the 3-year period from January 2003 to December 2005. The mean age of the group was 39 years (range 1-83) with more males (159, 58%) than females. Heat illnesses, injuries and medical problems each constituted about one third of the cases. Lower limb injuries accounted for nearly half of the injuries. About 30% of the rescued hikers did not register to be seen at the ED. Only 48 hikers (17.5%) required admission and four were admitted to intensive/coronary care units for heat stroke and acute coronary syndrome. Five cases of pre-hospital cardiac arrest were recorded. CONCLUSION: Most hikers evacuated by the GFS did not suffer from serious conditions. GFS should still be prepared for the occasional cases that require advanced life support. PMID:25214963
Distribution of gelatinous fibers in seedling roots of living cycads.

PubMed

Magellan, Tracy M; Griffith, M Patrick; Tomlinson, P Barry

2015-08-01

• The presence of gelatinous (tension) fibers (GFs) in the roots of two extant cycadales (Cycas and Zamia) in a recent publication raises interesting issues of GF distribution in seed plants. An immediate question that arises from this discovery is whether GFs occur consistently in the radicle of all extant cycad genera and therefore might have a similar role in root contraction. We present results of a survey of nursery-grown material of all 10 genera.• We sequentially sectioned seedling root material and used simple staining and histochemical methods to follow anatomical changes along the radicle of all 10 genera.• We found GFs in nine genera; Stangeria appears to be the only genus without them. In all genera, there is a wide variation in the number of GFs and also variation in the development of thickened, fleshy roots. "Tertiary expansion" is a useful term to describe late cell division and enlargement of both primary and secondary parenchyma, the latter produced by the vascular cambium. Certain other histological features can be diagnostically useful at the generic level.• The functional interpretation of GFs as being wholly responsible for apparent tissue contraction is now somewhat compromised, especially as distortion of tracheary elements by changes in dimensions of parenchyma cells can falsely suggest root contraction when it may not occur. These preliminary results point the way to a more precise investigation of study material grown in more uniform environments using advanced technological methods. © 2015 Botanical Society of America, Inc.

Primary adipose-derived stem cells enriched by growth factor treatment improves cell adaptability toward cardiovascular differentiation in a rodent model of acute myocardial infarction.

PubMed

Chang, Jui-Chih; Lee, Ping-Chun; Lin, Yu-Chun; Lee, Kung-Wei; Hsu, Shan-hui

2011-01-01

The heterogeneous cell population in primary adipose-derived adult stem cells (ADAS) and difficulty in keeping their primitive properties have posed certain limitations on using these cells for cell therapy. Therefore, our objective was to generate a population of cells enriched from the adipose stromal-vascular fraction (SVF) with greater differentiation potential than ADAS and to explore the mechanism behind the repair of the injured myocardium in vivo. The distinct population of adipose stromal cells was enriched by immediate treatment of the growth factor cocktail (EGF and PDGF-BB) to the freshly isolated SVF. These cells (ADAS-GFs) had distinct cell morphology from ADAS and in average had a smaller size. They presented co-expression of CD140a (pericytic markers) and CD34 (hematopoietic marker), more obvious mesenchymal (CD13, CD29, CD44, CD90 and CD117) markers, but rare KDR, and were negative for CD45 and CD31. ADAS-GFs not only spontaneously expressed endothelial cell markers and formed capillary-like tubes on Matrigel but also clearly expressed early cardiomyocyte marker genes when embedded in methylcellulose-based medium. In Sprague-Dawley (SD) rats with left anterior descending artery (LAD)-induced myocardial infarction (MI), the ADAS-GFs transplanted group had the left ventricular function significantly improved compared with the ADAS transplanted group or the control group at 12 weeks post transplantation. The immunofluorescence staining revealed that the transplanted ADAS-GFs expressed GATA4, betamyosin heavy chain and troponin T protein but not vWF. More capillaries were also observed around the infarcted zone in the ADAS-GFs transplanted group. These data suggested that ADAS-GFs with a higher proangiogenic potential may restore the cardiac function of infarcted myocardium via the direct cardiomyocyte differentiation as well as angiogenesis recruitment.
Rilonacept for gout flare prevention during initiation of uric acid-lowering therapy: results from the PRESURGE-2 international, phase 3, randomized, placebo-controlled trial.

PubMed

Mitha, Essack; Schumacher, H Ralph; Fouche, Leon; Luo, Shue-Fen; Weinstein, Steven P; Yancopoulos, George D; Wang, Jian; King-Davis, Shirletta; Evans, Robert R

2013-07-01

To evaluate the efficacy and safety of IL-1 inhibitor rilonacept (IL-1 Trap) for gout flare (GF) prevention during initiation of uric acid-lowering therapy (ULT) with allopurinol in a multiregional phase 3 clinical trial. Hyperuricaemic adults (n = 248) from South Africa, Germany and Asia with gout and two or more GFs within the past year were initiated on allopurinol and randomized 1:1:1 to once-weekly s.c. treatment with placebo (PBO), rilonacept 80 mg (R80) or rilonacept 160 mg (R160) for 16 weeks. The primary endpoint was the number of GFs per patient through week 16. The population was predominantly male and racially diverse (white, 53.2%; Asian, 33.1%; black, 13.7%). Across treatments, most patients completed the study (87.8-92.9%). At 16 weeks the mean number of GFs per patient was reduced by 71.3% with R80 (0.35) and by 72.6% with R160 (0.34) relative to PBO (1.23; both P < 0.0001). The proportion of patients without GFs was higher with R80 (74.4%) and R160 (79.5%) than with PBO (43.9%; both P ≤ 0.0001), and the proportions of patients on rilonacept with multiple GFs were significantly lower (P < 0.001). Overall, the incidence of adverse events (AEs) was similar between PBO (61.0%) and rilonacept (65.1%). Injection site reactions, generally mild, were the most frequent AE with rilonacept (1.2% PBO, 12.2% R80 and 17.9% R160); none of these injection site reactions led to withdrawal. There were no study drug-related serious AEs or deaths. Rilonacept significantly reduced the occurrence of GFs associated with initiation of ULT, with >70% of patients having no flares, and demonstrated an acceptable safety and tolerability profile. ClinicalTrials.gov, http://clinicaltrials.gov/, NCT00958438.
Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Johnathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the planetary boundary layer (PBL) of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface, particularly within weakly-sheared environments such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in land surface and numerical weather prediction (NWP) models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-impact weather over eastern Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) NWP model in real time to support its daily forecasting operations, making use of the NOAA/National Weather Service (NWS) Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the KMS-WRF runs on a regional grid over eastern Africa. Two organizations at the NASA Marshall Space Flight Center in Huntsville, AL, SERVIR and the Shortterm Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMS for enhancing its regional modeling capabilities through new datasets and tools. To accomplish this goal, SPoRT and SERVIR is providing enhanced, experimental land surface initialization datasets and model verification capabilities to KMS as part of this collaboration. To produce a land-surface initialization more consistent with the resolution of the KMS-WRF runs, the NASA Land Information System (LIS) is run at a comparable resolution to provide real-time, daily soil initialization data in place of data interpolated from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model soil moisture and temperature fields. Additionally, realtime green vegetation fraction (GVF) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi- NPP) satellite will be incorporated into the KMS-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service (NESDIS). Finally, model verification capabilities will be transitioned to KMS using the Model Evaluation Tools (MET; Brown et al. 2009) package in conjunction with a dynamic scripting package developed by SPoRT (Zavodsky et al. 2014), to help quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. Furthermore, the transition of these MET tools will enable KMS to monitor model forecast accuracy in near real time. This paper presents preliminary efforts to improve land surface model initialization over eastern Africa in support of operations at KMS. The remainder of this extended abstract is organized as follows: The collaborating organizations involved in the project are described in Section 2; background information on LIS and the configuration for eastern Africa is presented in Section 3; the WRF configuration used in this modeling experiment is described in Section 4; sample experimental WRF output with and without LIS initialization data are given in Section 5; a summary is given in Section 6 followed by acknowledgements and references.
The DEDS: DSTO’s Environmental-Data Server for Research Applications

DTIC Science & Technology

2013-07-01

AccuWeather.com, 2010. Available from: http:// www.accuweather.com/ [cited 15 June 2010]. 44. National Oceanic and Atmospheric Administration, /data/gfs- avn ...hi. Available from: http://nomads.ncdc.noaa.gov/data/gfs- avn -hi/ [cited 15 June 2010]. 45. Wang, Y., L.R. Leung, J.L. McGregor, D.-K. Lee, W.-C
Preparation of Three-Dimensional Graphene Foams Using Powder Metallurgy Templates.

PubMed

Sha, Junwei; Gao, Caitian; Lee, Seoung-Ki; Li, Yilun; Zhao, Naiqin; Tour, James M

2016-01-26

A simple and scalable method which combines traditional powder metallurgy and chemical vapor deposition is developed for the synthesis of mesoporous free-standing 3D graphene foams. The powder metallurgy templates for 3D graphene foams (PMT-GFs) consist of particle-like carbon shells which are connected by multilayered graphene that shows high specific surface area (1080 m(2) g(-1)), good crystallization, good electrical conductivity (13.8 S cm(-1)), and a mechanically robust structure. The PMT-GFs did not break under direct flushing with DI water, and they were able to recover after being compressed. These properties indicate promising applications of PMT-GFs for fields requiring 3D carbon frameworks such as in energy-based electrodes and mechanical dampening.
Wind Power predictability a risk factor in the design, construction and operation of Wind Generation Turbines

NASA Astrophysics Data System (ADS)

Thiesen, J.; Gulstad, L.; Ristic, I.; Maric, T.

2010-09-01

Summit: The wind power predictability is often a forgotten decision and planning factor for most major wind parks, both onshore and offshore. The results of the predictability are presented after having examined a number of European offshore and offshore parks power predictability by using three(3) mesoscale model IRIE_GFS and IRIE_EC and WRF. Full description: It is well known that the potential wind production is changing with latitude and complexity in terrain, but how big are the changes in the predictability and the economic impacts on a project? The concept of meteorological predictability has hitherto to some degree been neglected as a risk factor in the design, construction and operation of wind power plants. Wind power plants are generally built in places where the wind resources are high, but these are often also sites where the predictability of the wind and other weather parameters is comparatively low. This presentation addresses the question of whether higher predictability can outweigh lower average wind speeds with regard to the overall economy of a wind power project. Low predictability also tends to reduce the value of the energy produced. If it is difficult to forecast the wind on a site, it will also be difficult to predict the power production. This, in turn, leads to increased balance costs and a less reduced carbon emission from the renewable source. By investigating the output from three(3) mesoscale models IRIE and WRF, using ECMWF and GFS as boundary data over a forecasting period of 3 months for 25 offshore and onshore wind parks in Europe, the predictability are mapped. Three operational mesoscale models with two different boundary data have been chosen in order to eliminate the uncertainty with one mesoscale model. All mesoscale models are running in a 10 km horizontal resolution. The model output are converted into "day a head" wind turbine generation forecasts by using a well proven advanced physical wind power model. The power models are using a number of weather parameters like wind speed in different heights, friction velocity and DTHV. The 25 wind sites are scattered around in Europe and contains 4 offshore parks and 21 onshore parks in various terrain complexity. The "day a head" forecasts are compared with production data and predictability for the period February 2010-April 2010 are given in Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE). The power predictability results are mapped for each turbine giving a clear picture of the predictability in Europe. . Finally a economic analysis are shown for each wind parks in different regimes of predictability will be compared with regard to the balance costs that result from errors in the wind power prediction. Analysis shows that it may very well be profitable to place wind parks in regions of lower, but more predictable wind ressource. Authors: Ivan Ristic, CTO Weather2Umberlla D.O.O Tomislav Maric, Meteorologist at Global Flow Solutions Vestas Wind Technology R&D Line Gulstad, Manager Global Flow Solutions Vestas Wind Technology R&D Jesper Thiesen, CEO ConWx ApS
High-throughput investigation of endothelial-to-mesenchymal transformation (EndMT) with combinatorial cellular microarrays.

PubMed

Wang, Zongjie; Calpe, Blaise; Zerdani, Jalil; Lee, Youngsang; Oh, Jonghyun; Bae, Hojae; Khademhosseini, Ali; Kim, Keekyoung

2016-07-01

In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
Cloud System Evolution in the Trades—CSET

NASA Astrophysics Data System (ADS)

Albrecht, B. A.; Zuidema, P.; Bretherton, C. S.; Wood, R.; Ghate, V. P.

2015-12-01

The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the north-Pacific trade-winds. The observational component of this study centered on 7 round-trips made by the NSF NCAR Gulfstream V (GV) between Sacramento, CA and Kona, Hawaii between 1 July and 15 August 2015. The CSET observing strategy used a Lagrangian approach to sample aerosol, cloud, and boundary layer properties upwind from the transition zone over the North Pacific and to resample these areas two days later. GFS forecast trajectories were used to plan the outbound flight to Hawaii and then updated forecast trajectories helped set the return flight plan two days later. Two key elements of the CSET observing system were the newly developed HIAPER Cloud Radar (HCR) and the HIAPER Spectral Resolution Lidar (HSRL). Together they provided unprecedented characterizations of aerosol, cloud and precipitation structures. A full suite of probes on the aircraft were used for in situ measurements of aerosol, cloud, precipitation, and turbulence properties during the low-level aircraft profiling portions of the flights. A wide range of boundary layer structures and aerosol, cloud, and precipitation conditions were observed during CSET. The cloud systems sampled included solid stratocumulus infused with smoke from Canadian wildfires, mesoscale (100-200 km) cloud-precipitation complexes, and patches of shallow cumuli in environments with accumulation mode aerosol concentrations of less than 50 cm-3. Ultra clean layers (UCLs with accumulation mode concentrations of less than 10 cm-3) were observed frequently near the top of the boundary layer and were often associated with shallow, gray (optically thin) layered clouds—features that are the subject of focused investigations by the CSET science team. The extent of aerosol, cloud, drizzle and boundary layer sampling that was made over open areas of the North Pacific along 2-day trajectories during CSET is unprecedented and will enable focused modeling studies of cloud system evolution and the role of aerosol-cloud-precipitation interactions in that evolution.
Platelet-Rich Plasma Derived Growth Factors Contribute to Stem Cell Differentiation in Musculoskeletal Regeneration.

PubMed

Qian, Yun; Han, Qixin; Chen, Wei; Song, Jialin; Zhao, Xiaotian; Ouyang, Yuanming; Yuan, Weien; Fan, Cunyi

2017-01-01

Stem cell treatment and platelet-rich plasma (PRP) therapy are two significant issues in regenerative medicine. Stem cells such as bone marrow mesenchymal stem cells, adipose-derived stem cells and periodontal ligament stem cells can be successfully applied in the field of tissue regeneration. PRP, a natural product isolated from whole blood, can secrete multiple growth factors (GFs) for regulating physiological activities. These GFs can stimulate proliferation and differentiation of different stem cells in injury models. Therefore, combination of both agents receives wide expectations in regenerative medicine, especially in bone, cartilage and tendon repair. In this review, we thoroughly discussed the interaction and underlying mechanisms of PRP derived GFs with stem cells, and assessed their functions in cell differentiation for musculoskeletal regeneration.
The Modified VFT law of glass former materials under pressure: Part II: Relation with the equation of state.

PubMed

Rault, Jacques

2015-08-01

The dynamical properties of glass formers (GFs) as a function of P, V, and T are reanalyzed in relation with the equations of state (EOS) proposed recently (Eur. Phys. J. E 37, 113 (2014)). The relaxation times τ of the cooperative non-Arrhenius α process and the individual Arrhenius β process are coupled via the Kohlrausch exponent n S(T, P). In the model n S is the sigmoidal logistic function depending on T (and P, and the α relaxation time τ α of GFs above T g verifies the pressure-modified VFT law: log τ α ∼ E β /nsRT, which can be put into a form with separated variables: log τ α ∼ f(T)g(P). From the variation of n S and τ α with T and P the Vogel temperature T 0 (τ α → ∝, n S = 0) and the crossover temperature (also called the merging or splitting temperature) T B (τ α ∼ τ β, n S ∼ 1) are determined. The proposed sm-VFT equation fits with excellent accuracy the experimental data of fragile and strong GFs under pressure. The properties generally observed in organic mineral and metallic GFs are explained: a) The Vogel temperature is independent of P (as suggested by the EOS properties), the crossover is pressure-dependent. b) In crystallizable GFs the T B (P) and Clapeyron curves T m(P) coincide. c) The α and β processes have the same ratio of the activation energies and volume, E*/V* (T- and P-independent), the compensation law is observed, this ratio depends on the anharmonicity Slater-Grüneisen parameter and on the critical pressure P* deduced from the EOS. d) The properties of the Fan Structure of the Tangents (FST) to the isotherms and isobars curves log τ versus P and T and to the isochrones curves P(T). e) The scaling law log τ = f(V (Λ) ) and the relation between Γ and γ. We conclude that these properties should be studied in detail in GFs submitted to negative pressures.
Effect of bait delivery rate in a GreenFeed system on methane emission estimates from cattle grazing rangeland

USDA-ARS?s Scientific Manuscript database

Two experiments were conducted to evaluate the effect of bait delivery rate on methane emission estimates measured by a GreenFeed system (GFS; C-Lock, Inc., Rapid City, SD). The manufacture recommends that cattle have a minimum visit time of 3 minutes so that at least 3 eructations are captured to ...
Green functions of graphene: An analytic approach

NASA Astrophysics Data System (ADS)

Lawlor, James A.; Ferreira, Mauro S.

2015-04-01

In this article we derive the lattice Green Functions (GFs) of graphene using a Tight Binding Hamiltonian incorporating both first and second nearest neighbour hoppings and allowing for a non-orthogonal electron wavefunction overlap. It is shown how the resulting GFs can be simplified from a double to a single integral form to aid computation, and that when considering off-diagonal GFs in the high symmetry directions of the lattice this single integral can be approximated very accurately by an algebraic expression. By comparing our results to the conventional first nearest neighbour model commonly found in the literature, it is apparent that the extended model leads to a sizeable change in the electronic structure away from the linear regime. As such, this article serves as a blueprint for researchers who wish to examine quantities where these considerations are important.
Electrophysiological Recordings from the Giant Fiber System

PubMed Central

Allen, Marcus J

2010-01-01

The giant fiber system (GFS) of Drosophila is a well-characterized neuronal circuit that mediates the escape response in the fly. It is one of the few adult neural circuits from which electrophysiological recordings can be made routinely. This article describes a simple procedure for stimulating the giant fiber neurons directly in the brain of the adult fly and obtaining recordings from the output muscles of the giant fiber system. PMID:20647357
The Early-Warning System for incoming storm surge and tide in the Republic of Mauritius

NASA Astrophysics Data System (ADS)

Bogaard, Tom; de Lima Rego, Joao; Vatvani, Deepak; Virasami, Renganaden; Verlaan, Martin

2016-04-01

The Republic of Mauritius (ROM) is a group of islands in the South West of the Indian Ocean, consisting of the main islands of Mauritius, Rodrigues and Agalega and the archipelago of Saint Brandon. The ROM is particularly vulnerable to the adverse effects of climate change, especially in the coastal zone, where a convergence of accelerating sea level rise and increasing intensity of tropical cyclones is expected to result in considerable economic loss, humanitarian stresses, and environmental degradation. Storm surges and swell waves are expected to be aggravated through sea level rise and climate change effects on weather patterns. Adaptation to increased vulnerability requires a re-evaluation of existing preparedness measures. The focus of this project is on more effective preparedness and issuing of alerts developing a fully-automated Early-Warning System for incoming storm surge and tide, together with the Mauritius Meteorological Services and the National Disaster Risk Reduction and Management Centre (NDRRMC), such that coastal communities in Mauritius, Rodrigues and Agalega Islands are able to evacuate timely and safely in case of predicted extreme water levels. The Mauritius Early-Warning System for storm surge and tide was implemented using software from Deltares' Open-Source and free software Community. A set of five depth-averaged Delft3D-FLOW hydrodynamic models are run every six-hours with a forecast horizon of three days, simulating water levels along the coast of the three main islands. Two regional models of horizontal resolution 5km force the three detailed models of 500m resolution; all models are forced at the surface by the 0.25° NOAA/GFS meteorological forecasts. In addition, our Wind-Enhancement Scheme is used to blend detailed cyclone track bulletin's info with the larger-scale Numerical Weather Predictions. Measured data is retrieved near real-time from available Automatic Weather Stations. All these workflows are managed by the operational platform software, Delft-FEWS. The presently operational Mauritius Early-Warning System produces a set of intuitive tables for each island, containing time- and space-varying information on threshold crossings by predicted water levels. At multiple locations for each island of the ROM, the operator is informed in one glance about the recommended preparedness level, from "Safe" to "Watch", "Alert" or "Warning" based on water level forecasts. The HTML page was designed together with the MMS and the NDRRMC, in order to be easy to interpret and disseminate by local authorities.
Toward improving hurricane forecasts using the JPL Tropical Cyclone Information System (TCIS): A framework to address the issues of Big Data

NASA Astrophysics Data System (ADS)

Hristova-Veleva, S. M.; Boothe, M.; Gopalakrishnan, S.; Haddad, Z. S.; Knosp, B.; Lambrigtsen, B.; Li, P.; montgomery, M. T.; Niamsuwan, N.; Tallapragada, V. S.; Tanelli, S.; Turk, J.; Vukicevic, T.

2013-12-01

Accurate forecasting of extreme weather requires the use of both regional models as well as global General Circulation Models (GCMs). The regional models have higher resolution and more accurate physics - two critical components needed for properly representing the key convective processes. GCMs, on the other hand, have better depiction of the large-scale environment and, thus, are necessary for properly capturing the important scale interactions. But how to evaluate the models, understand their shortcomings and improve them? Satellite observations can provide invaluable information. And this is where the issues of Big Data come: satellite observations are very complex and have large variety while model forecast are very voluminous. We are developing a system - TCIS - that addresses the issues of model evaluation and process understanding with the goal of improving the accuracy of hurricane forecasts. This NASA/ESTO/AIST-funded project aims at bringing satellite/airborne observations and model forecasts into a common system and developing on-line tools for joint analysis. To properly evaluate the models we go beyond the comparison of the geophysical fields. We input the model fields into instrument simulators (NEOS3, CRTM, etc.) and compute synthetic observations for a more direct comparison to the observed parameters. In this presentation we will start by describing the scientific questions. We will then outline our current framework to provide fusion of models and observations. Next, we will illustrate how the system can be used to evaluate several models (HWRF, GFS, ECMWF) by applying a couple of our analysis tools to several hurricanes observed during the 2013 season. Finally, we will outline our future plans. Our goal is to go beyond the image comparison and point-by-point statistics, by focusing instead on understanding multi-parameter correlations and providing robust statistics. By developing on-line analysis tools, our framework will allow for consistent model evaluation, providing results that are much more robust than those produced by case studies - the current paradigm imposed by the Big Data issues (voluminous data and incompatible analysis tools). We believe that this collaborative approach, with contributions of models, observations and analysis approaches used by the research and operational communities, will help untangle the complex interactions that lead to hurricane genesis and rapid intensity changes - two processes that still pose many unanswered questions. The developed framework for evaluation of the global models will also have implications for the improvement of the climate models, which output only a limited amount of information making it difficult to evaluate them. Our TCIS will help by investigating the GCMs under current weather scenarios and with much more detailed model output, making it possible to compare the models to multiple observed parameters to help narrow down the uncertainty in their performance. This knowledge could then be transferred to the climate models to lower the uncertainty in their predictions. The work described here was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Multiphysics and Multiscale Model Coupling Using Gerris

NASA Astrophysics Data System (ADS)

Keen, T. R.; Dykes, J. D.; Campbell, T. J.

2012-12-01

This work is implementing oceanographic processes encompassing multiple physics and scales using the Gerris Flow Solver (GFS) in order to examine their interdependence and sensitivity to changes in the physical environment. The processes include steady flow due to tides and the wind, phase-averaged wave-forced flow and oscillatory currents, and sediment transport. The 2D steady flow is calculated by the Ocean module contained within GFS. This model solves the Navier-Stokes (N-S) equations using the finite volume method. The model domain is represented by quad-tree adaptive mesh refinement (AMR). A stationary wave field is computed for a specified wave spectrum is uniformly distributed over the domain as a tracer with local wind input parameterized as a source, and dissipation by friction and breaking as a sink. Alongshore flow is included by a radiation stress term; this current is added to the steady flow component from tides and wind. Wave-current interaction is parameterized using a bottom boundary layer model. Sediment transport as suspended and bed load is implemented using tracers that are transported via the advection equations. A bed-conservation equation is implemented to allow changes in seafloor elevation to be used in adjusting the AMR refinement. These processes are being coupled using programming methods that are inherent to GFS and that do not require modification or recompiling of the code. These techniques include passive tracers, C functions that operate as plug-ins, and user-defined C-type macros included with GFS. Our results suggest that the AMR model coupling method is useful for problems where the dynamics are governed by several processes. This study is examining the relative influence of the steady currents, wave field, and sedimentation. Hydrodynamic and sedimentation interaction in nearshore environments is being studied for an idealized beach and for the Sandy Duck storm of Oct. 1998. The potential behavior of muddy sediments on the inner shelf is being evaluated for cold fronts near Atchafalaya Bay in the Gulf of Mexico. Due to the complexity of the model output results, fields are loaded into ArcMAP, a GIS-based application developed by Environmental Systems Research Institute (ESRI), with additional software that facilitates analysis of the results and assessment of model performance. GFS provides output with sufficient georeferencing information to be suitable for nearly seamless ingestion by ArcMAP. Analysis tools include comparisons between data layers; these may be intra-model, inter-model, or model-observation data. The comparisons become new data layers with additional parameters such as enhancements curves, time series, and statistics.
An isolated tornadic supercell of 14 July 2012 in Poland - A prediction technique within the use of coarse-grid WRF simulation

NASA Astrophysics Data System (ADS)

Taszarek, Mateusz; Czernecki, Bartosz; Walczakiewicz, Szymon; Mazur, Andrzej; Kolendowicz, Leszek

2016-09-01

On 14 July 2012 a shortwave trough with a cold front passed through Poland. A few tornadoes were reported in the north central part of the country within an isolated cyclic supercell. The cell moved along the thermal and moisture horizontal gradients and the support of a synoptic scale lift. An analysis allowed for setting up four tornado damage tracks in a distance of 100 km and with a total length of 60 km. Tornadoes damaged 105 buildings with predominant intensity of F1-F2/T3-T4 (maximum F3/T6) in Fujita/TORRO scale, caused 1 fatality, 10 injures and felled 500 hectares of Bory Tucholskie forest. The main aim of this article was to analyze this event and assess the possibilities of its short-term prediction. In order to achieve this, a model forecast data derived from WRF-ARW simulation with a spatial resolution of 15 km and initial conditions extracted from 0000 UTC GFS was used. An analysis yielded that the cell moved in the environment of a low lifting condensation level, rich boundary layer's moisture content and a steepening vertical lapse rates that provided the presence of a thermodynamic instability. A wind vectors tilting with height and an increased vertical wind shear occurred as well. A forecasting method that combined a Universal Tornadic Index composite parameter with a convective precipitation filter showed that convective cells at 1500 UTC in the north central Poland had a potential to become tornadic. Within the use of a proposed methodology, it was possible to issue a tornado forecast for the areas where an index pointed the risk.
Synthesis, spectroscopic, thermogravimetric and antimicrobial studies of mixed ligands complexes

NASA Astrophysics Data System (ADS)

Mahmoud, Walaa H.; Mahmoud, Nessma F.; Mohamed, Gehad G.; El-Sonbati, Adel Z.; El-Bindary, Ashraf A.

2015-09-01

An interesting series of mixed ligand complexes have been synthesized by the reaction of metal chloride with guaifenesin (GFS) in the presence of 2-aminoacetic acid (HGly) (1:1:1 molar ratio). The elemental analysis, magnetic moments, molar conductance, spectral (UV-Vis, IR, 1H NMR and ESR) and thermal studies were used to characterize the isolated complexes. The molecular structure of GFS is optimized theoretically and the quantum chemical parameters are calculated. The IR showed that the ligand (GFS) acts as monobasic tridentate through the hydroxyl, phenoxy etheric and methoxy oxygen atoms and co-ligand (HGly) as monobasic bidentate through the deprotonated carboxylate oxygen atom and nitrogen atom of amino group. The molar conductivities showed that all the complexes are non-electrolytes except Cr(III) complex is electrolyte. Electronic and magnetic data proposed the octahedral structure for all complexes under investigation. ESR spectrum for Cu(II) revealed data which confirm the proposed structure. Antibacterial screening of the compounds were carried out in vitro on gram positive (Bacillus subtilis and Staphylococcus aureus), gram negative (Escherichia coli and Neisseria gonorrhoeae) bacteria and for in vitro antifungal activity against Candida albicans organism. However, some complexes showed more chemotherapeutic efficiency than the parent GFS drug. The complexes were also screened for their in vitro anticancer activity against the breast cell line (MFC7) and the results obtained showed that they exhibit a considerable anticancer activity.
Polymer-Enriched 3D Graphene Foams for Biomedical Applications.

PubMed

Wang, Jun Kit; Xiong, Gordon Minru; Zhu, Minmin; Özyilmaz, Barbaros; Castro Neto, Antonio Helio; Tan, Nguan Soon; Choong, Cleo

2015-04-22

Graphene foams (GFs) are versatile nanoplatforms for biomedical applications because of their excellent physical, chemical, and mechanical properties. However, the brittleness and inflexibility of pristine GF (pGF) are some of the important factors restricting their widespread application. Here, a chemical-vapor-deposition-assisted method was used to synthesize 3D GFs, which were subsequently spin-coated with polymer to produce polymer-enriched 3D GFs with high conductivity and flexibility. Compared to pGF, both poly(vinylidene fluoride)-enriched GF (PVDF/GF) and polycaprolactone-enriched GF (PCL/GF) scaffolds showed improved flexibility and handleability. Despite the presence of the polymers, the polymer-enriched 3D GF scaffolds retained high levels of electrical conductivity because of the presence of microcracks that allowed for the flow of electrons through the material. In addition, polymer enrichment of GF led to an enhancement in the formation of calcium phosphate (Ca-P) compounds when the scaffolds were exposed to simulated body fluid. Between the two polymers tested, PCL enrichment of GF resulted in a higher in vitro mineralization nucleation rate because the oxygen-containing functional group of PCL had a higher affinity for Ca-P deposition and formation compared to the polar carbon-fluorine (C-F) bond in PVDF. Taken together, our current findings are a stepping stone toward future applications of polymer-enriched 3D GFs in the treatment of bone defects as well as other biomedical applications.
Assessing the aerosol direct and first indirect effects using ACM/GCM simulation results

NASA Astrophysics Data System (ADS)

Huang, H.; Gu, Y.; Xue, Y.; Lu, C. H.

2016-12-01

Atmospheric aerosols have been found to play an important role in global climate change but there are still large uncertainty in evaluating its role in the climate system. The aerosols generally affect global and regional climate through the scattering and the absorption of solar radiation (direct effect) and through their influences on cloud particle, number and sizes (first indirect effect). The indirect effect will further affects cloud water content, cloud top albedo and surface precipitations. In this study, we investigate the global climatic effect of aerosols using a coupled NCEP Global Forecast System (GFS) and a land surface model (SSiB2) The OPAC (Optical Properties of Aerosols and Clouds) database is used for aerosol effect. The OPAC data provides the optical properties (i.e., the extinction, scattering and absorption coefficient, single-scattering albedo, asymmetry factor and phase function) of ten types of aerosols under various relative humidity conditions for investigating the global direct and first indirect effects of dust aerosols. For indirect forcings due to liquid water, we follow the approach presented by Jiang et al (2011), in which a parameterization of cloud effective radius was calculated to describe its variance with convective strength and aerosol concentration. Since the oceans also play an important role on aerosol climatic effect, we also design a set of simulations using a coupled atmosphere/ocean model (CFS) to evaluate the sensitivity of aerosol effect with two-way atmosphere-ocean interactions.

Zoledronic acid suppresses transforming growth factor-β-induced fibrogenesis by human gingival fibroblasts.

PubMed

Komatsu, Yuko; Ibi, Miho; Chosa, Naoyuki; Kyakumoto, Seiko; Kamo, Masaharu; Shibata, Toshiyuki; Sugiyama, Yoshiki; Ishisaki, Akira

2016-07-01

Bisphosphonates (BPs) are analogues of pyrophosphate that are known to prevent bone resorption by inhibiting osteoclast activity. Nitrogen-containing BPs, such as zoledronic acid (ZA), are widely used in the treatment of osteoporosis and bone metastasis. However, despite having benefits, ZA has been reported to induce BP-related osteonecrosis of the jaw (BRONJ) in cancer patients. The molecular pathological mechanisms responsible for the development of BRONJ, including necrotic bone exposure after tooth extraction, remain to be elucidated. In this study, we examined the effects of ZA on the transforming growth factor-β (TGF‑β)-induced myofibroblast (MF) differentiation of human gingival fibroblasts (hGFs) and the migratory activity of hGFs, which are important for wound closure by fibrous tissue formation. The ZA maximum concentration in serum (Cmax) was found to be approximately 1.47 µM, which clinically, is found after the intravenous administration of 4 mg ZA, and ZA at this dose is considered appropriate for the treatment of cancer bone metastasis or bone diseases, such as Erdheim-Chester disease. At Cmax, ZA significantly suppressed i) the TGF‑β-induced promotion of cell viability, ii) the TGF‑β-induced expression of MF markers such as α-smooth muscle actin (α-SMA) and type I collagen, iii) the TGF‑β-induced migratory activity of hGFs and iv) the expression level of TGF‑β type I receptor on the surfaces of hGFs, as well as the TGF‑β-induced phosphorylation of Smad2/3. Thus, ZA suppresses TGF‑β-induced fibrous tissue formation by hGFs, possibly through the inhibition of Smad‑dependent signal transduction. Our findings partly elucidate the molecular mechanisms underlying BRONJ and may prove to be beneficial to the identification of drug targets for the treatment of this symptom at the molecular level.
Reduced insulin signaling maintains electrical transmission in a neural circuit in aging flies

PubMed Central

McGourty, Kieran; Allen, Marcus J.; Madem, Sirisha Kudumala; Adcott, Jennifer; Kerr, Fiona; Wong, Chi Tung; Vincent, Alec; Godenschwege, Tanja; Boucrot, Emmanuel; Partridge, Linda

2017-01-01

Lowered insulin/insulin-like growth factor (IGF) signaling (IIS) can extend healthy lifespan in worms, flies, and mice, but it can also have adverse effects (the “insulin paradox”). Chronic, moderately lowered IIS rescues age-related decline in neurotransmission through the Drosophila giant fiber system (GFS), a simple escape response neuronal circuit, by increasing targeting of the gap junctional protein innexin shaking-B to gap junctions (GJs). Endosomal recycling of GJs was also stimulated in cultured human cells when IIS was reduced. Furthermore, increasing the activity of the recycling small guanosine triphosphatases (GTPases) Rab4 or Rab11 was sufficient to maintain GJs upon elevated IIS in cultured human cells and in flies, and to rescue age-related loss of GJs and of GFS function. Lowered IIS thus elevates endosomal recycling of GJs in neurons and other cell types, pointing to a cellular mechanism for therapeutic intervention into aging-related neuronal disorders. PMID:28902870
VIIRS Marine Isoprene Product and Initial Applications

NASA Astrophysics Data System (ADS)

Tong, D.; Wang, M.; Wang, B.; Pan, L.; Lee, P.; Goldberg, M.

2017-12-01

Isoprene is a reactive biogenic hydrocarbon that affects atmospheric chemistry, aerosol loading, and cloud formation. We have developed a marine isoprene emission algorithm based on ocean color data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). and global meteorology simulated by NOAA Global Forecasting System (GFS). This algorithm is implemented to generate a multi-year data record (2012-2015) of marine isoprene. The product was validated using historic ocean observations of marine isoprene, as well as in-situ data collected during two recent cruises (SPACES/OASIS in 2014 and ASTRA-OMZ in 2015). Result shows that the VIIRS product has captured the seasonal and spatial variability of global oceanic isoprene emission, which is controlled by a myriad of biological and environmental variables including chlorophyll-a concentration, phytoplankton functional types, seawater light attenuation rate, wind speed, and sea surface temperature. The VIIRS isoprene emission displays considerable seasonal and spatial variations, with peaks in spring over seawater abundant with nutrient inputs. Year to year variations are small, with the annual global emissions ranging from 0.20 to 0.25 Tg C/yr. This new dataset provides the first multi-year observations of global isoprene emissions that can be used to study a variety of environmental issues such as coastal air quality, global aerosol, and cloud formation. Some "early-adopter" applications of this product are briefly discussed.
Mechanism of ENSO influence on the South Asian monsoon rainfall in global model simulations

NASA Astrophysics Data System (ADS)

Joshi, Sneh; Kar, Sarat C.

2018-02-01

Coupled ocean atmosphere global climate models are increasingly being used for seasonal scale simulation of the South Asian monsoon. In these models, sea surface temperatures (SSTs) evolve as coupled air-sea interaction process. However, sensitivity experiments with various SST forcing can only be done in an atmosphere-only model. In this study, the Global Forecast System (GFS) model at T126 horizontal resolution has been used to examine the mechanism of El Niño-Southern Oscillation (ENSO) forcing on the monsoon circulation and rainfall. The model has been integrated (ensemble) with observed, climatological and ENSO SST forcing to document the mechanism on how the South Asian monsoon responds to basin-wide SST variations in the Indian and Pacific Oceans. The model simulations indicate that the internal variability gets modulated by the SSTs with warming in the Pacific enhancing the ensemble spread over the monsoon region as compared to cooling conditions. Anomalous easterly wind anomalies cover the Indian region both at 850 and 200 hPa levels during El Niño years. The locations and intensity of Walker and Hadley circulations are altered due to ENSO SST forcing. These lead to reduction of monsoon rainfall over most parts of India during El Niño events compared to La Niña conditions. However, internally generated variability is a major source of uncertainty in the model-simulated climate.
The Impact of Different Complexity on Numerical Weather Predictions within the Coupled Global Online Modeling System

NASA Astrophysics Data System (ADS)

Zhang, L.; Grell, G. A.; McKeen, S. A.; Ahmadov, R.

2017-12-01

The global Flow-following finite-volume Icosahedra Model (FIM), which was developed in the Global Systems Division of NOAA/ESRL and the Finite-volume cubed-sphere dynamical core (FV3) developed by GFDL, have been coupled online with aerosol and gas-phase chemistry schemes (FIM-Chem and FV3-Chem). Within the aerosol and chemistry modules, the models handle wet and dry deposition, chemical reactions, aerosol direct and semi-direct effect, anthropogenic emissions, biogenic emissions, biomass burning, dust and sea-salt emissions. They are able to provide chemical weather predictions at various spatial resolutions and with different levels of complexity. FIM-Chem is also able to quantify the impact of aerosol on numerical weather predictions (NWP). Currently, three different chemical schemes have been coupled with the FIM model. The simplest aerosol modules are from the GOCART model with its simplified parameterization of sulfur/sulfate chemistry. The photochemical gas-phase mechanism RACM was included to determine the impact of additional complexity on the aerosol and gas simulations. We have also implemented a more sophisticated aerosol scheme that includes secondary organic aerosols (SOA) based on the VBS approach. The model performance has been evaluated by comparing with the ATom-1 observations. FIM-Chem is able to reproduce many observed aerosol and gas features very well. A five-day NWP on 120 km horizontal resolution using FIM-Chem has been done for the end of July, 2016 to quantify the impact of the three different chemical schemes on weather forecasts. Compared to a meteorological run that excludes the model chemical schemes, and is driven only by background AODs from the GFS model, the 5-day forecast results shows significant impact on weather predictions when including the prognostic aerosol schemes. This includes convective precipitation, surface temperature, and 700 hPa air temperature. We also use FIM-Chem to investigate the 2012 South American Biomass Burning Analysis (SAMBBA) campaign period to determine whether more complex chemistry provides benefits for global numerical weather prediction.
Manifestations, acquisition and diagnostic categories of dental fear in a self-referred population.

PubMed

Moore, R; Brødsgaard, I; Birn, H

1991-01-01

This study aimed to clarify how manifestations and acquisition relate to diagnostic categories of dental fear in a population of self-referred dental fear patients, since diagnostic criteria specifically related to dental fear have not been validated. DSM III-R diagnostic criteria for phobias were used to compare with four existing dental fear diagnostic categories, referred to as the Seattle system. Subjects were 208 persons with dental fear who were telephone interviewed, of whom a subsample of 155 responded to a mailed Dental Anxiety Scale (DAS), State-Trait Anxiety Inventory and a modified FSS-II Geer Fear Scale (GFS). Personal interviews and a Dental Beliefs Scale of perceived trust and social interaction with dentists were also used to evaluate a subsample of 80 patients selected by sex and high dental fear. Results showed that the majority of the 80 patients (66%), suffered from social embarrassment about their dental fear problem and their inability to do something about it. The largest cause of their fear (84%) was reported to be traumatic dental experiences, especially in childhood (70%). A minority of patients (16%) could not isolate traumatic experiences and had a history of general fearfulness or anxiety. Analysis of GFS data for the 155 subjects showed that fear of snakes and injuries were highest among women; heights and injections among men. Fear of blood was rarely reported. Spearman correlations between GFS individual items and DAS scores indicated functional independence between dental fear and common fears such as blood, injections and enclosures in most cases. Only in specific types of dental fear did these results support Rachman and Lopatka's contention that fears are thought to summate.(ABSTRACT TRUNCATED AT 250 WORDS)
Improving Satellite Observation Utilization for Model Initialization with Machine Learning: An Introduction and Tackling the "Labeled Dataset" Challenge for Cyclones Around the World

NASA Astrophysics Data System (ADS)

Bonfanti, C. E.; Stewart, J.; Lee, Y. J.; Govett, M.; Trailovic, L.; Etherton, B.

2017-12-01

One of the National Oceanic and Atmospheric Administration (NOAA) goals is to provide timely and reliable weather forecasts to support important decisions when and where people need it for safety, emergencies, planning for day-to-day activities. Satellite data is essential for areas lacking in-situ observations for use as initial conditions in Numerical Weather Prediction (NWP) Models, such as spans of the ocean or remote areas of land. Currently only about 7% of total received satellite data is selected for use and from that, an even smaller percentage ever are assimilated into NWP models. With machine learning, the computational and time costs needed for satellite data selection can be greatly reduced. We study various machine learning approaches to process orders of magnitude more satellite data in significantly less time allowing for a greater quantity and more intelligent selection of data to be used for assimilation purposes. Given the future launches of satellites in the upcoming years, machine learning is capable of being applied for better selection of Regions of Interest (ROI) in the magnitudes more of satellite data that will be received. This paper discusses the background of machine learning methods as applied to weather forecasting and the challenges of creating a "labeled dataset" for training and testing purposes. In the training stage of supervised machine learning, labeled data are important to identify a ROI as either true or false so that the model knows what signatures in satellite data to identify. Authors have selected cyclones, including tropical cyclones and mid-latitude lows, as ROI for their machine learning purposes and created a labeled dataset of true or false for ROI from Global Forecast System (GFS) reanalysis data. A dataset like this does not yet exist and given the need for a high quantity of samples, is was decided this was best done with automation. This process was done by developing a program similar to the National Center for Environmental Prediction (NCEP) tropical cyclone tracker by Marchok that was used to identify cyclones based off its physical characteristics. We will discuss the methods and challenges to creating this dataset and the dataset's use for our current supervised machine learning model as well as use for future work on events such as convection initiation.
The impact of model prediction error in designing geodetic networks for crustal deformation applications

NASA Astrophysics Data System (ADS)

Murray, J. R.

2017-12-01

Earth surface displacements measured at Global Navigation Satellite System (GNSS) sites record crustal deformation due, for example, to slip on faults underground. A primary objective in designing geodetic networks to study crustal deformation is to maximize the ability to recover parameters of interest like fault slip. Given Green's functions (GFs) relating observed displacement to motion on buried dislocations representing a fault, one can use various methods to estimate spatially variable slip. However, assumptions embodied in the GFs, e.g., use of a simplified elastic structure, introduce spatially correlated model prediction errors (MPE) not reflected in measurement uncertainties (Duputel et al., 2014). In theory, selection algorithms should incorporate inter-site correlations to identify measurement locations that give unique information. I assess the impact of MPE on site selection by expanding existing methods (Klein et al., 2017; Reeves and Zhe, 1999) to incorporate this effect. Reeves and Zhe's algorithm sequentially adds or removes a predetermined number of data according to a criterion that minimizes the sum of squared errors (SSE) on parameter estimates. Adapting this method to GNSS network design, Klein et al. select new sites that maximize model resolution, using trade-off curves to determine when additional resolution gain is small. Their analysis uses uncorrelated data errors and GFs for a uniform elastic half space. I compare results using GFs for spatially variable strike slip on a discretized dislocation in a uniform elastic half space, a layered elastic half space, and a layered half space with inclusion of MPE. I define an objective criterion to terminate the algorithm once the next site removal would increase SSE more than the expected incremental SSE increase if all sites had equal impact. Using a grid of candidate sites with 8 km spacing, I find the relative value of the selected sites (defined by the percent increase in SSE that further removal of each site would cause) is more uniform when MPE is included. However, the number and distribution of selected sites depends primarily on site location relative to the fault. For this test case, inclusion of MPE has minimal practical impact; I will investigate whether these findings hold for more densely spaced candidate grids and dipping faults.
A Comparison of HWRF, ARW and NMM Models in Hurricane Katrina (2005) Simulation

PubMed Central

Dodla, Venkata B.; Desamsetti, Srinivas; Yerramilli, Anjaneyulu

2011-01-01

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina. PMID:21776239
A comparison of HWRF, ARW and NMM models in Hurricane Katrina (2005) simulation.

PubMed

Dodla, Venkata B; Desamsetti, Srinivas; Yerramilli, Anjaneyulu

2011-06-01

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina.
Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

USGS Publications Warehouse

Artan, G.A.; Verdin, J.P.; Lietzow, R.

2013-01-01

We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.
Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model

NASA Astrophysics Data System (ADS)

Raju, P. V. S.; Potty, Jayaraman; Mohanty, U. C.

2011-09-01

Comprehensive sensitivity analyses on physical parameterization schemes of Weather Research Forecast (WRF-ARW core) model have been carried out for the prediction of track and intensity of tropical cyclones by taking the example of cyclone Nargis, which formed over the Bay of Bengal and hit Myanmar on 02 May 2008, causing widespread damages in terms of human and economic losses. The model performances are also evaluated with different initial conditions of 12 h intervals starting from the cyclogenesis to the near landfall time. The initial and boundary conditions for all the model simulations are drawn from the global operational analysis and forecast products of National Center for Environmental Prediction (NCEP-GFS) available for the public at 1° lon/lat resolution. The results of the sensitivity analyses indicate that a combination of non-local parabolic type exchange coefficient PBL scheme of Yonsei University (YSU), deep and shallow convection scheme with mass flux approach for cumulus parameterization (Kain-Fritsch), and NCEP operational cloud microphysics scheme with diagnostic mixed phase processes (Ferrier), predicts better track and intensity as compared against the Joint Typhoon Warning Center (JTWC) estimates. Further, the final choice of the physical parameterization schemes selected from the above sensitivity experiments is used for model integration with different initial conditions. The results reveal that the cyclone track, intensity and time of landfall are well simulated by the model with an average intensity error of about 8 hPa, maximum wind error of 12 m s-1and track error of 77 km. The simulations also show that the landfall time error and intensity error are decreasing with delayed initial condition, suggesting that the model forecast is more dependable when the cyclone approaches the coast. The distribution and intensity of rainfall are also well simulated by the model and comparable with the TRMM estimates.
Application for the Drosophila ventral nerve cord standard in neuronal circuit reconstruction and in-depth analysis of mutant morphology.

PubMed

Boerner, Jana; Godenschwege, Tanja Angela

2010-09-01

The Drosophila standard brain has been a useful tool that provides information about position and size of different brain structures within a wild-type brain and allows the comparison of imaging data that were collected from individual preparations. Therefore the standard can be used to reveal and visualize differences of brain regions between wild-type and mutant brains and can provide spatial description of single neurons within the nervous system. Recently the standard brain was complemented by the generation of a ventral nerve cord (VNC) standard. Here the authors have registered the major components of a simple neuronal circuit, the Giant Fiber System (GFS), into this standard. The authors show that they can also virtually reconstruct the well-characterized synaptic contact of the Giant Fiber with its motorneuronal target when they register the individual neurons from different preparations into the VNC standard. In addition to the potential application for the standard thorax in neuronal circuit reconstruction, the authors show that it is a useful tool for in-depth analysis of mutant morphology of single neurons. The authors find quantitative and qualitative differences when they compared the Giant Fibers of two different neuroglian alleles, nrg(849) and nrg(G00305), using the averaged wild-type GFS in the standard VNC as a reference.
Growth Friendly Surgery and Serial Cast Correction in the Treatment of Early-onset Scoliosis for Patients With Prader-Willi Syndrome.

PubMed

Oore, Jonathan; Connell, Braydon; Yaszay, Burt; Samdani, Amer; Hilaire, Tricia St; Flynn, Tara; El-Hawary, Ron

2018-02-02

Prader-Willi syndrome (PWS) patients can present with scoliosis which can be treated with serial cast correction (SCC) or with growth friendly surgery (GFS). This study's purpose was to describe the results of SCC as well as GFS for PWS patients with early-onset scoliosis (EOS). PWS patients were identified from 2 international multicenter EOS databases. Scoliosis, kyphosis, spine height (T1-S1), right/left hemithoracic heights/widths (RHTH, LHTH, RHTW, LHTW) were measured pretreatment, postoperation, and at 2-year follow-up. Complications were recorded. Overall, 23 patients with 2-year follow-up were identified. Pretreatment; patients treated with SCC (n=10) had mean age of 1.8±0.6 years; body mass index (BMI), 16±1.5 kg/m; scoliosis, 45±18 degrees; kyphosis, 56±9 degrees; T1-S1, 22.4±2.4 cm; RHTH, 8.0±2.0 cm; LHTH, 8.5±1.7 cm; RHTW, 6.6±1.3 cm; and LHTW, 8.0±1.0 cm. Patients treated with GFS (n=13) had mean age of 5.8±2.6 years; BMI, 21±5.4 kg/m; scoliosis, 76±14 degrees; kyphosis, 59±25 degrees; T1-S1, 24.1±3.6 cm; RHTH, 10.0±1.6 cm; LHTH, 10.6±1.6 cm; RHTW, 9.4±2.5 cm; and LHTW, 8.1±2.8 cm. At 2-year follow-up, patients treated with SCC had mean scoliosis 37±11 degrees (18% correction, P=0.06); kyphosis, 42±6 degrees (NS); T1-S1, 26.4±2.1 cm (P<0.01); RHTH, 9.0±1.1 cm (13%; P=0.30); LHTH, 10.0±1.5 cm (18%, P<0.01); RHTW, 7.4±1.1 cm (12%, P<0.01); and LHTW, 8.0±1.0 cm (0%, P=0.34). At 2-year follow-up, patients treated with GFS had mean scoliosis 42±13 degrees (45% correction, P<0.000001); kyphosis, 53±13 degrees (10%, P=0.19); T1-S1, 31.5±5.4 cm (P<0.00001); RHTH, 12.0±2.4 cm (20%; P<0.01); LHTH, 12.0±1.7 cm (13%; P<0.01); RHTW, 9.8±1.3 cm (4%; P=0.27); and LHTW, 7.9±2.3 cm (3%;P=0.11). As an entire group, patients with a BMI>17 kg/m² had more device-related than disease-related complications (P=0.09). Patients treated with SCC had 0.9 complications per patient. Patients treated with GFS had 2.2 complications per patient [≤5 y more often had ≥2 complications (P=0.05)]. At 2-year follow-up, SCC and GFS were both effective in treating EOS in PWS patients. Patients treated with SCC had significant improvements in spine height and LHTH. Patients treated with GFS had significant improvements in scoliosis magnitude, spine height, RHTH, and LHTH. Level IV-therapeutic study.
Tumor Necrosis Factor-α and Interleukin (IL)-1β, IL-6, and IL-8 Impair In Vitro Migration and Induce Apoptosis of Gingival Fibroblasts and Epithelial Cells, Delaying Wound Healing.

PubMed

Basso, Fernanda G; Pansani, Taisa N; Turrioni, Ana Paula S; Soares, Diana G; de Souza Costa, Carlos Alberto; Hebling, Josimeri

2016-08-01

Multiple factors affect oral mucosal healing, such as the persistence of an inflammatory reaction. The present study evaluates effects of tumor necrosis factor (TNF)-α and interleukin (IL)-1β, IL-6, and IL-8 on epithelial cells (ECs) and human gingival fibroblasts (GFs) in vitro. GFs and ECs were seeded in 96-well plates (1 × 10(4) cells/well) in plain culture medium (Dulbecco's modified Eagle's medium [DMEM]) containing 1% antibiotic/antimycotic solution and 10% fetal bovine serum, and incubated for 24 hours. Both cell lines were exposed for 24 hours to the following cytokines: 1) TNF-α (100 ng/mL); 2) IL-1β (1 ng/mL); 3) IL-6 (10 ng/mL); and 4) IL-8 (10 ng/mL). All cytokines were diluted in serum-free DMEM. Control cultures were exposed only to serum-free DMEM. Effects of exposure to inflammatory cytokines were determined by means of: 1) apoptosis (anexin V); 2) cell migration (wound healing assay); 3) inflammatory cytokine synthesis (enzyme-linked immunosorbent assay). Data were analyzed by Kruskal-Wallis and Mann-Whitney U tests (α = 0.05). Increased apoptosis rates were noted when cells were exposed to inflammatory cytokines, except ECs exposed to IL-1β. Cell migration was negatively affected by all inflammatory cytokines for both cell lines. ECs and GFs exposed to IL-6 and IL-8 significantly increased synthesis of TNF-α and IL-1β. Demonstrated results indicate negative effects of tested inflammatory cytokines on ECs and GFs, inducing apoptosis and impairing cell migration. These results can justify delayed oral mucosa healing in the presence of inflammatory reaction.
Heparin-Poloxamer Thermosensitive Hydrogel Loaded with bFGF and NGF Enhances Peripheral Nerve Regeneration in Diabetic Rats.

PubMed

Li, Rui; Li, Yiyang; Wu, Yanqing; Zhao, Yingzheng; Chen, Huanwen; Yuan, Yuan; Xu, Ke; Zhang, Hongyu; Lu, Yingfeng; Wang, Jian; Li, Xiaokun; Jia, Xiaofeng; Xiao, Jian

2018-06-01

Peripheral nerve injury (PNI) is a major burden to society with limited therapeutic options, and novel biomaterials have great potential for shifting the current paradigm of treatment. With a rising prevalence of chronic illnesses such as diabetes mellitus (DM), treatment of PNI is further complicated, and only few studies have proposed therapies suitable for peripheral nerve regeneration in DM. To provide a supportive environment to restore structure and/or function of nerves in DM, we developed a novel thermo-sensitive heparin-poloxamer (HP) hydrogel co-delivered with basic fibroblast growth factor (bFGF) and nerve growth factor (NGF) in diabetic rats with sciatic nerve crush injury. The delivery vehicle not only had a good affinity for large amounts of growth factors (GFs), but also controlled their release in a steady fashion, preventing degradation in vitro. In vivo, compared with HP hydrogel alone or direct GFs administration, GFs-HP hydrogel treatment is more effective at facilitating Schwann cell (SC) proliferation, leading to an increased expression of nerve associated structural proteins, enhanced axonal regeneration and remyelination, and improved recovery of motor function (all p < 0.05). Our mechanistic investigation also revealed that these neuroprotective and neuroregenerative effects of the GFs-HP hydrogel may be associated with activations of phosphatidylinositol 3 kinase and protein kinase B (PI3K/Akt), janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3), and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MAPK/ERK) signaling pathways. Our work provides a promising therapy option for peripheral nerve regeneration in patients with DM. Copyright © 2018 Elsevier Ltd. All rights reserved.
A 3-D Finite-Volume Non-hydrostatic Icosahedral Model (NIM)

NASA Astrophysics Data System (ADS)

Lee, Jin

2014-05-01

The Nonhydrostatic Icosahedral Model (NIM) formulates the latest numerical innovation of the three-dimensional finite-volume control volume on the quasi-uniform icosahedral grid suitable for ultra-high resolution simulations. NIM's modeling goal is to improve numerical accuracy for weather and climate simulations as well as to utilize the state-of-art computing architecture such as massive parallel CPUs and GPUs to deliver routine high-resolution forecasts in timely manner. NIM dynamic corel innovations include: * A local coordinate system remapped spherical surface to plane for numerical accuracy (Lee and MacDonald, 2009), * Grid points in a table-driven horizontal loop that allow any horizontal point sequence (A.E. MacDonald, et al., 2010), * Flux-Corrected Transport formulated on finite-volume operators to maintain conservative positive definite transport (J.-L, Lee, ET. Al., 2010), *Icosahedral grid optimization (Wang and Lee, 2011), * All differentials evaluated as three-dimensional finite-volume integrals around the control volume. The three-dimensional finite-volume solver in NIM is designed to improve pressure gradient calculation and orographic precipitation over complex terrain. NIM dynamical core has been successfully verified with various non-hydrostatic benchmark test cases such as internal gravity wave, and mountain waves in Dynamical Cores Model Inter-comparisons Projects (DCMIP). Physical parameterizations suitable for NWP are incorporated into NIM dynamical core and successfully tested with multimonth aqua-planet simulations. Recently, NIM has started real data simulations using GFS initial conditions. Results from the idealized tests as well as real-data simulations will be shown in the conference.
Delivery of Alginate Scaffold Releasing Two Trophic Factors for Spinal Cord Injury Repair

PubMed Central

Grulova, I.; Slovinska, L.; Blaško, J.; Devaux, S.; Wisztorski, M.; Salzet, M.; Fournier, I.; Kryukov, O.; Cohen, S.; Cizkova, D.

2015-01-01

Spinal cord injury (SCI) has been implicated in neural cell loss and consequently functional motor and sensory impairment. In this study, we propose an alginate -based neurobridge enriched with/without trophic growth factors (GFs) that can be utilized as a therapeutic approach for spinal cord repair. The bioavailability of key GFs, such as Epidermal Growth factor (EGF) and basic Fibroblast Growth Factor (bFGF) released from injected alginate biomaterial to the central lesion site significantly enhanced the sparing of spinal cord tissue and increased the number of surviving neurons (choline acetyltransferase positive motoneurons) and sensory fibres. In addition, we document enhanced outgrowth of corticospinal tract axons and presence of blood vessels at the central lesion. Tissue proteomics was performed at 3, 7 and 10 days after SCI in rats indicated the presence of anti-inflammatory factors in segments above the central lesion site, whereas in segments below, neurite outgrowth factors, inflammatory cytokines and chondroitin sulfate proteoglycan of the lectican protein family were overexpressed. Collectively, based on our data, we confirm that functional recovery was significantly improved in SCI groups receiving alginate scaffold with affinity-bound growth factors (ALG +GFs), compared to SCI animals without biomaterial treatment. PMID:26348665
Neocortex expansion is linked to size variations in gene families with chemotaxis, cell-cell signalling and immune response functions in mammals.

PubMed

Castillo-Morales, Atahualpa; Monzón-Sandoval, Jimena; de Sousa, Alexandra A; Urrutia, Araxi O; Gutierrez, Humberto

2016-10-01

Increased brain size is thought to have played an important role in the evolution of mammals and is a highly variable trait across lineages. Variations in brain size are closely linked to corresponding variations in the size of the neocortex, a distinct mammalian evolutionary innovation. The genomic features that explain and/or accompany variations in the relative size of the neocortex remain unknown. By comparing the genomes of 28 mammalian species, we show that neocortical expansion relative to the rest of the brain is associated with variations in gene family size (GFS) of gene families that are significantly enriched in biological functions associated with chemotaxis, cell-cell signalling and immune response. Importantly, we find that previously reported GFS variations associated with increased brain size are largely accounted for by the stronger link between neocortex expansion and variations in the size of gene families. Moreover, genes within these families are more prominently expressed in the human neocortex during early compared with adult development. These results suggest that changes in GFS underlie morphological adaptations during brain evolution in mammalian lineages. © 2016 The Authors.
Neocortex expansion is linked to size variations in gene families with chemotaxis, cell–cell signalling and immune response functions in mammals

PubMed Central

Castillo-Morales, Atahualpa; Monzón-Sandoval, Jimena; de Sousa, Alexandra A.

2016-01-01

Increased brain size is thought to have played an important role in the evolution of mammals and is a highly variable trait across lineages. Variations in brain size are closely linked to corresponding variations in the size of the neocortex, a distinct mammalian evolutionary innovation. The genomic features that explain and/or accompany variations in the relative size of the neocortex remain unknown. By comparing the genomes of 28 mammalian species, we show that neocortical expansion relative to the rest of the brain is associated with variations in gene family size (GFS) of gene families that are significantly enriched in biological functions associated with chemotaxis, cell–cell signalling and immune response. Importantly, we find that previously reported GFS variations associated with increased brain size are largely accounted for by the stronger link between neocortex expansion and variations in the size of gene families. Moreover, genes within these families are more prominently expressed in the human neocortex during early compared with adult development. These results suggest that changes in GFS underlie morphological adaptations during brain evolution in mammalian lineages. PMID:27707894

Development of a two-step protocol for culture expansion of human annulus fibrosus cells with TGF-β1 and FGF-2.

PubMed

Chou, Po-Hsin; Wang, Shih-Tien; Ma, Hsiao-Li; Liu, Chien-Lin; Chang, Ming-Chau; Lee, Oscar Kuang-Sheng

2016-07-12

Different biologic approaches to treat disc regeneration, including growth factors (GFs) application, are currently under investigation. Human annulus fibrosus (hAF) repair or regeneration is one of the key elements for maintenance and restoration of nucleus pulposus function. However, so far there is no effective treatment for this purpose. The aim of the present study was to investigate the response of hAF cells to different combinations of GFs, and develop a protocol for efficient culture expansion. hAF cells were harvested from degenerated disc tissues during surgical intervertebral disc removal, and hAF cells were expanded in a monolayer. The experiments were categorized based on different protocols with transforming growth factor (TGF-β1) and fibroblast growth factor (FGF-2) culture for 14 days: group 1 had no GFs (control group); group 2 received TGF-β1; group 3 received FGF-2; group 4 received both GFs; and group 5 (two-step) received both GFs for the first 10 days and TGF-β1 only for the next 4 days. Cell proliferation, collagen, and noncollagen extracellular matrix (ECM) production and genes expression were compared among these groups. At days 3, 7 and 10 of cultivation, groups 4 and 5 had significantly more cell numbers and faster cell proliferation rates than groups 1, 2, and 3. At 14 days of cultivation, significantly more cell numbers were observed in groups 3 and 4 than in group 5. The group 4 had the most cell numbers and the fastest proliferation rate at 14 days of cultivation. After normalization for cell numbers, group 5 (two-step) produced the most collagen and noncollagen ECM at 10 and 14 days of cultivation among the five groups. In group 5, ECM gene expression was significantly upregulated. High expression of matrix metalloproteinase-1 was upregulated with FGF-2 on the different days as compared to the other groups. Annulus fibrosus cell phenotypes were only marginally retained under the different protocols based on quantitative polymerase chain reaction results. Taken together, the two-step protocol was the most efficient among these different protocols with the most abundant ECM production after normalization for cell numbers for culture expansion of hAF cells. The protocol may be useful in further cell therapy and tissue engineering approaches for disc regeneration.
Establishing the Common Community Physics Package by Transitioning the GFS Physics to a Collaborative Software Framework

NASA Astrophysics Data System (ADS)

Xue, L.; Firl, G.; Zhang, M.; Jimenez, P. A.; Gill, D.; Carson, L.; Bernardet, L.; Brown, T.; Dudhia, J.; Nance, L. B.; Stark, D. R.

2017-12-01

The Global Model Test Bed (GMTB) has been established to support the evolution of atmospheric physical parameterizations in NCEP global modeling applications. To accelerate the transition to the Next Generation Global Prediction System (NGGPS), a collaborative model development framework known as the Common Community Physics Package (CCPP) is created within the GMTB to facilitate engagement from the broad community on physics experimentation and development. A key component to this Research to Operation (R2O) software framework is the Interoperable Physics Driver (IPD) that hooks the physics parameterizations from one end to the dynamical cores on the other end with minimum implementation effort. To initiate the CCPP, scientists and engineers from the GMTB separated and refactored the GFS physics. This exercise demonstrated the process of creating IPD-compliant code and can serve as an example for other physics schemes to do the same and be considered for inclusion into the CCPP. Further benefits to this process include run-time physics suite configuration and considerably reduced effort for testing modifications to physics suites through GMTB's physics test harness. The implementation will be described and the preliminary results will be presented at the conference.
Application for the Drosophila ventral nerve cord standard in neuronal circuit reconstruction and in-depth analysis of mutant morphology

PubMed Central

Boerner, Jana; Godenschwege, Tanja Angela

2010-01-01

The Drosophila standard brain has been a useful tool that provides information about position and size of different brain structures within a wild-type brain and allows the comparison of imaging data that were collected from individual preparations. Therefore the standard can be used to reveal and visualize differences of brain regions between wild-type and mutant brains and can provide spatial description of single neurons within the nervous system. Recently the standard brain was complemented by the generation of a ventral nerve cord (VNC) standard. Here the authors have registered the major components of a simple neuronal circuit, the Giant Fiber System (GFS), into this standard. The authors show that they can also virtually reconstruct the well-characterized synaptic contact of the Giant Fiber with its motorneuronal target when they register the individual neurons from different preparations into the VNC standard. In addition to the potential application for the standard thorax in neuronal circuit reconstruction, the authors show that it is a useful tool for in-depth analysis of mutant morphology of single neurons. The authors find quantitative and qualitative differences when they compared the Giant Fibers of two different neuroglian alleles, nrg849 and nrgG00305, using the averaged wild-type GFS in the standard VNC as a reference. PMID:20615087
Layer-by-layer assembled cell instructive nanocoatings containing platelet lysate.

PubMed

Oliveira, Sara M; Santo, Vítor E; Gomes, Manuela E; Reis, Rui L; Mano, João F

2015-04-01

Great efforts have been made to introduce growth factors (GFs) onto 2D/3D constructs in order to control cell behavior. Platelet lysate (PL) presents itself as a cost-effective source of multiple GFs and other proteins. The instruction given by a construct-PL combination will depend on how its instructive cues are presented to the cells. The content, stability and conformation of the GFs affect their instruction. Strategies for a controlled incorporation of PL are needed. Herein, PL was incorporated into nanocoatings by layer-by-layer assembling with polysaccharides presenting different sulfation degrees (SD) and charges. Heparin and several marine polysaccharides were tested to evaluate their PL and GF incorporation capability. The consequent effects of those multilayers on human adipose derived stem cells (hASCs) were assessed in short-term cultures. Both nature of the polysaccharide and SD were important properties that influenced the adsorption of PL, vascular endothelial growth factor (VEGF), fibroblast growth factor b (FGFb) and platelet derived growth factor (PDGF). The sulfated polysaccharides-PL multilayers showed to be efficient in the promotion of morphological changes, serum-free adhesion and proliferation of high passage hASCs (P > 5). These biomimetic multilayers promise to be versatile platforms to fabricate instructive devices allowing a tunable incorporation of PL. Copyright © 2015 Elsevier Ltd. All rights reserved.
Ultralight Graphene Foam/Conductive Polymer Composites for Exceptional Electromagnetic Interference Shielding.

PubMed

Wu, Ying; Wang, Zhenyu; Liu, Xu; Shen, Xi; Zheng, Qingbin; Xue, Quan; Kim, Jang-Kyo

2017-03-15

Ultralight, high-performance electromagnetic interference (EMI) shielding graphene foam (GF)/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) composites are developed by drop coating of PEDOT:PSS on cellular-structured, freestanding GFs. To enhance the wettability and the interfacial bonds with PEDOT:PSS, GFs are functionalized with 4-dodecylbenzenesulfonic acid. The GF/PEDOT:PSS composites possess an ultralow density of 18.2 × 10 -3 g/cm 3 and a high porosity of 98.8%, as well as an enhanced electrical conductivity by almost 4 folds from 11.8 to 43.2 S/cm after the incorporation of the conductive PEDOT:PSS. Benefiting from the excellent electrical conductivity, ultralight porous structure, and effective charge delocalization, the composites deliver remarkable EMI shielding performance with a shielding effectiveness (SE) of 91.9 dB and a specific SE (SSE) of 3124 dB·cm 3 /g, both of which are the highest among those reported in the literature for carbon-based polymer composites. The excellent electrical conductivities of composites arising from both the GFs with three-dimensionally interconnected conductive networks and the conductive polymer coating, as well as the left-handed composites with absolute permittivity and/or permeability larger than one give rise to significant microwave attenuation by absorption.
Influence of high-resolution surface databases on the modeling of local atmospheric circulation systems

NASA Astrophysics Data System (ADS)

Paiva, L. M. S.; Bodstein, G. C. R.; Pimentel, L. C. G.

2013-12-01

Large-eddy simulations are performed using the Advanced Regional Prediction System (ARPS) code at horizontal grid resolutions as fine as 300 m to assess the influence of detailed and updated surface databases on the modeling of local atmospheric circulation systems of urban areas with complex terrain. Applications to air pollution and wind energy are sought. These databases are comprised of 3 arc-sec topographic data from the Shuttle Radar Topography Mission, 10 arc-sec vegetation type data from the European Space Agency (ESA) GlobCover Project, and 30 arc-sec Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation data from the ESA GlobCarbon Project. Simulations are carried out for the Metropolitan Area of Rio de Janeiro using six one-way nested-grid domains that allow the choice of distinct parametric models and vertical resolutions associated to each grid. ARPS is initialized using the Global Forecasting System with 0.5°-resolution data from the National Center of Environmental Prediction, which is also used every 3 h as lateral boundary condition. Topographic shading is turned on and two soil layers with depths of 0.01 and 1.0 m are used to compute the soil temperature and moisture budgets in all runs. Results for two simulated runs covering the period from 6 to 7 September 2007 are compared to surface and upper-air observational data to explore the dependence of the simulations on initial and boundary conditions, topographic and land-use databases and grid resolution. Our comparisons show overall good agreement between simulated and observed data and also indicate that the low resolution of the 30 arc-sec soil database from United States Geological Survey, the soil moisture and skin temperature initial conditions assimilated from the GFS analyses and the synoptic forcing on the lateral boundaries of the finer grids may affect an adequate spatial description of the meteorological variables.
Bayesian inverse modeling and source location of an unintended 131I release in Europe in the fall of 2011

NASA Astrophysics Data System (ADS)

Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Šindelářová, Kateřina; Hýža, Miroslav; Stohl, Andreas

2017-10-01

In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model.
NAME Modeling and Climate Process Team

NASA Astrophysics Data System (ADS)

Schemm, J. E.; Williams, L. N.; Gutzler, D. S.

2007-05-01

NAME Climate Process and Modeling Team (CPT) has been established to address the need of linking climate process research to model development and testing activities for warm season climate prediction. The project builds on two existing NAME-related modeling efforts. One major component of this project is the organization and implementation of a second phase of NAMAP, based on the 2004 season. NAMAP2 will re-examine the metrics proposed by NAMAP, extend the NAMAP analysis to transient variability, exploit the extensive observational database provided by NAME 2004 to analyze simulation targets of special interest, and expand participation. Vertical column analysis will bring local NAME observations and model outputs together in a context where key physical processes in the models can be evaluated and improved. The second component builds on the current NAME-related modeling effort focused on the diurnal cycle of precipitation in several global models, including those implemented at NCEP, NASA and GFDL. Our activities will focus on the ability of the operational NCEP Global Forecast System (GFS) to simulate the diurnal and seasonal evolution of warm season precipitation during the NAME 2004 EOP, and on changes to the treatment of deep convection in the complicated terrain of the NAMS domain that are necessary to improve the simulations, and ultimately predictions of warm season precipitation These activities will be strongly tied to NAMAP2 to ensure technology transfer from research to operations. Results based on experiments conducted with the NCEP CFS GCM will be reported at the conference with emphasis on the impact of horizontal resolution in predicting warm season precipitation over North America.
Evaluation of Recombinant Human FGF-2 and PDGF-BB in Periodontal Regeneration: A Systematic Review and Meta-Analysis.

PubMed

Li, Feifei; Yu, Fanyuan; Xu, Xin; Li, Chunjie; Huang, Dingming; Zhou, Xuedong; Ye, Ling; Zheng, Liwei

2017-03-06

The prognosis for successful treatment of periodontal diseases is generally poor. Current therapeutic strategies often fail to regenerate infected periodontium. Recently an alternative strategy has been developed that combines conventional treatment with the application of recombinant human growth factors (rhGFs). But ambiguities in existed studies on the clinical efficacy of rhGFs do not permit either the identification of the specific growth factors effective for therapeutic interventions or the optimal concentration of them. Neither is it known whether the same rhGF can stimulate regeneration of both soft tissue and bone, or whether different patient populations call for differential use of the growth factors. In order to explore these issues, a meta-analysis was carried out. Particular attention was given to the therapeutic impact of fibroblast growth factor 2(FGF-2) and platelet derived growth factor BB (PDGF-BB). Our findings indicate that 0.3% rhFGF-2 and 0.3 mg/ml rhPDGF-BB show a greater capacity for periodontal regeneration than other concentrations and superiority to control groups with statistical significance. In the case of patients suffering only from gingival recession, however, the application of rhPDGF-BB produces no significant regenerative advantage. The findings of this study can potentially endow clinicians with guidelines for the appropriate application of these two rhGFs.
In Vitro and In Vivo Investigation of the Potential of Amorphous Microporous Silica as a Protein Delivery Vehicle

PubMed Central

Vanmellaert, Lieve; Vermaelen, Peter; Deroose, Christophe M.; Naert, Ignace; Cardoso, Marcio Vivan; Martens, Johan A.

2013-01-01

Delivering growth factors (GFs) at bone/implant interface needs to be optimized to achieve faster osseointegration. Amorphous microporous silica (AMS) has a potential to be used as a carrier and delivery platform for GFs. In this work, adsorption (loading) and release (delivery) mechanism of a model protein, bovine serum albumin (BSA), from AMS was investigated in vitro as well as in vivo. In general, strong BSA adsorption to AMS was observed. The interaction was stronger at lower pH owing to favorable electrostatic interaction. In vitro evaluation of BSA release revealed a peculiar release profile, involving a burst release followed by a 6 h period without appreciable BSA release and a further slower release later. Experimental data supporting this observation are discussed. Apart from understanding protein/biomaterial (BSA/AMS) interaction, determination of in vivo protein release is an essential aspect of the evaluation of a protein delivery system. In this regard micropositron emission tomography (μ-PET) was used in an exploratory experiment to determine in vivo BSA release profile from AMS. Results suggest stronger in vivo retention of BSA when adsorbed on AMS. This study highlights the possible use of AMS as a controlled protein delivery platform which may facilitate osseointegration. PMID:23991413
Applications of the PUFF model to forecasts of volcanic clouds dispersal from Etna and Vesuvio

NASA Astrophysics Data System (ADS)

Daniele, P.; Lirer, L.; Petrosino, P.; Spinelli, N.; Peterson, R.

2009-05-01

PUFF is a numerical volcanic ash tracking model developed to simulate the behaviour of ash clouds in the atmosphere. The model uses wind field data provided by meteorological models and adds dispersion and sedimentation physics to predict the evolution of the cloud once it reaches thermodynamic equilibrium with the atmosphere. The software is intended for use in emergency response situations during an eruption to quickly forecast the position and trajectory of the ash cloud in the near (˜1-72 h) future. In this paper, we describe the first application of the PUFF model in forecasting volcanic ash dispersion from the Etna and Vesuvio volcanoes. We simulated the daily occurrence of an eruptive event of Etna utilizing ash cloud parameters describing the paroxysm of 22nd July 1998 and wind field data for the 1st September 2005-31st December 2005 time span from the Global Forecast System (GFS) model at the approximate location of the Etna volcano (38N 15E). The results show that volcanic ash particles are dispersed in a range of directions in response to changing wind field at various altitudes and that the ash clouds are mainly dispersed toward the east and southeast, although the exact trajectory is highly variable, and can change within a few hours. We tested the sensitivity of the model to the mean particle grain size and found that an increased concentration of ash particles in the atmosphere results when the mean grain size is decreased. Similarly, a dramatic variation in dispersion results when the logarithmic standard deviation of the particle-size distribution is changed. Additionally, we simulated the occurrence of an eruptive event at both Etna and Vesuvio, using the same parameters describing the initial volcanic plume, and wind field data recorded for 1st September 2005, at approximately 38N 15E for Etna and 41N 14E for Vesuvio. The comparison of the two simulations indicates that identical eruptions occurring at the same time at the two volcanic centres display significantly different dispersal axes as a consequence of the different local wind field acting at the respective eruptive vents. At the Vesuvio the volcano, a plinian eruptive event with the dynamical parameters of the 79 A.D. eruption was simulated daily for one year, from 1st July 2005 to 30th June 2006. The statistical processing of results points out that, although in most cases the ash cloud dispersal encompasses many different areas, generally the easterly southeasterly direction is preferred. Our results highlight the significant role of wind field trends in influencing the distribution of ash particles from eruptive columns and prove that the dynamical parameters that most influence the variability of plume dispersal are the duration of the eruption and the maximum column height. Finally, the possible use of cloud simulations for refining hazard maps of areas exposed to volcanic ash dispersal is proposed.
Design and Use of Chimeric Proteins Containing a Collagen-Binding Domain for Wound Healing and Bone Regeneration.

PubMed

Addi, Cyril; Murschel, Frederic; De Crescenzo, Gregory

2017-04-01

Collagen-based biomaterials are widely used in the field of tissue engineering; they can be loaded with biomolecules such as growth factors (GFs) to modulate the biological response of the host and thus improve its potential for regeneration. Recombinant chimeric GFs fused to a collagen-binding domain (CBD) have been reported to improve their bioavailability and the host response, especially when combined with an appropriate collagen-based biomaterial. This review first provides an extensive description of the various CBDs that have been fused to proteins, with a focus on the need for accurate characterization of their interaction with collagen. The second part of the review highlights the benefits of various CBD/GF fusion proteins that have been designed for wound healing and bone regeneration.
Platelet-rich plasma derived growth factors contribute to stem cell differentiation in musculoskeletal regeneration

NASA Astrophysics Data System (ADS)

Qian, Yun; Han, Qixin; Chen, Wei; Song, Jialin; Zhao, Xiaotian; Ouyang, Yuanming; Yuan, Weien; Fan, Cunyi

2017-10-01

Stem cell treatment and platelet-rich plasma (PRP) therapy are two significant issues in regenerative medicine. Stem cells such as bone marrow mesenchymal stem cells, adipose-derived stem cells and periodontal ligament stem cells can be successfully applied in the field of tissue regeneration. PRP, a natural product isolated from whole blood, can secrete multiple growth factors (GFs) for regulating physiological activities. These GFs can stimulate proliferation and differentiation of different stem cells in injury models. Therefore, combination of both agents receives wide expectations in regenerative medicine, especially in bone, cartilage and tendon repair. In this review, we thoroughly discussed the interaction and underlying mechanisms of platelet-rich plasma derived growth factors with stem cells, and assessed their functions in cell differentiation for musculoskeletal regeneration.
Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.
Clinical Applications of Platelet-Rich Plasma in Patellar Tendinopathy

PubMed Central

Jeong, D. U.; Lee, C.-R.; Lee, J. H.; Pak, J.; Kang, L.-W.; Jeong, B. C.

2014-01-01

Platelet-rich plasma (PRP), a blood derivative with high concentrations of platelets, has been found to have high levels of autologous growth factors (GFs), such as transforming growth factor-β (TGF-β), platelet-derived growth factor (PDGF), fibroblastic growth factor (FGF), vascular endothelial growth factor (VEGF), and epidermal growth factor (EGF). These GFs and other biological active proteins of PRP can promote tissue healing through the regulation of fibrosis and angiogenesis. Moreover, PRP is considered to be safe due to its autologous nature and long-term usage without any reported major complications. Therefore, PRP therapy could be an option in treating overused tendon damage such as chronic tendinopathy. Here, we present a systematic review highlighting the clinical effectiveness of PRP injection therapy in patellar tendinopathy, which is a major cause of athletes to retire from their respective careers. PMID:25136568
Rod-like polyaniline supported on three-dimensional boron and nitrogen-co-doped graphene frameworks for high-performance supercapacitors

NASA Astrophysics Data System (ADS)

Liao, Kexuan; Gao, Jialu; Fan, Jinchen; Mo, Yao; Xu, Qunjie; Min, Yulin

2017-12-01

In this work, novel three-dimensional (3D) boron and nitrogen-co-doped three-dimensional (3D) graphene frameworks (BN-GFs) supporting rod-like polyaniline (PANI) are facilely prepared and used as electrodes for high-performance supercapacitors. The results demonstrated that BN-GFs with tuned electronic structure can not only provide a large surface area for rod-like PANI to anchor but also effectively facilitate the ion transfer and charge storage in the electrode. The PANI/BN-GF composite with wrinkled boron and nitrogen-co-doped graphene sheets interconnected by rod-like PANI exhibits excellent capacitive properties with a maximum specific capacitance of 596 F/g at a current density of 0.5 A/g. Notably, they also show excellent cycling stability with more than 81% capacitance retention after 5000 charge-discharge cycles.
Understanding the role of growth factors in modulating stem cell tenogenesis.

PubMed

Gonçalves, Ana I; Rodrigues, Márcia T; Lee, Sang-Jin; Atala, Anthony; Yoo, James J; Reis, Rui L; Gomes, Manuela E

2013-01-01

Current treatments for tendon injuries often fail to fully restore joint biomechanics leading to the recurrence of symptoms, and thus resulting in a significant health problem with a relevant social impact worldwide. Cell-based approaches involving the use of stem cells might enable tailoring a successful tendon regeneration outcome. As growth factors (GFs) powerfully regulate the cell biological response, their exogenous addition can further stimulate stem cells into the tenogenic lineage, which might eventually depend on stem cells source. In the present study we investigate the tenogenic differentiation potential of human- amniotic fluid stem cells (hAFSCs) and adipose-derived stem cells (hASCs) with several GFs associated to tendon development and healing; namely, EGF, bFGF, PDGF-BB and TGF-β1. Stem cells response to biochemical stimuli was studied by screening of tendon-related genes (collagen type I, III, decorin, tenascin C and scleraxis) and proteins found in tendon extracellular matrix (ECM) (Collagen I, III, and Tenascin C). Despite the fact that GFs did not seem to influence the synthesis of tendon ECM proteins, EGF and bFGF influenced the expression of tendon-related genes in hAFSCs, while EGF and PDGF-BB stimulated the genetic expression in hASCs. Overall results on cellular alignment morphology, immunolocalization and PCR analysis indicated that both stem cell source can be biochemically induced towards tenogenic commitment, validating the potential of hASCs and hAFSCs for tendon regeneration strategies.
Diversity of killer cell immunoglobulin-like receptor genes in Indonesian populations of Java, Kalimantan, Timor and Irian Jaya.

PubMed

Velickovic, M; Velickovic, Z; Panigoro, R; Dunckley, H

2009-01-01

Killer cell immunoglobulin-like receptors (KIRs) regulate the activity of natural killer and T cells through interactions with specific human leucocyte antigen class I molecules on target cells. Population studies performed over the last several years have established that KIR gene frequencies (GFs) and genotype content vary considerably among different ethnic groups, indicating the extent of KIR diversity, some of which have also shown the effect of the presence or absence of specific KIR genes in human disease. We have determined the frequencies of 16 KIR genes and pseudogenes and genotypes in 193 Indonesian individuals from Java, East Timor, Irian Jaya (western half of the island of New Guinea) and Kalimantan provinces of Indonesian Borneo. All 16 KIR genes were observed in all four populations. Variation in GFs between populations was observed, except for KIR2DL4, KIR3DL2, KIR3DL3, KIR2DP1 and KIR3DP1 genes, which were present in every individual tested. When comparing KIR GFs between populations, both principal component analysis and a phylogenetic tree showed close clustering of the Kalimantan and Javanese populations, while Irianese populations were clearly separated from the other three populations. Our results indicate a high level of KIR polymorphism in Indonesian populations that probably reflects the large geographical spread of the Indonesian archipelago and the complex evolutionary history and population migration in this region.
Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698
Global system for hydrological monitoring and forecasting in real time at high resolution

NASA Astrophysics Data System (ADS)

Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

2016-04-01

This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet connection) and not need update (all upgrade are deployed on the remote server)and DSS is a classical client-server application. The client side will be an HTML 5-CSS 3 application, it runs in one of the most common browser. The server side consist in: A web server (Apache web server); a map server (Geoserver); a Geographical q3456Relational Database Management Sytem (Postgresql+Postgis); Tools based on GDAL Lybraries. A customized web page will be implemented to publish all hydrometeorological information and forecast runs (free) for all users in the world. In this first presentation of the project are invited to attend all those scientific / technical people, Universities, Research Centers (public or private) who want to collaborate in it, opening a brainstorming to improve the System. References: • Liu Z. and Todini E., (2002). Towards a comprehensive physically based rainfall-runoff model. Hydrology and Earth System Sciences (HESS), 6(5):859-881, 2002. • Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A., (2009): The European Flood Alert System - Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125-140, 2009. • Coccia C., Mazzetti C., Ortiz E., Todini E., (2010) - A different soil conceptualization for the TOPKAPI model application within the DMIP 2. American Geophysical Union. Fall Meeting, San Francisco H21H-07, 2010. • Pappenberger, F., Cloke, H. L., Balsamo, G., Ngo-Duc, T., and Oki,T., (2010) Global runoff routing with the hydrological component of the ECMWF NWP system, Int. J. Climatol., 30, 2155-2174, 2010. • Coccia, G. and Todini, E., (2011). Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274, 2011. • Wu, H., Adler, R. F., Hong, Y., Tian, Y., and Policelli, F.,(2012): Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model, J. Hydrometeorol., 13, 1268-1284, 2012. • Simth M. et al., (2013). The Distributed Model Intercomparison Project - Phase 2: Experiment Design and Summary Results of the Western Basin Experiments, Journal of Hydrology 507, 300-329, 2013. • Pontificiae Academiae Scientiarvm (2014). Proceedings of the Joint Workshop on 2-6 May 2014: Sustainable Humanity Sustainable Nature Our Responsibility. Pontificiae Academiae Scientiarvm Extra Series 41. Vatican City. 2014 • Encyclical letter CARITAS IN VERITATE of the supreme pontiff Benedict XVI to the bishops, priests and deacons, men and women religious the lay faithful and all people of good will on integral human development in charity and truth. Vatican City . 2009. • Encyclical letter LAUDATO SI' of the holy father Francis on care for our common home. Vatican City. 2015

Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

NASA Astrophysics Data System (ADS)

Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

2012-04-01

Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.
The Fleet Numerical Meteorology and Oceanography Center (FNMOC) - Naval

Science.gov Websites

Prediction Charts (EFS). WxMAP depictions of NAVGEM predictions for side-by-side comparison with NCEP global NWP model (GFS) are also available. Oceanography Products This area provides Global & Regional
A study on super-sulfated cement using Dinh Vu phosphogypsum

NASA Astrophysics Data System (ADS)

Lam, Nguyen Ngoc

2018-04-01

Super-sulfated cement (SSC) is a newly developed unburnt cementitious material. It is a kind of environmental-friendly cementitious material due to its energy-saving, carbon emission reducing, and waste-utilization. It mainly composes of phosphogysum (PG) and ground granulated blast furnace slag (GFS), with a small amount of cement. In Vietnam, the Diammonium Phosphate DAP – Dinh Vu fertilizer plant in Dinh Vu industrial zone in the northern port city of Hai Phong – has discharged millions of tons of solid waste containing gypsum after 9 years of operation. The waste has changed the color of the water, eroded metal and destroyed fauna and floral systems in the surrounding area. Notably, according to the environmental impact assessment, the gypsum landfill area is supposed to be 13 hectares and the storage time reaches up to five years. This paper presents the experimental results on SSC using a high amount of Dinh Vu phosphogypsum and GFS in comparison with those of ordinary Portland cement (PC). The results show that the setting time of SSC is much longer than that of Portland cement but the compressive strength of SSC can be obtained 45-50 MPa at the age of 28 days, similar to that of the control sample using 100% PC40, and 69MPa at the age of 90 days. This value even exceeds the compressive strength of the PC40 cement.
Intelligent Machine Learning Approaches for Aerospace Applications

NASA Astrophysics Data System (ADS)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire detection problem and the aircraft conflict resolution problem. During the last decade, CNNs have become increasingly popular in the domain of image and speech processing. CNNs have a lot more parameters compared to GFSs that are tuned using the back-propagation algorithm. CNNs typically have hundreds of thousands or maybe millions of parameters that are tuned using common cost functions such as integral squared error, softmax loss etc. Chapter 5 discusses a classification problem to classify images as humans or not and Chapter 6 discusses a regression task using CNN for producing an approximate near-optimal route for the Traveling Salesman Problem (TSP) which is regarded as one of the most complicated decision making problem. Both the GFS and CNN are used to develop intelligent systems specific to the application providing them computational efficiency, robustness in the face of uncertainties and scalability.
Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.
A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.
A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.
Mesoporous cellular-structured carbons derived from glucose-fructose syrup and their adsorption properties towards acetaminophen

NASA Astrophysics Data System (ADS)

Tzvetkov, George; Spassov, Tony; Kaneva, Nina; Tsyntsarski, Boyko

Here, a series of cellular-structured and predominantly mesoporous carbons were prepared via carbonization of glucose-fructose syrup (GFS) with sulfuric acid and subsequent calcination between 400∘C and 700∘C. Comparative results on the microstructure, chemical and textural properties of the newly produced carbons are presented. Furthermore, their adsorption performance for removal of acetaminophen from water was tested and it was found that the carbon calcined at 700∘C has a maximum adsorption capacity (98.7mgṡg-1) among all samples due to its suitable textural properties (BET surface area of 418m2ṡg-1 and total pore volume of 0.2cm3ṡg-1). This study demonstrates the potential use of GFS as a precursor in the preparation of carbonaceous materials for removal of biologically-active micropollutants from water.
GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

NASA Astrophysics Data System (ADS)

Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

2017-04-01

Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal outlook while providing useful information to users and partners. We demonstrate the first version of an operational GloFAS seasonal outlook, outlining the model set-up and presenting a first look at the seasonal forecasts that will be displayed in the GloFAS interface, and discuss the initial results of the forecast evaluation.
Impact of a warm core eddy on near-surface wind at Brazil-Malvinas Confluence region in high resolution simulations

NASA Astrophysics Data System (ADS)

Hackerott, J. A.; Mesquita, M. D. S.; Camargo, R. D.; Pezzi, L. P.

2014-12-01

Several studies show that near surface winds acquire anticyclonic (cyclonic) vorticity and accelerate (decelerate) when flow in the same direction as positive (negative) orientation of the Sea Surface Temperature (SST) gradient. Many of them were made over different oceanic thermal fronts in the world analyzing contrasts in SST gradients. However, still remains much uncertainty about how strong is this wind modulation, particularly on areas in need of studies and in-situ data, such as the Brazil-Malvinas Confluence Region (BMC) where intense SST gradients are found. This study brings results of the Weather Research and Forecasting (WRF) model simulations, configured with nested grids, where it is compared the influence of distinct synoptic patterns observed at BMC where three different SST patterns are imposed to WRF. These patterns are: (1) with a typical smoothed SST field, named as Control; (2) Small Eddy, which is the same as Control but adding an eddy of 1° radius and a +2°C amplitude; and (3) Intense Eddy, which is also the same as Control, but where an eddy of 1° radius and +4°C amplitude is added. The artificial imposed eddy is analogous to the SST patterns observed at BMC, with different intensities. The simulations were integrated for 76 hours using initial and lateral boundary conditions from the Global Forecast System (GFS) model with 0.5° resolution. The results showed that the wind at 10m height is influenced by the diurnal cycle of turbulence in the Marine Atmospheric Boundary Layer (MABL) modified by variations in SST. The wind magnitude changes up to 1m.s-1 over a 4/50°C.km-1 SST gradient and 0.6m.s-1 over a 2/50°C.km-1 SST gradient. This effect generates meso-scale disturbances that propagate to larger scales leading to disturbances in remote areas. Thus, the preliminary analyses are suggesting that there is an interaction between the meso and synoptic scale playing a role. Mechanisms such this one might not be captured by atmospheric global models used in low spatial resolution. Often, that is the case seen on operational models.
Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.
National Centers for Environmental Prediction

Science.gov Websites

SYSTEM CFS CLIMATE FORECAST SYSTEM NAQFC NAQFC MODEL GEFS GLOBAL ENSEMBLE FORECAST SYSTEM HWRF HURRICANE WEATHER RESEARCH and FORECASTING HMON HMON - OPERATIONAL HURRICANE FORECASTING WAVEWATCH III WAVEWATCH III
Predicting and attributing recent East African Spring droughts with dynamical-statistical climate model ensembles

NASA Astrophysics Data System (ADS)

Funk, C. C.; Shukla, S.; Hoerling, M. P.; Robertson, F. R.; Hoell, A.; Liebmann, B.

2013-12-01

During boreal spring, eastern portions of Kenya and Somalia have experienced more frequent droughts since 1999. Given the region's high levels of food insecurity, better predictions of these droughts could provide substantial humanitarian benefits. We show that dynamical-statistical seasonal climate forecasts, based on the latest generation of coupled atmosphere-ocean and uncoupled atmospheric models, effectively predict boreal spring rainfall in this area. Skill sources are assessed by comparing ensembles driven with full-ocean forcing with ensembles driven with ENSO-only sea surface temperatures (SSTs). Our analysis suggests that both ENSO and non-ENSO Indo-Pacific SST forcing have played an important role in the increase in drought frequencies. Over the past 30 years, La Niña drought teleconnections have strengthened, while non-ENSO Indo-Pacific convection patterns have also supported increased (decreased) Western Pacific (East African) rainfall. To further examine the relative contribution of ENSO, low frequency warming and the Pacific Decadal Oscillation, we present decompositions of ECHAM5, GFS, CAM4 and GMAO AMIP simulations. These decompositions suggest that rapid warming in the western Pacific and steeper western-to-central Pacific SST gradients have likely played an important role in the recent intensification of the Walker circulation, and the associated increase in East African aridity. A linear combination of time series describing the Pacific Decadal Oscillation and the strength of Indo-Pacific warming are shown to track East African rainfall reasonably well. The talk concludes with a few thoughts linking the potentially important interplay of attribution and prediction. At least for recent East African droughts, it appears that a characteristic Indo-Pacific SST and precipitation anomaly pattern can be linked statistically to support forecasts and attribution analyses. The combination of traditional AGCM attribution analyses with simple yet physically plausible statistical estimation procedures may help us better untangle some climate mysteries.
Immunomodulatory properties of human periodontal ligament stem cells.

PubMed

Wada, Naohisa; Menicanin, Danijela; Shi, Songtao; Bartold, P Mark; Gronthos, Stan

2009-06-01

Tissue engineering utilizing periodontal ligament stem cells (PDLSCs) has recently been proposed for the development of new periodontal regenerative therapies. Although the use of autologous PDLSC transplantation eliminates the potential of a significant host immune response against the donor cells, it is often difficult to generate enough PDLSCs from one donor source due to the variation of stem cell potential between donors and disease state of each patient. In this study, we examined the immunomodulatory properties of PDLSCs as candidates for new allogeneic stem cell-based therapies. Human PDLSCs displayed cell surface marker characteristics and differentiation potential similar to bone marrow stromal stem cells (BMSSCs) and dental pulp stem cells (DPSCs). PDLSCs, BMSSCs, and DPSCs inhibited peripheral blood mononuclear cell (PBMNC) proliferation stimulated with mitogen or in an allogeneic mixed lymphocyte reaction (MLR). Interestingly, gingival fibroblasts (GFs) also suppressed allogeneic PBMNC proliferation under both assay conditions. PDLSCs, BMSSCs, DPSCs, and GFs exhibited non-cell contact dependent suppression of PBMNC proliferation in co-cultures using transwells. Furthermore, conditioned media (CM) derived from each cell type pretreated with IFN-gamma partially suppressed PBMNC proliferation when compared to CMs without IFN-gamma stimulation. In all of these mesenchymal cell types cultured with activated PBMNCs, the expression of TGF-beta1, hepatocyte growth factor (HGF) and indoleamine 2, 3-dioxygenase (IDO) was upregulated while IDO expression was upregulated following stimulation with IFN-gamma. These results suggest that PDLSCs, BMSSCs, DPSCs, and GFs possess immunosuppressive properties mediated, in part, by soluble factors, produced by activated PBMNCs. J. Cell. Physiol. 219: 667-676, 2009. (c) 2009 Wiley-Liss, Inc.
Structural Biology and Evolution of the TGF-β Family

PubMed Central

Hinck, Andrew P.; Mueller, Thomas D.; Springer, Timothy A.

2017-01-01

We review the evolution and structure of members of the transforming growth factor β (TGF-β) family, antagonistic or agonistic modulators, and receptors that regulate TGF-β signaling in extracellular environments. The growth factor (GF) domain common to all family members and many of their antagonists evolved from a common cystine knot growth factor (CKGF) domain. The CKGF superfamily comprises six distinct families in primitive metazoans, including the TGF-β and Dan families. Compared with Wnt/Frizzled and Notch/Delta families that also specify body axes, cell fate, tissues, and other families that contain CKGF domains that evolved in parallel, the TGF-β family was the most fruitful in evolution. Complexes between the prodomains and GFs of the TGF-β family suggest a new paradigm for regulating GF release by conversion from closed- to open-arm procomplex conformations. Ternary complexes of the final step in extracellular signaling show how TGF-β GF dimers bind type I and type II receptors on the cell surface, and enable understanding of much of the specificity and promiscuity in extracellular signaling. However, structures suggest that when GFs bind repulsive guidance molecule (RGM) family coreceptors, type I receptors do not bind until reaching an intracellular, membrane-enveloped compartment, blurring the line between extra- and intracellular signaling. Modulator protein structures show how structurally diverse antagonists including follistatins, noggin, and members of the chordin family bind GFs to regulate signaling; complexes with the Dan family remain elusive. Much work is needed to understand how these molecular components assemble to form signaling hubs in extracellular environments in vivo. PMID:27638177
Working With the Wave Equation in Aeroacoustics: The Pleasures of Generalized Functions

NASA Technical Reports Server (NTRS)

Farassat, F.; Brentner, Kenneth S.; Dunn, mark H.

2007-01-01

The theme of this paper is the applications of generalized function (GF) theory to the wave equation in aeroacoustics. We start with a tutorial on GFs with particular emphasis on viewing functions as continuous linear functionals. We next define operations on GFs. The operation of interest to us in this paper is generalized differentiation. We give many applications of generalized differentiation, particularly for the wave equation. We discuss the use of GFs in finding Green s function and some subtleties that only GF theory can clarify without ambiguities. We show how the knowledge of the Green s function of an operator L in a given domain D can allow us to solve a whole range of problems with operator L for domains situated within D by the imbedding method. We will show how we can use the imbedding method to find the Kirchhoff formulas for stationary and moving surfaces with ease and elegance without the use of the four-dimensional Green s theorem, which is commonly done. Other subjects covered are why the derivatives in conservation laws should be viewed as generalized derivatives and what are the consequences of doing this. In particular we show how we can imbed a problem in a larger domain for the identical differential equation for which the Green s function is known. The primary purpose of this paper is to convince the readers that GF theory is absolutely essential in aeroacoustics because of its powerful operational properties. Furthermore, learning the subject and using it can be fun.
Distribution and transport of water vapor in the UTLS over the Tibetan Plateau as inferred from the MLS satellite data and WRF model simulations

NASA Astrophysics Data System (ADS)

Jain, S.; Kar, S. C.

2016-12-01

Water vapor is an important minor constituent in the lower stratosphere as it influences the stratospheric chemistry and total radiation budget. The spatial distribution of water vapor mixing ratio (WVMR) obtained from Aura Microwave Limb Sounder (MLS) satellite at 100 hPa level shows prominent maxima over the Tibetan Plateau during August 2015. The Asian monsoon upper level anticyclone is also known to occur over this region during this period. The Indian Meteorological Department (IMD) and National Centre of Medium Range Weather Forecasting (NCMRWF) observed daily gridded rainfall data shows moderate to heavy rainfall over the Tibetan Plateau, suggesting active convection from 26 July to 10 August 2015. The atmospheric conditions are simulated over the Asian region for the 15-day period using the Weather Research Forecasting (WRF) model. The simulations are carried out using two nested domains with resolution of 12 km and 4 km. The initial and boundary conditions are taken from the NGFS (up-graded version of the NCEP GFS) data. The WRF WVMR profiles are observed to be comparatively moist than the MLS profiles in the UTLS region over the Tibetan Plateau. This may be due to the relatively higher temperatures (1-2 K) simulated in the WRF model near 100 hPa level. It is noted that the WRF model has a drying tendency at all the levels. The UTLS WVMR and temperatures show poor sensitivity to the convective schemes. The parent domain and the explicit convective scheme simulate almost same moisture over time in the inner domain. The cloud micro-physics is observed to play a rather important role in controlling the UTLS water vapor content. The WSM-6 convective scheme is observed to simulate the UTLS moisture comparatively well and therefore the processes associated with the formation of ice, snow and graupel formation may be of much more importance in controlling the UTLS WVMR in the WRF model. The 24 hr, 48 hr and 72 hr forecast averaged for the 15-day period shows that over the Tibetan Plateau, high WVMR in the UTLS is not centered within the anticyclone, contrary to what has been shown by earlier studies. Similar simulations are also being carried out using the Era-interim initial and boundary conditions to confirm the above findings.
EMC HWRF Weekly Meeting Homepage

Science.gov Websites

structure using idealized HWRF simulations with GFS and MYJ PBL schemes by Jun Zhang (HRD/AOML) NOAA- MoES Zhan Zhang(EMC) (NOTE: Links open presentations in a new window) Please e-mail comments, questions, or
Performance of the multi-model SREPS precipitation probabilistic forecast over Mediterranean area

NASA Astrophysics Data System (ADS)

Callado, A.; Escribà, P.; Santos, C.; Santos-Muñoz, D.; Simarro, J.; García-Moya, J. A.

2009-09-01

The performance of the Short-Range Ensemble Prediction system (SREPS) probabilistic precipitation forecast over the Mediterranean area has been evaluated comparing with both, an Atlantic-European area excluding the first one, and a more general area including the two previous ones. The main aim is to assess whether the performance of the system due to its meso-alpha horizontal resolution of 25 kilometres is affected over the Mediterranean area, where the meteorological mesoscale events play a more important role than in an Atlantic-European area, more related to synoptic scale with an Atlantic influence. Furthermore, two different verification methods have been applied and compared for the three areas in order to assess its performance. The SREPS is a daily experimental LAM EPS focused on the short range (up to 72 hours) which has been developed at the Spanish Meteorological Agency (AEMET). To take into account implicitly the model errors, five purely independent different limited area models are used (COSMO (COSMO), HIRLAM (HIRLAM Consortium), HRM (DWD), MM5 (NOAA) and UM-NAE (UKMO)), and in order to sample the initial and boundary condition uncertainties each model is integrated using data from four different global deterministic models (GFS (NCEP), GME (DWD), IFS (ECMWF) and UM (UKMO)). As a result, crossing models and initial conditions the EPS is composed by 20 members. The underlying idea is that the ensemble performance has to improve as far as each member has itself the better possible performance, i.e. the better operational configuration limited area models are combined with the better global deterministic model configurations initialized with the best analysis. Because of this neither global EPS as initial conditions nor different model settings as multi-parameterizations or multi-parameters are used to generate SREPS. The performance over the three areas has been assessed focusing on 24 hour accumulation precipitation with four different usual forecasting thresholds: 1, 5 , 10 and 20 mm. A standard probabilistic verification exercise (following ECMWF recommendations) has been carried out, assessing quality with well known properties like reliability, resolution and discrimination, using usual performance measures: Reliability (Attributes) Diagram, Brier and Brier Skill Score Decomposition, Relative Operating Characteristic (ROC) and ROC area. The value of the forecasts w.r.t. sample climatology is shown with Relative value envelopes. This exercise has been carried out for a one year period (May 2007 to May 2008). Observed precipitation data from High Resolution (HR) networks over Europe have been used as reference. To avoid the potential lack of statistical significance due to spatial dependence between close observations, up-scaling processed observations have been used, provided by ECMWF, who collects the raw data from different member and cooperating states over Europe. This advanced up-scaling methodology has the feature to be more independent of the density of precipitation observations than the more classical simple methodology of interpolate the model outputs to the observation station points. In particular, the observations have been up-scaled to a 0.25ºx0.25º box taking each box as representative only when more than five observations are available in it. In the first one verifying method the box-average is taken, and for the second one a set of quantiles is considered, specifically 10, 25, 50 , 75 and 90 quantiles. The difference between both methods is that the first one takes over each box a single value as representative of precipitation. Whereas the second one takes a probability density function as representation of precipitation over the box, thus introducing uncertainty (related with spatial distribution) in the observations. The results are consistent, and show that in general SREPS is a reliable probabilistic forecasting system for the three selected areas. Concerning performance over different regions, the SREPS probabilistic precipitation forecasts over the selected Mediterranean area have a little less reliability and resolution than over the North Europe area, specially with the higher thresholds 10 and 20 mm. The latter results suggests that in SREPS the representation of the mesoscale meteorological events around the Mediterranean basin has to be improved, and probably also the orographic-related processes as the orographic enhancement of the precipitation. So it is suggested that the predictability skill of SREPS system around the Mediterranean could be expected to improve if the horizontal and vertical resolution of each limited area model of the system is increased in order to take into account the meso-beta scale. When comparing the two verification methods, one using up-scaled box average and the other using an up-scaled set of quantiles (i.e. a box PDF), it is shown that the validation of the probabilistic forecast is quite more consistent in the latter method when uncertainties in the observations are introduced and probably gives a more realistic idea of performance.
A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.
A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.
Adding Four- Dimensional Data Assimilation (aka grid ...

EPA Pesticide Factsheets

Adding four-dimensional data assimilation (a.k.a. grid nudging) to MPAS.The U.S. Environmental Protection Agency is investigating the use of MPAS as the meteorological driver for its next-generation air quality model. To function as such, MPAS needs to operate in a diagnostic mode in much the same manner as the current meteorological driver, the Weather Research and Forecasting (WRF) model. The WRF operates in diagnostic mode using Four-Dimensional Data Assimilation, also known as "grid nudging". MPAS version 4.0 has been modified with the addition of an FDDA routine to the standard physics drivers to nudge the state variables for wind, temperature and water vapor towards MPAS initialization fields defined at 6-hour intervals from GFS-derived data. The results to be shown demonstrate the ability to constrain MPAS simulations to known historical conditions and thus provide the U.S. EPA with a practical meteorological driver for global-scale air quality simulations. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use bo
Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.
Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.
Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

NASA Astrophysics Data System (ADS)

Vislocky, Robert L.; Fritsch, J. Michael

1997-12-01

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.
Nitric oxide donor restores lung growth factor and receptor expression in hyperoxia-exposed rat pups.

PubMed

Lopez, Emmanuel; Boucherat, Olivier; Franco-Montoya, Marie-Laure; Bourbon, Jacques R; Delacourt, Christophe; Jarreau, Pierre-Henri

2006-06-01

Exposure of newborn rats to hyperoxia impairs alveolarization. Nitric oxide (NO) may prevent this evolution. Angiogenesis and factors involved in this process, but also other growth factors (GFs) involved in alveolar development, are likely potential therapeutic targets for NO. We studied the effects of the NO donor, [Z]-1-[N-(2-aminoethyl)-N-(2-ammonioethyl)aminio]diazen-1-ium-1, 2-diolate, also termed DETANONOate (D-NO), on hyperoxia-induced changes in key regulatory factors of alveolar development in neonatal rats, and its possible preventive effect on the physiologic consequences of hyperoxia. Newborn rat pups were randomized at birth to hyperoxia (> 95% O2) or room air exposure for 6 or 10 d, while receiving D-NO or its diluent. On Day 6, several GFs and their receptors were studied at pre- and/or post-translational levels. Elastin transcript determination on Day 6, and elastin deposition in tissue and morphometric analysis of the lungs on Day 10, were also performed. Hyperoxia decreased the expression of vascular endothelial growth factor (VEGF) receptor (VEGFR) 2, fibroblast growth factor (FGF)-18, and FGF receptors (FGFRs) FGFR3 and FGFR4, increased mortality, and impaired alveolarization and capillary growth. D-NO treatment of hyperoxia-exposed pups restored the expression level of FGF18 and FGFR4, induced an increase of both VEGF mRNA and protein, enhanced elastin expression, and partially restored elastin deposition in alveolar walls. Although, under the present conditions, D-NO failed to prevent the physiologic consequences of hyperoxia in terms of survival and lung alveolarization, our findings demonstrate molecular effects of NO on GFs involved in alveolar development that may have contributed to the protective effects previously reported for NO.
Two novel mutations in the BCKDK (branched-chain keto-acid dehydrogenase kinase) gene are responsible for a neurobehavioral deficit in two pediatric unrelated patients.

PubMed

García-Cazorla, Angels; Oyarzabal, Alfonso; Fort, Joana; Robles, Concepción; Castejón, Esperanza; Ruiz-Sala, Pedro; Bodoy, Susanna; Merinero, Begoña; Lopez-Sala, Anna; Dopazo, Joaquín; Nunes, Virginia; Ugarte, Magdalena; Artuch, Rafael; Palacín, Manuel; Rodríguez-Pombo, Pilar; Alcaide, Patricia; Navarrete, Rosa; Sanz, Paloma; Font-Llitjós, Mariona; Vilaseca, Ma Antonia; Ormaizabal, Aida; Pristoupilova, Anna; Agulló, Sergi Beltran

2014-04-01

Inactivating mutations in the BCKDK gene, which codes for the kinase responsible for the negative regulation of the branched-chain α-keto acid dehydrogenase complex (BCKD), have recently been associated with a form of autism in three families. In this work, two novel exonic BCKDK mutations, c.520C>G/p.R174G and c.1166T>C/p.L389P, were identified at the homozygous state in two unrelated children with persistently reduced body fluid levels of branched-chain amino acids (BCAAs), developmental delay, microcephaly, and neurobehavioral abnormalities. Functional analysis of the mutations confirmed the missense character of the c.1166T>C change and showed a splicing defect r.[520c>g;521_543del]/p.R174Gfs1*, for c.520C>G due to the presence of a new donor splice site. Mutation p.L389P showed total loss of kinase activity. Moreover, patient-derived fibroblasts showed undetectable (p.R174Gfs1*) or barely detectable (p.L389P) levels of BCKDK protein and its phosphorylated substrate (phospho-E1α), resulting in increased BCKD activity and the very rapid BCAA catabolism manifested by the patients' clinical phenotype. Based on these results, a protein-rich diet plus oral BCAA supplementation was implemented in the patient homozygous for p.R174Gfs1*. This treatment normalized plasma BCAA levels and improved growth, developmental and behavioral variables. Our results demonstrate that BCKDK mutations can result in neurobehavioral deficits in humans and support the rationale for dietary intervention. © 2014 WILEY PERIODICALS, INC.
Antinutritional factors in pearl millet grains: Phytate and goitrogens content variability and molecular characterization of genes involved in their pathways

PubMed Central

Gangashetty, Prakash Irappa; Grando, Stefania; Kwaku Zu, Theophilus Tenutse; Daminati, Maria Gloria

2018-01-01

Pearl millet [Pennisetum glaucum (L.) R. Br.] is an important “orphan” cereal and the most widely grown of all the millet species worldwide. It is also the sixth most important cereal in the world after wheat, rice, maize, barley, and sorghum, being largely grown and used in West Africa as well as in India and Pakistan. The present study was carried out in the frame of a program designed to increase benefits and reduce potential health problems deriving from the consumption of pearl millet. The specific goal was to provide a database of information on the variability existing in pearl millet germplasm as to the amounts of phytate, the most relevant antinutrient compound, and the goitrogenic compounds C-glycosylflavones (C-GFs) accumulated in the grain.Results we obtained clearly show that, as indicated by the range in values, a substantial variability subsists across the investigated pearl millet inbred lines as regards the grain level of phytic acid phosphate, while the amount of C-GFs shows a very high variation. Suitable potential parents to be used in breeding programs can be therefore chosen from the surveyed material in order to create new germplasm with increased nutritional quality and food safety. Moreover, we report novel molecular data showing which genes are more relevant for phytic acid biosynthesis in the seeds as well as a preliminary analysis of a pearl millet orthologous gene for C-GFs biosynthesis. These results open the way to dissect the genetic determinants controlling key seed nutritional phenotypes and to the characterization of their impact on grain nutritional value in pearl millet. PMID:29856884
α5β1-Integrin inhibitor (CLT-28643) effective in rabbit trabeculectomy model.

PubMed

Schultheiss, Maximilian; Schnichels, Sven; Konrad, Eva-Maria; Bartz-Schmidt, Karl U; Zahn, Grit; Caldirola, Patrizia; Fsadni, Mario G; Caram-Lelham, Ninus; Spitzer, Martin S

2017-02-01

Glaucoma filtration surgery (GFS) fails due to fibrosis. The α5β1-integrin plays a pivotal role in fibrosis, angiogenesis and inflammation. This is the first experiment evaluating the prevention of fibrosis after GFS by a specific small molecule α5β1-integrin inhibitor (CLT-28643). Twenty-four rabbits received trabeculectomy on their right eyes. The rabbits were randomized into three groups of eight eyes each. CLT-28643 was given as a single subconjunctival injection intraoperatively to two of the right eye groups followed by postoperative vehicle eye drops (CLT+ group) or CLT-28643 eye drops 4 times daily (CLT++ group). A third group received mitomycin-C (MMC) intraoperatively (sponge application, 0.04%, 2 min) followed by vehicle eye drops postoperatively. The control-surgery group consisted of 12 left eyes having trabeculectomy with no adjunctive therapy. The remaining 12 left eyes formed the untreated group. Clinical assessment included intraocular pressure (IOP) measurement, slit-lamp examination (including bleb survival and morphology) and bleb photography. The rabbits were killed after four weeks for histology. Both CLT-28643-treated groups showed significantly prolonged bleb survival, and better bleb score compared to the control-surgery group. At end of the study, most functioning blebs were found in the MMC group (MMC group 75%; CLT+ group 12.5%, CLT++ group 25%; CLT+ group 12.5%, control-surgery group 0%). CLT-28643 was non-toxic and well tolerated. This rabbit GFS study indicates that inhibition of α5β1-integrin by the novel α5β1-integrin antagonist CLT-28643 significantly improved the outcome. The effect of a single intro-operative application of CLT-28643 seems to be inferior to 0.04% MMC. © 2016 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.
The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less
Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.
Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.
Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

NASA Astrophysics Data System (ADS)

Holmukhe, R. M.; Dhumale, Mrs. Sunita; Chaudhari, Mr. P. S.; Kulkarni, Mr. P. P.

2010-10-01

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.
Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.
Recent Trends in Variable Generation Forecasting and Its Value to the Power System

DOE PAGES

Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; ...

2014-12-23

We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less
Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.
Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).
Antimicrobial effect of platelet-rich plasma and platelet-rich fibrin.

PubMed

Badade, Pallavi S; Mahale, Swapna A; Panjwani, Alisha A; Vaidya, Prutha D; Warang, Ayushya D

2016-01-01

Platelet concentrates have been extensively used in a variety of medical fields to promote soft- and hard-tissue regeneration. The significance behind their use lies in the abundance of growth factors (GFs) in platelets α-granules that promote wound healing. Other than releasing a pool of GFs upon activation, platelets also have many features that indicate their role in the anti-infective host defense. The aim of this study is to evaluate the antimicrobial activities of platelet-rich plasma (PRP) and platelet-rich fibrin (PRF) against periodontal disease-associated bacteria. Blood samples were obtained from ten adult male patients. PRP and PRF were procured using centrifugation. The antimicrobial activity of PRP and PRF was evaluated by microbial culturing using bacterial strains of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. P. gingivalis and A. actinomycetemcomitans were inhibited by PRP but not by PRF. PRP is a potentially useful substance in the fight against periodontal pathogens. This might represent a valuable property in adjunct to the enhancement of tissue regeneration.

A Role of Oral Bacteria in Bisphosphonate-induced Osteonecrosis of the Jaw

PubMed Central

Mawardi, H.; Giro, G.; Kajiya, M.; Ohta, K.; Almazrooa, S.; Alshwaimi, E.; Woo, S.-B.; Nishimura, I.; Kawai, T.

2011-01-01

No consensus has yet been reached to associate oral bacteria conclusively with the etio-pathogenesis of bisphosphonate-induced osteonecrosis of the jaw (BONJ). Therefore, the present study examined the effects of oral bacteria on the development of BONJ-like lesions in a mouse model. In the pamidronate (Pam)-treated mice, but not control non-drug-treated mice, tooth extraction followed by oral infection with Fusobacterium nucleatum caused BONJ-like lesions and delayed epithelial healing, both of which were completely suppressed by a broad-spectrum antibiotic cocktail. Furthermore, in both in vitro and in vivo experiments, the combination of Pam and Fusobacterium nucleatum caused the death of gingival fibroblasts (GFs) and down-regulated their production of keratinocyte growth factor (KGF), which induces epithelial cell growth and migration. Therefore, in periodontal tissues pre-exposed to bisphosphonate, bacterial infection at tooth extraction sites caused diminished KGF expression in GFs, leading to a delay in the epithelial wound-healing process that was mitigated by antibiotics. PMID:21921248
Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara; ...

2017-06-16

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less
Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less
Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.
The Texas Children's Hospital immunization forecaster: conceptualization to implementation.

PubMed

Cunningham, Rachel M; Sahni, Leila C; Kerr, G Brady; King, Laura L; Bunker, Nathan A; Boom, Julie A

2014-12-01

Immunization forecasting systems evaluate patient vaccination histories and recommend the dates and vaccines that should be administered. We described the conceptualization, development, implementation, and distribution of a novel immunization forecaster, the Texas Children's Hospital (TCH) Forecaster. In 2007, TCH convened an internal expert team that included a pediatrician, immunization nurse, software engineer, and immunization subject matter experts to develop the TCH Forecaster. Our team developed the design of the model, wrote the software, populated the Excel tables, integrated the software, and tested the Forecaster. We created a table of rules that contained each vaccine's recommendations, minimum ages and intervals, and contraindications, which served as the basis for the TCH Forecaster. We created 15 vaccine tables that incorporated 79 unique dose states and 84 vaccine types to operationalize the entire United States recommended immunization schedule. The TCH Forecaster was implemented throughout the TCH system, the Indian Health Service, and the Virginia Department of Health. The TCH Forecast Tester is currently being used nationally. Immunization forecasting systems might positively affect adherence to vaccine recommendations. Efforts to support health care provider utilization of immunization forecasting systems and to evaluate their impact on patient care are needed.
Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of stakeholder-driven applications of the K-PREP system, including empirical forecasts for circumboreal fire activity.
Hybrid Intrusion Forecasting Framework for Early Warning System

NASA Astrophysics Data System (ADS)

Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.
The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.
Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less
Wind Power Forecasting Error Distributions: An International Comparison; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2012-09-01

Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.
Real-time emergency forecasting technique for situation management systems

NASA Astrophysics Data System (ADS)

Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

2018-05-01

The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.
Louisiana Airport System Plan aviation activity forecasts 1990-2010.

DOT National Transportation Integrated Search

1991-07-01

This report documents the methodology used to develop the aviation activity forecasts prepared as a part of the update to the Louisiana Airport System Plan and provides Louisiana aviation forecasts for the years 1990 to 2010. In general, the forecast...
Antifibrotic effects of pirfenidone on Tenon's fibroblasts in glaucomatous eyes: comparison with mitomycin C and 5-fluorouracil.

PubMed

Na, Jung Hwa; Sung, Kyung Rim; Shin, Jin A; Moon, Jung Il

2015-09-01

The purpose of this study was to evaluate the antifibrotic effects of pirfenidone (PFD) on primary cultured human Tenon's fibroblasts (HTFs) from primary open-angle glaucoma (POAG) eyes, compared to mitomicin C (MMC) and 5-fluorouracil (5-FU). Samples of human Tenon's capsule were obtained during respective surgeries from three groups of patients: patients with cataract (CAT group), patients with POAG who underwent glaucoma filtration surgery (GFS) (POAG1 group), and patients with POAG who underwent GFS due to failed bleb of previous GFS (POAG2 group). Cell toxicity, cell migration, and the expression level of α-smooth muscle actin (α-SMA) protein were evaluated in primary cultured HTFs from the three patient groups after treatment (PFD, MMC, or 5-FU). Overall, cell viability after PFD treatment was higher compared to MMC treatment (82.3 ± 5.1 % vs 56.7 ± 3.8 %; p = 0.001) and comparable to 5-FU treatment (82.3 ± 5.1 % vs 85.7 ± 10.7 %, p = 0.214) at the same concentration (0.4 mg/ml). Both 0.3 mg/ml PFD and 0.1 mg/ml MMC inhibited cell migration compared to control (without treatment) cells (p = 0.014 and 0.005, respectively), while 0.2 mg/ml 5-FU showed the highest degree of cell migration among the three agents in the POAG1 group (PFD vs MMC vs 5-FU; 29.5 ± 2.1 % vs 34.5 ± 0.7 % vs 76.0 ± 8.5 %, PFD vs MMC; p = 1.000, PFD vs 5-FU; p = 0.008, MMC vs 5-FU; p = 0.011). PFD (0.1 or 0.3 mg/ml) and MMC (0.05 and 0.1 mg/ml) treatment significantly reduced the protein expression level of α-SMA in the POAG 1 group (all p < 0.05), and the α-SMA protein level following treatment with 0.3 mg/ml PFD was lower than that of 0.1 mg/ml MMC (p = 0.040). PFD showed less cytotoxicity compared to MMC. PFD and MMC inhibited cell migration and reduced α-SMA protein expression levels, while 5-FU showed neither inhibition of cell migration nor reduction in α-SMA expression level. These findings indicate PFD as a potential adjunctive antifibrotic agent to prevent bleb failure during GFS.
Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

NASA Astrophysics Data System (ADS)

ÁLvarez, A.; Orfila, A.; Tintoré, J.

2004-03-01

Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.
Great Lakes Maps - NOAA's National Weather Service

Science.gov Websites

Coastal Forecast System) Waves (GLERL Great Lakes Coastal Forecast System) Ice Cover (GLERL Great Lakes Coastal Forecast System) NOAA's National Weather Service Central Region Headquarters Regional Office 7220
Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.
3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less
Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.
Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.
First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Design of a Forecasting Service System for Monitoring of Vulnerabilities of Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a forecasting service system. The system is to remove or monitor causes of breach incidents in advance. To that end, this research first examines general security vulnerabilities of sensor networks and analyzes characteristics of existing forecasting systems. Then, 5 steps of a forecasting service system are proposed in order to improve security responses.
Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; Galelli, Stefano

2017-09-01

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made - namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.
Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less
Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE PAGES

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; ...

2017-09-28

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less
Progress and challenges with Warn-on-Forecast

NASA Astrophysics Data System (ADS)

Stensrud, David J.; Wicker, Louis J.; Xue, Ming; Dawson, Daniel T.; Yussouf, Nusrat; Wheatley, Dustan M.; Thompson, Therese E.; Snook, Nathan A.; Smith, Travis M.; Schenkman, Alexander D.; Potvin, Corey K.; Mansell, Edward R.; Lei, Ting; Kuhlman, Kristin M.; Jung, Youngsun; Jones, Thomas A.; Gao, Jidong; Coniglio, Michael C.; Brooks, Harold E.; Brewster, Keith A.

2013-04-01

The current status and challenges associated with two aspects of Warn-on-Forecast-a National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations-are outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.
Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

In many areas of the world, landslides occur every year, claiming lives and producing severe economic and environmental damage. Many of the landslides with human or economic consequences are the result of intense or prolonged rainfall. For this reason, in many areas the timely forecast of rainfall-induced landslides is of both scientific interest and social relevance. In the recent years, there has been a mounting interest and an increasing demand for operational landslide forecasting, and for associated landslide early warning systems. Despite the relevance of the problem, and the increasing interest and demand, only a few systems have been designed, and are currently operated. Inspection of the - limited - literature on operational landslide forecasting, and on the associated early warning systems, reveals that common criteria and standards for the design, the implementation, the operation, and the evaluation of the performances of the systems, are lacking. This limits the possibility to compare and to evaluate the systems critically, to identify their inherent strengths and weaknesses, and to improve the performance of the systems. Lack of common criteria and of established standards can also limit the credibility of the systems, and consequently their usefulness and potential practical impact. Landslides are very diversified phenomena, and the information and the modelling tools used to attempt landslide forecasting vary largely, depending on the type and size of the landslides, the extent of the geographical area considered, the timeframe of the forecasts, and the scope of the predictions. Consequently, systems for landslide forecasting and early warning can be designed and implemented at several different geographical scales, from the local (site or slope specific) to the regional, or even national scale. The talk focuses on regional to national scale landslide forecasting systems, and specifically on operational systems based on empirical rainfall threshold models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.
Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.
Development of an operational African Drought Monitor prototype

NASA Astrophysics Data System (ADS)

Chaney, N.; Sheffield, J.; Wood, E. F.; Lettenmaier, D. P.

2011-12-01

Droughts have severe economic, environmental, and social impacts. However, timely detection and monitoring can minimize these effects. Based on previous drought monitoring over the continental US, a drought monitor has been developed for Africa. Monitoring drought in data sparse regions such as Africa is difficult due to a lack of historical or real-time observational data at a high spatial and temporal resolution. As a result, a land surface model is used to estimate hydrologic variables, which are used as surrogate observations for monitoring drought. The drought monitoring system consists of two stages: the first is to create long-term historical background simulations against which current conditions can be compared. The second is the real-time estimation of current hydrological conditions that results in an estimated drought index value. For the first step, a hybrid meteorological forcing dataset was created that assimilates reanalysis and observational datasets from 1950 up to real-time. Furthermore, the land surface model (currently the VIC land surface model is being used) was recalibrated against spatially disaggregated runoff fields derived from over 500 GRDC stream gauge measurements over Africa. The final result includes a retrospective database from 1950 to real-time of soil moisture, evapotranspiration, river discharge at the GRDC gauged sites (etc.) at a 1/4 degree spatial resolution, and daily temporal resolution. These observation-forced simulations are analyzed to detect and track historical drought events according to a drought index that is calculated from the soil moisture fields and river discharge relative to their seasonal climatology. The real-time monitoring requires the use of remotely sensed and weather-model analysis estimates of hydrological model forcings. For the current system, NOAA's Global Forecast System (GFS) is used along with remotely sensed precipitation from the NASA TMPA system. The historical archive of these data is evaluated against the data set used to create the background simulations. Real-time adjustments are used to preserve consistency between the historical and real-time data. The drought monitor will be presented together with the web-interface that has been developed for the scientific community to access and retrieve the data products. This system will be deployed for operational use at AGRHYMET in Niamey, Niger before the end of 2011.
Inventory of File gfs.t06z.sfluxgrbf06.grib2

Science.gov Websites

hour ave Visible Diffuse Downward Solar Flux [W/m^2] 036 surface NBDSF 0-6 hour ave Near IR Beam Downward Solar Flux [W/m^2] 037 surface NDDSF 0-6 hour ave Near IR Diffuse Downward Solar Flux [W/m^2] 038
Carbohydrate composition of mature and immature faba bean (Vicia faba L.) seeds from diverse origins

USDA-ARS?s Scientific Manuscript database

Faba bean (Vicia faba L.) is a valuable pulse crop for human consumption. The low molecular weight carbohydrates (LMWC): glucose, fructose, sucrose (GFS), raffinose, stachyose, and verbascose (RFO- raffinose family oligosaccharides) in faba bean seeds are significant components of human nutrition an...
Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.
Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): an alternate for nonstructural flood management in Bangladesh.

PubMed

Chowdhury, Rashed

2005-06-01

Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.
A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.
A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.
Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.
Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.
Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource allocation are provided.
A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial resolution appropriate to the NWP system. We then demonstrate how these warning areas could eventually complement existing global systems such as the Global Flood Awareness System (GloFAS), to give warnings of flash floods. This work demonstrates the possibility of creating a global flash flood forecasting system based on forecasts from existing global NWP systems. Future developments, in post-processing for example, will need to address an under-prediction bias, for extreme point rainfall, that is innate to current-generation global models.
Development of novel electrospun dual-drug fiber mats loaded with a combination of ampicillin and metronidazole.

PubMed

Schkarpetkin, Dennis; Reise, Markus; Wyrwa, Ralf; Völpel, Andrea; Berg, Albrecht; Schweder, Martina; Schnabelrauch, Matthias; Watts, David C; Sigusch, Bernd W

2016-08-01

Our study was performed with the aim of preparing electrospun polylactide fibers with a combination of ampicillin (AMP) and metronidazole (MNZ) and investigating their drug release behavior and the antibacterial effect on Aggregatibacter actinomycetemcomitans and other oral pathogens. AMP and MNZ were integrated as a combination in two separate fibers (dual fiber mats - DFW mix) of electrospun PLA fiber mats by means of multijet electrospinning and in a single fiber (single fiber mats - SFW mix). HPLC (high-performance liquid chromatography) was used to measure the released drug quantities. Agar diffusion tests were used to determine the antibacterial effect of the eluates on A. actinomycetemcomitans, Fusobacterium nucleatum, Porphyromonas gingivalis and Enterococcus faecalis. The neutral red test was made to examine the cytocompatibility of the eluates with human gingival fibroblasts (hGFs). The release of the active agents varied with the antibiotic concentrations initially used in the fiber mats, but also with the distribution of the active agents in one or two fibers. Of the total quantity of MNZ (AMP), the SFW mix fiber mats released >60% (>70%) within a span of 5min, and 76% (71%) after 96h. With these drug concentrations released by the fiber mats (≥5m%), an antibacterial effect was achieved on A. actinomycetemcomitans and on all other species tested. Fiber mats and their eluates have no cytotoxic influence on human gingival fibroblasts (hGFs). Electrospun AMP/MNZ-loaded polymer fibers are a potential drug delivery system for use in periodontal and endodontic infections. Copyright © 2016 The Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.
Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less
Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System

DTIC Science & Technology

2011-01-01

USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water
Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2013-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC. Distribution Statement A: Approved for Public Release; distribution is unlimited
Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2012-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC.
The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. PMID:27219341
The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.
Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

2016-04-01

Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less
The Use of an Atmospheric Model for Study the Gas Dispersion at the Brazilian Space Launching Center

NASA Astrophysics Data System (ADS)

Fisch, G.; Iriart, P. G.; Andrade Schuch, D.; Couto Milanez, V.

2015-09-01

The present work aims to use an atmospheric mesoscale model (Weather Research and Forecasting model - WRF) coupled with its chemical module (CHEM) in order to study the simulation of the dispersion of exhausted gas released from a typical rockets (in this case the Satellite Vehicle Launcher characteristics was used) from the Alcântara Launch Center (ALC). For the initialization of the coupled model, the preprocessor PREP-Chem was assigned to the Reanalysis of the TROpospheric chemical composition (RETRO). However, as this repository has no pollutants at the ALC area, a new method of insertion of chemical data assigned to the exact geographical position where the VLS is launched was used with all emissions null unless at the Launcher pad. Also, the model was initialized with meteorological data extracted from the Global Forecasting System (GFS). The simulations were made for different 4 cases representatives of the diurnal (daytime and nighttime) and seasonal (dry and wet seasons) scales. Observational data (radiosondes and wind tower data) was used to validate the wind field. There are 3 grids nested with 9, 3 and 1 km spatial resolution and the model has 45 levels in the vertical (15 levels up to 2000 m). All the simulations showed approximately the same patterns as the wind flow are very persistent (this is a characteristic of the trade winds). Typically, the simulations showed that the CO concentration (the variable used to represent the gases exhausted by the solid motors) at the launch pad is 2 order of magnitude higher than at the gate (1 km far) and 4 order of magnitude higher than Alcantara village (20 km far). It can reach 30000 ppm at the launching pad after Ho + 1 mm. Also, it was computed that the launch pad must stay isolated by 1 5 mm before any other action for the complete dispersion and, consequently, for safety reasons. As the turbulent intensity is higher at 12 UTC (daytime conditions), the total time for the complete dispersion of the plume is reduced (around 40-45 mm) related to the nighttime conditions (60-75 mm). This is an ongoing work that aims to improve this model configuration to include a vertical distribution of the exhausted gases due to the normal launching and to include small scale features at the scale of 100 m. In the near future, this model should be operational for the launchings at ALC.
Temporal and spatial variability of Fe and Mn in perched groundwater flowing through weathered argillite underlying a steep forested hillslope

NASA Astrophysics Data System (ADS)

Kim, H.; Bishop, J. K.

2013-12-01

Groundwater flowing through weathered bedrock dictates the runoff chemistry to streams in many catchments yet; its chemical evolution has been rarely documented. In particular, observations of Fe and Mn dynamics in groundwater are extremely challenging due to their high reactivity. To preserve the sample integrity for these elements we have developed a new sampling scheme that is applicable to autosamplers; a gravitational filtration system (GFS). GFS is capable of filtering samples by gravity within 30 minutes after the sampling. The GFS samples showed a good agreement with reference samples, which were collected following the standard sampling method for trace metals (i.e. immediate filtration and acidification). Since October 2011, GFS has been employed to monitor Fe and Mn in perched groundwater that moves through weathered argillite in an intensively instrumented hillslope (Rivendell), in the Angelo Coast Range Reserve. The study site is located at the headwaters of the Eel River, northern California, characterized by a typical coastal Californian Mediterranean climate. We collected groundwater samples at 3 wells along the hillslope (upslope (W10), mid-slope (W3) and near the creek (W1)) with 1-3 day intervals. Additionally, rainwater and throughfall samples were collected at a meadow near the hillslope and at the middle of the hillslope, respectively. The results from our observations indicate that Fe and Mn exhibit distinct spatial and temporal behavior under variable hydrologic conditions. The concentrations of Fe in throughfall vs. rainwater were similar (0.45μM vs. 0.49μM), but Mn in throughfall was 10-fold higher than that in rainwater (1.2 μM vs. 0.1 μM). In the early rainy season, W10's water table was deep (-18m) and Fe and Mn in W10 were 30-150 nM and 1-2 μM, respectively. As the rainy season proceeds, W10's water table rose by 4-6m, indicating the arrival of new water. At this time, Mn in W10 decreased to ~0.1 μM, synchronizing with the water table rise, and remained unchanged throughout the season. In contrast, Fe slowly declined to <10nM for this high water table regime. During the summer recession limb, Fe and Mn concentrations in W10 began to increase. During the dry summer, the concentrations of Fe and Mn at W3 were 2-3μM and 15-20 μM, respectively. At the beginning of the rainy seasons, the W3 water table slowly rose (<1 m) and both Fe and Mn decreased by 10-fold. The concentrations of Fe and Mn decreased to 20-70nM and 0.1 μM, respectively, when W3's water table became highly dynamic and fluctuated about 4 m. At W1, Fe and Mn remained in the 50-100nM and 5-10 μM ranges, respectively; however, the water table was extremely responsive to rainfall inputs. Mn in W1 was briefly diluted to <0.1 μM during large rainstorms and rebounded within several days. In the late summer of 2012, Fe and Mn in W1 increased up to 2-6 μM and 80 μM, respectively. These high-frequency observations of Fe and Mn will provide insight into the biogeochemical cycles of redox sensitive elements in upland terrains, allowing for better quantitative estimation of these elemental fluxes.
Seasonal Forecasting of Reservoir Inflow for the Segura River Basin, Spain

NASA Astrophysics Data System (ADS)

de Tomas, Alberto; Hunink, Johannes

2017-04-01

A major threat to the agricultural sector in Europe is an increasing occurrence of low water availability for irrigation, affecting the local and regional food security and economies. Especially in the Mediterranean region, such as in the Segura river basin (Spain), drought epidodes are relatively frequent. Part of the irrigation water demand in this basin is met by a water transfer from the Tagus basin (central Spain), but also in this basin an increasing pressure on the water resources has reduced the water available to be transferred. Currently, Drought Management Plans in these Spanish basins are in place and mitigate the impact of drought periods to some extent. Drought indicators that are derived from the available water in the storage reservoirs impose a set of drought mitigation measures. Decisions on water transfers are dependent on a regression-based time series forecast from the reservoir inflows of the preceding months. This user-forecast has its limitations and can potentially be improved using more advanced techniques. Nowadays, seasonal climate forecasts have shown to have increasing skill for certain areas and for certain applications. So far, such forecasts have not been evaluated in a seasonal hydrologic forecasting system in the Spanish context. The objective of this work is to develop a prototype of a Seasonal Hydrologic Forecasting System and compare this with a reference forecast. The reference forecast in this case is the locally used regression-based forecast. Additionally, hydrological simulations derived from climatological reanalysis (ERA-Interim) are taken as a reference forecast. The Spatial Processes in Hydrology model (SPHY - http://www.sphy.nl/) forced with the ECMWF- SFS4 (15 ensembles) Seasonal Forecast Systems is used to predict reservoir inflows of the upper basins of the Segura and Tagus rivers. The system is evaluated for 4 seasons with a forecasting lead time of 3 months. First results show that only for certain initialization months and lead times, the developed system outperforms the reference forecast. This research is carried out within the European research project IMPREX (www.imprex.eu) that aims at investigating the value of improving predictions of hydro-meteorological extremes in a number of water sectors, including agriculture . The next step is to integrate improved seasonal forecasts into the system and evaluate these. This should finally lead to a more robust forecasting system that allows water managers and irrigators to better anticipate to drought episodes and putting into practice more effective water allocation and mitigation practices.
The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

NASA Astrophysics Data System (ADS)

Liechti, K.; Panziera, L.; Germann, U.; Zappa, M.

2013-10-01

This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
Centralized Storm Information System (CSIS)

NASA Technical Reports Server (NTRS)

Norton, C. C.

1985-01-01

A final progress report is presented on the Centralized Storm Information System (CSIS). The primary purpose of the CSIS is to demonstrate and evaluate real time interactive computerized data collection, interpretation and display techniques as applied to severe weather forecasting. CSIS objectives pertaining to improved severe storm forecasting and warning systems are outlined. The positive impact that CSIS has had on the National Severe Storms Forecast Center (NSSFC) is discussed. The benefits of interactive processing systems on the forecasting ability of the NSSFC are described.
Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests that for the Blue Nile basin, (1) the combination of GEOS-5 and CFSv2 is equivalent in skill to the full North American Multimodel Ensemble (NMME); and (2) the seasonal water deficit forecasting system skill for both soil moisture and streamflow anomalies is greater than the standard Ensemble Streamflow Prediction (ESP) approach.
Potential for malaria seasonal forecasting in Africa

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

2014-05-01

As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.
Origin of the pre-tropical storm Debby (2006) African easterly wave-mesoscale convective system

NASA Astrophysics Data System (ADS)

Lin, Yuh-Lang; Liu, Liping; Tang, Guoqing; Spinks, James; Jones, Wilson

2013-05-01

The origins of the pre-Debby (2006) mesoscale convective system (MCS) and African easterly wave (AEW) and their precursors were traced back to the southwest Arabian Peninsula, Asir Mountains (AS), and Ethiopian Highlands (EH) in the vicinity of the ITCZ using satellite imagery, GFS analysis data and ARW model. The sources of the convective cloud clusters and vorticity perturbations were attributed to the cyclonic convergence of northeasterly Shamal wind and the Somali jet, especially when the Mediterranean High shifted toward east and the Indian Ocean high strengthened and its associated Somali jet penetrated farther to the north. The cyclonic vorticity perturbations were strengthened by the vorticity stretching associated with convective cloud clusters in the genesis region—southwest Arabian Peninsula. A conceptual model was proposed to explain the genesis of convective cloud clusters and cyclonic vorticity perturbations preceding the pre-Debby (2006) AEW-MCS system.
The value of information as applied to the Landsat Follow-on benefit-cost analysis

NASA Technical Reports Server (NTRS)

Wood, D. B.

1978-01-01

An econometric model was run to compare the current forecasting system with a hypothetical (Landsat Follow-on) space-based system. The baseline current system was a hybrid of USDA SRS domestic forecasts and the best known foreign data. The space-based system improved upon the present Landsat by the higher spatial resolution capability of the thematic mapper. This satellite system is a major improvement for foreign forecasts but no better than SRS for domestic forecasts. The benefit analysis was concentrated on the use of Landsat Follow-on to forecast world wheat production. Results showed that it was possible to quantify the value of satellite information and that there are significant benefits in more timely and accurate crop condition information.
An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.
Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.
Weather Safety - NOAA's National Weather Service

Science.gov Websites

Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Safety Weather Safety This page weather safety. StormReady NOAA Weather Radio Emergency Managers Information Network U.S. Hazard Assmt

Growth Factor Signaling and Memory Formation: Temporal and Spatial Integration of a Molecular Network

ERIC Educational Resources Information Center

Kopec, Ashley M.; Carew, Thomas J.

2013-01-01

Growth factor (GF) signaling is critically important for developmental plasticity. It also plays a crucial role in adult plasticity, such as that required for memory formation. Although different GFs interact with receptors containing distinct types of kinase domains, they typically signal through converging intracellular cascades (e.g.,…
Forecasting Influenza Epidemics in Hong Kong.

PubMed

Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y; Shaman, Jeffrey

2015-07-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.
Forecasting Influenza Epidemics in Hong Kong

PubMed Central

Yang, Wan; Cowling, Benjamin J.; Lau, Eric H. Y.; Shaman, Jeffrey

2015-01-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions. PMID:26226185
Three-Dimensional Rebar Graphene.

PubMed

Sha, Junwei; Salvatierra, Rodrigo V; Dong, Pei; Li, Yilun; Lee, Seoung-Ki; Wang, Tuo; Zhang, Chenhao; Zhang, Jibo; Ji, Yongsung; Ajayan, Pulickel M; Lou, Jun; Zhao, Naiqin; Tour, James M

2017-03-01

Free-standing robust three-dimensional (3D) rebar graphene foams (GFs) were developed by a powder metallurgy template method with multiwalled carbon nanotubes (MWCNTs) as a reinforcing bar, sintered Ni skeletons as a template and catalyst, and sucrose as a solid carbon source. As a reinforcement and bridge between different graphene sheets and carbon shells, MWCNTs improved the thermostability, storage modulus (290.1 kPa) and conductivity (21.82 S cm -1 ) of 3D GF resulting in a high porosity and structurally stable 3D rebar GF. The 3D rebar GF can support >3150× the foam's weight with no irreversible height change, and shows only a ∼25% irreversible height change after loading >8500× the foam's weight. The 3D rebar GF also shows stable performance as a highly porous electrode in lithium ion capacitors (LICs) with an energy density of 32 Wh kg -1 . After 500 cycles of testing at a high current density of 6.50 mA cm -2 , the LIC shows 78% energy density retention. These properties indicate promising applications with 3D rebar GFs in devices requiring stable mechanical and electrochemical properties.
Application of platelet-rich plasma with stem cells in bone and periodontal tissue engineering

PubMed Central

Fernandes, Gabriela; Yang, Shuying

2016-01-01

Presently, there is a high paucity of bone grafts in the United States and worldwide. Regenerating bone is of prime concern due to the current demand of bone grafts and the increasing number of diseases causing bone loss. Autogenous bone is the present gold standard of bone regeneration. However, disadvantages like donor site morbidity and its decreased availability limit its use. Even allografts and synthetic grafting materials have their own limitations. As certain specific stem cells can be directed to differentiate into an osteoblastic lineage in the presence of growth factors (GFs), it makes stem cells the ideal agents for bone regeneration. Furthermore, platelet-rich plasma (PRP), which can be easily isolated from whole blood, is often used for bone regeneration, wound healing and bone defect repair. When stem cells are combined with PRP in the presence of GFs, they are able to promote osteogenesis. This review provides in-depth knowledge regarding the use of stem cells and PRP in vitro, in vivo and their application in clinical studies in the future. PMID:28018706
Air quality modelling over the Eastern Mediterranean using the WRF/Chem model: Comparison of gas-phase chemistry and aerosol mechanisms

NASA Astrophysics Data System (ADS)

Georgiou, George K.; Christoudias, Theodoros; Proestos, Yiannis; Kushta, Jonilda; Hadjinicolaou, Panos; Lelieveld, Jos

2017-04-01

A comprehensive analysis of the performance of three coupled gas-phase chemistry and aerosol mechanisms included in the WRF/Chem model has been performed over the Eastern Mediterranean focusing on Cyprus during the CYPHEX campaign in 2014, using high temporal and spatial resolution. The model performance was evaluated by comparing calculations to measurements of gas phase species (O3, CO, NOx, SO2) and aerosols (PM10, PM2.5) from 13 ground stations. Initial results indicate that the calculated day-to-day and diurnal variations of the aforementioned species show good agreement with observations. The model was set up with three nested grids, downscaling to 4km over Cyprus. The meteorological boundary conditions were updated every 3 hours throughout the simulation using the Global Forecast System (GFS), while chemical boundary conditions were updated every 6 hours using the MOZART global chemical transport model. Biogenic emissions were calculated online by the the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). Anthropogenic emissions were based on the EDGAR HTAP v2 global emission inventory, provided on a horizontal grid resolution of 0.1o × 0.1o. Three simulations were performed employing different chemistry and aerosol mechanisms; i) RADM2 chemical mechanism and MADE/SORGAM aerosols, ii) CBMZ chemical mechanism and MOSAIC aerosols, iii) MOZART chemical mechanism and MOSAIC aerosols. Results show that the WRF/Chem model satisfactorily estimates the trace gases relative concentrations at the background sites but not at the urban and traffic sites, while some differences appear between the simulated concentrations by the three mechanisms. The resulting discrepancies between the model outcome and measurements, especially at the urban and traffic sites, suggest that a higher resolution anthropogenic emission inventory might help improve fine resolution, regional air quality modelling. Differences in the simulated concentrations by the three chemical mechanisms are attributed to the different chemical species and reaction rate constants used.
The annual amount of lightning in just 20 minutes: the October 25th, 2015 super-cell thunderstorm over central Israel

NASA Astrophysics Data System (ADS)

Yair, Y.; Ziv, B.; Lynn, B. H.; Evgeni, K.

2016-12-01

An exceptionally rare Eastern Mediterranean super-cell thunderstorm occurred during the morning hours of October 25th 2015. The storm developed within the northern tip of a Red-Sea trough (extending from Sudan to the Southeastern Mediterranean Sea) off the Egyptian coastline near Alexandria and moved north-west, crossing the Israeli coast just north of Tel-Aviv at 0900 local time. Deep convective cells developed rapidly over the sea, with thunderclouds exhibiting cloud top temperatures colder than -70°C (18 km) and radar reflectivity cores > 65 dBz at 10 km. The storms were accompanied by intensive lightning activity, severe hail, downbursts, and intense rain. The super-cell subsided upon reaching the Jordan rift in eastern Israel. The super-cell caused 1 fatality, extensive flooding and agricultural damages. It also impacted the national electrical network with power outages lasting for 3 days in central Israel. More than 17,000 cloud-to-ground lightning strokes were registered by the lightning detection system of the Israeli Electrical Corporation, exceeding the annual average for the entire country. The average cloud-to-ground flash rates between 0940-0950 and 0950-1000 (local time) were greater than 436 and 430 strokes per minute respectively, exceeding the global record flash rates found in the Argentina-Paraguay border (Zipser et al., 2006). This was the most powerful thunderstorm ever observed in Israel since lightning detection became operational in 1997. Medium-range forecast models such as ECMWF and the GFS missed the timing and severity of this unusual storm. We will present a mesoscale and microphysical analysis of this event to better understand the origins and severity of this rare super-cell. WRF high-resolution simulations with lightning assimilation (Fierro et al., 2012; Lynn et al., 2015) coupled with the Dynamic Lightning Scheme (Lynn et al., 2012) will be used in order to evaluate the performance of the WRF for accurately nowcasting such events.
Quantifying Uncertainty of Wind Power Production Through an Analog Ensemble

NASA Astrophysics Data System (ADS)

Shahriari, M.; Cervone, G.

2016-12-01

The Analog Ensemble (AnEn) method is used to generate probabilistic weather forecasts that quantify the uncertainty in power estimates at hypothetical wind farm locations. The data are from the NREL Eastern Wind Dataset that includes more than 1,300 modeled wind farms. The AnEn model uses a two-dimensional grid to estimate the probability distribution of wind speed (the predictand) given the values of predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind. The meteorological data is taken from the NCEP GFS which is available on a 0.25 degree grid resolution. The methodology first divides the data into two classes: training period and verification period. The AnEn selects a point in the verification period and searches for the best matching estimates (analogs) in the training period. The predictand value at those analogs are the ensemble prediction for the point in the verification period. The model provides a grid of wind speed values and the uncertainty (probability index) associated with each estimate. Each wind farm is associated with a probability index which quantifies the degree of difficulty to estimate wind power. Further, the uncertainty in estimation is related to other factors such as topography, land cover and wind resources. This is achieved by using a GIS system to compute the correlation between the probability index and geographical characteristics. This study has significant applications for investors in renewable energy sector especially wind farm developers. Lower level of uncertainty facilitates the process of submitting bids into day ahead and real time electricity markets. Thus, building wind farms in regions with lower levels of uncertainty will reduce the real-time operational risks and create a hedge against volatile real-time prices. Further, the links between wind estimate uncertainty and factors such as topography and wind resources, provide wind farm developers with valuable information regarding wind farm siting.
Self-Organizing Maps-based ocean currents forecasting system.

PubMed

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-03-16

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.
Self-Organizing Maps-based ocean currents forecasting system

PubMed Central

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-01-01

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129
Action-based flood forecasting for triggering humanitarian action

NASA Astrophysics Data System (ADS)

Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

2016-09-01

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.
Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

NASA Astrophysics Data System (ADS)

Ma, Feng; Ye, Aizhong; Duan, Qingyun

2017-03-01

An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.
Visualization of ocean forecast in BYTHOS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Zodiatis, G.; Nikolaidis, A.; Stylianou, S.; Karaolia, A.

2016-08-01

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center's monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System - CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.
Forecast simulation of rapidly-intensified typhoon in the Eddy-Rich Northwest Pacific region

NASA Astrophysics Data System (ADS)

Kim, Kyeong Ok; Yuk, Jin-Hee; Jung, Kyung Tae; Kuh Kang, Suk

2017-04-01

The real-time typhoon predictions in the Northwest Pacific (NWP) are being distributed by various agencies (for example, KMA, JMA, JTWC, NMC, CWB, HKO and PAGASA). Currently the movement of the typhoon can be predicted with an error of less than 100 km in 48 hours, however it is difficult to the predict of the intensity of the typhoon especially the Rapidly Intensified (RI) Typhoons. The mean occurrence of RI typhoon amounts to 5.4 times a year during 39 years (1977-2015), occupying 21% of typhoons in NWP. Especially the RI typhoon in the Eddy-Rich Northwest Pacific (ER-NWP) occurred 1.8 times a year, covering 29% of typhoons in ER-NWP. A RI typhoon, NEPARTAK (T201601), occurred in July 2016. It was formed in Caroline Islands and moved northwest, straightly heading for Taiwan. However, at the beginning stage many forecasting agencies predicts as move to the Yellow Sea. The accuracy of prediction data of the Typhoon NEPARTAK (T201601) from KMA, JMA and JTWC was compared with the adjusted best-track data from Digital-Typhoon (JMA-RSMC). The sequential prediction data are summarized with 6-hour interval from 3th to 10th July 2016.The JMA prediction of the typhoon track and the JTWC predictions of the maximum wind speed were found to be best. The numerical simulations using WRF model forced with NCEP GFS prediction data and microwave SST is compared. The simulations using one domain (D1), two domains (D2) using a moving nest scheme, and with or without the spectral nudging (-SN) are compared. Comparison of the errors on the track shows the differences of 100 km in 48-hour prediction and200 km in 72-hour prediction on average. The best results on the track prediction are shown in the D2 case of WRF model. However, underestimation of the maximum wind speed of WRF prediction still exists, obviously requiring better understanding of RI-related processes to improve the model prediction.
Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.
The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

NASA Astrophysics Data System (ADS)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events, has become evident. However, despite the demonstrated advantages, worldwide the incorporation of HEPS in operational flood forecasting is still limited. The applicability of HEPS for smaller river basins was tested in MAP D-Phase, an acronym for "Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region" which was launched in 2005 as a Forecast Demonstration Project of World Weather Research Programme of WMO, and entered a pre-operational and still active testing phase in 2007. In Europe, a comparatively high number of EPS driven systems for medium-large rivers exist. National flood forecasting centres of Sweden, Finland and the Netherlands, have already implemented HEPS in their operational forecasting chain, while in other countries including France, Germany, Czech Republic and Hungary, hybrids or experimental chains have been installed. As an example of HEPS, the European Flood Alert System (EFAS) is being presented. EFAS provides medium-range probabilistic flood forecasting information for large trans-national river basins. It incorporates multiple sets of weather forecast including different types of EPS and deterministic forecasts from different providers. EFAS products are evaluated and visualised as exceedance of critical levels only - both in forms of maps and time series. Different sources of uncertainty and its impact on the flood forecasting performance for every grid cell has been tested offline but not yet incorporated operationally into the forecasting chain for computational reasons. However, at stations where real-time discharges are available, a hydrological uncertainty processor is being applied to estimate the total predictive uncertainty from the hydrological and input uncertainties. Research on long-term EFAS results has shown the need for complementing statistical analysis with case studies for which examples will be shown.
Traffic flow forecasting for intelligent transportation systems.

DOT National Transportation Integrated Search

1995-01-01

The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accur...
Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

NASA Astrophysics Data System (ADS)

Singh, Sanjeev Kumar; Prasad, V. S.

2018-02-01

This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June-September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble-variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.
Assessment of Forecast Sensitivity to Observation and Its Application to Satellite Radiances

NASA Astrophysics Data System (ADS)

Ide, K.

2017-12-01

The Forecast sensitivity to observation provides practical and useful metric for the assessment of observation impact without conducting computationally intensive data denial experiments. Quite often complex data assimilation systems use a simplified version of the forecast sensitivity formulation based on ensembles. In this talk, we first present the comparison of forecast sensitivity for 4DVar, Hybrid-4DEnVar, and 4DEnKF with or without such simplifications using a highly nonlinear model. We then present the results of ensemble forecast sensitivity to satellite radiance observations for Hybrid-4DEnVart using NOAA's Global Forecast System.
The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

Uniformly coated highly porous graphene/MnO2 foams for flexible asymmetric supercapacitors

NASA Astrophysics Data System (ADS)

Drieschner, Simon; von Seckendorff, Maximilian; del Corro, Elena; Wohlketzetter, Jörg; Blaschke, Benno M.; Stutzmann, Martin; Garrido, Jose A.

2018-06-01

Supercapacitors are called to play a prominent role in the newly emerging markets of electric vehicles, flexible displays and sensors, and wearable electronics. In order to compete with current battery technology, supercapacitors have to be designed with highly conductive current collectors exhibiting high surface area per unit volume and uniformly coated with pseudocapacitive materials, which is crucial to boost the energy density while maintaining a high power density. Here, we present a versatile technique to prepare thickness-controlled thin-film micro graphene foams (μGFs) with pores in the lower micrometer range grown by chemical vapor deposition which can be used as highly conductive current collectors in flexible supercapacitors. To fabricate the μGF, we use porous metallic catalytic substrates consisting of nickel/copper alloy synthesized on nickel foil by electrodeposition in an electrolytic solution. Changing the duration of the electrodeposition allows the control of the thickness of the metal foam, and thus of the μGF, ranging from a few micrometers to the millimeter scale. The resulting μGF with a thickness and pores in the micrometer regime exhibits high structural quality which leads to a very low intrinsic resistance of the devices. Transferred onto flexible substrates, we demonstrate a uniform coating of the μGFs with manganese oxide, a pseudocapacitively active material. Considering the porous structure and the thickness of the μGFs, square wave potential pulses are used to ensure uniform coverage by the oxide material boosting the volumetric and areal capacitance to 14 F cm‑3 and 0.16 F cm‑2. The μGF with a thickness and pores in the micrometer regime in combination with a coating technique tuned to the porosity of the μGF is of great relevance for the development of supercapacitors based on state-of-the-art graphene foams.
Perceptual suppression revealed by adaptive multi-scale entropy analysis of local field potential in monkey visual cortex.

PubMed

Hu, Meng; Liang, Hualou

2013-04-01

Generalized flash suppression (GFS), in which a salient visual stimulus can be rendered invisible despite continuous retinal input, provides a rare opportunity to directly study the neural mechanism of visual perception. Previous work based on linear methods, such as spectral analysis, on local field potential (LFP) during GFS has shown that the LFP power at distinctive frequency bands are differentially modulated by perceptual suppression. Yet, the linear method alone may be insufficient for the full assessment of neural dynamic due to the fundamentally nonlinear nature of neural signals. In this study, we set forth to analyze the LFP data collected from multiple visual areas in V1, V2 and V4 of macaque monkeys while performing the GFS task using a nonlinear method - adaptive multi-scale entropy (AME) - to reveal the neural dynamic of perceptual suppression. In addition, we propose a new cross-entropy measure at multiple scales, namely adaptive multi-scale cross-entropy (AMCE), to assess the nonlinear functional connectivity between two cortical areas. We show that: (1) multi-scale entropy exhibits percept-related changes in all three areas, with higher entropy observed during perceptual suppression; (2) the magnitude of the perception-related entropy changes increases systematically over successive hierarchical stages (i.e. from lower areas V1 to V2, up to higher area V4); and (3) cross-entropy between any two cortical areas reveals higher degree of asynchrony or dissimilarity during perceptual suppression, indicating a decreased functional connectivity between cortical areas. These results, taken together, suggest that perceptual suppression is related to a reduced functional connectivity and increased uncertainty of neural responses, and the modulation of perceptual suppression is more effective at higher visual cortical areas. AME is demonstrated to be a useful technique in revealing the underlying dynamic of nonlinear/nonstationary neural signal.
A test harness for accelerating physics parameterization advancements into operations

NASA Astrophysics Data System (ADS)

Firl, G. J.; Bernardet, L.; Harrold, M.; Henderson, J.; Wolff, J.; Zhang, M.

2017-12-01

The process of transitioning advances in parameterization of sub-grid scale processes from initial idea to implementation is often much quicker than the transition from implementation to use in an operational setting. After all, considerable work must be undertaken by operational centers to fully test, evaluate, and implement new physics. The process is complicated by the scarcity of like-to-like comparisons, availability of HPC resources, and the ``tuning problem" whereby advances in physics schemes are difficult to properly evaluate without first undertaking the expensive and time-consuming process of tuning to other schemes within a suite. To address this process shortcoming, the Global Model TestBed (GMTB), supported by the NWS NGGPS project and undertaken by the Developmental Testbed Center, has developed a physics test harness. It implements the concept of hierarchical testing, where the same code can be tested in model configurations of varying complexity from single column models (SCM) to fully coupled, cycled global simulations. Developers and users may choose at which level of complexity to engage. Several components of the physics test harness have been implemented, including a SCM and an end-to-end workflow that expands upon the one used at NOAA/EMC to run the GFS operationally, although the testbed components will necessarily morph to coincide with changes to the operational configuration (FV3-GFS). A standard, relatively user-friendly interface known as the Interoperable Physics Driver (IPD) is available for physics developers to connect their codes. This prerequisite exercise allows access to the testbed tools and removes a technical hurdle for potential inclusion into the Common Community Physics Package (CCPP). The testbed offers users the opportunity to conduct like-to-like comparisons between the operational physics suite and new development as well as among multiple developments. GMTB staff have demonstrated use of the testbed through a comparison between the 2017 operational GFS suite and one containing the Grell-Freitas convective parameterization. An overview of the physics test harness and its early use will be presented.
Uniformly coated highly porous graphene/MnO2 foams for flexible asymmetric supercapacitors.

PubMed

Drieschner, Simon; Seckendorff, Maximilian von; Corro, Elena Del; Wohlketzetter, Jörg; Blaschke, Benno M; Stutzmann, Martin; Garrido, Jose A

2018-06-01

Supercapacitors are called to play a prominent role in the newly emerging markets of electric vehicles, flexible displays and sensors, and wearable electronics. In order to compete with current battery technology, supercapacitors have to be designed with highly conductive current collectors exhibiting high surface area per unit volume and uniformly coated with pseudocapacitive materials, which is crucial to boost the energy density while maintaining a high power density. Here, we present a versatile technique to prepare thickness-controlled thin-film micro graphene foams (μGFs) with pores in the lower micrometer range grown by chemical vapor deposition which can be used as highly conductive current collectors in flexible supercapacitors. To fabricate the μGF, we use porous metallic catalytic substrates consisting of nickel/copper alloy synthesized on nickel foil by electrodeposition in an electrolytic solution. Changing the duration of the electrodeposition allows the control of the thickness of the metal foam, and thus of the μGF, ranging from a few micrometers to the millimeter scale. The resulting μGF with a thickness and pores in the micrometer regime exhibits high structural quality which leads to a very low intrinsic resistance of the devices. Transferred onto flexible substrates, we demonstrate a uniform coating of the μGFs with manganese oxide, a pseudocapacitively active material. Considering the porous structure and the thickness of the μGFs, square wave potential pulses are used to ensure uniform coverage by the oxide material boosting the volumetric and areal capacitance to 14 F cm -3 and 0.16 F cm -2 . The μGF with a thickness and pores in the micrometer regime in combination with a coating technique tuned to the porosity of the μGF is of great relevance for the development of supercapacitors based on state-of-the-art graphene foams.
Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less
How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.
Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.
The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.
Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.
Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

USGS Publications Warehouse

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.
An Approach to Assess Observation Impact Based on Observation-Minus-Forecast Residuals

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2009-01-01

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations) ultimately providing information about the impact of individual observations on the forecast. Some features of the approach are to be noted. The typical choice of forecast aspect employed in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this presentation, an observation-space metric is used that, for a relatively time-homogeneous observing system, allows inferring observation impact on the forecast without some of the limitations above. Specifically, using observation-minus-forecast residuals leads to an approach with the following features: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints; therefore being applicable to any length of forecast. The state and observation-space approaches might be complementary to some degree, and involve different limitations and complexities. Illustrations are given using the NASA GEOS-5 data.
Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

2015-12-01

In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.
The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and weaknesses of each strategy map onto the requirements for different types of forecasting services (e.g., flash flooding, river flooding, seasonal water supply prediction).
Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

2017-11-01

Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.
Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less
VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

EPA Science Inventory

An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...
Operational seasonal forecasting of crop performance.

PubMed

Stone, Roger C; Meinke, Holger

2005-11-29

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
Operational seasonal forecasting of crop performance

PubMed Central

Stone, Roger C; Meinke, Holger

2005-01-01

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097
Satellite based Ocean Forecasting, the SOFT project

NASA Astrophysics Data System (ADS)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.
Developing Environmental Scanning/Forecasting Systems To Augment Community College Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Held, William G.

A description is provided of a conference session that was conducted to explore the structure and function of an environmental scanning/forecasting system that could be used in a community college to facilitate planning. Introductory comments argue that a college that establishes an environmental scanning and forecasting system is able to identify…

Flash-flood early warning using weather radar data: from nowcasting to forecasting

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Panziera, Luca; Germann, Urs; Zappa, Massimiliano

2013-04-01

In our study we explore the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 hours between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic forcing.
Flash-flood early warning using weather radar data: from nowcasting to forecasting

NASA Astrophysics Data System (ADS)

Liechti, K.; Panziera, L.; Germann, U.; Zappa, M.

2013-01-01

This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less
78 FR 10656 - Self-Regulatory Organizations; Municipal Securities Rulemaking Board; Notice of Filing of a...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-14

... dealers and the public and is generally consistent with the type of information currently required to be... leads to corruption. GFS stated that it would be helpful to place in the public record information... proposed rule change provide the appropriate types of information that should be disclosed to the general...
Realtime Hourly Data Table

Science.gov Websites

Tables View the latest hourly text summary CLICK ON UNDERLINED HOUR / SHADED BOX FOR THE LATEST CYCLE 00z Dump Tables View the latest rap text summary CLICK ON UNDERLINED HOUR / SHADED BOX FOR THE LATEST CYCLE Data Dump Tables View the latest model data text summary NAM GFS GDS CLICK ON UNDERLINED HOUR / SHADED
NCEP Model Verification

Science.gov Websites

daily and monthly statistics. The daily and monthly verification processing is broken down into three geopotential height and wind using daily statistics from the gdas1 prepbufr files at 00Z; 06Z; 12Z; and, 18Z Hemisphere; the Southern Hemisphere; and the Tropics. Daily S1 scores from the GFS and NAM models are
The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.
Regional early flood warning system: design and implementation

NASA Astrophysics Data System (ADS)

Chang, L. C.; Yang, S. N.; Kuo, C. L.; Wang, Y. F.

2017-12-01

This study proposes a prototype of the regional early flood inundation warning system in Tainan City, Taiwan. The AI technology is used to forecast multi-step-ahead regional flood inundation maps during storm events. The computing time is only few seconds that leads to real-time regional flood inundation forecasting. A database is built to organize data and information for building real-time forecasting models, maintaining the relations of forecasted points, and displaying forecasted results, while real-time data acquisition is another key task where the model requires immediately accessing rain gauge information to provide forecast services. All programs related database are constructed in Microsoft SQL Server by using Visual C# to extracting real-time hydrological data, managing data, storing the forecasted data and providing the information to the visual map-based display. The regional early flood inundation warning system use the up-to-date Web technologies driven by the database and real-time data acquisition to display the on-line forecasting flood inundation depths in the study area. The friendly interface includes on-line sequentially showing inundation area by Google Map, maximum inundation depth and its location, and providing KMZ file download of the results which can be watched on Google Earth. The developed system can provide all the relevant information and on-line forecast results that helps city authorities to make decisions during typhoon events and make actions to mitigate the losses.
On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.
A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.
Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
Assessing skill of a global bimonthly streamflow ensemble prediction system

NASA Astrophysics Data System (ADS)

van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

2011-12-01

Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing observations of initial catchment conditions should help to increase actual skill in future. Future work also could address the potential skill gain from using weather and climate forecasts and from a calibrated and/or alternative hydrological model or model ensemble. The approach and data might be useful as a benchmark for joint seasonal forecasting experiments planned under GEWEX.
Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present verification statistics using error-spread metrics, along with figures from operational ensemble forecasts before and after calibration.
Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

NASA Astrophysics Data System (ADS)

Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong

2016-05-01

As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.
Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling

NASA Astrophysics Data System (ADS)

Foroutan, E.; Delavar, M. R.; Araabi, B. N.

2012-07-01

Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.
pERK1/2 Peripheral Recruitment and Filopodia Protrusion Augment Oligodendrocyte Progenitor Cell Migration: Combined Effects of PDGF-A and Fibronectin.

PubMed

Tripathi, Ashutosh; Parikh, Zalak S; Vora, Parvez; Frost, Emma E; Pillai, Prakash P

2017-03-01

Oligodendrocyte progenitor cell (OPC) migration is critical for effective myelination of the central nervous system. Not only during normal myelination but also during remyelination, the growth factors (GFs) and extracellular matrix (ECM) protein affect the OPC migration. Studies showed the altered levels of GFs and ECM in the demyelinating lesions. In our earlier studies, we have shown that the effect of platelet-derived growth factor alpha (PDGF-A) on OPC migration is dose- and time-dependent. In that we have shown that the physiological concentration (1 ng/ml) of PDGF-A was unable to induce OPC migration at transient exposure (30 min). However, the involvement of ECM in the regulation of PDGF-A mediated OPC migration was not clear. In the present study, we have used fibronectin (FN) as ECM. PDGF-A and FN have similar and overlapping intracellular signaling pathways including the extracellular regulated kinases 1 and 2 (ERK1/2). Here we demonstrate how physiological concentration of PDGF-A combines with FN to augment OPC migration in vitro. The present study is first of its kind to show the importance of the synergistic effects of PDGF-A and FN on peripheral recruitment of phosphorylated/activated ERK1/2 (pERK1/2), actin-pERK1/2 co-localization, and filopodia formation, which are essential for the enhanced OPC migration. These findings were further confirmed by ERK1/2 inhibition studies, using the pharmacological inhibitor U0126. An understanding of these complex interactions may lead to additional strategies for transplanting genetically modified OPCs to repair widespread demyelinated lesions.
Combinatorial Analysis of Growth Factors Reveals the Contribution of Bone Morphogenetic Proteins to Chondrogenic Differentiation of Human Periosteal Cells.

PubMed

Mendes, Luis Filipe; Tam, Wai Long; Chai, Yoke Chin; Geris, Liesbet; Luyten, Frank P; Roberts, Scott J

2016-05-01

Successful application of cell-based strategies in cartilage and bone tissue engineering has been hampered by the lack of robust protocols to efficiently differentiate mesenchymal stem cells into the chondrogenic lineage. The development of chemically defined culture media supplemented with growth factors (GFs) has been proposed as a way to overcome this limitation. In this work, we applied a fractional design of experiment (DoE) strategy to screen the effect of multiple GFs (BMP2, BMP6, GDF5, TGF-β1, and FGF2) on chondrogenic differentiation of human periosteum-derived mesenchymal stem cells (hPDCs) in vitro. In a micromass culture (μMass) system, BMP2 had a positive effect on glycosaminoglycan deposition at day 7 (p < 0.001), which in combination with BMP6 synergistically enhanced cartilage-like tissue formation that displayed in vitro mineralization capacity at day 14 (p < 0.001). Gene expression of μMasses cultured for 7 days with a medium formulation supplemented with 100 ng/mL of BMP2 and BMP6 and a low concentration of GDF5, TGF-β1, and FGF2 showed increased expression of Sox9 (1.7-fold) and the matrix molecules aggrecan (7-fold increase) and COL2A1 (40-fold increase) compared to nonstimulated control μMasses. The DoE analysis indicated that in GF combinations, BMP2 was the strongest effector for chondrogenic differentiation of hPDCs. When transplanted ectopically in nude mice, the in vitro-differentiated μMasses showed maintenance of the cartilaginous phenotype after 4 weeks in vivo. This study indicates the power of using the DoE approach for the creation of new medium formulations for skeletal tissue engineering approaches.
WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.
Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

2009-01-01

An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.
How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

2015-03-01

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.; Ranjithan, R. S.; Brill, E. D.

2014-08-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.
Improved Regional Water Management Utilizing Climate Forecasts: An Inter-basin Transfer Model with a Risk Management Framework

NASA Astrophysics Data System (ADS)

Li, W.; Arumugam, S.; Ranjithan, R. S.; Brill, E. D., Jr.

2014-12-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study presents a framework for regional water management by proposing an Inter-Basin Transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end- of-season target storage across the participating reservoirs. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle area. Results show that inter-basin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) Inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no- transfer scenario as well as under transfers obtained with climatology; (b) Spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting inter-basin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating reservoirs in the regional water supply system.
Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System

NASA Astrophysics Data System (ADS)

Dumbović, Mateja; Srivastava, Nandita; Rao, Yamini K.; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

2017-08-01

Under the European Union 7th Framework Programme (EU FP7) project Coronal Mass Ejections and Solar Energetic Particles (COMESEP, http://comesep.aeronomy.be), an automated space weather alert system has been developed to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. The COMESEP alert system uses the automated detection tool called Computer Aided CME Tracking (CACTus) to detect potentially threatening CMEs, a drag-based model (DBM) to predict their arrival, and a CME geoeffectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, the DBM calculates its arrival time at Earth and the CGFT calculates its geomagnetic risk level. The geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geoeffectiveness, as well as an estimate of the geomagnetic storm duration. We present the evaluation of the CME risk level forecast with the COMESEP alert system based on a study of geoeffective CMEs observed during 2014. The validation of the forecast tool is made by comparing the forecasts with observations. In addition, we test the success rate of the automatic forecasts (without human intervention) against the forecasts with human intervention using advanced versions of the DBM and CGFT (independent tools available at the Hvar Observatory website, http://oh.geof.unizg.hr). The results indicate that the success rate of the forecast in its current form is unacceptably low for a realistic operation system. Human intervention improves the forecast, but the false-alarm rate remains unacceptably high. We discuss these results and their implications for possible improvement of the COMESEP alert system.
An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.
Forecasting the short-term passenger flow on high-speed railway with neural networks.

PubMed

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.
NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

2016-12-01

Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.
Forecasting the spatial transmission of influenza in the United States.

PubMed

Pei, Sen; Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2018-03-13

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.
Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

DTIC Science & Technology

2014-04-01

WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch
An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.
Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

2018-04-01

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.
The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.
An investigation into incident duration forecasting for FleetForward

DOT National Transportation Integrated Search

2000-08-01

Traffic condition forecasting is the process of estimating future traffic conditions based on current and archived data. Real-time forecasting is becoming an important tool in Intelligent Transportation Systems (ITS). This type of forecasting allows ...
Satellite provided fixed communications services: A forecast of potential domestic demand through the year 2000: Volume 2: Main text

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Potential satellite-provided fixed communications services, baseline forecasts, net long haul forecasts, cost analysis, net addressable forecasts, capacity requirements, and satellite system market development are considered.
Scientific assessment of accuracy, skill and reliability of ocean probabilistic forecast products.

NASA Astrophysics Data System (ADS)

Wei, M.; Rowley, C. D.; Barron, C. N.; Hogan, P. J.

2016-02-01

As ocean operational centers are increasingly adopting and generating probabilistic forecast products for their customers with valuable forecast uncertainties, how to assess and measure these complicated probabilistic forecast products objectively is challenging. The first challenge is how to deal with the huge amount of the data from the ensemble forecasts. The second one is how to describe the scientific quality of probabilistic products. In fact, probabilistic forecast accuracy, skills, reliability, resolutions are different attributes of a forecast system. We briefly introduce some of the fundamental metrics such as the Reliability Diagram, Reliability, Resolution, Brier Score (BS), Brier Skill Score (BSS), Ranked Probability Score (RPS), Ranked Probability Skill Score (RPSS), Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS). The values and significance of these metrics are demonstrated for the forecasts from the US Navy's regional ensemble system with different ensemble members. The advantages and differences of these metrics are studied and clarified.
Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less
A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Zhang, Xinmin; Li, Yuan

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, formore » both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.« less
The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers

NASA Astrophysics Data System (ADS)

Foster, Kean; Bertacchi Uvo, Cintia; Olsson, Jonas

2018-05-01

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981-2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ˜ 6 % across all sub-basins and forecast dates.
Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.
Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.
Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

ENSO Prediction in the NASA GMAO GEOS-5 Seasonal Forecasting System

NASA Astrophysics Data System (ADS)

Kovach, R. M.; Borovikov, A.; Marshak, J.; Pawson, S.; Vernieres, G.

2016-12-01

Seasonal-to-Interannual coupled forecasts are conducted in near-real time with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model (AOGCM). A 30-year suite of 9-month hindcasts is available, initialized with the MERRA-Ocean, MERRA-Land, and MERRA atmospheric fields. These forecasts are used to predict the timing and magnitude of ENSO and other short-term climate variability. The 2015 El Niño peaked in November 2015 and was considered a "very strong" event with the Equatorial Pacific Ocean sea-surface-temperature (SST) anomalies higher than 2.0 °C. These very strong temperature anomalies began in Sep/Oct/Nov (SON) of 2015 and persisted through Dec/Jan/Feb (DJF) of 2016. The other two very strong El Niño events recently recorded occurred in 1981/82 and 1997/98. The GEOS-5 system began predicting a very strong El Niño for SON starting with the March 2015 forecast. At this time, the GMAO forecast was an outlier in both the NMME and IRI multi-model ensemble prediction plumes. The GMAO May 2015 forecast for the November 2015 peak in temperature anomaly in the Niño3.4 region was in excellent agreement with the real event, but in May this forecast was still one of the outliers in the multi-model forecasts. The GEOS-5 May 2015 forecast also correctly predicted the weakening of the Eastern Pacific (Niño1+2) anomalies for SON. We will present a summary of the NASA GMAO GEOS-5 Seasonal Forecast System skills based on historic hindcasts. Initial conditions, prediction of ocean surface and subsurface evolution for the 2015/16 El Niño will be compared to the 1998/97 event. GEOS-5 capability to predict the precipitation, i.e. to model the teleconnection patterns associated with El Niño will also be shown. To conclude, we will highlight some new developments in the GEOS forecasting system.
Against all odds -- Probabilistic forecasts and decision making

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Zappa, Massimiliano

2015-04-01

In the city of Zurich (Switzerland) the setting is such that the damage potential due to flooding of the river Sihl is estimated to about 5 billion US dollars. The flood forecasting system that is used by the administration for decision making runs continuously since 2007. It has a time horizon of max. five days and operates at hourly time steps. The flood forecasting system includes three different model chains. Two of those are run by the deterministic NWP models COSMO-2 and COSMO-7 and one is driven by the probabilistic NWP COSMO-Leps. The model chains are consistent since February 2010, so five full years are available for the evaluation for the system. The system was evaluated continuously and is a very nice example to present the added value that lies in probabilistic forecasts. The forecasts are available on an online-platform to the decision makers. Several graphical representations of the forecasts and forecast-history are available to support decision making and to rate the current situation. The communication between forecasters and decision-makers is quite close. To put it short, an ideal situation. However, an event or better put a non-event in summer 2014 showed that the knowledge about the general superiority of probabilistic forecasts doesn't necessarily mean that the decisions taken in a specific situation will be based on that probabilistic forecast. Some years of experience allow gaining confidence in the system, both for the forecasters and for the decision-makers. Even if from the theoretical point of view the handling during crisis situation is well designed, a first event demonstrated that the dialog with the decision-makers still lacks of exercise during such situations. We argue, that a false alarm is a needed experience to consolidate real-time emergency procedures relying on ensemble predictions. A missed event would probably also fit, but, in our case, we are very happy not to report about this option.
Evaluation of Clear-Air Turbulence Diagnostics: GTG in Korea

NASA Astrophysics Data System (ADS)

Kim, J.-H.; Chun, H.-Y.; Jang, W.; Sharman, R. D.

2009-04-01

Turbulence forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (Sharman et al., 2006), is evaluated with available turbulence observations (e.g. pilot reports; PIREPs) reported in South Korea during the recent 4 years (2003-2007). Clear-air turbulence (CAT) is extracted from PIREPs by using cloud-to-ground lightning flash data from Korean Meteorological Administration (KMA). The GTG system includes several steps. First, 45 turbulence indices are calculated in the East Asian region near Korean peninsula using the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA. Second, 10 CAT indices that performed ten best forecasting score are selected. The scoring method is based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. Various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs in South Korea. Performance of GTG is more consistent and stable than that of any individual diagnostic in each year and season. In addition, current-year forecasting based on yearly PIREPs is better than adjacent-year forecasting and year-after-year forecasting. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to jet stream, and turbulence associated with the jet can be most activated in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than in wintertime. To acquire better performance for summertime forecasting, it is likely to develop more turbulence indices related to, for example, convections. By sensitivity test to the number of combined indices, it is found that yearly and seasonal GTG is the best when about 7 CAT indices are combined.
Some economic benefits of a synchronous earth observatory satellite

NASA Technical Reports Server (NTRS)

Battacharyya, R. K.; Greenberg, J. S.; Lowe, D. S.; Sattinger, I. J.

1974-01-01

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.
Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
Optimising seasonal streamflow forecast lead time for operational decision making in Australia

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

2016-10-01

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.
Real-time Social Internet Data to Guide Forecasting Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Valle, Sara Y.

Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematicalmore » approaches and heterogeneous data streams.« less
Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.
Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.
Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.
A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.
Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.
Enhanced seasonal forecast skill following stratospheric sudden warmings

NASA Astrophysics Data System (ADS)

Sigmond, M.; Scinocca, J. F.; Kharin, V. V.; Shepherd, T. G.

2013-02-01

Advances in seasonal forecasting have brought widespread socio-economic benefits. However, seasonal forecast skill in the extratropics is relatively modest, prompting the seasonal forecasting community to search for additional sources of predictability. For over a decade it has been suggested that knowledge of the state of the stratosphere can act as a source of enhanced seasonal predictability; long-lived circulation anomalies in the lower stratosphere that follow stratospheric sudden warmings are associated with circulation anomalies in the troposphere that can last up to two months. Here, we show by performing retrospective ensemble model forecasts that such enhanced predictability can be realized in a dynamical seasonal forecast system with a good representation of the stratosphere. When initialized at the onset date of stratospheric sudden warmings, the model forecasts faithfully reproduce the observed mean tropospheric conditions in the months following the stratospheric sudden warmings. Compared with an equivalent set of forecasts that are not initialized during stratospheric sudden warmings, we document enhanced forecast skill for atmospheric circulation patterns, surface temperatures over northern Russia and eastern Canada and North Atlantic precipitation. We suggest that seasonal forecast systems initialized during stratospheric sudden warmings are likely to yield significantly greater forecast skill in some regions.
Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.
A PERFORMANCE EVALUATION OF THE ETA- CMAQ AIR QUALITY FORECAST SYSTEM FOR THE SUMMER OF 2005

EPA Science Inventory

This poster presents an evaluation of the Eta-CMAQ Air Quality Forecast System's experimental domain using O₃ observations obtained from EPA's AIRNOW program and a suite of statistical metrics examining both discrete and categorical forecasts.
System load forecasts for an electric utility. [Hourly loads using Box-Jenkins method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Uri, N.D.

This paper discusses forecasting hourly system load for an electric utility using Box-Jenkins time-series analysis. The results indicate that a model based on the method of Box and Jenkins, given its simplicity, gives excellent results over the forecast horizon.
A Decision Support System for effective use of probability forecasts

NASA Astrophysics Data System (ADS)

De Kleermaeker, Simone; Verkade, Jan

2013-04-01

Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.
Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

PubMed

Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

2017-03-15

As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.
Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less
Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted
A report from the Space Science and Engineering Center, the University of Wisconsin-Madison, Madison, Wisconsin

NASA Technical Reports Server (NTRS)

1985-01-01

Operational forecasters have habitually been plagued with the problems associated with acquisition, display, and dissemination of data used in preparing forecasts. The centralized storm information system (CSIS) experiment provided an operational forecaster with an interactive computer system which could perform these preliminary tasks more quickly and accurately than any human could. CSIS objectives pertaining to improved severe storms forecasting and warning procedures are addressed.
Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Florita, A.; Orwig, K.

2012-07-01

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent Systemmore » Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.« less
NOMADS-NOAA Operational Model Archive and Distribution System

Science.gov Websites

Forecast Maps Climate Climate Prediction Climate Archives Weather Safety Storm Ready NOAA Central Library (16km) 6 hours grib filter http OpenDAP-alt URMA hourly - http - Climate Models Climate Forecast System Flux Products 6 hours grib filter http - Climate Forecast System 3D Pressure Products 6 hours grib
Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less
Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

NASA Astrophysics Data System (ADS)

Hou, Tuanjie; Kong, Fanyou; Chen, Xunlai; Lei, Hengchi; Hu, Zhaoxia

2015-07-01

To improve the accuracy of short-term (0-12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.
Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).
An expert system-based approach to prediction of year-to-year climatic variations in the North Atlantic region

NASA Astrophysics Data System (ADS)

Rodionov, S. N.; Martin, J. H.

1999-07-01

A novel approach to climate forecasting on an interannual time scale is described. The approach is based on concepts and techniques from artificial intelligence and expert systems. The suitability of this approach to climate diagnostics and forecasting problems and its advantages compared with conventional forecasting techniques are discussed. The article highlights some practical aspects of the development of climatic expert systems (CESs) and describes an implementation of such a system for the North Atlantic (CESNA). Particular attention is paid to the content of CESNA's knowledge base and those conditions that make climatic forecasts one to several years in advance possible. A detailed evaluation of the quality of the experimental real-time forecasts made by CESNA for the winters of 1995-1996, 1996-1997 and 1997-1998 are presented.
Stochastic Forcing for High-Resolution Regional and Global Ocean and Atmosphere-Ocean Coupled Ensemble Forecast System

NASA Astrophysics Data System (ADS)

Rowley, C. D.; Hogan, P. J.; Martin, P.; Thoppil, P.; Wei, M.

2017-12-01

An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC), and a global ocean ensemble generation capability to represent uncertainty in the ocean initial conditions has been developed. At extended forecast times, the uncertainty due to the model error overtakes the initial condition as the primary source of forecast uncertainty. Recently, stochastic parameterization or stochastic forcing techniques have been applied to represent the model error in research and operational atmospheric, ocean, and coupled ensemble forecasts. A simple stochastic forcing technique has been developed for application to US Navy high resolution regional and global ocean models, for use in ocean-only and coupled atmosphere-ocean-ice-wave ensemble forecast systems. Perturbation forcing is added to the tendency equations for state variables, with the forcing defined by random 3- or 4-dimensional fields with horizontal, vertical, and temporal correlations specified to characterize different possible kinds of error. Here, we demonstrate the stochastic forcing in regional and global ensemble forecasts with varying perturbation amplitudes and length and time scales, and assess the change in ensemble skill measured by a range of deterministic and probabilistic metrics.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less
Alternative Approaches to Land Initialization for Seasonal Precipitation and Temperature Forecasts

NASA Technical Reports Server (NTRS)

Koster, Randal; Suarez, Max; Liu, Ping; Jambor, Urszula

2004-01-01

The seasonal prediction system of the NASA Global Modeling and Assimilation Office is used to generate ensembles of summer forecasts utilizing realistic soil moisture initialization. To derive the realistic land states, we drive offline the system's land model with realistic meteorological forcing over the period 1979-1993 (in cooperation with the Global Land Data Assimilation System project at GSFC) and then extract the state variables' values on the chosen forecast start dates. A parallel series of forecast ensembles is performed with a random (though climatologically consistent) set of land initial conditions; by comparing the two sets of ensembles, we can isolate the impact of land initialization on forecast skill from that of the imposed SSTs. The base initialization experiment is supplemented with several forecast ensembles that use alternative initialization techniques. One ensemble addresses the impact of minimizing climate drift in the system through the scaling of the initial conditions, and another is designed to isolate the importance of the precipitation signal from that of all other signals in the antecedent offline forcing. A third ensemble includes a more realistic initialization of the atmosphere along with the land initialization. The impact of each variation on forecast skill is quantified.
On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559
Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.
Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)
Atmospheric Transport Modelling assessing radionuclide detection chances after the nuclear test announced by the DPRK in January 2016

NASA Astrophysics Data System (ADS)

Ross, J. Ole; Ceranna, Lars

2016-04-01

The Comprehensive Nuclear-Test-Ban Treaty (CTBT) prohibits all kinds of nuclear explosions. The International Monitoring System (IMS) is in place and at about 90% complete to verify compliance with the CTBT. The stations of the waveform technologies are capable to detect seismic, hydro-acoustic and infrasonic signals for detection, localization, and characterization of explosions. The seismic signals of the DPRK event on 6 January 2016 were detected by many seismic stations around the globe and allow for localization of the event and identification as explosion (see poster by G. Hartmann et al.). However, the direct evidence for a nuclear explosion is only possible through the detection of nuclear fission products which may be released. For that 80 Radionuclide (RN) Stations are part of the designed IMS, about 60 are already operational. All RN stations are highly sensitive for tiny traces of particulate radionuclides in large volume air samplers. There are 40 of the RN stations designated to be equipped with noble gas systems detecting traces of radioactive xenon isotopes which are more likely to escape from an underground test cavity than particulates. Already 30 of the noble gas systems are operational. Atmospheric Transport Modelling supports the interpretation of radionuclide detections (and as appropriate non-detections) by connecting the activity concentration measurements with potential source locations and release times. In our study forecasts with the Lagrangian Particle Dispersion Model HYSPLIT (NOAA) and GFS (NCEP) meteorological data are considered to assess the plume propagation patterns for hypothetical releases at the known DPRK nuclear test site. The results show a considerable sensitivity of the IMS station RN 38 Takasaki (Japan) to a potential radionuclide release at the test site in the days and weeks following the explosion in January 2016. In addition, backtracking simulations with ECMWF analysis data in 0.2° horizontal resolution are performed for selected samples to get a complementary estimation of the sensitivities and the connected thresholds for detectable releases.The meteorological situation is compared to the aftermath of the nuclear explosion on 12 February 2013 after which a specific occurrence of an unusual 131mXe signature at RN 38 eight weeks after the test could be very likely attributed to a late release from the DPRK event.
Sensitivity of Satellite-Based Skin Temperature to Different Surface Emissivity and NWP Reanalysis Sources Demonstrated Using a Single-Channel, Viewing-Angle-Corrected Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Scarino, B. R.; Minnis, P.; Yost, C. R.; Chee, T.; Palikonda, R.

2015-12-01

Single-channel algorithms for satellite thermal-infrared- (TIR-) derived land and sea surface skin temperature (LST and SST) are advantageous in that they can be easily applied to a variety of satellite sensors. They can also accommodate decade-spanning instrument series, particularly for periods when split-window capabilities are not available. However, the benefit of one unified retrieval methodology for all sensors comes at the cost of critical sensitivity to surface emissivity (ɛs) and atmospheric transmittance estimation. It has been demonstrated that as little as 0.01 variance in ɛs can amount to more than a 0.5-K adjustment in retrieved LST values. Atmospheric transmittance requires calculations that employ vertical profiles of temperature and humidity from numerical weather prediction (NWP) models. Selection of a given NWP model can significantly affect LST and SST agreement relative to their respective validation sources. Thus, it is necessary to understand the accuracies of the retrievals for various NWP models to ensure the best LST/SST retrievals. The sensitivities of the single-channel retrievals to surface emittance and NWP profiles are investigated using NASA Langley historic land and ocean clear-sky skin temperature (Ts) values derived from high-resolution 11-μm TIR brightness temperature measured from geostationary satellites (GEOSat) and Advanced Very High Resolution Radiometers (AVHRR). It is shown that mean GEOSat-derived, anisotropy-corrected LST can vary by up to ±0.8 K depending on whether CERES or MODIS ɛs sources are used. Furthermore, the use of either NOAA Global Forecast System (GFS) or NASA Goddard Modern-Era Retrospective Analysis for Research and Applications (MERRA) for the radiative transfer model initial atmospheric state can account for more than 0.5-K variation in mean Ts. The results are compared to measurements from the Surface Radiation Budget Network (SURFRAD), an Atmospheric Radiation Measurement (ARM) Program ground station, and NOAA ESRL high-resolution Optimum Interpolation SST (OISST). Precise understanding of the influence these auxiliary inputs have on final satellite-based Ts retrievals may help guide refinement in ɛs characterization and NWP development, e.g., future Goddard Earth Observing System Data Assimilation System versions.
Wave ensemble forecast system for tropical cyclones in the Australian region

NASA Astrophysics Data System (ADS)

Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

2018-05-01

Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.
Worldwide satellite market demand forecast

NASA Technical Reports Server (NTRS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.
Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

Worldwide satellite market demand forecast

NASA Astrophysics Data System (ADS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-06-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.
Decision Support on the Sediments Flushing of Aimorés Dam Using Medium-Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Collischonn, Walter; Assis dos Reis, Alberto; Alvarado Montero, Rodolfo; Alencar Siqueira, Vinicius

2015-04-01

In the present study we investigate the use of medium-range streamflow forecasts in the Doce River basin (Brazil), at the reservoir of Aimorés Hydro Power Plant (HPP). During daily operations this reservoir acts as a "trap" to the sediments that originate from the upstream basin of the Doce River. This motivates a cleaning process called "pass through" to periodically remove the sediments from the reservoir. The "pass through" or "sediments flushing" process consists of a decrease of the reservoir's water level to a certain flushing level when a determined reservoir inflow threshold is forecasted. Then, the water in the approaching inflow is used to flush the sediments from the reservoir through the spillway and to recover the original reservoir storage. To be triggered, the sediments flushing operation requires an inflow larger than 3000m³/s in a forecast horizon of 7 days. This lead-time of 7 days is far beyond the basin's concentration time (around 2 days), meaning that the forecasts for the pass through procedure highly depends on Numerical Weather Predictions (NWP) models that generate Quantitative Precipitation Forecasts (QPF). This dependency creates an environment with a high amount of uncertainty to the operator. To support the decision making at Aimorés HPP we developed a fully operational hydrological forecasting system to the basin. The system is capable of generating ensemble streamflow forecasts scenarios when driven by QPF data from meteorological Ensemble Prediction Systems (EPS). This approach allows accounting for uncertainties in the NWP at a decision making level. This system is starting to be used operationally by CEMIG and is the one shown in the present study, including a hindcasting analysis to assess the performance of the system for the specific flushing problem. The QPF data used in the hindcasting study was derived from the TIGGE (THORPEX Interactive Grand Global Ensemble) database. Among all EPS available on TIGGE, three were selected: ECMWF, GEFS, and CPTEC. As a deterministic reference forecast, we adopt the high resolution ECMWF forecast for comparison. The experiment consisted on running retrospective forecasts for a full five-year period. To verify the proposed objectives of the study, we use different metrics to evaluate the forecast: ROC Curves, Exceedance Diagrams, Forecast Convergence Score (FCS). Metrics results enabled to understand the benefits of the hydrological ensemble prediction system as a decision making tool for the HPP operation. The ROC scores indicate that the use of the lower percentiles of the ensemble scenarios issues for a true alarm rate around 0,5 to 0,8 (depending on the model and on the percentile), for the lead time of seven days. While the false alarm rate is between 0 and 0,3. Those rates were better than the ones resulting from the deterministic reference forecast. Exceedance diagrams and forecast convergence scores indicate that the ensemble scenarios provide an early signal about the threshold crossing. Furthermore, the ensemble forecasts are more consistent between two subsequent forecasts in comparison to the deterministic forecast. The assessments results also give more credibility to CEMIG in the realization and communication of flushing operation with the stakeholders involved.
Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852
A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...
A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less
Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.
77 FR 9298 - Office of Hazardous Materials Safety; Actions on Special Permit Applications

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-16

... follows: 1--Motor vehicle, 2--Rail freight, 3--Cargo vessel, 4--Cargo aircraft only, 5--Passenger-carrying... permit to Bethlehem, PA. , (b)(3) and authorize (b)(4); ultrasonic testing 180.205(c) and of DOT-SP 9001... every 10 years. (modes 1, 2, 3, 4, 5) 15257-N......... GFS Chemicals 49 CFR To authorize the Columbus...
The GFS Atmospheric Model description

Science.gov Websites

model has only one type of cloud cover represented by C. In the tropics the cloudiness is primarily due mainly through grid-scale condensation. The fractional cloud cover C is available at all model levels , 1996: Parameterizations for the absorption of solar radiation by water vapor and ozone. J. Atmos. Sci
Inventory of File gfs.t06z.sfluxgrbf00.grib2

Science.gov Websites

Volumetric Soil Moisture Content [Fraction] 007 0.1-0.4 m below ground SOILW analysis Volumetric Soil Volumetric Soil Moisture Content [Fraction] 068 1-2 m below ground SOILW analysis Volumetric Soil Moisture analysis Temperature [K] 071 0-0.1 m below ground SOILL analysis Liquid Volumetric Soil Moisture (non
In vitro analyses of the anti-fibrotic effect of SPARC silencing in human Tenon’s fibroblasts: comparisons with mitomycin C

PubMed Central

Seet, Li-Fong; Su, Roseline; Toh, Li Zhen; Wong, Tina T

2012-01-01

Abstract Failure of glaucoma filtration surgery (GFS) is commonly attributed to scarring at the surgical site. The human Tenon’s fibroblasts (HTFs) are considered the major cell type contributing to the fibrotic response. We previously showed that SPARC (secreted protein, acidic, rich in cysteine) knockout mice had improved surgical success in a murine model of GFS. To understand the mechanisms of SPARC deficiency in delaying subconjunctival fibrosis, we used the gene silencing approach to reduce SPARC expression in HTFs and examined parameters important for wound repair and fibrosis. Mitomycin C-treated HTFs were used for comparison. We demonstrate that SPARC-silenced HTFs showed normal proliferation and negligible cellular necrosis but were impaired in motility and collagen gel contraction. The expression of pro-fibrotic genes including collagen I, MMP-2, MMP-9, MMP-14, IL-8, MCP-1 and TGF-β2 were also reduced. Importantly, TGF-β2 failed to induce significant collagen I and fibronectin expressions in the SPARC-silenced HTFs. Together, these data demonstrate that SPARC knockdown in HTFs modulates fibroblast functions important for wound fibrosis and is therefore a promising strategy in the development of anti-scarring therapeutics. PMID:21801304
The effect of surface modification of glass fiber on the performance of poly(lactic acid) composites: Graphene oxide vs. silane coupling agents

NASA Astrophysics Data System (ADS)

Jing, Mengfan; Che, Junjin; Xu, Shuman; Liu, Zhenwei; Fu, Qiang

2018-03-01

In this work, a comparison study was carried out to investigate the efficacy of glass fiber (GF) in reinforcing poly(lactic acid) (PLA) by using traditional silane coupling agents (GF-S) and novel graphene oxide (GF-GO) as surface modifiers. The crystallization behavior of the PLA matrix was investigated by differential scanning calorimetry. The mechanical performances and the thermomechanical properties of the composites were evaluated by uniaxial tensile testing and dynamic mechanical analysis, respectively. For neat GF without any treatment, the poor interfacial adhesion and the sharp shortening of the GF length result in the relatively poor mechanical performances of PLA/GF composites. However, the incorporation of GF-S significantly improves the mechanical strength and keeps relatively good toughness of the composites, while GF-GO exhibits excellent nucleation ability for PLA and could moderately increase the modulus of the composites. The thermomechanical properties of the composites are improved markedly resulting from the crystallinity increase. The different surface modification of glass fiber influences the crystallinity of matrix, the interfacial interaction and the length of fiber, which altogether affect the mechanical performances of the prepared PLA/GF composites.
Antioxidant lactobacilli could protect gingival fibroblasts against hydrogen peroxide: a preliminary in vitro study.

PubMed

Mendi, Ayşegül; Aslım, Belma

2014-12-01

Oxidative stress and tissue destruction are at the heart of periodontal diseases. The dental research area is geared toward the prevention of free radicals by nutrient antioxidants. Lactic acid bacteria (LAB) have recently attracted attention in alternative dental therapies. We aimed at highlighting the antioxidative property of Lactobacilli and Bifidobacterium strains and at determining their protective effect on gingival fibroblasts (GFs). Two Lactobacilli and 2 Bifidobacterium strains were screened for their exopolysaccharide (EPSs) production. Antioxidative assays were conducted by spectrophotometer analysis. Resistance to different concentrations of hydrogen peroxide (H2O2) was determined by the serial dilution technique. The protective effect of strains on GFs on hydrogen peroxide exposure was also examined by a new trypan blue exclusion assay method. Bifidobacterium breve A28 showed the highest EPS production (122 mg/l) and remarkable antioxidant activity, which were demonstrated by its ability to scavenge 72% α,α-diphenyl-1-picrylhydrazyl free radical and chelate 88% of iron ion, respectively. Inhibition of lipid peroxidation was determined as 71% for the A28 strain. We suggest that LAB with antioxidative activity could be a good natural therapy agent for periodontal disorders.
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.
Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.
A Scheme for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

DTIC Science & Technology

1984-07-01

D.A. Lowry, 1978: Use of a real - time computer graphics system for diagnosis and forecasting . Preprints, Conf. on Wes. Forecasting and Analysis and...28 Figure 4.2.1. Graph for forecasting the night minimum temperature from observations at 1800-2000 local time . From Zverev (1972...3u 1. 2 much weather is produced by organized systems that translate, and forecast gains were made through use of the concepts of steering
An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.
iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system simulations will help to develop a seamless integration with the boundary systems in the service-gap area with new insights into our scientific understanding of such complex systems. A visualization system is being developed to allow stake holders and the community to have access to the flood forecasting for their region with sufficient lead time.
Operational Earthquake Forecasting of Aftershocks for New England

NASA Astrophysics Data System (ADS)

Ebel, J.; Fadugba, O. I.

2015-12-01

Although the forecasting of mainshocks is not possible, recent research demonstrates that probabilistic forecasts of expected aftershock activity following moderate and strong earthquakes is possible. Previous work has shown that aftershock sequences in intraplate regions behave similarly to those in California, and thus the operational aftershocks forecasting methods that are currently employed in California can be adopted for use in areas of the eastern U.S. such as New England. In our application, immediately after a felt earthquake in New England, a forecast of expected aftershock activity for the next 7 days will be generated based on a generic aftershock activity model. Approximately 24 hours after the mainshock, the parameters of the aftershock model will be updated using the observed aftershock activity observed to that point in time, and a new forecast of expected aftershock activity for the next 7 days will be issued. The forecast will estimate the average number of weak, felt aftershocks and the average expected number of aftershocks based on the aftershock statistics of past New England earthquakes. The forecast also will estimate the probability that an earthquake that is stronger than the mainshock will take place during the next 7 days. The aftershock forecast will specify the expected aftershocks locations as well as the areas over which aftershocks of different magnitudes could be felt. The system will use web pages, email and text messages to distribute the aftershock forecasts. For protracted aftershock sequences, new forecasts will be issued on a regular basis, such as weekly. Initially, the distribution system of the aftershock forecasts will be limited, but later it will be expanded as experience with and confidence in the system grows.
The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less
Constraints on Rational Model Weighting, Blending and Selecting when Constructing Probability Forecasts given Multiple Models

NASA Astrophysics Data System (ADS)

Higgins, S. M. W.; Du, H. L.; Smith, L. A.

2012-04-01

Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.

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