Assimilation of TOPEX/Poseidon altimeter data into a global ocean circulation model: How good are the results?

NASA Astrophysics Data System (ADS)

Fukumori, Ichiro; Raghunath, Ramanujam; Fu, Lee-Lueng; Chao, Yi

1999-11-01

The feasibility of assimilating satellite altimetry data into a global ocean general circulation model is studied. Three years of TOPEX/Poseidon data are analyzed using a global, three-dimensional, nonlinear primitive equation model. The assimilation's success is examined by analyzing its consistency and reliability measured by formal error estimates with respect to independent measurements. Improvements in model solution are demonstrated, in particular, properties not directly measured. Comparisons are performed with sea level measured by tide gauges, subsurface temperatures and currents from moorings, and bottom pressure measurements. Model representation errors dictate what can and cannot be resolved by assimilation, and its identification is emphasized.

The Global Observing System in the Assimilation Context

NASA Technical Reports Server (NTRS)

Reinecker, Michele M.; Gelaro, R.; Pawson, S.; Reichle, R.; McCarty, W.

2011-01-01

Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues.

The Effects of Chlorophyll Assimilation on Carbon Fluxes in a Global Biogeochemical Model. [Technical Report Series on Global Modeling and Data Assimilation

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); Rousseaux, Cecile Severine; Gregg, Watson W.

2014-01-01

In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-2010, we found that the global FCO2 values produced in the free-run and after assimilation were within -0.6 mol C m(sup -2) y(sup -1) of the observations. The effect of satellite chlorophyll assimilation was assessed in 12 major oceanographic regions. The region with the highest bias was the North Atlantic. Here the model underestimated the fluxes by 1.4 mol C m(sup -2) y(sup -1) whereas all the other regions were within 1 mol C m(sup -2) y(sup -1) of the data. The FCO2 values were not strongly impacted by the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the uncertainty in chlorophyll concentration. Chlorophyll concentrations were within approximately 25% of the database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration in the regions with the highest bias by 10-20%. These results suggest that the assimilation of chlorophyll data does not considerably improve FCO2 estimates and that other components of the carbon cycle play a role that could further improve our FCO2 estimates.

A Global Carbon Assimilation System using a modified EnKF assimilation method

NASA Astrophysics Data System (ADS)

Zhang, S.; Zheng, X.; Chen, Z.; Dan, B.; Chen, J. M.; Yi, X.; Wang, L.; Wu, G.

2014-10-01

A Global Carbon Assimilation System based on Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 abundance data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is based on the ensemble Kalman filter (EnKF), but with several new developments, including using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is tested in observing system simulation experiments and then used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

Scalar and Vector Spherical Harmonics for Assimilation of Global Datasets in the Ionosphere and Thermosphere

NASA Astrophysics Data System (ADS)

Miladinovich, D.; Datta-Barua, S.; Bust, G. S.; Ramirez, U.

2017-12-01

Understanding physical processes during storm time in the ionosphere-thermosphere (IT) system is limited, in part, due to the inability to obtain accurate estimates of IT states on a global scale. One reason for this inability is the sparsity of spatially distributed high quality data sets. Data assimilation is showing promise toward enabling global estimates by blending high quality observational data sets with established climate models. We are continuing development of an algorithm called Estimating Model Parameters for Ionospheric Reverse Engineering (EMPIRE) to enable assimilation of global datasets for storm time estimates of IT drivers. EMPIRE is a data assimilation algorithm that uses a Kalman filtering routine to ingest model and observational data. The EMPIRE algorithm is based on spherical harmonics which provide a spherically symmetric, smooth, continuous, and orthonormal set of basis functions suitable for a spherical domain such as Earth's IT region (200-600 km altitude). Once the basis function coefficients are determined, the newly fitted function represents the disagreement between observational measurements and models. We apply spherical harmonics to study the March 17, 2015 storm. Data sources include Fabry-Perot interferometer neutral wind measurements and global Ionospheric Data Assimilation 4 Dimensional (IDA4D) assimilated total electron content (TEC). Models include Weimer 2000 electric potential, International Geomagnetic Reference Field (IGRF) magnetic field, and Horizontal Wind Model 2014 (HWM14) neutral winds. We present the EMPIRE assimilation results of Earth's electric potential and thermospheric winds. We also compare EMPIRE storm time E cross B ion drift estimates to measured drifts produced from the Super Dual Auroral Radar Network (SuperDARN) and Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) measurement datasets. The analysis from these results will enable the generation of globally assimilated storm time IT state estimates for future studies. In particular, the ability to provide data assimilated estimation of the drivers of the IT system from high to low latitudes is a critical step toward forecasting the influence of geomagnetic storms on the near Earth space environment.

Assessment of Two Types of Observations (SATWND and GPSRO) for the Operational Global 4DVAR System

NASA Astrophysics Data System (ADS)

Leng, H.

2017-12-01

The performance of a data assimilation system is significantly dependent on the quality and quantity of observations assimilated. In these years, more and more satellite observations have been applied in many operational assimilation systems. In this paper, the assessment of satellite-derived winds (SATWND) and GPS radio occultation (GPSRO) bending angles has been performed using a range of diagnostics. The main positive impacts are made when satellite-derived cloud data (GOES cloud data and MODIS cloud data) is assimilated, but benefit is hardly obtained from GPSRO data in the Operational Global 4DVAR System. In a full system configuration, the assimilation of satellite-derived observations is globally beneficial on the analysis, and the benefit can be well propagated into the forecast. The assimilation of the GPSRO observations has a slightly positive impact in the Tropics, but is neutral in the Northern Hemisphere and in the Southern Hemisphere. To assess the synergies of satellite-derived observations with other types of observation, experiments assimilating satellite-derived data and AMSU-A and AMSU-B observations were run. The results show that the analysis increments structure is not modified when AMSU-A and AMSU-B observations are also assimilated. This suggests that the impact of satellite-derived observations is not limited by the large impact of satellite radiance observations.

Coupled Data Assimilation in Navy ESPC

NASA Astrophysics Data System (ADS)

Barron, C. N.; Spence, P. L.; Frolov, S.; Rowley, C. D.; Bishop, C. H.; Wei, M.; Ruston, B.; Smedstad, O. M.

2017-12-01

Data assimilation under global coupled Earth System Prediction Capability (ESPC) presents significantly greater challenges than data assimilation in forecast models of a single earth system like the ocean and atmosphere. In forecasts of a single component, data assimilation has broad flexibility in adjusting boundary conditions to reduce forecast errors; coupled ESPC requires consistent simultaneous adjustment of multiple components within the earth system: air, ocean, ice, and others. Data assimilation uses error covariances to express how to consistently adjust model conditions in response to differences between forecasts and observations; in coupled ESPC, these covariances must extend from air to ice to ocean such that changes within one fluid are appropriately balanced with corresponding adjustments in the other components. We show several algorithmic solutions that allow us to resolve these challenges. Specifically, we introduce the interface solver method that augments existing stand-alone systems for ocean and atmosphere by allowing them to be influenced by relevant measurements from the coupled fluid. Plans are outlined for implementing coupled data assimilation within ESPC for the Navy's global coupled model. Preliminary results show the impact of assimilating SST-sensitive radiances in the atmospheric model and first results of hybrid DA in a 1/12 degree model of the global ocean.

HBCU Summer Undergraduate Training Program in Prostate Cancer: A Partnership Between USU-CPDR and UDC

DTIC Science & Technology

2016-10-01

Award Number: W81XWH-14-2-0142 TITLE: HBCU Summer Undergraduate Training Program in Prostate Cancer : A Partnership Between USU-CPDR and UDC... Cancer : A Partnership Between USU-CPDR and UDC 5a. CONTRACT NUMBER: 5b. GRANT NUMBER: W81XWH-14-2-0142 5c. PROGRAM ELEMENT NUMBER: 6. AUTHOR(S...second year of the award (2016), 4 meritorious students were selected under HBCU Summer Undergraduate Training Program in Prostate Cancer by USU-CPDR

Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model.

PubMed

Dai, Tie; Schutgens, Nick A J; Goto, Daisuke; Shi, Guangyu; Nakajima, Teruyuki

2014-12-01

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of data assimilation on the global aerosol key optical properties simulations

NASA Astrophysics Data System (ADS)

Yin, Xiaomei; Dai, Tie; Schutgens, Nick A. J.; Goto, Daisuke; Nakajima, Teruyuki; Shi, Guangyu

2016-09-01

We present the one month results of global aerosol optical properties for April 2006, using the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), by assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) with Local Ensemble Transform Kalman Filter (LETKF). The simulated AOD, Ångström Exponent (AE) and single scattering albedo (SSA) are validated by independent Aerosol Robotic Network (AERONET) observations over the global sites. The data assimilation has the strongest positive effect on the AOD simulation and slight positive influences on the AE and SSA simulations. For the time-averaged globally spatial distribution, the data assimilation increases the model skill score (S) of AOD, AE, and SSA from 0.55, 0.92, and 0.75 to 0.79, 0.94, and 0.80, respectively. Over the North Africa (NAF) and Middle East region where the aerosol composition is simple (mainly dust), the simulated AODs are best improved by the data assimilation, indicating the assimilation correctly modifies the wrong dust burdens caused by the uncertainties of the dust emission parameterization. Assimilation also improves the simulation of the temporal variations of the aerosol optical properties over the AERONET sites, with improved S at 60 (62%), 45 (55%) and 11 (50%) of 97, 82 and 22 sites for AOD, AE and SSA. By analyzing AOD and AE at five selected sites with best S improvement, this study further indicates that the assimilation can reproduce short duration events and ratios between fine and coarse aerosols more accurately.

Simultaneous assimilation of ozone profiles from multiple UV-VIS satellite instruments

NASA Astrophysics Data System (ADS)

van Peet, Jacob C. A.; van der A, Ronald J.; Kelder, Hennie M.; Levelt, Pieternel F.

2018-02-01

A three-dimensional global ozone distribution has been derived from assimilation of ozone profiles that were observed by satellites. By simultaneous assimilation of ozone profiles retrieved from the nadir looking satellite instruments Global Ozone Monitoring Experiment 2 (GOME-2) and Ozone Monitoring Instrument (OMI), which measure the atmosphere at different times of the day, the quality of the derived atmospheric ozone field has been improved. The assimilation is using an extended Kalman filter in which chemical transport model TM5 has been used for the forecast. The combined assimilation of both GOME-2 and OMI improves upon the assimilation results of a single sensor. The new assimilation system has been demonstrated by processing 4 years of data from 2008 to 2011. Validation of the assimilation output by comparison with sondes shows that biases vary between -5 and +10 % between the surface and 100 hPa. The biases for the combined assimilation vary between -3 and +3 % in the region between 100 and 10 hPa where GOME-2 and OMI are most sensitive. This is a strong improvement compared to direct retrievals of ozone profiles from satellite observations.

Study on aerodynamics characteristics an urban concept car for energy-efficient race

NASA Astrophysics Data System (ADS)

Ambarita, H.; Siregar, M. R.; Kawai, H.

2018-03-01

"Horas Mesin USU" is a prototype of urban concept vehicle designed by University of Sumatera Utara to participate in the energy-efficient competition. This paper deals with a numerical study on aerodynamic characteristics of the Horas Mesin USU. The numerical analyses are carried out by solving the governing equations using CFD FLUENT commercial code. The turbulent flow is closed using k-epsilon turbulence model. In the results, pathline, velocity vector and pressure distribution are plotted. By using the pressure distributions, drag and lift coefficients are calculated. In order to make a comparison, the aerodynamic characteristics of the present design are compared with commercial city car Ford-Fiesta. The averaged drag coefficients of Horas Mesin USU and Ford-Fiesta are 0.24320 and 0.29598, respectively. On the other hand, the averaged lift coefficients of the Horas Mesin USU and Ford-Fiesta are 0.03192202 and 0.09485621, respectively. This fact suggests that Ford-Fiesta has a better aerodynamic performance in comparison with Horas Mesin USU. The flow field analysis shows that there are many modifications can be proposed to improve the aerodynamic performance of the Horas Mesin USU. It is suggested to perform further analysis to improve the aerodynamic performance of Horas Mesin USU.

Ionospheric Simulation System for Satellite Observations and Global Assimilative Modeling Experiments (ISOGAME)

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Mannucci, Anthony J.; Verkhoglyadova, Olga P.; Stephens, Philip; Wilson, Brian D.; Akopian, Vardan; Komjathy, Attila; Lijima, Byron A.

2013-01-01

ISOGAME is designed and developed to assess quantitatively the impact of new observation systems on the capability of imaging and modeling the ionosphere. With ISOGAME, one can perform observation system simulation experiments (OSSEs). A typical OSSE using ISOGAME would involve: (1) simulating various ionospheric conditions on global scales; (2) simulating ionospheric measurements made from a constellation of low-Earth-orbiters (LEOs), particularly Global Navigation Satellite System (GNSS) radio occultation data, and from ground-based global GNSS networks; (3) conducting ionospheric data assimilation experiments with the Global Assimilative Ionospheric Model (GAIM); and (4) analyzing modeling results with visualization tools. ISOGAME can provide quantitative assessment of the accuracy of assimilative modeling with the interested observation system. Other observation systems besides those based on GNSS are also possible to analyze. The system is composed of a suite of software that combines the GAIM, including a 4D first-principles ionospheric model and data assimilation modules, an Internal Reference Ionosphere (IRI) model that has been developed by international ionospheric research communities, observation simulator, visualization software, and orbit design, simulation, and optimization software. The core GAIM model used in ISOGAME is based on the GAIM++ code (written in C++) that includes a new high-fidelity geomagnetic field representation (multi-dipole). New visualization tools and analysis algorithms for the OSSEs are now part of ISOGAME.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2010-09-30

oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or...from Aqua and NASA Tropical Rainfall Measuring Mission (TRMM), 2) developing mesoscale data assimilation techniques to assimilate satellite, radar

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

Assimilation of Satellite Ozone Observations

NASA Technical Reports Server (NTRS)

Stajner, I.; Winslow, N.; Wargan, K.; Hayashi, H.; Pawson, S.; Rood, R.

2003-01-01

This talk will discuss assimilation of ozone data from satellite-borne instruments. Satellite observations of ozone total columns and profiles have been measured by a series of Total Ozone Mapping Spectrometer (TOMS), Solar Backscatter Ultraviolet (SBUV) instruments, and more recently by the Global Ozone Monitoring Experiment. Additional profile data are provided by instruments on NASA's Upper Atmosphere Research Satellite and by occultation instruments on other platforms. Instruments on Envisat' and future EOS Aura satellite will supply even more comprehensive data about the ozone distribution. Satellite data contain a wealth of information, but they do not provide synoptic global maps of ozone fields. These maps can be obtained through assimilation of satellite data into global chemistry and transport models. In the ozone system at NASA's Data Assimilation Office (DAO) any combination of TOMS, SBUV, and Microwave Limb sounder (MLS) data can be assimilated. We found that the addition of MLS to SBUV and TOMS data in the system helps to constrain the ozone distribution, especially in the polar night region and in the tropics. The assimilated ozone distribution in the troposphere and lower stratosphere is sensitive also to finer changes in the SBUV and TOMS data selection and to changes in error covariance models. All results are established by comparisons of assimilated ozone with independent profiles from ozone sondes and occultation instruments.

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

NASA Technical Reports Server (NTRS)

Vernieres, Guillaume Rene Jean; Kovach, Robin M.; Keppenne, Christian L.; Akella, Santharam; Brucker, Ludovic; Dinnat, Emmanuel Phillippe

2014-01-01

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

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

Information flow in an atmospheric model and data assimilation

NASA Astrophysics Data System (ADS)

Yoon, Young-noh

2011-12-01

Weather forecasting consists of two processes, model integration and analysis (data assimilation). During the model integration, the state estimate produced by the analysis evolves to the next cycle time according to the atmospheric model to become the background estimate. The analysis then produces a new state estimate by combining the background state estimate with new observations, and the cycle repeats. In an ensemble Kalman filter, the probability distribution of the state estimate is represented by an ensemble of sample states, and the covariance matrix is calculated using the ensemble of sample states. We perform numerical experiments on toy atmospheric models introduced by Lorenz in 2005 to study the information flow in an atmospheric model in conjunction with ensemble Kalman filtering for data assimilation. This dissertation consists of two parts. The first part of this dissertation is about the propagation of information and the use of localization in ensemble Kalman filtering. If we can perform data assimilation locally by considering the observations and the state variables only near each grid point, then we can reduce the number of ensemble members necessary to cover the probability distribution of the state estimate, reducing the computational cost for the data assimilation and the model integration. Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. We address these issues and elucidate the role played by chaotic wave dynamics in the propagation of information and the resulting impact on forecasts. The second part of this dissertation is about ensemble regional data assimilation using joint states. Assuming that we have a global model and a regional model of higher accuracy defined in a subregion inside the global region, we propose a data assimilation scheme that produces the analyses for the global and the regional model simultaneously, considering forecast information from both models. We show that our new data assimilation scheme produces better results both in the subregion and the global region than the data assimilation scheme that produces the analyses for the global and the regional model separately.

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

2009-01-01

Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

NASA Astrophysics Data System (ADS)

Moradi, I.; Prive, N.; McCarty, W.; Errico, R. M.; Gelaro, R.

2017-12-01

This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.

The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

NASA Technical Reports Server (NTRS)

Moradi, Isaac; Prive, Nikki; McCarty, Will; Errico, Ronald M.; Gelaro, Ron

2017-01-01

This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.

Observability of global rivers with future SWOT observations

NASA Astrophysics Data System (ADS)

Fisher, Colby; Pan, Ming; Wood, Eric

2017-04-01

The Surface Water and Ocean Topography (SWOT) mission is designed to provide global observations of water surface elevation and slope from which river discharge can be estimated using a data assimilation system. This mission will provide increased spatial and temporal coverage compared to current altimeters, with an expected accuracy for water level elevations of 10 cm on rivers greater than 100 m wide. Within the 21-day repeat cycle, a river reach will be observed 2-4 times on average. Due to the relationship between the basin orientation and the orbit, these observations are not evenly distributed in time, which will impact the derived discharge values. There is, then, a need for a better understanding of how the mission will observe global river basins. In this study, we investigate how SWOT will observe global river basins and how the temporal and spatial sampling impacts the discharge estimated from assimilation. SWOT observations can be assimilated using the Inverse Streamflow Routing (ISR) model of Pan and Wood [2013] with a fixed interval Kalman smoother. Previous work has shown that the ISR assimilation method can be used to reproduce the spatial and temporal dynamics of discharge within many global basins: however, this performance was strongly impacted by the spatial and temporal availability of discharge observations. In this study, we apply the ISR method to 32 global basins with different geometries and crossing patterns for the future orbit, assimilating theoretical SWOT-retrieved "gauges". Results show that the model performance varies significantly across basins and is driven by the orientation, flow distance, and travel time in each. Based on these properties, we quantify the "observability" of each basin and relate this to the performance of the assimilation. Applying this metric globally to a large variety of basins we can gain a better understanding of the impact that SWOT observations may have across basin scales. By determining the availability of SWOT observations in this manner, hydrologic data assimilation approaches like ISR can be optimized to provide useful discharge estimates in sparsely gauged regions where spatially and temporally consistent discharge records are most valuable. Pan, M; Wood, E F 2013 Inverse streamflow routing, HESS 17(11):4577-4588

Exploring New Pathways in Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara Q.

2004-01-01

Precipitation assimilation poses a special challenge in that the forward model for rain in a global forecast system is based on parameterized physics, which can have large systematic errors that must be rectified to use precipitation data effectively within a standard statistical analysis framework. We examine some key issues in precipitation assimilation and describe several exploratory studies in assimilating rainfall and latent heating information in NASA's global data assimilation systems using the forecast model as a weak constraint. We present results from two research activities. The first is the assimilation of surface rainfall data using a time-continuous variational assimilation based on a column model of the full moist physics. The second is the assimilation of convective and stratiform latent heating retrievals from microwave sensors using a variational technique with physical parameters in the moist physics schemes as a control variable. We will show the impact of assimilating these data on analyses and forecasts. Among the lessons learned are (1) that the time-continuous application of moisture/temperature tendency corrections to mitigate model deficiencies offers an effective strategy for assimilating precipitation information, and (2) that the model prognostic variables must be allowed to directly respond to an improved rain and latent heating field within an analysis cycle to reap the full benefit of assimilating precipitation information. of microwave radiances versus retrieval information in raining areas, and initial efforts in developing ensemble techniques such as Kalman filter/smoother for precipitation assimilation. Looking to the future, we discuss new research directions including the assimilation

Technical Report Series on Global Modeling and Data Assimilation, Volume 43. MERRA-2; Initial Evaluation of the Climate

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); Bosilovich, Michael G.; Akella, Santha; Lawrence, Coy; Cullather, Richard; Draper, Clara; Gelaro, Ronald; Kovach, Robin; Liu, Qing; Molod, Andrea;

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Riishojgaard, Lars Peter; Rood, Richard B.

2000-01-01

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Does Ocean Color Data Assimilation Improve Estimates of Global Ocean Inorganic Carbon?

NASA Technical Reports Server (NTRS)

Gregg, Watson

2012-01-01

Ocean color data assimilation has been shown to dramatically improve chlorophyll abundances and distributions globally and regionally in the oceans. Chlorophyll is a proxy for phytoplankton biomass (which is explicitly defined in a model), and is related to the inorganic carbon cycle through the interactions of the organic carbon (particulate and dissolved) and through primary production where inorganic carbon is directly taken out of the system. Does ocean color data assimilation, whose effects on estimates of chlorophyll are demonstrable, trickle through the simulated ocean carbon system to produce improved estimates of inorganic carbon? Our emphasis here is dissolved inorganic carbon, pC02, and the air-sea flux. We use a sequential data assimilation method that assimilates chlorophyll directly and indirectly changes nutrient concentrations in a multi-variate approach. The results are decidedly mixed. Dissolved organic carbon estimates from the assimilation model are not meaningfully different from free-run, or unassimilated results, and comparisons with in situ data are similar. pC02 estimates are generally worse after data assimilation, with global estimates diverging 6.4% from in situ data, while free-run estimates are only 4.7% higher. Basin correlations are, however, slightly improved: r increase from 0.78 to 0.79, and slope closer to unity at 0.94 compared to 0.86. In contrast, air-sea flux of C02 is noticeably improved after data assimilation. Global differences decline from -0.635 mol/m2/y (stronger model sink from the atmosphere) to -0.202 mol/m2/y. Basin correlations are slightly improved from r=O.77 to r=0.78, with slope closer to unity (from 0.93 to 0.99). The Equatorial Atlantic appears as a slight sink in the free-run, but is correctly represented as a moderate source in the assimilation model. However, the assimilation model shows the Antarctic to be a source, rather than a modest sink and the North Indian basin is represented incorrectly as a sink rather than the source indicated by the free-run model and data estimates.

Welcome to Utah State University

Science.gov Websites

illustrious alumni, USU students are fully equipped to succeed in anything. Learn more about Academics Find a Monument Valley Extension Info for Future Students Current Students Faculty/Staff Alumni Parents With so . Overview Who we are About USU USU at a Glance History and Traditions Life in Logan Notable Alumni Contact

Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

NASA Technical Reports Server (NTRS)

Arellano, A. F., Jr.; Raeder, K.; Anderson, J. L.; Hess, P. G.; Emmons, L. K.; Edwards, D. P.; Pfister, G. G.; Campos, T. L.; Sachse, G. W.

2007-01-01

We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical resolution of the measurement, the retrievals at different altitudes are correlated which can lead to problems with numerical error and overall efficiency. This has resulted in a manuscript that is about to be submitted to JGR:

Global isotope metabolomics reveals adaptive strategies for nitrogen assimilation

DOE PAGES

Kurczy, Michael E.; Forsberg, Erica M.; Thorgersen, Michael P.; ...

2016-04-05

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using 15NO 3 as a nitrogen source. In contrast to a control ( Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee ( Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2more » strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. In conclusion, the use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.« less

Global isotope metabolomics reveals adaptive strategies for nitrogen assimilation

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

Kurczy, Michael E.; Forsberg, Erica M.; Thorgersen, Michael P.

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using 15NO 3 as a nitrogen source. In contrast to a control ( Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee ( Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2more » strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. In conclusion, the use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.« less

Substrate and environmental controls on microbial assimilation of soil organic carbon: a framework for Earth System Models

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

Xu, Xiaofeng; Schimel, Joshua; Thornton, Peter E

2014-01-01

Microbial assimilation of soil organic carbon is one of the fundamental processes of global carbon cycling and it determines the magnitude of microbial biomass in soils. Mechanistic understanding of microbial assimilation of soil organic carbon and its controls is important for to improve Earth system models ability to simulate carbon-climate feedbacks. Although microbial assimilation of soil organic carbon is broadly considered to be an important parameter, it really comprises two separate physiological processes: one-time assimilation efficiency and time-dependent microbial maintenance energy. Representing of these two mechanisms is crucial to more accurately simulate carbon cycling in soils. In this study, amore » simple modeling framework was developed to evaluate the substrate and environmental controls on microbial assimilation of soil organic carbon using a new term: microbial annual active period (the length of microbes remaining active in one year). Substrate quality has a positive effect on microbial assimilation of soil organic carbon: higher substrate quality (lower C:N ratio) leads to higher ratio of microbial carbon to soil organic carbon and vice versa. Increases in microbial annual active period from zero stimulate microbial assimilation of soil organic carbon; however, when microbial annual active period is longer than an optimal threshold, increasing this period decreases microbial biomass. The simulated ratios of soil microbial biomass to soil organic carbon are reasonably consistent with a recently compiled global dataset at the biome-level. The modeling framework of microbial assimilation of soil organic carbon and its controls developed in this study offers an applicable ways to incorporate microbial contributions to the carbon cycling into Earth system models for simulating carbon-climate feedbacks and to explain global patterns of microbial biomass.« less

All-Sky Microwave Imager Data Assimilation at NASA GMAO

NASA Technical Reports Server (NTRS)

Kim, Min-Jeong; Jin, Jianjun; El Akkraoui, Amal; McCarty, Will; Todling, Ricardo; Gu, Wei; Gelaro, Ron

2017-01-01

Efforts in all-sky satellite data assimilation at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been focused on the development of GSI configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) and Global Change Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 (AMSR-2). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be relatively transparent to atmospheric gases and thin ice clouds, and highly sensitive to precipitation. Therefore, GMAOs all-sky data assimilation efforts are primarily focused on utilizing these data in precipitating regions. The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar configuration of the Goddard Earth Observing System (GEOS) data assimilation system, which will be included in the next formal update of GEOS. This article provides an overview of the development of all-sky radiance assimilation in GEOS, including some performance metrics. In addition, various projects underway at GMAO designed to enhance the all-sky implementation will be introduced.

Sulfate and Pb-210 Simulated in a Global Model Using Assimilated Meteorological Fields

NASA Technical Reports Server (NTRS)

Chin, Mian; Rood, Richard; Lin, S.-J.; Jacob, Daniel; Muller, Jean-Francois

1999-01-01

This report presents the results of distributions of tropospheric sulfate, Pb-210 and their precursors from a global 3-D model. This model is driven by assimilated meteorological fields generated by the Goddard Data Assimilation Office. Model results are compared with observations from surface sites and from multiplatform field campaigns of Pacific Exploratory Missions (PEM) and Advanced Composition Explorer (ACE). The model generally captures the seasonal variation of sulfate at the surface sites, and reproduces well the short-term in-situ observations. We will discuss the roles of various processes contributing to the sulfate levels in the troposphere, and the roles of sulfate aerosol in regional and global radiative forcing.

Tropospheric and stratospheric ozone from assimilation of Aura data

NASA Technical Reports Server (NTRS)

Stajner, I.; Wargan, K.; Chang, L.-P.; Hayashi, H.; Pawwson, S.; Froidevaux, L.; Livesey, N.; Bhartia, P. K.

2006-01-01

Ozone is an atmospheric trace gas with multiple impacts on the environment. Global ozone fields are needed for air quality predictions, estimation of the ultraviolet radiation reaching the surface, climate-radiation studies, and may also have an impact on longer-term weather predictions. We estimate global ozone fields in the stratosphere and troposphere by combining the data from EOS Aura satellite with an ozone model using data assimilation. Ozone exhibits a large temporal variability in the lower stratosphere. Our previous work showed that assimilation of satellite data from limb-sounding geometry helps constrain ozone profiles in that region. We assimilated ozone data from the Aura Microwave Limb Sounder (MLS) and the Ozone Monitoring Instrument (OMI) into the ozone system at NASA's Global Modeling and Assimilation Office (GMAO). Ozone is transported within a general circulation model (GCM) which includes parameterizations for stratospheric photochemistry, tropospheric chemistry, and a simple scheme for heterogeneous ozone loss. The focus of this study is on the representation of ozone in the lower stratosphere and tropospheric ozone columns. We plan to extend studies of tropospheric ozone distribution through assimilation of ozone data from the Tropospheric Emission Spectrometer (TES). Comparisons with ozone sondes and occultation data show that assimilation of Aura data reproduces ozone gradients and variability in the lower stratosphere well. We proceed by separating the contributions to temporal changes in the ozone field into those that are due to the model and those that are due to the assimilation of Aura data. The impacts of Aura data are illustrated and their role in the representation of ozone variability in the lower stratosphere and troposphere is shown.

Remote sensing data assimilation for a prognostic phenology model

Treesearch

R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

2008-01-01

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

Role of Satellite Rainfall Information in Improving Understanding of the Dynamical Link Between the Tropics and Extratropics Prospects of Improved Forecasts of Weather and Short-Term Climate Variability on Sub-Seasonal Time Scales

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2002-01-01

The tropics and extratropics are two dynamically distinct regimes. The coupling between these two regimes often defies simple analytical treatment. Progress in understanding of the dynamical interaction between the tropics and extratropics relies on better observational descriptions to guide theoretical development. However, global analyses currently contain significant errors in primary hydrological variables such as precipitation, evaporation, moisture, and clouds, especially in the tropics. Tropical analyses have been shown to be sensitive to parameterized precipitation processes, which are less than perfect, leading to order-one discrepancies between estimates produced by different data assimilation systems. One strategy for improvement is to assimilate rainfall observations to constrain the analysis and reduce uncertainties in variables physically linked to precipitation. At the Data Assimilation Office at the NASA Goddard Space Flight Center, we have been exploring the use of tropical rain rates derived from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments in global data assimilation. Results show that assimilating these data improves not only rainfall and moisture fields but also related climate parameters such as clouds and radiation, as well as the large-scale circulation and short-range forecasts. These studies suggest that assimilation of microwave rainfall observations from space has the potential to significantly improve the quality of 4-D assimilated datasets for climate investigations (Hou et al. 2001). In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007. Continued improvements in assimilation methodology, rainfall error estimates, and model parameterizations are needed to ensure that we derive maximum benefits from these observations.

A Global Data Assimilation System for Atmospheric Aerosol

NASA Technical Reports Server (NTRS)

daSilva, Arlindo

1999-01-01

We will give an overview of an aerosol data assimilation system which combines advances in remote sensing of atmospheric aerosols, aerosol modeling and data assimilation methodology to produce high spatial and temporal resolution 3D aerosol fields. Initially, the Goddard Aerosol Assimilation System (GAAS) will assimilate TOMS, AVHRR and AERONET observations; later we will include MODIS and MISR. This data assimilation capability will allows us to integrate complementing aerosol observations from these platforms, enabling the development of an assimilated aerosol climatology as well as a global aerosol forecasting system in support of field campaigns. Furthermore, this system provides an interactive retrieval framework for each aerosol observing satellites, in particular TOMS and AVHRR. The Goddard Aerosol Assimilation System (GAAS) takes advantage of recent advances in constituent data assimilation at DAO, including flow dependent parameterizations of error covariances and the proper consideration of model bias. For its prognostic transport model, GAAS will utilize the Goddard Ozone, Chemistry, Aerosol, Radiation and Transport (GOCART) model developed at NASA/GSFC Codes 916 and 910.3. GOCART includes the Lin-Rood flux-form, semi-Langrangian transport model with parameterized aerosol chemistry and physical processes for absorbing (dust and black carbon) and non-absorbing aerosols (sulfate and organic carbon). Observations and model fields are combined using a constituent version of DAO's Physical-space Statistical Analysis System (PSAS), including its adaptive quality control system. In this talk we describe the main components of this assimilation system and present preliminary results obtained by assimilating TOMS data.

Improving Incremental Balance in the GSI 3DVAR Analysis System

NASA Technical Reports Server (NTRS)

Errico, Ronald M.; Yang, Runhua; Kleist, Daryl T.; Parrish, David F.; Derber, John C.; Treadon, Russ

2008-01-01

The Gridpoint Statistical Interpolation (GSI) analysis system is a unified global/regional 3DVAR analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a Tangent Linear Normal Mode Constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation both at NCEP and at the NASA Global Modeling and Assimilation Office (GMAO).

Improving Assimilated Global Data Sets using TMI Rainfall and Columnar Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.

1999-01-01

A global analysis that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. In this study, we show that assimilating precipitation and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). In particular, assimilating TMI rain improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. The improved analysis also improves short-range forecasts in the tropics. Ensemble forecasts initialized with the GEOS analysis incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day and better 500 hPa geopotential height forecasts up to 5 days. Results of this study demonstrate the potential of using high-quality space-borne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications

Assimilation of MLS and OMI Ozone Data

NASA Technical Reports Server (NTRS)

Stajner, I.; Wargan, K.; Chang, L.-P.; Hayashi, H.; Pawson, S.; Froidevaux, L.; Livesey, N.

2005-01-01

Ozone data from Aura Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) were assimilated into the ozone model at NASA's Global Modeling and Assimilation Office (GMAO). This assimilation produces ozone fields that are superior to those from the operational GMAO assimilation of Solar Backscatter Ultraviolet (SBUV/2) instrument data. Assimilation of Aura data improves the representation of the "ozone hole" and the agreement with independent Stratospheric Aerosol and Gas Experiment (SAGE) III and ozone sonde data. Ozone in the lower stratosphere is captured better: mean state, vertical gradients, spatial and temporal variability are all improved. Inclusion of OMI and MLS data together, or separately, in the assimilation system provides a way of checking how consistent OMI and MLS data are with each other, and with the ozone model. We found that differences between OMI total ozone column data and model forecasts decrease after MLS data are assimilated. This indicates that MLS stratospheric ozone profiles are consistent with OMI total ozone columns. The evaluation of error characteristics of OMI and MLS ozone will continue as data from newer versions of retrievals becomes available. We report on the initial step in obtaining global assimilated ozone fields that combine measurements from different Aura instruments, the ozone model at the GMAO, and their respective error characteristics. We plan to use assimilated ozone fields in estimation of tropospheric ozone. We also plan to investigate impacts of assimilated ozone fields on numerical weather prediction through their use in radiative models and in the assimilation of infrared nadir radiance data from NASA's Advanced Infrared Sounder (AIRS).

Low Frequency Oscillations in Assimilated Global Datasets Using TRMM Rainfall Observations

NASA Technical Reports Server (NTRS)

Tao, Li; Yang, Song; Zhang, Zhan; Hou, Arthur; Olson, William S.

2004-01-01

Global datasets for the period May-August 1998 from the Goddard Earth Observing System (GEOS) data assimilation system (DAS) with/without assimilated Tropical Rainfall Measuring Mission (TRMM) precipitation are analyzed against European Center for Medium-Range Weather Forecast (ECMWF) output, NOAA observed outgoing longwave radiation (OLR) data, and TRMM measured rainfall. The purpose of this study is to investigate the representation of the Madden-Julian Oscillation (MJO) in GEOS assimilated global datasets, noting the impact of TRMM observed rainfall on the MJO in GEOS data assimilations. A space-time analysis of the OLR data indicates that the observed OLR exhibits a spectral maximum for eastward-propagating wavenumber 1-3 disturbances with periods of 20-60 days in the 0deg-30degN latitude band. The assimilated OLR has a similar feature but with a smaller magnitude. However, OLR spectra from assimilations including TRMM rainfall data show better agreement with observed OLR spectra than spectra from assimilations without TRMM rainfall. Similar results are found for wavenumber 4-6 disturbances. There is a spectral peak for eastward-propagating wavenumber 4-6 disturbances with periods of 20-40 days near the equator, while for westward-moving disturbances, a spectral peak is noted for periods of 30-50 days near 25degN. To isolate the MJO, a 30-50 day band filter is selected for this study. It was found that the eastward-propagating waves from the band-filtered observed OLR between 10degs- 10degN are located in the eastern hemisphere. Similar patterns are evident in surface rainfall and the 850 hPa wind field. Assimilation of TRMM-observed rainfall reveals more distinct MJO features in the analysis than without rainfall assimilation. Similar analyses are also conducted over the Indian summer monsoon and East Asia summer monsoon regions, where the MJO is strongly related to the summer monsoon active-break patterns.

Description and verification of a U.S. Naval Research Lab's loosely coupled data assimilation system for the Navy's Earth System Model

NASA Astrophysics Data System (ADS)

Barton, N. P.; Metzger, E. J.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T.; Ridout, J. A.; Zamudio, L.; Posey, P.; Reynolds, C. A.; Richman, J. G.; Phelps, M.

2017-12-01

The Naval Research Laboratory is developing an Earth System Model (NESM) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. This system consists of a global atmosphere, ocean, ice, wave, and land prediction models and the individual models include: atmosphere - NAVy Global Environmental Model (NAVGEM); ocean - HYbrid Coordinate Ocean Model (HYCOM); sea ice - Community Ice CodE (CICE); WAVEWATCH III™; and land - NAVGEM Land Surface Model (LSM). Data assimilation is currently loosely coupled between the atmosphere component using a 6-hour update cycle in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR) and the ocean/ice components using a 24-hour update cycle in the Navy Coupled Ocean Data Assimilation (NCODA) with 3 hours of incremental updating. This presentation will describe the US Navy's coupled forecast model, the loosely coupled data assimilation, and compare results against stand-alone atmosphere and ocean/ice models. In particular, we will focus on the unique aspects of this modeling system, which includes an eddy resolving ocean model and challenges associated with different update-windows and solvers for the data assimilation in the atmosphere and ocean. Results will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled system is performing with comparable skill to the stand-alone systems.

Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

NASA Astrophysics Data System (ADS)

Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

2017-12-01

Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

National Centers for Environmental Prediction

Science.gov Websites

System as follows: Changes to the model components Changes to the data assimilation and tropical storm relocation components Changes to the post-processing Changes to output products 1) Changes to the Global the land-atmosphere system from decoupling. 2) Changes to the Global Data Assimilation System (GDAS

Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

NASA Astrophysics Data System (ADS)

Allen, Douglas R.; Hoppel, Karl W.; Kuhl, David D.

2018-03-01

Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the flow-of-the-day ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ˜ 2 m s-1 and ˜ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ˜ 3 m s-1 and ˜ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ˜ 1 m s-1 and ˜ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polar-orbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) charge, or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

NASA Technical Reports Server (NTRS)

Girotto, Manuela; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Rodell, Matthew

2017-01-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Joint assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for improved soil moisture estimation

NASA Astrophysics Data System (ADS)

Girotto, M.; Reichle, R. H.; De Lannoy, G.; Rodell, M.

2017-12-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0-5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2004-01-01

Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 4% since 1998. The North Pacific ocean basin has increased nearly 19%. These trend analyses follow earlier results showing decadal declines in global ocean chlorophyll and primary production. To understand the causes of these changes and trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The model utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. This enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll. A full discussion of the changes and trends, possible causes, modeling approaches, and data assimilation will be the focus of the seminar.

The Global Ocean Data Assimilation Experiment (GODAE)

NASA Astrophysics Data System (ADS)

Le Traon, P.; Smith, N.

The Global Ocean Data Assimilation Experiment (GODAE) will conduct its main demonstration phase from 2003 to 2005. From 2003 to 2005, operational and research institutions from Australia, Japan, United States, United Kingdom, France and Norway will be performing global oceanic data assimilation and ocean forecast in order to provide regular and comprehensive descriptions of ocean fields such as temperature, salinity and currents at high temporal and spatial resolution. A central objective of GODAE is to provide an integrated description that combines remote sensing data, in-situ data and models through data assimilation. Climate and seasonal forecasting, navy applications, marine safety, fisheries, the offshore industry and management of shelf/coastal areas are among the expected beneficiaries of GODAE. The integrated description of the ocean that GODAE will provide will also be highly beneficial to the research community. An overview of GODAE will be given; we will detail the GODAE objectives and strategy and the way it is implemented as an international experiment. Results from first pre-operational or prototype systems will finally be shown.

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

NASA Technical Reports Server (NTRS)

Lievens, H.; Martens, B.; Verhoest, N. E. C.; Hahn, S.; Reichle, R. H.; Miralles, D. G.

2017-01-01

Active radar backscatter (s?) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s? and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s? or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s? and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

NASA Technical Reports Server (NTRS)

Li, Xuanli; Srikishen, Jayanthi; Zavodsky, Bradley; Mecikalski, John

2018-01-01

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2014-11-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS Package for Observation Processing (KPOP) system for data assimilation, preprocessing and quality control modules for bending angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending angle operator and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model-Spectral Element (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS-LETKF data assimilation system, which has been successfully implemented to a cubed-sphere model with fully unstructured quadrilateral meshes. As a result of data processing, the bending angle departure statistics between observation and background shows significant improvement. Also, the first experiment in assimilating GPS-RO bending angle resulting from KPOP within KIAPS-LETKF shows encouraging results.

Performance and Evaluation of the Global Modeling and Assimilation Office Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki; Errico, R. M.; Carvalho, D.

2018-01-01

The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) has spent more than a decade developing and implementing a global Observing System Simulation Experiment framework for use in evaluting both new observation types as well as the behavior of data assimilation systems. The NASA/GMAO OSSE has constantly evolved to relect changes in the Gridpoint Statistical Interpolation data assimiation system, the Global Earth Observing System model, version 5 (GEOS-5), and the real world observational network. Software and observational datasets for the GMAO OSSE are publicly available, along with a technical report. Substantial modifications have recently been made to the NASA/GMAO OSSE framework, including the character of synthetic observation errors, new instrument types, and more sophisticated atmospheric wind vectors. These improvements will be described, along with the overall performance of the current OSSE. Lessons learned from investigations into correlated errors and model error will be discussed.

Real-time Assimilation of Altimeter Derived Synthetic Profiles Into a Global version of the Naval Research Laboratory's Coastal Ocean Model (NCOM)

NASA Astrophysics Data System (ADS)

Rhodes, R. C.; Barron, C. N.; Fox, D. N.; Smedstad, L. F.

2001-12-01

A global implementation of the Navy Coastal Ocean Model (NCOM), developed by the Naval Research Laboratory (NRL) at Stennis Space Center is currently running in real-time and is planned for transition to the Naval Oceanographic Office (NAVOCEANO) in 2002. The model encompasses the open ocean to 5 m depth on a curvilinear global model grid with 1/8 degree grid spacing at 45N, extending from 80 S to a complete arctic cap with grid singularities mapped into Canada and Russia. Vertically, the model employs 41 sigma-z levels with sigma in the upper-ocean and coastal regions and z in the deeper ocean. The Navy Operational Global Atmospheric Prediction System (NOGAPS) provides 6-hourly wind stresses and heat fluxes for forcing, while the operational Modular Ocean Data Assimilation System (MODAS) provides the background climatology and tools for data pre-processing. Operationally available sea surface temperature (SST) and altimetry (SSH) data are assimilated into the NAVOCEANO global 1/8 degree MODAS 2-D analysis and the 1/16 degree Navy Layered Ocean Model (NLOM) to provide analyses and forecasts of SSH and SST. The 2-D SSH and SST nowcast fields are used as input to the MODAS synthetic climatology database to yield three-dimensional fields of synthetic temperature and salinity for assimilation into global NCOM. The synthetic profiles are weighted higher at depth in the assimilation process to allow the numerical model to properly develop the mixed-layer structure driven by the real-time atmospheric forcing. Global NCOM nowcasts and forecasts provide a valuable resource for rapid response to the varied and often unpredictable operational requests for 3-dimensional fields of ocean temperature, salinity, and currents. In some cases, the resolution of the global product is sufficient for guidance. In cases requiring higher resolution, the global product offers a quick overview of local circulation and provides initial and boundary conditions for higher resolution coastal models that may be more specialized for a particular task or domain. Nowcast and forecast results are presented globally and in selected areas of interest and model results are compared with historical and concurrent observations and analyses.

Assimilation of GNSS radio occultation observations in GRAPES

NASA Astrophysics Data System (ADS)

Liu, Y.; Xue, J.

2014-07-01

This paper reviews the development of the global navigation satellite system (GNSS) radio occultation (RO) observations assimilation in the Global/Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration, including the choice of data to assimilate, the data quality control, the observation operator, the tuning of observation error, and the results of the observation impact experiments. The results indicate that RO data have a significantly positive effect on analysis and forecast at all ranges in GRAPES not only in the Southern Hemisphere where conventional observations are lacking but also in the Northern Hemisphere where data are rich. It is noted that a relatively simple assimilation and forecast system in which only the conventional and RO observation are assimilated still has analysis and forecast skill even after nine months integration, and the analysis difference between both hemispheres is gradually reduced with height when compared with NCEP (National Centers for Enviromental Prediction) analysis. Finally, as a result of the new onboard payload of the Chinese FengYun-3 (FY-3) satellites, the research status of the RO of FY-3 satellites is also presented.

Tropospheric Ozone from Assimilation of Aura Data using Different Definitions of the Tropopause

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Wargan, K.; Chang, L.-P.; Hayashi, H.; Pawson, S.; Pawson, Steven; Livesey, N.; Bhartia, P. K.

2006-01-01

Ozone data from Aura OMI and MLS instruments were assimilated into the general circulation model (GCM) constrained by assimilated meteorological fields from the Global Modeling and Assimilation Office at NASA Goddard. Properties of tropospheric ozone and their sensitivity to the definition of the tropopause are investigated. Three definitions of the tropopause are considered: (1) dynamical (using potential vorticity and potential temperature), (2) using temperature lapse rate, and (3) using a fixed ozone value. Comparisons of the tropospheric ozone columns using these tropopause definitions will be presented and evaluated against coincident profiles from ozone sondes. Assimilated ozone profiles are used to identify possible tropopause folding events, which are important for stratosphere-troposphere exchange. Each profile is searched for multiple levels at which ozone attains the value typical of the troposphere-stratosphere transition in order to identify possible tropopause folds. Constrained by the dynamics from a global model and by assimilation of Aura ozone data every 3-hours, this data set provides an opportunity to study ozone evolution in the upper troposphere and lower stratosphere with high temporal resolution.

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi

2014-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Assimilation of satellite color observations in a coupled ocean GCM-ecosystem model

NASA Technical Reports Server (NTRS)

Sarmiento, Jorge L.

1992-01-01

Monthly average coastal zone color scanner (CZCS) estimates of chlorophyll concentration were assimilated into an ocean global circulation model(GCM) containing a simple model of the pelagic ecosystem. The assimilation was performed in the simplest possible manner, to allow the assessment of whether there were major problems with the ecosystem model or with the assimilation procedure. The current ecosystem model performed well in some regions, but failed in others to assimilate chlorophyll estimates without disrupting important ecosystem properties. This experiment gave insight into those properties of the ecosystem model that must be changed to allow data assimilation to be generally successful, while raising other important issues about the assimilation procedure.

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.

2012-01-01

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5).

Technical report series on global modeling and data assimilation. Volume 6: A multiyear assimilation with the GEOS-1 system: Overview and results

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Schubert, Siegfried; Rood, Richard; Park, Chung-Kyu; Wu, Chung-Yu; Kondratyeva, Yelena; Molod, Andrea; Takacs, Lawrence; Seablom, Michael; Higgins, Wayne

1995-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the GEOS-1 system, was to produce a research quality data set suitable for the study of short-term climate variability. The output, which is global and gridded, includes all prognostic fields and a large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information about the observations input to the GEOS-1 DAS is provided in terms of maps of spatial coverage, bar graphs of data counts, and tables of all time periods with significant data gaps. The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output. Documentation is provided on all the data archives, including sample read programs and methods of data access. Extensive comparisons are made with the corresponding operational European Center for Medium-Range Weather Forecasts analyses, as well as various in situ and satellite observations. This document is also intended to alert users of the data about potential limitations of assimilated data, in general, and the GEOS-1 data, in particular. Results are presented for the period March 1985-February 1990.

Global SWOT Data Assimilation of River Hydrodynamic Model; the Twin Simulation Test of CaMa-Flood

NASA Astrophysics Data System (ADS)

Ikeshima, D.; Yamazaki, D.; Kanae, S.

2016-12-01

CaMa-Flood is a global scale model for simulating hydrodynamics in large scale rivers. It can simulate river hydrodynamics such as river discharge, flooded area, water depth and so on by inputting water runoff derived from land surface model. Recently many improvements at parameters or terrestrial data are under process to enhance the reproducibility of true natural phenomena. However, there are still some errors between nature and simulated result due to uncertainties in each model. SWOT (Surface water and Ocean Topography) is a satellite, which is going to be launched in 2021, can measure open water surface elevation. SWOT observed data can be used to calibrate hydrodynamics model at river flow forecasting and is expected to improve model's accuracy. Combining observation data into model to calibrate is called data assimilation. In this research, we developed data-assimilated river flow simulation system in global scale, using CaMa-Flood as river hydrodynamics model and simulated SWOT as observation data. Generally at data assimilation, calibrating "model value" with "observation value" makes "assimilated value". However, the observed data of SWOT satellite will not be available until its launch in 2021. Instead, we simulated the SWOT observed data using CaMa-Flood. Putting "pure input" into CaMa-Flood produce "true water storage". Extracting actual daily swath of SWOT from "true water storage" made simulated observation. For "model value", we made "disturbed water storage" by putting "noise disturbed input" to CaMa-Flood. Since both "model value" and "observation value" are made by same model, we named this twin simulation. At twin simulation, simulated observation of "true water storage" is combined with "disturbed water storage" to make "assimilated value". As the data assimilation method, we used ensemble Kalman filter. If "assimilated value" is closer to "true water storage" than "disturbed water storage", the data assimilation can be marked effective. Also by changing the input disturbance of "disturbed water storage", acceptable rate of uncertainty at the input may 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.

Global Soil Moisture Estimation through a Coupled CLM4-RTM-DART Land Data Assimilation System

NASA Astrophysics Data System (ADS)

Zhao, L.; Yang, Z. L.; Hoar, T. J.

2016-12-01

Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, we have developed such a framework by linking the Community Land Model version 4 (CLM4) and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global multi-layer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to drive CLM4 simulations. Spatial-specific time-invariant microwave parameters are pre-calibrated to minimize uncertainties in RTM. Besides, various methods are designed in consideration of computational efficiency. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4-RTM-DART framework improves the open-loop CLM4 simulated soil moisture. Pre-calibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers, while simultaneously updating multi-layer soil moisture fails to obtain intended improvements. We will show in this presentation the architecture of the CLM4-RTM-DART system and the evaluations on AMSR-E DA. Preliminary results on multi-sensor DA that integrates various satellite observations including GRACE, MODIS, and AMSR-E will also be presented. ReferenceZhao, L., Z.-L. Yang, and T. J. Hoar, 2016. Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4-RTM-DART System. Journal of Hydrometeorology, DOI: 10.1175/JHM-D-15-0218.1.

ASTER Images Mt. Usu Volcano

NASA Image and Video Library

2000-04-26

On April 3, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra Satellite captured this image of the erupting Mt. Usu volcano in Hokkaido, Japan. With its 14 spectral bands from the visible to the thermal infrared wavelength region, and its high spatial resolution of 15 to 90 meters (about 50 to 300 feet), ASTER will image the Earth for the next 6 years to map and monitor the changing surface of our planet. This false color infrared image of Mt Usu volcano is dominated by Lake Toya, an ancient volcanic caldera. On the south shore is the active Usu volcano. On Friday, March 31, more than 11,000 people were evacuated by helicopter, truck and boat from the foot of Usu, that began erupting from the northwest flank, shooting debris and plumes of smoke streaked with blue lightning thousands of feet in the air. Although no lava gushed from the mountain, rocks and ash continued to fall after the eruption. The region was shaken by thousands of tremors before the eruption. People said they could taste grit from the ash that was spewed as high as 2,700 meters (8,850 ft) into the sky and fell to coat surrounding towns with ash. "Mount Usu has had seven significant eruptions that we know of, and at no time has it ended quickly with only a small scale eruption," said Yoshio Katsui, a professor at Hokkaido University. This was the seventh major eruption of Mount Usu in the past 300 years. Fifty people died when the volcano erupted in 1822, its worst known eruption. In the image, most of the land is covered by snow. Vegetation, appearing red in the false color composite, can be seen in the agricultural fields, and forests in the mountains. Mt. Usu is crossed by three dark streaks. These are the paths of ash deposits that rained out from eruption plumes two days earlier. The prevailing wind was from the northwest, carrying the ash away from the main city of Date. Ash deposited can be traced on the image as far away as 10 kilometers (16 miles) from the volcano. http://photojournal.jpl.nasa.gov/catalog/PIA02608

Variational data assimilation with a semi-Lagrangian semi-implicit global shallow-water equation model and its adjoint

NASA Technical Reports Server (NTRS)

Li, Y.; Navon, I. M.; Courtier, P.; Gauthier, P.

1993-01-01

An adjoint model is developed for variational data assimilation using the 2D semi-Lagrangian semi-implicit (SLSI) shallow-water equation global model of Bates et al. with special attention being paid to the linearization of the interpolation routines. It is demonstrated that with larger time steps the limit of the validity of the tangent linear model will be curtailed due to the interpolations, especially in regions where sharp gradients in the interpolated variables coupled with strong advective wind occur, a synoptic situation common in the high latitudes. This effect is particularly evident near the pole in the Northern Hemisphere during the winter season. Variational data assimilation experiments of 'identical twin' type with observations available only at the end of the assimilation period perform well with this adjoint model. It is confirmed that the computational efficiency of the semi-Lagrangian scheme is preserved during the minimization process, related to the variational data assimilation procedure.

Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil moisture retrievals

NASA Astrophysics Data System (ADS)

Tian, Siyuan; Tregoning, Paul; Renzullo, Luigi J.; van Dijk, Albert I. J. M.; Walker, Jeffrey P.; Pauwels, Valentijn R. N.; Allgeyer, Sébastien

2017-03-01

The accuracy of global water balance estimates is limited by the lack of observations at large scale and the uncertainties of model simulations. Global retrievals of terrestrial water storage (TWS) change and soil moisture (SM) from satellites provide an opportunity to improve model estimates through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. For the first time, TWS observations from the Gravity Recovery and Climate Experiment (GRACE) and near-surface SM observations from the Soil Moisture and Ocean Salinity (SMOS) were jointly assimilated into a water balance model using the Ensemble Kalman Smoother from January 2010 to December 2013 for the Australian continent. The performance of joint assimilation was assessed against open-loop model simulations and the assimilation of either GRACE TWS anomalies or SMOS SM alone. The SMOS-only assimilation improved SM estimates but reduced the accuracy of groundwater and TWS estimates. The GRACE-only assimilation improved groundwater estimates but did not always produce accurate estimates of SM. The joint assimilation typically led to more accurate water storage profile estimates with improved surface SM, root-zone SM, and groundwater estimates against in situ observations. The assimilation successfully downscaled GRACE-derived integrated water storage horizontally and vertically into individual water stores at the same spatial scale as the model and SMOS, and partitioned monthly averaged TWS into daily estimates. These results demonstrate that satellite TWS and SM measurements can be jointly assimilated to produce improved water balance component estimates.

A Variable-Resolution Stretched-Grid General Circulation Model and Data Assimilation System with Multiple Areas of Interest: Studying the Anomalous Regional Climate Events of 1998

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.; Takacs, Lawrence; Govindaraju, Ravi C.; Atlas, Robert (Technical Monitor)

2002-01-01

The new stretched-grid design with multiple (four) areas of interest, one at each global quadrant, is implemented into both a stretched-grid GCM (general circulation model) and a stretched-grid data assimilation system (DAS). The four areas of interest include: the U.S./Northern Mexico, the El Nino area/Central South America, India/China, and the Eastern Indian Ocean/Australia. Both the stretched-grid GCM and DAS annual (November 1997 through December 1998) integrations are performed with 50 km regional resolution. The efficient regional down-scaling to mesoscales is obtained for each of the four areas of interest while the consistent interactions between regional and global scales and the high quality of global circulation, are preserved. This is the advantage of the stretched-grid approach. The global variable resolution DAS incorporating the stretched-grid GCM has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The anomalous regional climate events of 1998 that occurred over the U.S., Mexico, South America, China, India, African Sahel, and Australia are investigated in both simulation and data assimilation modes. Tree assimilated products are also used, along with gauge precipitation data, for validating the simulation results. The obtained results show that the stretched-grid GCM and DAS are capable of producing realistic high quality simulated and assimilated products at mesoscale resolution for regional climate studies and applications.

Data assimilation of ground GPG total electron content into a physics-based ionosheric model by use of the Kalman filter

NASA Technical Reports Server (NTRS)

Hajj, G. A.; Wilson, B. D.; Wang, C.; Pi, X.; Rosen, I. G.

2004-01-01

A three-dimensional (3-D) Global Assimilative Ionospheric Model (GAIM) is currently being developed by a joint University of Southern California and Jet Propulsion Laboratory (JPL) team. To estimate the electron density on a global grid, GAIM uses a first-principles ionospheric physics model and the Kalman filter as one of its possible estimation techniques.

Integrated Modeling of the Battlespace Environment

DTIC Science & Technology

2010-10-01

Office of Counsel.Code 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs (Unclassified/ Unlimited Only). Code 7030 4 Division, Code...ESMF: the Hakamada- Akasofu-Fry version 2 (HAFv2) solar wind model and the global assimilation of ionospheric mea- surements (GAIM1) forecast...ground-truth measurements for comparison with the solar wind predictions. Global Assimilation of Ionospheric Measurements The GAIMv2.3 effort

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

Impact of TRMM and SSM/I-derived Precipitation and Moisture Data on the GEOS Global Analysis

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.

1999-01-01

Current global analyses contain significant errors in primary hydrological fields such as precipitation, evaporation, and related cloud and moisture in the tropics. The Data Assimilation Office at NASA's Goddard Space Flight Center has been exploring the use of space-based rainfall and total precipitable water (TPW) estimates to constrain these hydrological parameters in the Goddard Earth Observing System (GEOS) global data assimilation system. We present results showing that assimilating the 6-hour averaged rain rates and TPW estimates from the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/Imager (SSM/I) instruments improves not only the precipitation and moisture estimates but also reduce state-dependent systematic errors in key climate parameters directly linked to convection such as the outgoing longwave radiation, clouds, and the large-scale circulation. The improved analysis also improves short-range forecasts beyond 1 day, but the impact is relatively modest compared with improvements in the time-averaged analysis. The study shows that, in the presence of biases and other errors of the forecast model, improving the short-range forecast is not necessarily prerequisite for improving the assimilation as a climate data set. The full impact of a given type of observation on the assimilated data set should not be measured solely in terms of forecast skills.

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.

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2015-03-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS package for observation processing (KPOP) system for data assimilation, preprocessing, and quality control modules for bending-angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. The GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending-angle operator, and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS local ensemble transform Kalman filter (LETKF) data assimilation system, which has been successfully implemented to a cubed-sphere model with unstructured quadrilateral meshes. As a result of data processing, the bending-angle departure statistics between observation and background show significant improvement. Also, the first experiment in assimilating GPS-RO bending angle from KPOP within KIAPS-LETKF shows encouraging results.

Continental scale data assimilation of discharge and its effect on flow predictions

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; van Dijk, Albert

2017-04-01

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist (Emmerton et al., 2016). Current global flood forecasting system heavily rely on forecast forcing (precipitation, temperature, reference potential evaporation) to derive initial state estimates of the hydrological model for the next forecast (e.g. by glueing the first day of subsequent forecast as proxy for the historical observed forcing). It is clear that this approach is not perfect and that data assimilation can help to overcome some of the weaknesses of this approach. So far most hydrologic da studies have focused mostly on catchment scale. Here we conduct a da experiment by assimilating multiple streamflow observations across the contiguous united states (CONUS) and Europe into a global hydrological model (W3RA) and run with and without localization method using OpenDA in the global flood forecasting information system (GLOFFIS). It is shown that assimilation of streamflow holds considerable potential for improving global scale flood forecasting (improving NSE scores from 0 to 0.7 and beyond). Weakness in the model (e.g. structural problems and missing processes) and forcing that influence the performance will be highlighted.

Continental scale data assimilation of discharge and its effect on flow predictions across the contiguous US (CONUS)

NASA Astrophysics Data System (ADS)

Weerts, A.; Schellekens, J.; van Dijk, A.; Molenaar, R.

2016-12-01

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist (Emmerton et al., 2016). Current global flood forecasting system heavily rely on forecast forcing (precipitation, temperature, reference potential evaporation) to derive initial state estimates of the hydrological model for the next forecast (e.g. by glueing the first day of subsequent forecast as proxy for the historical observed forcing). It is clear that this approach is not perfect and that data assimilation can help to overcome some of the weaknesses of this approach. So far most hydrologic da studies have focused mostly on catchment scale. Here we conduct a da experiment by assimilating multiple streamflow observations across the contiguous united states (CONUS) into a global hydrological model (W3RA) and run with and without localization method using OpenDA in the global flood forecasting information system (GLOFFIS). It is shown that assimilation of streamflow holds considerable potential for improving global scale flood forecasting (improving NSE scores from 0 to 0.7 and beyond). Weakness in the model (e.g. structural problems and missing processes) and forcing that influence the performance will be highlighted.

G254: USU student payload flown on STS-64 in September, 1994

NASA Technical Reports Server (NTRS)

Raghuram, Tumkur; Monje, Oscar A.; Evans, Brett; Droter, Matt; Lemon, Mark; Redd, Kristen; Hubble, Tina; Wilkinson, Mark; Wilkinson, Michael; Tebbs, Dan

1995-01-01

G254 is the culmination of USU Get Away Special (GAS) students' efforts to get back into space. After a hiatus of a decade, the USU GAS program flew its sixth canister on STS-64 in September 1994. Like its predecessor payloads, this one contained a diverse set of experiments, six in all. Each experiment has its own lessons learned, which hopefully can be passed on to the next generation of GAS students. This presentation will give a balanced view of the successes and failures of G254. Emphasis will be placed on describing the stumbling blocks and the many lessons learned that come from experience rather than academic training. G254 has once again taken a team of about fifteen USU students, plus about one hundred fourth and fifth graders, and given them an immeasurable education.

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.

Method and Early Results of Applying the Global Land Data Assimilation System (GLDAS) in the Third Global Reanalysis of NCEP

NASA Astrophysics Data System (ADS)

Meng, J.; Mitchell, K.; Wei, H.; Yang, R.; Kumar, S.; Geiger, J.; Xie, P.

2008-05-01

Over the past several years, the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) of the U.S. National Weather Service has developed a Global Land Data Assimilation System (GLDAS). For its computational infrastructure, the GLDAS applies the NASA Land Information System (LIS), developed by the Hydrological Science Branch of NASA Goddard Space Flight Center. The land model utilized in the NCEP GLDAS is the NCEP Noah Land Surface Model (Noah LSM). This presentation will 1) describe how the GLDAS component has been included in the development of NCEP's third global reanalysis (with special attention to the input sources of global precipitation), and 2) will present results from the GLDAS component of pilot tests of the new NCEP global reanalysis. Unlike NCEP's past two global reanalysis projects, this new NCEP global reanalysis includes both a global land data assimilation system (GLDAS) and a global ocean data assimilation system (GODAS). The new global reanalysis will span 30-years (1979-2008) and will include a companion realtime operational component. The atmospheric, ocean, and land states of this global reanalysis will provide the initial conditions for NCEP's 3rd- generation global coupled Climate Forecast System (CFS). NCEP is now preparing to launch a 28-year seasonal reforecast project with its new CFS, to provide the reforecast foundation for operational NCEP seasonal climate forecasts using the new CFS. Together, the new global reanalysis and companion CFS reforecasts constitute what NCEP calls the Climate Forecast System Reanalysis and Reforecast (CFSRR) project. Compared to the previous two generations of NCEP global reanalysis, the hallmark of the GLDAS component of CFSRR is GLDAS use of global analyses of observed precipitation to drive the land surface component of the reanalysis (rather than the typical reanalysis approach of using precipitation from the assimilating background atmospheric model). Specifically, the GLDAS merges two global analyses of observed precipitation produced by the Climate Prediction Center (CPC) of NCEP, as follows: 1) a new CPC daily gauge-only land-only global precipitation analysis at 0.5-degree resolution and 2) the well-known CPC CMAP global 2.0 x 2.5 degree 5-day precipitation analysis, which utilizes satellite estimates of precipitation, as well as some gauge observations. The presentation will describe how these two analyses are merged with latitude-dependent weights that favor the gauge-only analysis in mid-latitudes and the satellite-dominated CMAP analysis in tropical latitudes. Finally, we will show some impacts of using GLDAS to initialize the land states of seasonal CFS reforecasts, versus using the previous generation of NCEP global reanalysis as the source for CFS initial land states.

Global 3-D ionospheric electron density reanalysis based on multisource data assimilation

NASA Astrophysics Data System (ADS)

Yue, Xinan; Schreiner, William S.; Kuo, Ying-Hwa; Hunt, Douglas C.; Wang, Wenbin; Solomon, Stanley C.; Burns, Alan G.; Bilitza, Dieter; Liu, Jann-Yenq; Wan, Weixing; Wickert, Jens

2012-09-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis demonstration study during 2002-2011 based on multisource data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground-based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 h in universal time, 5° in latitude, 10° in longitude, and ˜30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content derived from the international GNSS service global ionospheric maps, the observed electron density profiles from the Poker Flat Incoherent Scatter Radar during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Global trends in ocean phytoplankton: a new assessment using revised ocean colour data.

PubMed

Gregg, Watson W; Rousseaux, Cécile S; Franz, Bryan A

2017-01-01

A recent revision of the NASA global ocean colour record shows changes in global ocean chlorophyll trends. This new 18-year time series now includes three global satellite sensors, the Sea-viewing Wide Field of view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite (VIIRS). The major changes are radiometric drift correction, a new algorithm for chlorophyll, and a new sensor VIIRS. The new satellite data record shows no significant trend in global annual median chlorophyll from 1998 to 2015, in contrast to a statistically significant negative trend from 1998 to 2012 in the previous version. When revised satellite data are assimilated into a global ocean biogeochemical model, no trend is observed in global annual median chlorophyll. This is consistent with previous findings for the 1998-2012 time period using the previous processing version and only two sensors (SeaWiFS and MODIS). Detecting trends in ocean chlorophyll with satellites is sensitive to data processing options and radiometric drift correction. The assimilation of these data, however, reduces sensitivity to algorithms and radiometry, as well as the addition of a new sensor. This suggests the assimilation model has skill in detecting trends in global ocean colour. Using the assimilation model, spatial distributions of significant trends for the 18-year record (1998-2015) show recent decadal changes. Most notable are the North and Equatorial Indian Oceans basins, which exhibit a striking decline in chlorophyll. It is exemplified by declines in diatoms and chlorophytes, which in the model are large and intermediate size phytoplankton. This decline is partially compensated by significant increases in cyanobacteria, which represent very small phytoplankton. This suggests the beginning of a shift in phytoplankton composition in these tropical and subtropical Indian basins.

Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

NASA Technical Reports Server (NTRS)

Susskind, Joel; Rosenberg, Bob

2016-01-01

We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

Leaf nitrogen assimilation and partitioning differ among subtropical forest plants in response to canopy addition of nitrogen treatments

Treesearch

Nan Liu; Shuhua Wu; Qinfeng Guo; Jiaxin Wang; Ce Cao; Jun Wang

2018-01-01

Global increases in nitrogen deposition may alter forest structure and function by interferingwith plant nitrogen metabolism (e.g., assimilation and partitioning) and subsequent carbon assimilation, but it is unclear how these responses to nitrogen deposition differ among species. In this study, we conducted a 2-year experiment to investigate the effects of canopy...

A reanalysis of ozone on Mars from assimilation of SPICAM observations

NASA Astrophysics Data System (ADS)

Holmes, James A.; Lewis, Stephen R.; Patel, Manish R.; Lefèvre, Franck

2018-03-01

We have assimilated for the first time SPICAM retrievals of total ozone into a Martian global circulation model to provide a global reanalysis of the ozone cycle. Disagreement in total ozone between model prediction and assimilation is observed between 45°S-10°S from LS = 135-180° and at northern polar (60°N-90°N) latitudes during northern fall (LS = 150-195°). Large percentage differences in total ozone at northern fall polar latitudes identified through the assimilation process are linked with excessive northward transport of water vapour west of Tharsis and over Arabia Terra. Modelling biases in water vapour can also explain the underestimation of total ozone between 45°S-10°S from LS = 135-180°. Heterogeneous uptake of odd hydrogen radicals are unable to explain the outstanding underestimation of northern polar total ozone in late northern fall. Assimilation of total ozone retrievals results in alterations of the modelled spatial distribution of ozone in the southern polar winter high altitude ozone layer. This illustrates the potential use of assimilation methods in constraining total ozone where SPICAM cannot observe, in a region where total ozone is especially important for potential investigations of the polar dynamics.

Understanding the Global Water and Energy Cycle Through Assimilation of Precipitation-Related Observations: Lessons from TRMM and Prospects for GPM

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara; daSilva, Arlindo; Li, Frank; Atlas, Robert (Technical Monitor)

2002-01-01

Understanding the Earth's climate and how it responds to climate perturbations relies on what we know about how atmospheric moisture, clouds, latent heating, and the large-scale circulation vary with changing climatic conditions. The physical process that links these key climate elements is precipitation. Improving the fidelity of precipitation-related fields in global analyses is essential for gaining a better understanding of the global water and energy cycle. In recent years, research and operational use of precipitation observations derived from microwave sensors such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) have shown the tremendous potential of using these data to improve global modeling, data assimilation, and numerical weather prediction. We will give an overview of the benefits of assimilating TRMM and SSM/I rain rates and discuss developmental strategies for using space-based rainfall and rainfall-related observations to improve forecast models and climate datasets in preparation for the proposed multi-national Global Precipitation Mission (GPM).

GNSS-Based Space Weather Systems Including COSMIC Ionospheric Measurements

NASA Technical Reports Server (NTRS)

Komjathy, Attila; Mandrake, Lukas; Wilson, Brian; Iijima, Byron; Pi, Xiaoqing; Hajj, George; Mannucci, Anthony J.

2006-01-01

The presentation outline includes University Corporation for Atmospheric Research (UCAR) and Jet Propulsion Laboratory (JPL) product comparisons, assimilating ground-based global positioning satellites (GPS) and COSMIC into JPL/University of Southern California (USC) Global Assimilative Ionospheric Model (GAIM), and JPL/USC GAIM validation. The discussion of comparisons examines Abel profiles and calibrated TEC. The JPL/USC GAIM validation uses Arecibo ISR, Jason-2 VTEC, and Abel profiles.

Climate Data Guide - Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)

NASA Technical Reports Server (NTRS)

Cullather, Richard; Bosilovich, Michael

2017-01-01

The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) is a global atmospheric reanalysis produced by the NASA Global Modeling and Assimilation Office (GMAO). It spans the satellite observing era from 1980 to the present. The goals of MERRA-2 are to provide a regularly-gridded, homogeneous record of the global atmosphere, and to incorporate additional aspects of the climate system including trace gas constituents (stratospheric ozone), and improved land surface representation, and cryospheric processes. MERRA-2 is also the first satellite-era global reanalysis to assimilate space-based observations of aerosols and represent their interactions with other physical processes in the climate system. The inclusion of these additional components are consistent with the overall objectives of an Integrated Earth System Analysis (IESA). MERRA-2 is intended to replace the original MERRA product, and reflects recent advances in atmospheric modeling and data assimilation. Modern hyperspectral radiance and microwave observations, along with GPS-Radio Occultation and NASA ozone datasets are now assimilated in MERRA-2. Much of the structure of the data files remains the same in MERRA-2. While the original MERRA data format was HDF-EOS, the MERRA-2 supplied binary data format is now NetCDF4 (with lossy compression to save space).

Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

NASA Technical Reports Server (NTRS)

Buchard, V.; Da Silva, A.

2016-01-01

MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

Comparison of Two Global Ocean Reanalyses, NRL Global Ocean Forecast System (GOFS) and U. Maryland Simple Ocean Data Assimilation (SODA)

NASA Astrophysics Data System (ADS)

Richman, J. G.; Shriver, J. F.; Metzger, E. J.; Hogan, P. J.; Smedstad, O. M.

2017-12-01

The Oceanography Division of the Naval Research Laboratory recently completed a 23-year (1993-2015) coupled ocean-sea ice reanalysis forced by NCEP CFS reanalysis fluxes. The reanalysis uses the Global Ocean Forecast System (GOFS) framework of the HYbrid Coordinate Ocean Model (HYCOM) and the Los Alamos Community Ice CodE (CICE) and the Navy Coupled Ocean Data Assimilation 3D Var system (NCODA). The ocean model has 41 layers and an equatorial resolution of 0.08° (8.8 km) on a tri-polar grid with the sea ice model on the same grid that reduces to 3.5 km at the North Pole. Sea surface temperature (SST), sea surface height (SSH) and temperature-salinity profile data are assimilated into the ocean every day. The SSH anomalies are converted into synthetic profiles of temperature and salinity prior to assimilation. Incremental analysis updating of geostrophically balanced increments is performed over a 6-hour insertion window. Sea ice concentration is assimilated into the sea ice model every day. Following the lead of the Ocean Reanalysis Intercomparison Project (ORA-IP), the monthly mean upper ocean heat and salt content from the surface to 300 m, 700m and 1500 m, the mixed layer depth, the depth of the 20°C isotherm, the steric sea surface height and the Atlantic Meridional Overturning Circulation for the GOFS reanalysis and the Simple Ocean Data Assimilation (SODA 3.3.1) eddy-permitting reanalysis have been compared on a global uniform 0.5° grid. The differences between the two ocean reanalyses in heat and salt content increase with increasing integration depth. Globally, GOFS trends to be colder than SODA at all depth. Warming trends are observed at all depths over the 23 year period. The correlation of the upper ocean heat content is significant above 700 m. Prior to 2004, differences in the data assimilated lead to larger biases. The GOFS reanalysis assimilates SSH as profile data, while SODA doesn't. Large differences are found in the Western Boundary Currents, Southern Ocean and equatorial regions. In the Indian Ocean, the Equatorial Counter Current extends to far to the east and the subsurface flow in the thermocline is too weak in GOFS. The 20°C isotherm is biased 2 m shallow in SODA compared to GOFS, but the monthly anomalies in the depth are highly correlated.

On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state

NASA Astrophysics Data System (ADS)

Androsov, Alexey; Nerger, Lars; Schnur, Reiner; Schröter, Jens; Albertella, Alberta; Rummel, Reiner; Savcenko, Roman; Bosch, Wolfgang; Skachko, Sergey; Danilov, Sergey

2018-05-01

General ocean circulation models are not perfect. Forced with observed atmospheric fluxes they gradually drift away from measured distributions of temperature and salinity. We suggest data assimilation of absolute dynamical ocean topography (DOT) observed from space geodetic missions as an option to reduce these differences. Sea surface information of DOT is transferred into the deep ocean by defining the analysed ocean state as a weighted average of an ensemble of fully consistent model solutions using an error-subspace ensemble Kalman filter technique. Success of the technique is demonstrated by assimilation into a global configuration of the ocean circulation model FESOM over 1 year. The dynamic ocean topography data are obtained from a combination of multi-satellite altimetry and geoid measurements. The assimilation result is assessed using independent temperature and salinity analysis derived from profiling buoys of the AGRO float data set. The largest impact of the assimilation occurs at the first few analysis steps where both the model ocean topography and the steric height (i.e. temperature and salinity) are improved. The continued data assimilation over 1 year further improves the model state gradually. Deep ocean fields quickly adjust in a sustained manner: A model forecast initialized from the model state estimated by the data assimilation after only 1 month shows that improvements induced by the data assimilation remain in the model state for a long time. Even after 11 months, the modelled ocean topography and temperature fields show smaller errors than the model forecast without any data assimilation.

Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Lin, Xin; Zhang, Sara Q.; Hou, Arthur Y.

2006-01-01

Global microwave rainfall retrievals from a 5-satellite constellation, including TMI from TRMM, SSWI from DMSP F13, F14 and F15, and AMSR-E from EOS-AQUA, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) using a 1-D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses and forecasts is examined at various temporal and spatial scales. This study demonstrates that the 1-D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over the land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly-mean and 6-hour time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1-D VCA algorithm, by compensating for errors in the model s moist time-tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall-assimilation m especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicates that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity are more realistically captured across the front. Short-term forecasts using initial conditions assimilated with rainfall data also show slight improvements. 1

Air Quality Activities in the Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Pawson, Steven

2016-01-01

GMAO's mission is to enhance the use of NASA's satellite observations in weather and climate modeling. This presentation will be discussing GMAO's mission, value of data assimilation, and some relevant (available) GMAO data products.

NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2008-01-01

This talk will review the status and progress of the NASA/Global Modeling and Assimilation Office (GMAO) atmospheric global reanalysis project called the Modern Era Retrospective-Analysis for Research and Applications (MERRA). An overview of NASA's emerging capabilities for assimilating a variety of other Earth Science observations of the land, ocean, and atmospheric constituents will also be presented. MERRA supports NASA Earth science by synthesizing the current suite of research satellite observations in a climate data context (covering the period 1979-present), and by providing the science and applications communities with of a broad range of weather and climate data with an emphasis on improved estimates of the hydrological cycle. MERRA is based on a major new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5), that includes the Earth System Modeling Framework (ESMF)-based GEOS-5 atmospheric general circulation model and the new NOAA National Centers for Environmental Prediction (NCEP) unified grid-point statistical interpolation (GST) analysis scheme developed as a collaborative effort between NCEP and the GMAO. In addition to MERRA, the GMAO is developing new capabilities in aerosol and constituent assimilation, ocean, ocean biology, and land surface assimilation. This includes the development of an assimilation capability for tropospheric air quality monitoring and prediction, the development of a carbon-cycle modeling and assimilation system, and an ocean data assimilation system for use in coupled short-term climate forecasting.

Utah State University Global Assimilation of Ionospheric Measurements Gauss-Markov Kalman filter model of the ionosphere: Model description and validation

NASA Astrophysics Data System (ADS)

Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2006-11-01

The Utah State University Gauss-Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics-based model of the ionosphere and a Gauss-Markov Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts for five ion species and covers the E region, F region, and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration, the GMKF assimilates slant total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line-of-sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real-time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real-time operations, the model ran continuously and automatically and produced three-dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss-Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.

Why Is Rainfall Error Analysis Requisite for Data Assimilation and Climate Modeling?

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.

2004-01-01

Given the large temporal and spatial variability of precipitation processes, errors in rainfall observations are difficult to quantify yet crucial to making effective use of rainfall data for improving atmospheric analysis, weather forecasting, and climate modeling. We highlight the need for developing a quantitative understanding of systematic and random errors in precipitation observations by examining explicit examples of how each type of errors can affect forecasts and analyses in global data assimilation. We characterize the error information needed from the precipitation measurement community and how it may be used to improve data usage within the general framework of analysis techniques, as well as accuracy requirements from the perspective of climate modeling and global data assimilation.

Chemical OSSEs in Global Modeling and Assimilation Office (GMAO)

NASA Technical Reports Server (NTRS)

Pawson, Steven

2008-01-01

This presentation will summarize ongoing 'chemical observing system simulation experiment (OSSE)' work in the Global Modeling and Assimilation Office (GMAO). Weather OSSEs are being studied in detail, with a 'nature run' based on the European Centre for Medium-Range Weather Forecasts (ECMWF) model that can be sampled by a synthesized suite of satellites that reproduces present-day observations. Chemical OSSEs are based largely on the carbon-cycle project and aim to study (1) how well we can reproduce the observed carbon distribution with the Atmospheric Infrared Sounder (AIRS) and Orbiting Carbon Observatory (OCO) sensors and (2) with what accuracy can we deduce surface sources and sinks of carbon species in an assimilation system.

Operational Space Weather Models: Trials, Tribulations and Rewards

NASA Astrophysics Data System (ADS)

Schunk, R. W.; Scherliess, L.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2009-12-01

There are many empirical, physics-based, and data assimilation models that can probably be used for space weather applications and the models cover the entire domain from the surface of the Sun to the Earth’s surface. At Utah State University we developed two physics-based data assimilation models of the terrestrial ionosphere as part of a program called Global Assimilation of Ionospheric Measurements (GAIM). One of the data assimilation models is now in operational use at the Air Force Weather Agency (AFWA) in Omaha, Nebraska. This model is a Gauss-Markov Kalman Filter (GAIM-GM) model, and it uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside ionosphere from 90 to 1400 km. It takes account of five ion species (NO+, O2+, N2+, O+, H+), but the main output of the model is a 3-dimensional electron density distribution at user specified times. The second data assimilation model uses a physics-based Ionosphere-Plasmasphere Model (IPM) and an ensemble Kalman filter technique as a basis for assimilating a diverse set of real-time (or near real-time) measurements. This Full Physics model (GAIM-FP) is global, covers the altitude range from 90 to 30,000 km, includes six ions (NO+, O2+, N2+, O+, H+, He+), and calculates the self-consistent ionospheric drivers (electric fields and neutral winds). The GAIM-FP model is scheduled for delivery in 2012. Both of these GAIM models assimilate bottom-side Ne profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ Ne from four DMSP satellites, line-of-sight UV emissions measured by satellites, and occultation data. Quality control algorithms for all of the data types are provided as an integral part of the GAIM models and these models take account of latent data (up to 3 hours). The trials, tribulations and rewards of constructing and maintaining operational data assimilation models will be discussed.

Terminator field-aligned current system: A new finding from model-assimilated data set (MADS)

NASA Astrophysics Data System (ADS)

Zhu, L.; Schunk, R. W.; Scherliess, L.; Sojka, J. J.; Gardner, L. C.; Eccles, J. V.; Rice, D.

2013-12-01

Physics-based data assimilation models have been recognized by the space science community as the most accurate approach to specify and forecast the space weather of the solar-terrestrial environment. The model-assimilated data sets (MADS) produced by these models constitute an internally consistent time series of global three-dimensional fields whose accuracy can be estimated. Because of its internal consistency of physics and completeness of descriptions on the status of global systems, the MADS has also been a powerful tool to identify the systematic errors in measurements, reveal the missing physics in physical models, and discover the important dynamical physical processes that are inadequately observed or missed by measurements due to observational limitations. In the past years, we developed a data assimilation model for the high-latitude ionospheric plasma dynamics and electrodynamics. With a set of physical models, an ensemble Kalman filter, and the ingestion of data from multiple observations, the data assimilation model can produce a self-consistent time-series of the complete descriptions of the global high-latitude ionosphere, which includes the convection electric field, horizontal and field-aligned currents, conductivity, as well as 3-D plasma densities and temperatures, In this presentation, we will show a new field-aligned current system discovered from the analysis of the MADS produced by our data assimilation model. This new current system appears and develops near the ionospheric terminator. The dynamical features of this current system will be described and its connection to the active role of the ionosphere in the M-I coupling will be discussed.

A Note on NCOM Temperature Forecast Error Calibration Using the Ensemble Transform

DTIC Science & Technology

2009-01-01

Division Head Ruth H. Preller, 7300 Security, Code 1226 Office of Counsel,Code 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs...problem, local unbiased (correlation) and persistent errors (bias) of the Navy Coastal Ocean Modeling (NCOM) System nested in global ocean domains, are...system were made available in real-time without performing local data assimilation, though remote sensing and global data was assimilated on the

Regional Data Assimilation Using a Stretched-Grid Approach and Ensemble Calculations

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, M. S.; Takacs, L. L.; Govindaraju, R. C.; Atlas, Robert (Technical Monitor)

2002-01-01

The global variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM (Fox-Rabinovitz 2000, Fox-Rabinovitz et al. 2001a,b), has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The major area of interest with enhanced regional resolution used in different SG-DAS experiments includes a rectangle over the U.S. with 50 or 60 km horizontal resolution. The analyses and diagnostics are produced for all mandatory levels from the surface to 0.2 hPa. The assimilated regional mesoscale products are consistent with global scale circulation characteristics due to using the SG-approach. Both the stretched grid and basic uniform grid DASs use the same amount of global grid-points and are compared in terms of regional product quality.

Assimilative modeling of low latitude ionosphere

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Wang, Chunining; Hajj, George A.; Rosen, I. Gary; Wilson, Brian D.; Mannucci, Anthony J.

2004-01-01

In this paper we present an observation system simulation experiment for modeling low-latitude ionosphere using a 3-dimensional (3-D) global assimilative ionospheric model (GAIM). The experiment is conducted to test the effectiveness of GAIM with a 4-D variational approach (4DVAR) in estimation of the ExB drift and thermospheric wind in the magnetic meridional planes simultaneously for all longitude or local time sectors. The operational Global Positioning System (GPS) satellites and the ground-based global GPS receiver network of the International GPS Service are used in the experiment as the data assimilation source. 'The optimization of the ionospheric state (electron density) modeling is performed through a nonlinear least-squares minimization process that adjusts the dynamical forces to reduce the difference between the modeled and observed slant total electron content in the entire modeled region. The present experiment for multiple force estimations reinforces our previous assessment made through single driver estimations conducted for the ExB drift only.

Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.; Balmaseda, Magdalena; Awaji, Toshiyuki; Barnier, Bernard; Behringer, David; Bell, Mike; Bourassa, Mark; Brasseur, Pierre; Breivik, Lars-Anders; Carton, James;

Adjoint-Based Climate Model Tuning: Application to the Planet Simulator

NASA Astrophysics Data System (ADS)

Lyu, Guokun; Köhl, Armin; Matei, Ion; Stammer, Detlef

2018-01-01

The adjoint method is used to calibrate the medium complexity climate model "Planet Simulator" through parameter estimation. Identical twin experiments demonstrate that this method can retrieve default values of the control parameters when using a long assimilation window of the order of 2 months. Chaos synchronization through nudging, required to overcome limits in the temporal assimilation window in the adjoint method, is employed successfully to reach this assimilation window length. When assimilating ERA-Interim reanalysis data, the observations of air temperature and the radiative fluxes are the most important data for adjusting the control parameters. The global mean net longwave fluxes at the surface and at the top of the atmosphere are significantly improved by tuning two model parameters controlling the absorption of clouds and water vapor. The global mean net shortwave radiation at the surface is improved by optimizing three model parameters controlling cloud optical properties. The optimized parameters improve the free model (without nudging terms) simulation in a way similar to that in the assimilation experiments. Results suggest a promising way for tuning uncertain parameters in nonlinear coupled climate models.

Towards the Operational Ensemble-based Data Assimilation System for the Wave Field at the National Weather Service

NASA Astrophysics Data System (ADS)

Flampouris, Stylianos; Penny, Steve; Alves, Henrique

2017-04-01

The National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) provides the operational wave forecast for the US National Weather Service (NWS). Given the continuous efforts to improve forecast, NCEP is developing an ensemble-based data assimilation system, based on the local ensemble transform Kalman filter (LETKF), the existing operational global wave ensemble system (GWES) and on satellite and in-situ observations. While the LETKF was designed for atmospheric applications (Hunt et al 2007), and has been adapted for several ocean models (e.g. Penny 2016), this is the first time applied for oceanic waves assimilation. This new wave assimilation system provides a global estimation of the surface sea state and its approximate uncertainty. It achieves this by analyzing the 21-member ensemble of the significant wave height provided by GWES every 6h. Observations from four altimeters and all the available in-situ measurements are used in this analysis. The analysis of the significant wave height is used for initializing the next forecasting cycle; the data assimilation system is currently being tested for operational use.

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

NASA Astrophysics Data System (ADS)

Isern-Fontanet, Jordi; Ballabrera-Poy, Joaquim; Turiel, Antonio; García-Ladona, Emilio

2017-10-01

Ocean currents play a key role in Earth's climate - they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

Efforts in assimilating Indian satellite data in the NGFS and monitoring of their quality

NASA Astrophysics Data System (ADS)

Prasad, V. S.; Singh, Sanjeev Kumar

2016-05-01

Megha-Tropiques (MT) is an Indo-French Joint Satellite Mission, launched on 12 October 2011. MT-SAPHIR is a sounding instrument with 6 channels near the absorption band of water vapor at 183 GHz, for studying the water cycle and energy exchanges in the tropics. The main objective of this mission is to understand the life cycle of convective systems that influence the tropical weather and climate and their role in associated energy and moisture budget of the atmosphere in tropical regions. India also has a prestigious space programme and has launched the INSAT-3D satellite on 26 July 2013 which has an atmospheric sounder for the first time along with improved VHRR imager. NCMRWF (National Centre for Medium Range Weather Forecasting) is regularly receiving these new datasets and also making changes to its Global Data Assimilation Forecasting (GDAF) system from time-to-time to assimilate these new datasets. A well planned strategy involving various steps such as monitoring of data quality, development of observation operator and quality control procedures, and finally then studying its impact on forecasts is developed to include new observations in global data analysis system. By employing this strategy observations having positive impact on forecast quality such as MT-SAPHIR, and INSAT-3D Clear Sky Radiance (CSR) products are identified and being assimilated in the Global Data Assimilation and Forecasting (GDAF) system.

Aerosol EnKF at GMAO

NASA Technical Reports Server (NTRS)

Buchard, Virginie; Da Silva, Arlindo; Todling, Ricardo

2017-01-01

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASAs Global Modeling and Assimilation Office(GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to an ensemble square root filter(EnSRF; Whitaker and Hamill (2002)) type of scheme.We present a summary of our preliminary results of the assimilation of column integrated aerosol observations (Aerosol Optical Depth; AOD) using an Ensemble Square Root Filters (EnSRF) scheme and the ensemble members produced routinely by the meteorological assimilation.

New species of Schulzia (Nematoda: Molineidae) in Ptychoglossus festae (Squamata: Gymnophthalmidae) from Panama.

PubMed

Bursey, Charles R; Goldberg, Stephen R; Telford, Sam R

2006-10-01

Schulzia ptychoglossi n. sp. (Strongylida: Molineidae) from the intestines of Ptychoglossus festae (Squamata: Gymnophthalmidae) is described and illustrated. Schulzia ptychoglossi n. sp. represents the fourth species assigned to the genus and is most similar to the Venezuelan species S. usu by possessing a cervical inflation that begins a short distance from the anterior end of the body. Schulzia ptychoglossi differs from S. usu in that ray 8 separates midway between the root and tip of the dorsal ray in S. ptychoglossi, but separates close to the root of the dorsal ray in S. usu.

Verification and Validation of a Navy ESPC Hindcast with Loosely Coupled Data Assimilation

NASA Astrophysics Data System (ADS)

Metzger, E. J.; Barton, N. P.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T. R.; Ridout, J. A.; Franklin, D. S.; Zamudio, L.; Posey, P. G.; Reynolds, C. A.; Phelps, M.

2016-12-01

The US Navy is developing an Earth System Prediction Capability (ESPC) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. It will be a fully coupled global atmosphere/ocean/ice/wave/land prediction system providing daily deterministic forecasts out to 16 days at high horizontal and vertical resolution, and daily probabilistic forecasts out to 45 days at lower resolution. The system will run at the Navy DoD Supercomputing Resource Center with an initial operational capability scheduled for the end of FY18 and the final operational capability scheduled for FY22. The individual model and data assimilation components include: atmosphere - NAVy Global Environmental Model (NAVGEM) and Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR); ocean - HYbrid Coordinate Ocean Model (HYCOM) and Navy Coupled Ocean Data Assimilation (NCODA); ice - Community Ice CodE (CICE) and NCODA; WAVEWATCH III™ and NCODA; and land - NAVGEM Land Surface Model (LSM). Currently, NAVGEM/HYCOM/CICE are three-way coupled and each model component is cycling with its respective assimilation scheme. The assimilation systems do not communicate with each other, but future plans call for these to be coupled as well. NAVGEM runs with a 6-hour update cycle while HYCOM/CICE run with a 24-hour update cycle. The T359L50 NAVGEM/0.08° HYCOM/0.08° CICE system has been integrated in hindcast mode and verification/validation metrics have been computed against unassimilated observations and against stand-alone versions of NAVGEM and HYCOM/CICE. This presentation will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled ESPC system is performing with comparable skill to the stand-alone systems at the nowcast time.

A Global Perspective: NASA's Prediction of Worldwide Energy Resources (POWER) Project

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Whitlock, Charles H.

2007-01-01

The Prediction of the Worldwide Energy Resources (POWER) Project, initiated under the NASA Science Mission Directorate Applied Science Energy Management Program, synthesizes and analyzes data on a global scale that are invaluable to the renewable energy industries, especially to the solar and wind energy sectors. The POWER project derives its data primarily from NASA's World Climate Research Programme (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Version 2.9) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (Version 4). The latest development of the NASA POWER Project and its plans for the future are presented in this paper.

Development Of A Data Assimilation Capability For RAPID

NASA Astrophysics Data System (ADS)

Emery, C. M.; David, C. H.; Turmon, M.; Hobbs, J.; Allen, G. H.; Famiglietti, J. S.

2017-12-01

The global decline of in situ observations associated with the increasing ability to monitor surface water from space motivates the creation of data assimilation algorithms that merge computer models and space-based observations to produce consistent estimates of terrestrial hydrology that fill the spatiotemporal gaps in observations. RAPID is a routing model based on the Muskingum method that is capable of estimating river streamflow over large scales with a relatively short computing time. This model only requires limited inputs: a reach-based river network, and lateral surface and subsurface flow into the rivers. The relatively simple model physics imply that RAPID simulations could be significantly improved by including a data assimilation capability. Here we present the early developments of such data assimilation approach into RAPID. Given the linear and matrix-based structure of the model, we chose to apply a direct Kalman filter, hence allowing for the preservation of high computational speed. We correct the simulated streamflows by assimilating streamflow observations and our early results demonstrate the feasibility of the approach. Additionally, the use of in situ gauges at continental scales motivates the application of our new data assimilation scheme to altimetry measurements from existing (e.g. EnviSat, Jason 2) and upcoming satellite missions (e.g. SWOT), and ultimately apply the scheme globally.

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; deSilva, Arlindo M.

2000-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.

1999-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

NASA Astrophysics Data System (ADS)

Scholze, Marko; Buchwitz, Michael; Dorigo, Wouter; Guanter, Luis; Quegan, Shaun

2017-07-01

The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations.

Ocean Spectral Data Assimilation Without Background Error Covariance Matrix

DTIC Science & Technology

2016-01-01

float data (Chu et al. 2007 ), and 97 temporal and spatial variability of the global upper ocean heat content (Chu 2011) from the data 98 of the Global...Melnichenko OV, Wells NC ( 2007 ) Long baroclinic Rossby waves in the 558 tropical North Atlantic observed from profiling floats. J Geophys Res...Hall, J, Harrison D.E. and Stammer , D., Eds., ESA Publication WPP-610 306. 611 612 Tang Y, Kleeman R (2004) SST assimilation experiments in a

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2011-09-30

assimilating satellite, radar and in-situ observations for improved numerical simulations of major Typhoons (Jiangmi, Sinlaku, Nuri and Hagupit) during T- PARC ...oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or

Recent Trends in Global Ocean Chlorophyll

NASA Technical Reports Server (NTRS)

Gregg, Watson; Casey, Nancy

2004-01-01

Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 5% since 1998. The North Pacific ocean basin has increased nearly 19%. To understand the causes of these trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The mode1 utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. Ths enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll.

Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle; Reichle, Rolf; Gruber, Alexander; Bechtold, Michel; Quets, Jan; Vrugt, Jasper; Wigneron, Jean-Pierre

2018-01-01

The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

Global Scale Atmospheric Processes Research Program Review

NASA Technical Reports Server (NTRS)

Worley, B. A. (Editor); Peslen, C. A. (Editor)

1984-01-01

Global modeling; satellite data assimilation and initialization; simulation of future observing systems; model and observed energetics; dynamics of planetary waves; First Global Atmospheric Research Program Global Experiment (FGGE) diagnosis studies; and National Research Council Research Associateship Program are discussed.

Space and ground-based GNSS activities at NOAA

NASA Astrophysics Data System (ADS)

Cucurull, L.

2016-12-01

With the launch of the FORMOSAT-3/COSMIC satellites in April 2006, the availability of Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) observations for operational Numerical Weather Prediction (NWP) applications began. GNSS RO profiles started being assimilated operationally in the major worldwide weather centers soon after. NOAA started assimilating RO data operationally in early 2007. After COSMIC, other missions carrying GNSS RO receivers became available for operational uses. The incorporation of RO observations into the operational assimilation systems was shown to improve global model forecast skill. Since its launch in 2006, the COSMIC constellation has been the mainstay of the global RO system. However, COSMIC is already past the end of its formal lifetime, and only three satellites are still operating. This has motivated NOAA to invest on the COSMIC-2 mission, a 12-satellite constellation, that will replace COSMIC. The first launch, in equatorial orbit, is planned for March 2017. In addition to the space-based component of the GNSS technique, NOAA is assimilating ground-based products into its operational regional models. Although most stations over CONUS provide estimates of Precipitable Water (PW), this is not the case outside the U.S., where the required auxiliary meteorological information is generally not available. Thus, in order to evaluate the impact of ground-based GNSS products on a global weather model, the assimilation of less derived products, such as zenith total delays, rather than PW, is necessary. The talk will include an update on current activities and future plans for the utilization of space and ground-based GNSS products at NOAA. In addition, an update on the COSMIC-2 mission will be discussed.

America's medical school: 5,000 graduates since the "first class".

PubMed

Artino, Anthony R; Gilliland, William R; Cruess, David F; Durning, Steven J

2015-04-01

In 1980, the Uniformed Services University of the Health Sciences (USU) graduated its first class of medical students. As a national university intended to produce "career-committed" military officers and future leaders of the Military Health System, USU functions as the service academy for military medicine and public health. More than 40 years after the school's charter and 5,000 graduates since the first class, we describe the original purpose of USU and provide an update on its achievements. In particular, we address the question of the "staying power" of the University's alumni-the degree to which graduation from the nation's military medical school is associated with long years of devoted service to military medicine. At a time when the MHS is confronting the challenge of extended deployments, rising health care costs, and a growing array of threats to our nation's health, we suggest that America needs USU now more than ever. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

NASA Technical Reports Server (NTRS)

Zaitchik, Benjamin F.; Rodell, Matthew

2008-01-01

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

Assessment of Global Forecast Ocean Assimilation Model (FOAM) using new satellite SST data

NASA Astrophysics Data System (ADS)

Ascione Kenov, Isabella; Sykes, Peter; Fiedler, Emma; McConnell, Niall; Ryan, Andrew; Maksymczuk, Jan

2016-04-01

There is an increased demand for accurate ocean weather information for applications in the field of marine safety and navigation, water quality, offshore commercial operations, monitoring of oil spills and pollutants, among others. The Met Office, UK, provides ocean forecasts to customers from governmental, commercial and ecological sectors using the Global Forecast Ocean Assimilation Model (FOAM), an operational modelling system which covers the global ocean and runs daily, using the NEMO (Nucleus for European Modelling of the Ocean) ocean model with horizontal resolution of 1/4° and 75 vertical levels. The system assimilates salinity and temperature profiles, sea surface temperature (SST), sea surface height (SSH), and sea ice concentration observations on a daily basis. In this study, the FOAM system is updated to assimilate Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) SST data. Model results from one month trials are assessed against observations using verification tools which provide a quantitative description of model performance and error, based on statistical metrics, including mean error, root mean square error (RMSE), correlation coefficient, and Taylor diagrams. A series of hindcast experiments is used to run the FOAM system with AMSR2 and SEVIRI SST data, using a control run for comparison. Results show that all trials perform well on the global ocean and that largest SST mean errors were found in the Southern hemisphere. The geographic distribution of the model error for SST and temperature profiles are discussed using statistical metrics evaluated over sub-regions of the global ocean.

Impact of using scatterometer and altimeter data on storm surge forecasting

NASA Astrophysics Data System (ADS)

Bajo, Marco; De Biasio, Francesco; Umgiesser, Georg; Vignudelli, Stefano; Zecchetto, Stefano

2017-05-01

Satellite data are rarely used in storm surge models because of the lack of established methodologies. Nevertheless, they can provide useful information on surface wind and sea level, which can potentially improve the forecast. In this paper satellite wind data are used to correct the bias of wind originating from a global atmospheric model, while satellite sea level data are used to improve the initial conditions of the model simulations. In a first step, the capability of global winds (biased and unbiased) to adequately force a storm surge model are assessed against that of a high resolution local wind. Then, the added value of direct assimilation of satellite altimeter data in the storm surge model is tested. Eleven storm surge events, recorded in Venice from 2008 to 2012, are simulated using different configurations of wind forcing and altimeter data assimilation. Focusing on the maximum surge peak, results show that the relative error, averaged over the eleven cases considered, decreases from 13% to 7%, using both the unbiased wind and assimilating the altimeter data, while, if the high resolution local wind is used to force the hydrodynamic model, the altimeter data assimilation reduces the error from 9% to 6%. Yet, the overall capabilities in reproducing the surge in the first day of forecast, measured by the correlation and by the rms error, improve only with the use of the unbiased global wind and not with the use of high resolution local wind and altimeter data assimilation.

Customer-oriented Data Formats and Services for Global Land Data Assimilation System (GLDAS) Products at the NASA GES DISC

NASA Technical Reports Server (NTRS)

Fang, Hongliang; Beaudoing, Hiroko; Rodell, Matthew; Teng, BIll; Vollmer, Bruce

2008-01-01

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface Models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of NASA Goddard Earth Sciences Data and Information Services Center (GESDISC).

New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA

NASA Technical Reports Server (NTRS)

Rui, Hualan; Beaudoing, Hiroko Kato; Mocko, David M.; Rodell, Matthew; Teng, William L.; Vollmer. Bruce

2010-01-01

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.

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.

Decadal Prediction Efforts in GMAO (Global Modeling and Assimilation Office)

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.; Suarez, Max; Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) plans to use our GEOS-5 atmosphere-ocean general circulation model (AOGCM) to explore issues associated with predictability on decadal time scales and to contribute to the decadal prediction project that is part ofCMIP5. The GEOS-5 AOGCM is comprised of the GEOS-5 AGCM with the Catchment Land Surface Model, coupled to GFDL's MOM, version 4. We have assimilation systems for both the atmosphere and ocean. For our climate prediction efforts, the atmosphere will be initialized from the GEOS-5 Modem Era Retrospective-analysis for Research and Applications (MERRA), available from 1979 to present at 112 resolution, and from 1948 to present at 2 resolution. The ocean assimilation is conducted within the coupled model framework, using the MERRA as a constraint for both the atmosphere and the ocean. The decadal prediction experiments will be conducted with a 1 atmosphere and a 112 ocean. Some initial results will be presented, focusing on initialization aspects of the GEOS-5 system.

Assimilating All-Sky GPM Microwave Imager(GMI) Radiance Data in NASA GEOS-5 System for Global Cloud and Precipitation Analyses

NASA Astrophysics Data System (ADS)

Kim, M. J.; Jin, J.; McCarty, W.; Todling, R.; Holdaway, D. R.; Gelaro, R.

2014-12-01

The NASA Global Modeling and Assimilation Office (GMAO) works to maximize the impact of satellite observations in the analysis and prediction of climate and weather through integrated Earth system modeling and data assimilation. To achieve this goal, the GMAO undertakes model and assimilation development, generates products to support NASA instrument teams and the NASA Earth science program. Currently Atmospheric Data Assimilation System (ADAS) in the Goddard Earth Observing System Model, Version 5(GEOS-5) system combines millions of observations and short-term forecasts to determine the best estimate, or analysis, of the instantaneous atmospheric state. However, ADAS has been geared towards utilization of observations in clear sky conditions and the majority of satellite channel data affected by clouds are discarded. Microwave imager data from satellites can be a significant source of information for clouds and precipitation but the data are presently underutilized, as only surface rain rates from the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) are assimilated with small weight assigned in the analysis process. As clouds and precipitation often occur in regions with high forecast sensitivity, improvements in the temperature, moisture, wind and cloud analysis of these regions are likely to contribute to significant gains in numerical weather prediction accuracy. This presentation is intended to give an overview of GMAO's recent progress in assimilating the all-sky GPM Microwave Imager (GMI) radiance data in GEOS-5 system. This includes development of various new components to assimilate cloud and precipitation affected data in addition to data in clear sky condition. New observation operators, quality controls, moisture control variables, observation and background error models, and a methodology to incorporate the linearlized moisture physics in the assimilation system are described. In addition preliminary results showing impacts of assimilating all-sky GMI data on GEOS-5 forecasts are discussed.

The Impact of Withholding Observations from TOMS or SBUV Instruments on the GEOS Ozone Data Assimilation System

NASA Technical Reports Server (NTRS)

Stajner, Ovanka; Riishojgaard, Lars Peter; Rood, Richard B.

2000-01-01

In a data assimilation system (DAS), model forecast atmospheric fields, observations and their respective statistics are combined in an attempt to produce the best estimate of these fields. Ozone observations from two instruments are assimilated in the Goddard Earth Observing System (GEOS) ozone DAS: the Total Ozone Mapping Spectrometer (TOMS) and the Solar Backscatter Ultraviolet (SBUV) instrument. The assimilated observations are complementary; TOMS provides a global daily coverage of total column ozone, without profile information, while SBUV measures ozone profiles and total column ozone at nadir only. The purpose of this paper is to examine the performance of the ozone assimilation system in the absence of observations from one of the instruments as it can happen in the event of a failure of an instrument or when there are problems with an instrument for a limited time. Our primary concern is for the performance of the GEOS ozone DAS when it is used in the operational mode to provide near real time analyzed ozone fields in support of instruments on the Terra satellite. In addition, we are planning to produce a longer term ozone record by assimilating historical data. We want to quantify the differences in the assimilated ozone fields that are caused by the changes in the TOMS or SBUV observing network. Our primary interest is in long term and large scale features visible in global statistics of analysis fields, such as differences in the zonal mean of assimilated ozone fields or comparisons with independent observations, While some drifts in assimilated fields occur immediately, after assimilating just one day of different observations, the others develop slowly over several months. Thus, we are also interested in the length of time, which is determined from time series, that is needed for significant changes to take place.

Global assimilation of X Project Loon stratospheric balloon observations

NASA Astrophysics Data System (ADS)

Coy, L.; Schoeberl, M. R.; Pawson, S.; Candido, S.; Carver, R. W.

2017-12-01

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

Global Assimilation of X Project Loon Stratospheric Balloon Observations

NASA Technical Reports Server (NTRS)

Coy, Lawrence; Schoeberl, Mark R.; Pawson, Steven; Candido, Salvatore; Carver, Robert W.

2017-01-01

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

From Low Altitude to High Altitude: Assimilating SAMPEX Data in Global Radiation Belt Models by Quantifying Precipitation and Loss

NASA Astrophysics Data System (ADS)

Tu, W.; Reeves, G. D.; Cunningham, G.; Selesnick, R. S.; Li, X.; Looper, M. D.

2012-12-01

Since its launch in 1992, SAMPEX has been continuously providing measurements of radiation belt electrons at low altitude, which are not only ideal for the direct quantification of the electron precipitation loss in the radiation belt, but also provide data coverage in a critical region for global radiation belt data assimilation models. However, quantitatively combining high-altitude and low-earth-orbit (LEO) measurements on the same L-shell is challenging because LEO measurements typically contain a dynamic mixture of trapped and precipitating populations. Specifically, the electrons measured by SAMPEX can be distinguished as trapped, quasi-trapped (in the drift loss cone), and precipitating (in the bounce loss cone). To simulate the low-altitude electron distribution observed by SAMPEX/PET, a drift-diffusion model has been developed that includes the effects of azimuthal drift and pitch angle diffusion. The simulation provides direct quantification of the rates and variations of electron loss to the atmosphere, a direct input to our Dynamic Radiation Environment Assimilation Model (DREAM) as the electron loss lifetimes. The current DREAM uses data assimilation to combine a 1D radial diffusion model with observational data of radiation belt electrons. In order to implement the mixed electron measurements from SAMPEX into DREAM, we need to map the SAMPEX data from low altitude to high altitudes. To perform the mapping, we will first examine the well-known 'global coherence' of radiation belt electrons by comparing SAMPEX electron fluxes with the energetic electron data from LANL GEO and GPS spacecraft. If the correlation is good, we can directly map the SAMPEX fluxes to high altitudes based on the global coherence; if not, we will use the derived pitch angle distribution from the drift-diffusion model to map up the field and test the mapping by comparing to the high-altitude flux measurements. Then the globally mapped electron fluxes can be assimilated into DREAM. This new implementation of SAMPEX data will greatly augment the data coverage of DREAM and contribute to the global specification of the radiation belt environment.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

NASA Astrophysics Data System (ADS)

Pierre Auger Collaboration; Abreu, P.; Aglietta, M.; Ahlers, M.; Ahn, E. J.; Albuquerque, I. F. M.; Allard, D.; Allekotte, I.; Allen, J.; Allison, P.; Almela, A.; Alvarez Castillo, J.; Alvarez-Muñiz, J.; Ambrosio, M.; Aminaei, A.; Anchordoqui, L.; Andringa, S.; Antiči'C, T.; Aramo, C.; Arganda, E.; Arqueros, F.; Asorey, H.; Assis, P.; Aublin, J.; Ave, M.; Avenier, M.; Avila, G.; Bäcker, T.; Badescu, A. M.; Balzer, M.; Barber, K. B.; Barbosa, A. F.; Bardenet, R.; Barroso, S. L. C.; Baughman, B.; Bäuml, J.; Beatty, J. J.; Becker, B. R.; Becker, K. H.; Bellétoile, A.; Bellido, J. A.; Benzvi, S.; Berat, C.; Bertou, X.; Biermann, P. L.; Billoir, P.; Blanco, F.; Blanco, M.; Bleve, C.; Blümer, H.; Boháčová, M.; Boncioli, D.; Bonifazi, C.; Bonino, R.; Borodai, N.; Brack, J.; Brancus, I.; Brogueira, P.; Brown, W. C.; Bruijn, R.; Buchholz, P.; Bueno, A.; Burton, R. E.; Caballero-Mora, K. S.; Caccianiga, B.; Caramete, L.; Caruso, R.; Castellina, A.; Catalano, O.; Cataldi, G.; Cazon, L.; Cester, R.; Chauvin, J.; Cheng, S. H.; Chiavassa, A.; Chinellato, J. A.; Chirinos Diaz, J.; Chudoba, J.; Clay, R. W.; Coluccia, M. R.; Conceição, R.; Contreras, F.; Cook, H.; Cooper, M. J.; Coppens, J.; Cordier, A.; Coutu, S.; Covault, C. E.; Creusot, A.; Criss, A.; Cronin, J.; Curutiu, A.; Dagoret-Campagne, S.; Dallier, R.; Daniel, B.; Dasso, S.; Daumiller, K.; Dawson, B. R.; de Almeida, R. M.; de Domenico, M.; de Donato, C.; de Jong, S. J.; de La Vega, G.; de Mello Junior, W. J. M.; de Mello Neto, J. R. T.; de Mitri, I.; de Souza, V.; de Vries, K. D.; Del Peral, L.; Del Río, M.; Deligny, O.; Dembinski, H.; Dhital, N.; di Giulio, C.; Díaz Castro, M. L.; Diep, P. N.; Diogo, F.; Dobrigkeit, C.; Docters, W.; D'Olivo, J. C.; Dong, P. N.; Dorofeev, A.; Dos Anjos, J. C.; Dova, M. T.; D'Urso, D.; Dutan, I.; Ebr, J.; Engel, R.; Erdmann, M.; Escobar, C. O.; Espadanal, J.; Etchegoyen, A.; Facal San Luis, P.; Fajardo Tapia, I.; Falcke, H.; Farrar, G.; Fauth, A. C.; Fazzini, N.; Ferguson, A. P.; Fick, B.; Filevich, A.; Filipčič, A.; Fliescher, S.; Fracchiolla, C. E.; Fraenkel, E. D.; Fratu, O.; Fröhlich, U.; Fuchs, B.; Gaior, R.; Gamarra, R. F.; Gambetta, S.; García, B.; Garcia Roca, S. T.; Garcia-Gamez, D.; Garcia-Pinto, D.; Gascon, A.; Gemmeke, H.; Ghia, P. L.; Giaccari, U.; Giller, M.; Glass, H.; Gold, M. S.; Golup, G.; Gomez Albarracin, F.; Gómez Berisso, M.; Gómez Vitale, P. F.; Gonçalves, P.; Gonzalez, D.; Gonzalez, J. G.; Gookin, B.; Gorgi, A.; Gouffon, P.; Grashorn, E.; Grebe, S.; Griffith, N.; Grigat, M.; Grillo, A. F.; Guardincerri, Y.; Guarino, F.; Guedes, G. P.; Guzman, A.; Hansen, P.; Harari, D.; Harrison, T. A.; Harton, J. L.; Haungs, A.; Hebbeker, T.; Heck, D.; Herve, A. E.; Hojvat, C.; Hollon, N.; Holmes, V. C.; Homola, P.; Hörandel, J. R.; Horneffer, A.; Horvath, P.; Hrabovský, M.; Huber, D.; Huege, T.; Insolia, A.; Ionita, F.; Italiano, A.; Jarne, C.; Jiraskova, S.; Josebachuili, M.; Kadija, K.; Kampert, K. H.; Karhan, P.; Kasper, P.; Kégl, B.; Keilhauer, B.; Keivani, A.; Kelley, J. L.; Kemp, E.; Kieckhafer, R. M.; Klages, H. O.; Kleifges, M.; Kleinfeller, J.; Knapp, J.; Koang, D.-H.; Kotera, K.; Krohm, N.; Krömer, O.; Kruppke-Hansen, D.; Kuehn, F.; Kuempel, D.; Kulbartz, J. K.; Kunka, N.; La Rosa, G.; Lachaud, C.; Lahurd, D.; Latronico, L.; Lauer, R.; Lautridou, P.; Le Coz, S.; Leão, M. S. A. B.; Lebrun, D.; Lebrun, P.; Leigui de Oliveira, M. A.; Letessier-Selvon, A.; Lhenry-Yvon, I.; Link, K.; López, R.; Lopez Agüera, A.; Louedec, K.; Lozano Bahilo, J.; Lu, L.; Lucero, A.; Ludwig, M.; Lyberis, H.; Maccarone, M. C.; Macolino, C.; Maldera, S.; Mandat, D.; Mantsch, P.; Mariazzi, A. G.; Marin, J.; Marin, V.; Maris, I. C.; Marquez Falcon, H. R.; Marsella, G.; Martello, D.; Martin, L.; Martinez, H.; Martínez Bravo, O.; Mathes, H. J.; Matthews, J.; Matthews, J. A. J.; Matthiae, G.; Maurel, D.; Maurizio, D.; Mazur, P. O.; Medina-Tanco, G.; Melissas, M.; Melo, D.; Menichetti, E.; Menshikov, A.; Mertsch, P.; Meurer, C.; Mi'Canovi'C, S.; Micheletti, M. I.; Minaya, I. A.; Miramonti, L.; Molina-Bueno, L.; Mollerach, S.; Monasor, M.; Monnier Ragaigne, D.; Montanet, F.; Morales, B.; Morello, C.; Moreno, E.; Moreno, J. C.; Mostafá, M.; Moura, C. A.; Muller, M. A.; Müller, G.; Münchmeyer, M.; Mussa, R.; Navarra, G.; Navarro, J. L.; Navas, S.; Necesal, P.; Nellen, L.; Nelles, A.; Neuser, J.; Nhung, P. T.; Niechciol, M.; Niemietz, L.; Nierstenhoefer, N.; Nitz, D.; Nosek, D.; Nožka, L.; Oehlschläger, J.; Olinto, A.; Ortiz, M.; Pacheco, N.; Pakk Selmi-Dei, D.; Palatka, M.; Pallotta, J.; Palmieri, N.; Parente, G.; Parizot, E.; Parra, A.; Pastor, S.; Paul, T.; Pech, M.; Pȩkala, J.; Pelayo, R.; Pepe, I. M.; Perrone, L.; Pesce, R.; Petermann, E.; Petrera, S.; Petrinca, P.; Petrolini, A.; Petrov, Y.; Pfendner, C.; Piegaia, R.; Pierog, T.; Pieroni, P.; Pimenta, M.; Pirronello, V.; Platino, M.; Ponce, V. H.; Pontz, M.; Porcelli, A.; Privitera, P.; Prouza, M.; Quel, E. J.; Querchfeld, S.; Rautenberg, J.; Ravel, O.; Ravignani, D.; Revenu, B.; Ridky, J.; Riggi, S.; Risse, M.; Ristori, P.; Rivera, H.; Rizi, V.; Roberts, J.; Rodrigues de Carvalho, W.; Rodriguez, G.; Rodriguez Martino, J.; Rodriguez Rojo, J.; Rodriguez-Cabo, I.; Rodríguez-Frías, M. D.; Ros, G.; Rosado, J.; Rossler, T.; Roth, M.; Rouillé-D'Orfeuil, B.; Roulet, E.; Rovero, A. C.; Rühle, C.; Saftoiu, A.; Salamida, F.; Salazar, H.; Salesa Greus, F.; Salina, G.; Sánchez, F.; Santo, C. E.; Santos, E.; Santos, E. M.; Sarazin, F.; Sarkar, B.; Sarkar, S.; Sato, R.; Scharf, N.; Scherini, V.; Schieler, H.; Schiffer, P.; Schmidt, A.; Scholten, O.; Schoorlemmer, H.; Schovancova, J.; Schovánek, P.; Schröder, F.; Schulte, S.; Schuster, D.; Sciutto, S. J.; Scuderi, M.; Segreto, A.; Settimo, M.; Shadkam, A.; Shellard, R. C.; Sidelnik, I.; Sigl, G.; Silva Lopez, H. H.; Sima, O.; 'Smiałkowski, A.; Šmída, R.; Snow, G. R.; Sommers, P.; Sorokin, J.; Spinka, H.; Squartini, R.; Srivastava, Y. N.; Stanic, S.; Stapleton, J.; Stasielak, J.; Stephan, M.; Stutz, A.; Suarez, F.; Suomijärvi, T.; Supanitsky, A. D.; Šuša, T.; Sutherland, M. S.; Swain, J.; Szadkowski, Z.; Szuba, M.; Tapia, A.; Tartare, M.; Taşcău, O.; Tavera Ruiz, C. G.; Tcaciuc, R.; Thao, N. T.; Thomas, D.; Tiffenberg, J.; Timmermans, C.; Tkaczyk, W.; Todero Peixoto, C. J.; Toma, G.; Tomankova, L.; Tomé, B.; Tonachini, A.; Travnicek, P.; Tridapalli, D. B.; Tristram, G.; Trovato, E.; Tueros, M.; Ulrich, R.; Unger, M.; Urban, M.; Valdés Galicia, J. F.; Valiño, I.; Valore, L.; van den Berg, A. M.; Varela, E.; Vargascárdenas, B.; Vázquez, J. R.; Vázquez, R. A.; Veberič, D.; Verzi, V.; Vicha, J.; Videla, M.; Villaseñor, L.; Wahlberg, H.; Wahrlich, P.; Wainberg, O.; Walz, D.; Watson, A. A.; Weber, M.; Weidenhaupt, K.; Weindl, A.; Werner, F.; Westerhoff, S.; Whelan, B. J.; Widom, A.; Wieczorek, G.; Wiencke, L.; Wilczyńska, B.; Wilczyński, H.; Will, M.; Williams, C.; Winchen, T.; Wommer, M.; Wundheiler, B.; Yamamoto, T.; Yapici, T.; Younk, P.; Yuan, G.; Yushkov, A.; Zamorano, B.; Zas, E.; Zavrtanik, D.; Zavrtanik, M.; Zaw, I.; Zepeda, A.; Zhu, Y.; Zimbres Silva, M.; Ziolkowski, M.

2012-04-01

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargüe and averaged monthly models, the utility of the GDAS data is shown.

Description of Atmospheric Conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

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

Abreu, P.; /Lisbon, IST; Aglietta, M.

2012-01-01

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown.

Global Ocean Prediction with the HYbrid Coordinate Ocean Model, HYCOM

NASA Astrophysics Data System (ADS)

Chassignet, E.

A broad partnership of institutions is collaborating in developing and demonstrating the performance and application of eddy-resolving, real-time global and Atlantic ocean prediction systems using the the HYbrid Coordinate Ocean Model (HYCOM). These systems will be transitioned for operational use by both the U.S. Navy at the Naval Oceanographic Office (NAVOCEANO), Stennis Space Center, MS, and the Fleet Numerical Meteorology and Oceanography Centre (FNMOC), Monterey, CA, and by NOAA at the National Centers for Environmental Prediction (NCEP), Washington, D.C. These systems will run efficiently on a variety of massively parallel computers and will include sophisticated data assimilation techniques for assimilation of satellite altimeter sea surface height and sea surface temperature as well as in situ temperature, salinity, and float displacement. The Partnership addresses the Global Ocean Data Assimilation Experiment (GODAE) goals of three-dimensional (3D) depiction of the ocean state at fine resolution in real-time and provision of boundary conditions for coastal and regional models. An overview of the effort will be presented.

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Chou, Shih-Hung; Jedlovec, Gary

2012-01-01

Improvements to global and regional numerical weather prediction (NWP) have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) that mimics the analysis methodology, domain, and observational datasets for the regional North American Mesoscale (NAM) model run at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) are run to examine the impact of each type of AIRS data set. The first configuration will assimilate the AIRS radiance data along with other conventional and satellite data using techniques implemented within the operational system; the second configuration will assimilate AIRS retrieved profiles instead of AIRS radiances in the same manner. Preliminary results of this study will be presented and focus on the analysis impact of the radiances and profiles for selected cases.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

Ozone data assimilation with GEOS-Chem: a comparison between 3-D-Var, 4-D-Var, and suboptimal Kalman filter approaches

NASA Astrophysics Data System (ADS)

Singh, K.; Sandu, A.; Bowman, K. W.; Parrington, M.; Jones, D. B. A.; Lee, M.

2011-08-01

Chemistry transport models determine the evolving chemical state of the atmosphere by solving the fundamental equations that govern physical and chemical transformations subject to initial conditions of the atmospheric state and surface boundary conditions, e.g., surface emissions. The development of data assimilation techniques synthesize model predictions with measurements in a rigorous mathematical framework that provides observational constraints on these conditions. Two families of data assimilation methods are currently widely used: variational and Kalman filter (KF). The variational approach is based on control theory and formulates data assimilation as a minimization problem of a cost functional that measures the model-observations mismatch. The Kalman filter approach is rooted in statistical estimation theory and provides the analysis covariance together with the best state estimate. Suboptimal Kalman filters employ different approximations of the covariances in order to make the computations feasible with large models. Each family of methods has both merits and drawbacks. This paper compares several data assimilation methods used for global chemical data assimilation. Specifically, we evaluate data assimilation approaches for improving estimates of the summertime global tropospheric ozone distribution in August 2006 based on ozone observations from the NASA Tropospheric Emission Spectrometer and the GEOS-Chem chemistry transport model. The resulting analyses are compared against independent ozonesonde measurements to assess the effectiveness of each assimilation method. All assimilation methods provide notable improvements over the free model simulations, which differ from the ozonesonde measurements by about 20 % (below 200 hPa). Four dimensional variational data assimilation with window lengths between five days and two weeks is the most accurate method, with mean differences between analysis profiles and ozonesonde measurements of 1-5 %. Two sequential assimilation approaches (three dimensional variational and suboptimal KF), although derived from different theoretical considerations, provide similar ozone estimates, with relative differences of 5-10 % between the analyses and ozonesonde measurements. Adjoint sensitivity analysis techniques are used to explore the role of of uncertainties in ozone precursors and their emissions on the distribution of tropospheric ozone. A novel technique is introduced that projects 3-D-Variational increments back to an equivalent initial condition, which facilitates comparison with 4-D variational techniques.

Super Dwarf Wheat for Growth in Confined Spaces

NASA Technical Reports Server (NTRS)

Bugbee, Bruce

2011-01-01

USU-Perigee is a dwarf red spring wheat that is a hybrid of a high-yield early tall wheat (USU-Apogee) and a low-yield, extremely short wheat that has poor agronomic characteristics. USU-Perigee was selected for its extremely short height (.0.3 m) and high yield . characteristics that make it suitable for growth in confined spaces in controlled environments. Other desirable characteristics include rapid development and resistance to a leaf-tip necrosis, associated with calcium deficiency, that occurs in other wheat cultivars under rapid-growth conditions (particularly, continuous light). Heads emerge after only 21 days of growth in continuous light at a constant temperature of 25 C. In tests, USU-Perigee was found to outyield other full dwarf (defined as <0.4 m tall) wheat cultivars: The yield advantage at a constant temperature of 23 C was found to be about 30 percent. Originally intended as a candidate food crop to be grown aboard spacecraft on long missions, this cultivar could also be grown in terrestrial growth chambers and could be useful for plant-physiology and -pathology studies.

Simulating PACE Global Ocean Radiances

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Rousseaux, Cecile S.

2017-01-01

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P < 0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and a CDOC (r =0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250-800 nm. These unassimilated radiances were within 0.074 mW/sq cm/micron/sr of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of 10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability. PACE, ocean color, water-leaving radiances, biogeochemical model, radiative transfer model

Simulating PACE Global Ocean Radiances

PubMed Central

Gregg, Watson W.; Rousseaux, Cécile S.

2017-01-01

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P <0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and aCDOC (r = 0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250–800 nm. These unassimilated radiances were within −0.074 mW cm−2 μm1 sr−1 of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of −10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability. PMID:29292403

Assimilation of the AVISO Altimetry Data into the Ocean Dynamics Model with a High Spatial Resolution Using Ensemble Optimal Interpolation (EnOI)

NASA Astrophysics Data System (ADS)

Kaurkin, M. N.; Ibrayev, R. A.; Belyaev, K. P.

2018-01-01

A parallel realization of the Ensemble Optimal Interpolation (EnOI) data assimilation (DA) method in conjunction with the eddy-resolving global circulation model is implemented. The results of DA experiments in the North Atlantic with the assimilation of the Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data from the Jason-1 satellite are analyzed. The results of simulation are compared with the independent temperature and salinity data from the ARGO drifters.

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

Simultaneous shape repulsion and global assimilation in the perception of aspect ratio

PubMed Central

Sweeny, Timothy D.; Grabowecky, Marcia; Suzuki, Satoru

2012-01-01

Although local interactions involving orientation and spatial frequency are well understood, less is known about spatial interactions involving higher level pattern features. We examined interactive coding of aspect ratio, a prevalent two-dimensional feature. We measured perception of two simultaneously flashed ellipses by randomly post-cueing one of them and having observers indicate its aspect ratio. Aspect ratios interacted in two ways. One manifested as an aspect-ratio-repulsion effect. For example, when a slightly tall ellipse and a taller ellipse were simultaneously flashed, the less tall ellipse appeared flatter and the taller ellipse appeared even taller. This repulsive interaction was long range, occurring even when the ellipses were presented in different visual hemifields. The other interaction manifested as a global assimilation effect. An ellipse appeared taller when it was a part of a global vertical organization than when it was a part of a global horizontal organization. The repulsion and assimilation effects temporally dissociated as the former slightly strengthened, and the latter disappeared when the ellipse-to-mask stimulus onset asynchrony was increased from 40 to 140 ms. These results are consistent with the idea that shape perception emerges from rapid lateral and hierarchical neural interactions. PMID:21248223

The Global Precipitation Mission

NASA Technical Reports Server (NTRS)

Braun, Scott; Kummerow, Christian

2000-01-01

The Global Precipitation Mission (GPM), expected to begin around 2006, is a follow-up to the Tropical Rainfall Measuring Mission (TRMM). Unlike TRMM, which primarily samples the tropics, GPM will sample both the tropics and mid-latitudes. The primary, or core, satellite will be a single, enhanced TRMM satellite that can quantify the 3-D spatial distributions of precipitation and its associated latent heat release. The core satellite will be complemented by a constellation of very small and inexpensive drones with passive microwave instruments that will sample the rainfall with sufficient frequency to be not only of climate interest, but also have local, short-term impacts by providing global rainfall coverage at approx. 3 h intervals. The data is expected to have substantial impact upon quantitative precipitation estimation/forecasting and data assimilation into global and mesoscale numerical models. Based upon previous studies of rainfall data assimilation, GPM is expected to lead to significant improvements in forecasts of extratropical and tropical cyclones. For example, GPM rainfall data can provide improved initialization of frontal systems over the Pacific and Atlantic Oceans. The purpose of this talk is to provide information about GPM to the USWRP (U.S. Weather Research Program) community and to discuss impacts on quantitative precipitation estimation/forecasting and data assimilation.

Usefulness of Wave Data Assimilation to the WAVE WATCH III Modeling System

NASA Astrophysics Data System (ADS)

Choi, J. K.; Dykes, J. D.; Yaremchuk, M.; Wittmann, P.

2017-12-01

In-situ and remote-sensed wave data are more abundant currently than in years past, with excellent accuracy at global scales. Forecast skill of the WAVE WATCH III model is improved by assimilation of these measurements and they are also useful for model validation and calibration. It has been known that the impact of assimilation in wind-sea conditions is not large, but spectra that result in large swell with long term propagation are identified and assimilated, the improved accuracy of the initial conditions improve the long-term forecasts. The Navy's assimilation method started with the simple Optimal Interpolation (OI) method. Operationally, Fleet Numerical Meteorology and Oceanography Center uses the sequential 2DVar scheme, but a new approach has been tested based on an adjoint-free method to variational assimilation in WAVE WATCH III. We will present the status of wave data assimilation into the WAVE WATCH III numerical model and upcoming development of this new adjoint-free variational approach.

Global heating distributions for January 1979 calculated from GLA assimilated and simulated model-based datasets

NASA Technical Reports Server (NTRS)

Schaack, Todd K.; Lenzen, Allen J.; Johnson, Donald R.

1991-01-01

This study surveys the large-scale distribution of heating for January 1979 obtained from five sources of information. Through intercomparison of these distributions, with emphasis on satellite-derived information, an investigation is conducted into the global distribution of atmospheric heating and the impact of observations on the diagnostic estimates of heating derived from assimilated datasets. The results indicate a substantial impact of satellite information on diagnostic estimates of heating in regions where there is a scarcity of conventional observations. The addition of satellite data provides information on the atmosphere's temperature and wind structure that is important for estimation of the global distribution of heating and energy exchange.

Impact of TRMM and SSM/I Rainfall Assimilation on Global Analysis and QPF

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara; Reale, Oreste

2002-01-01

Evaluation of QPF skills requires quantitatively accurate precipitation analyses. We show that assimilation of surface rain rates derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) improves quantitative precipitation estimates (QPE) and many aspects of global analyses. Short-range forecasts initialized with analyses with satellite rainfall data generally yield significantly higher QPF threat scores and better storm track predictions. These results were obtained using a variational procedure that minimizes the difference between the observed and model rain rates by correcting the moist physics tendency of the forecast model over a 6h assimilation window. In two case studies of Hurricanes Bonnie and Floyd, synoptic analysis shows that this procedure produces initial conditions with better-defined tropical storm features and stronger precipitation intensity associated with the storm.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

SMAP Data Assimilation at the GMAO

NASA Technical Reports Server (NTRS)

Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.

2016-01-01

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days.

An earlier de motu cordis.

PubMed Central

Daly, Walter J.

2004-01-01

Thirteenth century medical science, like medieval scholarship in general, was directed at reconciliation of Greek philosophy/science with prevailing medieval theology and philosophy. Peter of Spain [later Pope John XXI] was the leading medical scholar of his time. Peter wrote a long book on the soul. Imbedded in it was a chapter on the motion of the heart. Peter's De Motu was based on his own medical experience and Galen's De Usu Partium and De Usu Respirationis and De Usu Pulsuum. This earlier De Motu defines a point on the continuum of intellectual development leading to us and into the future. Thirteenth century scholarship relied on past authority to a degree that continues to puzzle and beg explanation. Images Fig. 1 PMID:17060956

Japanese 25-year reanalysis (JRA-25)

NASA Astrophysics Data System (ADS)

Ohkawara, Nozomu

2006-12-01

A long term global atmospheric reanalysis Japanese 25-year Reanalysis (JRA-25) which covers from 1979 to 2004 was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. This is the first long term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and observational data collected as much as possible, were used in JRA-25 to generate a consistent and high quality reanalysis dataset to contribute to climate research and operational work. One purpose of JRA-25 is to enhance to a high quality the analysis in the Asian region. 6-hourly data assimilation cycles were performed and produced 6-hourly atmospheric analysis and forecast fields with various kinds of physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120km) and 40 vertical layers with the top level at 0.4hPa. For observational data, a great deal of satellite data was used in addition to conventional surface and upper air data. Atmospheric Motion Vector (AMV) data retrieved from geostationary satellites, brightness temperature (TBB) data from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from radiance of microwave radiometer from orbital satellites and some other satellite data were assimilated with 3-dimensional variational method (3DVAR). Many advantages have been found in the JRA-25 reanalysis. Firstly, forecast 6-hour global total precipitation in JRA-25 performs well, distribution and amount are properly represented both in space and time. JRA-25 has the best performance compared to other reanalysis with respect to time series of global precipitation over many years, with few unrealistic variations caused by degraded quality of satellite data due to volcanic eruptions. Secondly, JRA-25 is the first reanalysis which assimilated wind profiles surrounding tropical cyclones retrieved from historical best track information; tropical cyclones were analyzed correctly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is forecast very accurately, and snow depth analysis is also good.

Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

The Mars Analysis Correction Data Assimilation (MACDA): A reference atmospheric reanalysis

NASA Astrophysics Data System (ADS)

Montabone, Luca; Read, Peter; Lewis, Stephen; Steele, Liam; Holmes, James; Valeanu, Alexandru

2016-07-01

The Mars Analysis Correction Data Assimilation (MACDA) dataset version 1.0 contains the reanalysis of fundamental atmospheric and surface variables for the planet Mars covering a period of about three Martian years (late MY 24 to early MY 27). This has been produced by data assimilation of retrieved thermal profiles and column dust optical depths from NASA's Mars Global Surveyor/Thermal Emission Spectrometer (MGS/TES), which have been assimilated into a Mars global climate model (MGCM) using the Analysis Correction scheme developed at the UK Meteorological Office. The MACDA v1.0 reanalysis is publicly available, and the NetCDF files can be downloaded from the archive at the Centre for Environmental Data Analysis/British Atmospheric Data Centre (CEDA/BADC). The variables included in the dataset can be visualised using an ad-hoc graphical user interface (the "MACDA Plotter") at the following URL: http://macdap.physics.ox.ac.uk/ MACDA is an ongoing collaborative project, and work is currently undertaken to produce version 2.0 of the Mars atmospheric reanalysis. One of the key improvements is the extension of the reanalysis period to nine martian years (MY 24 through MY 32), with the assimilation of NASA's Mars Reconnaissance Orbiter/Mars Climate Sounder (MRO/MCS) retrievals of thermal and dust opacity profiles. MACDA 2.0 is also going to be based on an improved version of the underlying MGCM and an updated scheme to fully assimilate (radiative active) tracers, such as dust and water ice.

Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Evaluation of the Impact of Atmospheric Infrared Sounder (AIRS) Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of 6 weeks of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

A 15-year global biogeochemical reanalysis with ocean colour data assimilation

NASA Astrophysics Data System (ADS)

Ford, David; Barciela, Rosa

2013-04-01

A continuous global time-series of remotely sensed ocean colour observations is available from 1997 to the present day. However coverage is incomplete, and limited to the sea surface. Models are therefore required to provide full spatial coverage, and to investigate the relationships between physical and biological variables and the carbon cycle. Data assimilation can then be used to constrain models to fit the observations, thereby combining the advantages of both sources of information. As part of the European Space Agency's Climate Change Initiative (ESA-CCI), we assimilate chlorophyll concentration derived from ocean colour observations into a coupled physical-biogeochemical model. The data assimilation scheme (Hemmings et al., 2008, J. Mar. Res.; Ford et al., 2012, Ocean Sci.) uses the information from the observations to update all biological and carbon cycle state variables within the model. Global daily reanalyses have been produced, with and without assimilation of merged ocean colour data provided by GlobColour, for the period September 1997 to August 2012. The assimilation has been shown to significantly improve the model's representation of chlorophyll concentration, at the surface and at depth. Furthermore, there is evidence of improvement to the representation of pCO2, nutrients and zooplankton concentration compared to in situ observations. We use the results to quantify recent seasonal and inter-annual variability in variables including chlorophyll concentration, air-sea CO2 flux and alkalinity. In particular, we explore the impact of physical drivers such as the El Niño Southern Oscillation (ENSO) on the model's representation of chlorophyll and the carbon cycle, and the pros and cons of the model reanalyses compared to observation-based climatologies. Furthermore, we perform a comparison between the GlobColour product and an initial version of a new merged product being developed as part of the ESA-CCI. Equivalent year-long hindcasts are performed with assimilation of each data set, and compared to a control run. Differences in the products are discussed, along with their impact on model accuracy compared to in situ observations, and the representation of the carbon cycle in each hindcast.

The Impact of Prior Biosphere Models in the Inversion of Global Terrestrial CO2 Fluxes by Assimilating OCO-2 Retrievals

NASA Technical Reports Server (NTRS)

Philip, Sajeev; Johnson, Matthew S.

2018-01-01

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emissions and biospheric fluxes. The processes controlling terrestrial biosphere-atmosphere carbon exchange are currently not fully understood, resulting in terrestrial biospheric models having significant differences in the quantification of biospheric CO2 fluxes. Atmospheric transport models assimilating measured (in situ or space-borne) CO2 concentrations to estimate "top-down" fluxes, generally use these biospheric CO2 fluxes as a priori information. Most of the flux inversion estimates result in substantially different spatio-temporal posteriori estimates of regional and global biospheric CO2 fluxes. The Orbiting Carbon Observatory 2 (OCO-2) satellite mission dedicated to accurately measure column CO2 (XCO2) allows for an improved understanding of global biospheric CO2 fluxes. OCO-2 provides much-needed CO2 observations in data-limited regions facilitating better global and regional estimates of "top-down" CO2 fluxes through inversion model simulations. The specific objectives of our research are to: 1) conduct GEOS-Chem 4D-Var assimilation of OCO-2 observations, using several state-of-the-science biospheric CO2 flux models as a priori information, to better constrain terrestrial CO2 fluxes, and 2) quantify the impact of different biospheric model prior fluxes on OCO-2-assimilated a posteriori CO2 flux estimates. Here we present our assessment of the importance of these a priori fluxes by conducting Observing System Simulation Experiments (OSSE) using simulated OCO-2 observations with known "true" fluxes.

Exploring and Analyzing Climate Variations Online by Using MERRA-2 data at GES DISC

NASA Astrophysics Data System (ADS)

Shen, S.; Ostrenga, D.; Vollmer, B.; Kempler, S.

2016-12-01

NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) (http://giovanni.sci.gsfc.nasa.gov/giovanni/) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Recently, long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, and preprocessing the data. Example data include climate reanalysis from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS) which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM) which assimilates data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Model Parameter Estimation Using Ensemble Data Assimilation: A Case with the Nonhydrostatic Icosahedral Atmospheric Model NICAM and the Global Satellite Mapping of Precipitation Data

NASA Astrophysics Data System (ADS)

Kotsuki, Shunji; Terasaki, Koji; Yashiro, Hasashi; Tomita, Hirofumi; Satoh, Masaki; Miyoshi, Takemasa

2017-04-01

This study aims to improve precipitation forecasts from numerical weather prediction (NWP) models through effective use of satellite-derived precipitation data. Kotsuki et al. (2016, JGR-A) successfully improved the precipitation forecasts by assimilating the Japan Aerospace eXploration Agency (JAXA)'s Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112-km horizontal resolution. Kotsuki et al. mitigated the non-Gaussianity of the precipitation variables by the Gaussian transform method for observed and forecasted precipitation using the previous 30-day precipitation data. This study extends the previous study by Kotsuki et al. and explores an online estimation of model parameters using ensemble data assimilation. We choose two globally-uniform parameters, one is the cloud-to-rain auto-conversion parameter of the Berry's scheme for large scale condensation and the other is the relative humidity threshold of the Arakawa-Schubert cumulus parameterization scheme. We perform the online-estimation of the two model parameters with an ensemble transform Kalman filter by assimilating the GSMaP precipitation data. The estimated parameters improve the analyzed and forecasted mixing ratio in the lower troposphere. Therefore, the parameter estimation would be a useful technique to improve the NWP models and their forecasts. This presentation will include the most recent progress up to the time of the symposium.

Improving Global Analysis and Short-Range Forecast Using Rainfall and Moisture Observations Derived from TRMM and SSM/I Passive Microwave Instruments

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.; Kummerow, Christian D.; Simpson, Joanne

2000-01-01

The Global Precipitation Mission, a satellite project under consideration as a follow-on to the Tropical Rainfall Measuring Mission (TRMM) by the National Aeronautics and Space Agency (NASA) in the United States, the National Space Development Agency (NASDA) in Japan, and other international partners, comprises an improved TRMM-like satellite and a constellation of 8 satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-hour intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using, rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and 2 Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating the 6-h averaged TMI and SSM/I surface rainrate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument and brightness temperature observations by the TIROS Operational Vertical Sounder (TOVS) instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the tropics. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Mannucci, Anthony J.; Verkhoglyadova, Olga; Stephens, Philip; Iijima, Bryron A.

2013-01-01

Modeling and imaging the Earth's ionosphere as well as understanding its structures, inhomogeneities, and disturbances is a key part of NASA's Heliophysics Directorate science roadmap. This invention provides a design tool for scientific missions focused on the ionosphere. It is a scientifically important and technologically challenging task to assess the impact of a new observation system quantitatively on our capability of imaging and modeling the ionosphere. This question is often raised whenever a new satellite system is proposed, a new type of data is emerging, or a new modeling technique is developed. The proposed constellation would be part of a new observation system with more low-Earth orbiters tracking more radio occultation signals broadcast by Global Navigation Satellite System (GNSS) than those offered by the current GPS and COSMIC observation system. A simulation system was developed to fulfill this task. The system is composed of a suite of software that combines the Global Assimilative Ionospheric Model (GAIM) including first-principles and empirical ionospheric models, a multiple- dipole geomagnetic field model, data assimilation modules, observation simulator, visualization software, and orbit design, simulation, and optimization software.

Impact of lateral boundary conditions on regional analyses

NASA Astrophysics Data System (ADS)

Chikhar, Kamel; Gauthier, Pierre

2017-04-01

Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging improved significantly the circulation through the lateral boundaries which translated in a much better agreement with observations.

A storm-time plasmasphere evolution study using data assimilation

NASA Astrophysics Data System (ADS)

Nikoukar, R.; Bust, G. S.; Bishop, R. L.; Coster, A. J.; Lemon, C.; Turner, D. L.; Roeder, J. L.

2017-12-01

In this work, we study the evolution of the Earth's plasmasphere during geomagnetic active periods using the Plasmasphere Data Assimilation (PDA) model. The total electron content (TEC) measurements from an extensive network of global ground-based GPS receivers as well as GPS receivers on-board Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites and Communications/Navigation Outage Forecasting System (C/NOFS) satellite are ingested into the model. Global Core Plasma model, which is an empirical plasmasphere model, is utilized as the background model. Based on the 3D-VAR optimization, the PDA assimilative model benefits from incorporation of regularization techniques to prevent non-physical altitudinal variation in density estimates due to the limited-angle observational geometry. This work focuses on the plasmapause location, plasmasphere erosion time scales and refilling rates during the main and recovery phases of geomagnetic storms as estimated from the PDA 3-dimensional global maps of electron density in the ionosphere/plasmasphere. The comparison between the PDA results with in-situ density measurements from THEMIS and Van Allen Probes, and the RCM-E first-principle model will be also presented.

Assessing Hydrological and Energy Budgets in Amazonia through Regional Downscaling, and Comparisons with Global Reanalysis Products

NASA Astrophysics Data System (ADS)

Nunes, A.; Ivanov, V. Y.

2014-12-01

Although current global reanalyses provide reasonably accurate large-scale features of the atmosphere, systematic errors are still found in the hydrological and energy budgets of such products. In the tropics, precipitation is particularly challenging to model, which is also adversely affected by the scarcity of hydrometeorological datasets in the region. With the goal of producing downscaled analyses that are appropriate for a climate assessment at regional scales, a regional spectral model has used a combination of precipitation assimilation with scale-selective bias correction. The latter is similar to the spectral nudging technique, which prevents the departure of the regional model's internal states from the large-scale forcing. The target area in this study is the Amazon region, where large errors are detected in reanalysis precipitation. To generate the downscaled analysis, the regional climate model used NCEP/DOE R2 global reanalysis as the initial and lateral boundary conditions, and assimilated NOAA's Climate Prediction Center (CPC) MORPHed precipitation (CMORPH), available at 0.25-degree resolution, every 3 hours. The regional model's precipitation was successfully brought closer to the observations, in comparison to the NCEP global reanalysis products, as a result of the impact of a precipitation assimilation scheme on cumulus-convection parameterization, and improved boundary forcing achieved through a new version of scale-selective bias correction. Water and energy budget terms were also evaluated against global reanalyses and other datasets.

Uniformed Services University of the Health Sciences Journal. 2004/5 Edition

DTIC Science & Technology

2005-10-30

Goal 5: STEWARDSHIP: We will protect and enhance the human and physical resources of the University, optimize productivity , promote a...Other OSD- Recognized, Significant Areas of Support and Products Are Provided by USU for the MHS ...... 46-47 - Clinical Support for the Military...Comprehensive Annual Faculty Listing Report ......................................... 51-52 Two Significant OSD Awards Recognize the Multiple Products of USU

Student Outcomes from Experiential Learning on a Student-Run Certified Organic Farm

ERIC Educational Resources Information Center

Reeve, Jennifer R.; Hall, Kelsey; Kalkman, Carol

2014-01-01

The USU Student Organic Farm was initiated in April 2008 on 1 acre (0.4 ha) of land at Utah State University (USU). Students plant, weed, harvest, and market the produce on campus. Proceeds support the student farm in terms of operating expenses, equipment purchases, and modest student stipends. Since 2009, the students have operated a community…

Stereo Image of Mt. Usu Volcano

NASA Technical Reports Server (NTRS)

2002-01-01

On April 3, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra Satellite captured this image of the erupting Mt. Usu volcano in Hokkaido, Japan. This anaglyph stereo image is of Mt Usu volcano. On Friday, March 31, more than 15,000 people were evacuated by helicopter, truck and boat from the foot of Usu, that began erupting from the northwest flank, shooting debris and plumes of smoke streaked with blue lightning thousands of feet in the air. Although no lava gushed from the mountain, rocks and ash continued to fall after the eruption. The region was shaken by thousands of tremors before the eruption. People said they could taste grit from the ash that was spewed as high as 2,700 meters (8,850 ft) into the sky and fell to coat surrounding towns with ash. A 3-D view can be obtained by looking through stereo glasses, with the blue film through your left eye and red film with your right eye at the same time. North is on your right hand side. For more information, see When Rivers of Rock Flow ASTER web page Image courtesy of MITI, ERSDAC, JAROS, and the U.S./Japan ASTER Science Team

El Nino and the Global Ocean Observing System

NASA Technical Reports Server (NTRS)

Halpern, David

1999-01-01

Until a decade ago, an often-quoted expression in oceanography is that very few observations are recorded throughout the ocean. Now, the sentiment is no longer valid in the uppermost 10% of the tropical Pacific Ocean nor at the surface of the global ocean. One of the remarkable legacies of the 1985-1994 Tropical Oceans Global Atmosphere (TOGA) Program is an in situ marine meteorological and upper oceanographic measurement array throughout the equatorial Pacific to monitor the development and maintenance of El Nino episodes. The TOGA Observing System, which initially consisted of moored- and drifting-buoy arrays, a network of commercial ships, and coastal and island stations, now includes a constellation of satellites and data-assimilating models to simulate subsurface oceanographic conditions. The El Nino and La Nina tropical Pacific Ocean observing system represents the initial phase of an integrated global ocean observing system. Remarkable improvements have been made in ocean model simulation of subsurface currents, but some problems persist. For example, the simulation of the South Equatorial Current (SEC) remains an important challenge in the 2S-2N Pacific equatorial wave guide. During El Nino the SEC at the equator is reduced and sometimes the direction is reversed, becoming eastward. Both conditions allow warm water stored in the western Pacific to invade the eastern region, creating an El Nino episode. Assimilation of data is a tenet of faith to correct simulation errors caused by deficiencies in surface fluxes (especially wind stress) and parameterizations of subgrid-scale physical processes. In the first of two numerical experiments, the Pacific SEC was simulated with and without assimilation of subsurface temperature data. Along the equator, a very weak SEC occurred throughout the eastern Pacific, independent of assimilation of data. However, as displayed in the diagram, in the western Pacific there was no satisfactory agreement between the two simulations. To help determine reliability of the simulated SEC in the western Pacific, current measurements recorded during the 9-19 October 1994 voyage of the French research vessel L'Atalante are also shown in the diagram. With data assimilation, the simulated SEC was in much better agreement with L'Atalante observations. The simulated SEC with data assimilation was far from perfect, in part because of the sparsity of subsurface temperature observations. In the next experiment, TOPEX/POSEIDON sea surface height data in combination with subsurface temperatures will be assimilated to assess further improvement of the simulation of the SEC.

Assimilation of MODIS and VIIRS AOD to improve aerosols forecasts with FV3-GOCART

NASA Astrophysics Data System (ADS)

Pagowski, M.

2017-12-01

In 2016 NOAA chose the FV3 dynamical core as a basis for its future global modeling system. We present an implementation of aerosol module in the FV3 model and its assimilation framework. The parameterization of aerosols is based on the GOCART scheme. The assimilation methodology relies on hybrid 3D-Var and EnKF methods. Aerosol observations include aerosol optical depth at 550 nm from VIIRS satellite. Results and evaluation of the system against independent observations and NASA's MERRA-2 is shown.

High Resolution Land Surface Modeling with the next generation Land Data Assimilation Systems

NASA Astrophysics Data System (ADS)

Kumar, S. V.; Eylander, J.; Peters-Lidard, C.

2005-12-01

Knowledge of land surface processes is important to many real-world applications such as agricultural production, water resources management, and flood predication. The Air Force Weather Agency (AFWA) has provided the USDA and other customers global soil moisture and temperature data for the past 30 years using the agrometeorological data assimilation model (now called AGRMET), merging atmospheric data. Further, accurate initialization of land surface conditions has been shown to greatly influence and improve weather forecast model and seasonal-to-interannual climate predictions. The AFWA AGRMET model exploits real time precipitation observations and analyses, global forecast model and satellite data to generate global estimates of soil moisture, soil temperature and other land surface states at 48km spatial resolution. However, to truly address the land surface initialization and climate prediction problem, and to mitigate the errors introduced by the differences in spatial scales of models, representations of land surface conditions need to be developed at the same fine scales such as that of cloud resolving models. NASA's Goddard Space Flight Center has developed an offline land data assimilation system known as the Land Information System (LIS) capable of modeling land atmosphere interactions at spatial resolutions as fine as 1km. LIS provides a software architecture that integrates the use of the state of the art land surface models, data assimilation techniques, and high performance computing and data management tools. LIS also employs many high resolution surface parameters such as the NASA Earth Observing System (EOS)-era products. In this study we describe the development of a next generation high resolution land surface modeling and data assimilation system, combining the capabilities of LIS and AGRMET. We investigate the influence of high resolution land surface data and observations on the land surface conditions by comparing with the operational AGRMET outputs.

Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

NASA Astrophysics Data System (ADS)

Schürmann, Gregor J.; Kaminski, Thomas; Köstler, Christoph; Carvalhais, Nuno; Voßbeck, Michael; Kattge, Jens; Giering, Ralf; Rödenbeck, Christian; Heimann, Martin; Zaehle, Sönke

2016-09-01

We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the JSBACH land-surface scheme, which is part of the MPI-Earth System Model v1. The simulated phenology and net land carbon balance were constrained by globally distributed observations of the fraction of absorbed photosynthetically active radiation (FAPAR, using the TIP-FAPAR product) and atmospheric CO2 at a global set of monitoring stations for the years 2005 to 2009. When constrained by FAPAR observations alone, the system successfully, and computationally efficiently, improved simulated growing-season average FAPAR, as well as its seasonality in the northern extra-tropics. When constrained by atmospheric CO2 observations alone, global net and gross carbon fluxes were improved, despite a tendency of the system to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, thereby increasing the overall appropriateness of the simulated biosphere dynamics and underlying parameter values. Our study thus demonstrates the value of multiple-data-stream assimilation for the simulation of terrestrial biosphere dynamics. It further highlights the potential role of remote sensing data, here the TIP-FAPAR product, in stabilising the strongly underdetermined atmospheric inversion problem posed by atmospheric transport and CO2 observations alone. Notwithstanding these advances, the constraint of the observations on regional gross and net CO2 flux patterns on the MPI-CCDAS is limited through the coarse-scale parametrisation of the biosphere model. We expect improvement through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

Recent Updates to the GEOS-5 Linear Model

NASA Technical Reports Server (NTRS)

Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul

2014-01-01

Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.

Global Gridded Data from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS)

NASA Technical Reports Server (NTRS)

2001-01-01

The Goddard Earth Observing System Data Assimilation System (GEOS-DAS) timeseries is a globally gridded atmospheric data set for use in climate research. This near real-time data set is produced by the Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center in direct support of the operational EOS instrument product generation from the Terra (12/1999 launch), Aqua (05/2002 launch) and Aura (01/2004 launch) spacecrafts. The data is archived in the EOS Core System (ECS) at the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (GES DISC DAAC). The data is only a selection of the products available from the GEOS-DAS. The data is organized chronologically in timeseries format to facilitate the computation of statistics. GEOS-DAS data will be available for the time period January 1, 2000, through present.

Role of the Polar Oceans in Global Climate

NASA Technical Reports Server (NTRS)

Rothrock, D. A.

2003-01-01

The project focused on ice-ocean model development and in particular on the assimilation of ice motion data and ice concentration data into both regional and global models. Many of the resulting publications below deal with improvements made in the physics treated by the model and the procedures for assimilating data. Several papers examine how the ability of the model to simulate the past behavior of the ice cover, especially to represent the ice thickness and ice deformation, is improved by data assimilation. A second aspect of the work involved interpretation of modeled behavior. Resulting papers treat the decline of arctic ice thickness over the last thirty years, and how that decline was caused by a slight warming of the near-surface atmosphere, and also how large variation in ice thickness are due to changes in wind patterns associated with a well- known oscillation of the atmospheric circulation. The research resulted in over 20 published papers on these topics.

Global Eddy-Permitting Ocean Reanalyses and Simulations of the Period 1992 to Present

NASA Astrophysics Data System (ADS)

Parent, L.; Ferry, N.; Barnier, B.; Garric, G.; Bricaud, C.; Testut, C.-E.; Le Galloudec, O.; Lellouche, J.-M.; Greiner, E.; Drevillon, M.; Remy, E.; Moulines, J.-M.; Guinehut, S.; Cabanes, C.

2013-09-01

We present GLORYS2V1 global ocean and sea-ice eddy permitting reanalysis over the altimetric era (1993- 2009). This reanalysis is based on an ocean and sea-ice general circulation model at 1⁄4° horizontal resolution assimilating sea surface temperature, in situ profiles of temperature and salinity and along-track sea level anomaly observations. The reanalysis has been produced along with a reference simulation called MJM95 which allows evaluating the benefits of the data assimilation. In the introduction, we briefly describe the GLORYS2V1 reanalysis system. In sections 2, 3 and 4, the reanalysis skill is presented. Data assimilation diagnostics reveal that the reanalysis is stable all along the time period, with however an improved skill when Argo observation network establishes. GLORYS2V1 captures well climate signals and trends and describes meso-scale variability in a realistic manner.

Home Delivery Medicament Program: access, inactivity and cardiovascular risk.

PubMed

Araújo, Roque da Silva; Arcuri, Edna Apparecida Moura; Lopes, Victor Cauê

2016-10-10

to verify causes of inactivity in the Home Delivery Medicament Program, as referred by users from a Primary Health Care Service in São Paulo, comparing them to the causes registered in the program and analyzing them in the theoretical model Concept of Access to Health. cross-sectional study, interviewing 111 inactive users; and documentary study in the program records. half of the users did not know the condition of inactivity. Discrepancies were found between the user's and the program's information, observing different levels of agreement: Absence of physician and administrative staff member 0%; Transfer to other service 25%; Death 50%; Option to quit 50%; Address change 57% and Change in therapeutic schedule 80%. The users' feeling of accepting the program was observed. In the health access concept, inactivity can be explained in the information dimension, in the degree of asymmetry between the patient's and the health professional's knowledge, identified through the indicators: education, knowledge and information sources. due to the low education level, the user does not assimilate the information on the steps of the program flowchart, does not return for the assessment that guarantees its continuity. Consequently, (s)he stops receiving the medication and spends a long time without treatment, increasing the cardiovascular risk of hypertensive (92% of the sample), diabetic (44%) and dyslipidemic patients (31%). verificar causas de inatividade no Programa Remédio em Casa, referidas por usuários de Unidade Básica de Saúde de São Paulo, comparando-as às registradas pelo programa e analisando-as no modelo teórico Conceito de Acesso à Saúde. estudo transversal entrevistando 111 usuários inativos; e documental, nos registros do programa. metade dos usuários desconhecia a condição de inatividade. Constatadas discrepâncias nas informações usuário versus programa, observando-se diferentes níveis de concordância: Falta de médico e funcionário administrativo 0%; Transferência para outra unidade 25%; Óbito 50%; Opção desistir 50%; Mudança de endereço 57% e Mudança de esquema terapêutico 80%. Observados sentimentos de aceitação do programa pelos usuários. No conceito de acesso à saúde, a inatividade pode ser explicada na dimensão Informação, no grau de assimetria entre o conhecimento do paciente e do profissional de saúde, identificada pelos indicadores: escolaridade, conhecimento e fontes de informação. devido ao baixo nível de escolaridade, o usuário não assimila as informações sobre as etapas do fluxograma do programa, não retorna para a avaliação que garante sua continuidade. Consequentemente, para de receber medicamentos e permanece longo tempo sem tratamento, o que aumenta o risco cardiovascular dos hipertensos (92% da amostra), diabéticos (44%) e dislipidêmicos (31%). verificar causas de inactividad en el Programa Medicamento en Casa, referidas por usuarios de Unidad Básica de Salud de São Paulo, comparándolas a las registradas por el programa y analizándolas en el modelo teórico Concepto de Acceso a la Salud. estudio trasversal entrevistando 111 usuarios inactivos; y documental, en los registros del programa. la mitad de los usuario desconocía la condición de inactividad. Constatadas discrepancias en las informaciones usuario versus programa, observándose diferentes niveles de concordancia: Falta de médico y funcionario administrativo 0%; Trasferencia para otra unidad 25%; Óbito 50%; Opción desistir 50%; Cambio de dirección 57% y Cambio de esquema terapéutico 80%. Observados sentimientos de aceptación del programa por los usuarios. En el concepto de acceso a la salud, la inactividad puede ser explicada en la dimensión Información, en el grado de asimetría entre el conocimiento del paciente y del profesional de salud, identificada por los indicadores: escolaridad, conocimiento y fuentes de información. debido al bajo nivel de escolaridad, el usuario no asimila las informaciones sobre las etapas del diagrama de flujo del programa, no regresa para la evaluación que garantiza su continuidad. Consecuentemente, deja de recibir medicamentos y sigue largo tiempo sin tratamiento, lo que aumenta el riesgo cardiovascular de los hipertensivos (92% de la muestra), diabéticos (44%) y dislipidémicos (31%).

LDAS-Monde: Global scale satellite driven Land Data Assimilation System based on SURFEX modelling platform

NASA Astrophysics Data System (ADS)

Munier, Simon; Albergel, Clément; Leroux, Delphine; Calvet, Jean-Christophe

2017-04-01

In the past decades, large efforts have been made to improve our understanding of the dynamics of the terrestrial water cycle, including vertical and horizontal water fluxes as well as water stored in the biosphere. The soil water content is closely related to the development of the vegetation, which is in turn closely related to the water and energy exchanges with the atmosphere (through evapotranspiration) as well as to carbon fluxes. Land Surface Models (LSMs) are usually designed to represent biogeophysical variables, such as Surface and Root Zone Soil Moisture (SSM, RZSM) or Leaf Area Index (LAI), in order to simulate water, energy and carbon fluxes at the interface between land and atmosphere. With the recent increase of satellite missions and derived products, LSMs can benefit from Earth Observations via Data Assimilation systems to improve their representation of different biogeophysical variables. This study, which is part of the eartH2Observe European project (http://www.earth2observe.eu), presents LDAS-Monde, a global Land Data Assimilation System using an implementation of the Simplified Extended Kalman Filter (SEKF) in the Météo-France's modelling platform (SURFEX). SURFEX is based on the coupling of the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (CTRIP). Two global operational datasets derived from satellite observations are assimilated simultaneously: (i) SSM from the ESA Climate Change Initiative and (ii) LAI from the Copernicus Global Land Service project. Atmospheric forcing used in SURFEX are derived from the ERA-Interim reanalysis and corrected from GPCC precipitations. The simulations are conducted at the global scale at a 1 degree spatial resolution over the period 2000-2014. An analysis of the model sensitivity to the assimilated observations is performed over different regions of the globe under various hydro-climatic conditions. The impact of the SEKF on different biogeophysical and hydrological variables is assessed. It is shown that the assimilation scheme greatly improves the representation of the observed variables (SSM and LAI) and that it effectively affects most of the other variables related to the terrestrial water and vegetation cycles. Future developments include the optimization of LDAS-Monde in order to improve the spatial resolution and then take full advantage of the potential of Earth Observations.

Performance and quality assessment of the recent updated CMEMS global ocean monitoring and forecasting real-time system

NASA Astrophysics Data System (ADS)

Le Galloudec, Olivier; Lellouche, Jean-Michel; Greiner, Eric; Garric, Gilles; Régnier, Charly; Drévillon, Marie; Drillet, Yann

2017-04-01

Since May 2015, Mercator Ocean opened the Copernicus Marine Environment and Monitoring Service (CMEMS) and is in charge of the global eddy resolving ocean analyses and forecast. In this context, Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. R&D activities have been conducted at Mercator Ocean these last years to improve the real-time 1/12° global system for recent updated CMEMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefited of the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting, … This presentation will show the impact of some updates separately, with a particular focus on adaptive tuning experiments of satellite Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) observations errors. For the SLA, the a priori prescribed observation error is globally greatly reduced. The median value of the error changed from 5cm to 2.5cm in a few assimilation cycles. For the SST, we chose to maintain the median value of the error to 0.4°C. The spatial distribution of the SST error follows the model physics and atmospheric variability. Either for SLA or SST, we improve the performances of the system using this adaptive tuning. The overall behavior of the system integrating all updates reporting on the products quality improvements will be also discussed, highlighting the level of performance and the reliability of the new system.

Assimilation of Remotely Sensed Leaf Area Index into the Community Land Model with Explicit Carbon and Nitrogen Components using Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Ling, X.; Fu, C.; Yang, Z. L.; Guo, W.

2017-12-01

Information of the spatial and temporal patterns of leaf area index (LAI) is crucial to understand the exchanges of momentum, carbon, energy, and water between the terrestrial ecosystem and the atmosphere, while both in-situ observation and model simulation usually show distinct deficiency in terms of LAI coverage and value. Land data assimilation, combined with observation and simulation together, is a promising way to provide variable estimation. The Data Assimilation Research Testbed (DART) developed and maintained by the National Centre for Atmospheric Research (NCAR) provides a powerful tool to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to better understanding of all three. Here we systematically investigated the effects of data assimilation on improving LAI simulation based on NCAR Community Land Model with the prognostic carbon-nitrogen option (CLM4CN) linked with DART using the deterministic Ensemble Adjustment Kalman Filter (EAKF). Random 40-member atmospheric forcing was used to drive the CLM4CN with or without LAI assimilation. The Global Land Surface Satellite LAI data (GLASS LAI) LAI is assimilated into the CLM4CN at a frequency of 8 days, and LAI (and leaf carbon / nitrogen) are adjusted at each time step. The results show that assimilating remotely sensed LAI into the CLM4CN is an effective method for improving model performance. In detail, the CLM4-CN simulated LAI systematically overestimates global LAI, especially in low latitude with the largest bias of 5 m2/m2. While if updating both LAI and leaf carbon and leaf nitrogen simultaneously during assimilation, the analyzed LAI can be corrected, especially in low latitude regions with the bias controlled around ±1 m2/m2. Analyzed LAI could also represent the seasonal variation except for the Southern Temperate (23°S-90°S). The obviously improved regions located in the center of Africa, Amazon, the South of Eurasia, the northeast of China, and the west of Europe, where were mainly covered by evergreen/deciduous forests and mixed forests. In addition, the best method for LAI assimilation should include the EAKF method, the accepted percentage of all observation, as well as the carbon-nitrogen control.

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

DART: Tools and Support for Ensemble Data Assimilation Research, Operations, and Education

NASA Astrophysics Data System (ADS)

Hoar, T. J.; Anderson, J. L.; Collins, N.; Raeder, K.; Kershaw, H.; Romine, G. S.; Mizzi, A. P.; Chatterjee, A.; Karspeck, A. R.; Zarzycki, C. M.; Ha, S. Y.; Barre, J.; Gaubert, B.

2014-12-01

The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and supported by the National Center for Atmospheric Research. DART provides a comprehensive suite of software, documentation, examples and tutorials that can be used for ensemble data assimilation research, operations, and education. Scientists and software engineers from the Data Assimilation Research Section at NCAR are available to actively support DART users who want to use existing DART products or develop their own new applications. Current DART users range from university professors teaching data assimilation, to individual graduate students working with simple models, through national laboratories doing operational prediction with large state-of-the-art models. DART runs efficiently on many computational platforms ranging from laptops through thousands of cores on the newest supercomputers. This poster focuses on several recent research activities using DART with geophysical models. First, DART is being used with the Community Atmosphere Model Spectral Element (CAM-SE) and Model for Prediction Across Scales (MPAS) global atmospheric models that support locally enhanced grid resolution. Initial results from ensemble assimilation with both models are presented. DART is also being used to produce ensemble analyses of atmospheric tracers, in particular CO, in both the global CAM-Chem model and the regional Weather Research and Forecast with chemistry (WRF-Chem) model by assimilating observations from the Measurements of Pollution in the Troposphere (MOPITT) and Infrared Atmospheric Sounding Interferometer (IASI) instruments. Results from ensemble analyses in both models are presented. An interface between DART and the Community Atmosphere Biosphere Land Exchange (CABLE) model has been completed and ensemble land surface analyses with DART/CABLE will be discussed. Finally, an update on ensemble analyses in the fully-coupled Community Earth System (CESM) is presented. The poster includes instructions on how to get started using DART for research or educational applications.

Multi-RTM-based Radiance Assimilation to Improve Snow Estimates

NASA Astrophysics Data System (ADS)

Kwon, Y.; Zhao, L.; Hoar, T. J.; Yang, Z. L.; Toure, A. M.

2015-12-01

Data assimilation of microwave brightness temperature (TB) observations (i.e., radiance assimilation (RA)) has been proven to improve snowpack characterization at relatively small scales. However, large-scale applications of RA require a considerable amount of further efforts. Our objective in this study is to explore global-scale snow RA. In a RA scheme, a radiative transfer model (RTM) is an observational operator predicting TB; therefore, the quality of the assimilation results may strongly depend upon the RTM used as well as the land surface model (LSM). Several existing RTMs show different sensitivities to snowpack properties and thus they simulate significantly different TB. At the global scale, snow physical properties vary widely with local climate conditions. No single RTM has been shown to be able to accurately reproduce the observed TB for such a wide range of snow conditions. In this study, therefore, we hypothesize that snow estimates using a microwave RA scheme can be improved through the use of multiple RTMs (i.e., multi-RTM-based approaches). As a first step, here we use two snowpack RTMs, i.e., the Dense Media Radiative Transfer-Multi Layers model (DMRT-ML) and the Microwave Emission Model for Layered Snowpacks (MEMLS). The Community Land Model version 4 (CLM4) is used to simulate snow dynamics. The assimilation process is conducted by the Data Assimilation Research Testbed (DART), which is a community facility developed by the National Center for Atmospheric Research (NCAR) for ensemble-based data assimilation studies. In the RA experiments, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB at 18.7 and 36.5 GHz vertical polarization channels are assimilated into the RA system using the ensemble adjustment Kalman filter. The results are evaluated using the Canadian Meteorological Centre (CMC) daily snow depth, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, and in-situ snowpack and river discharge observations.

Assessment of Data Assimilation with the Prototype High Resolution Rapid Refresh for Alaska (HRRRAK)

NASA Technical Reports Server (NTRS)

Harrison, Kayla; Morton, Don; Zavodsky, Brad; Chou, Shih

2012-01-01

The Arctic Region Supercomputing Center has been running a quasi-operational prototype of a High Resolution Rapid Refresh for Alaska (HRRRAK) at 3km resolution, initialized by the 13km Rapid Refresh (RR). Although the RR assimilates a broad range of observations into its analyses, experiments with the HRRRAK suggest that there may be added value in assimilating observations into the 3km initial conditions, downscaled from the 13km RR analyses. The NASA Short-term Prediction Research and Transition (SPoRT) group has been using assimilated data from the Atmospheric Infrared Sounder (AIRS) in WRF and WRF-Var simulations since 2004 with promising results. The sounder is aboard NASA s Aqua satellite, and provides vertical profiles of temperature and humidity. The Gridpoint Statistical Interpolation (GSI) system is then used to assimilate these vertical profiles into WRF forecasts. In this work, we assess the use of AIRS data in combination with other global data assimilation products on non-assimilated HRRRAK case studies. Two separate weather events will be assessed to qualitatively and quantitatively assess the impacts of AIRS data on HRRRAK forecasts.

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

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

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi

2014-01-01

Radiances from hyperspectral sounders such as the Atmospheric Infrared Sounder (AIRS) are routinely assimilated both globally and regionally in operational numerical weather prediction (NWP) systems using the Gridpoint Statistical Interpolation (GSI) data assimilation system. However, only thinned, cloud-free radiances from a 281-channel subset are used, so the overall percentage of these observations that are assimilated is somewhere on the order of 5%. Cloud checks are performed within GSI to determine which channels peak above cloud top; inaccuracies may lead to less assimilated radiances or introduction of biases from cloud-contaminated radiances.Relatively large footprint from AIRS may not optimally represent small-scale cloud features that might be better resolved by higher-resolution imagers like the Moderate Resolution Imaging Spectroradiometer (MODIS). Objective of this project is to "swap" the MODIS-derived cloud top pressure (CTP) for that designated by the AIRS-only quality control within GSI to test the hypothesis that better representation of cloud features will result in higher assimilated radiance yields and improved forecasts.

Assimilation of SBUV Version 8 Radiances into the GEOS Ozone DAS

NASA Technical Reports Server (NTRS)

Mueller, Martin D.; Stajner, Ivanka; Bhartia, Pawan K.

2004-01-01

In operational weather forecasting, the assimilation of brightness temperatures from satellite sounders, instead of assimilation of 1D-retrievals has become increasingly common practice over the last two decades. Compared to these systems, assimilation of trace gases is still at a relatively early stage of development, and efforts to directly assimilate radiances instead of retrieved products have just begun a few years ago, partially because it requires much more computation power due to the employment of a radiative transport forward model (FM). This paper will focus on a method to assimilate SBUV/2 radiances (albedos) into the Global Earth Observation System Ozone Data Assimilation Scheme (GEOS-03DAS). While SBUV-type instruments cannot compete with newer sensors in terms of spectral and horizontal resolution, they feature a continuous data record back to 1978, which makes them very valuable for trend studies. Assimilation can help spreading their ground coverage over the whole globe, as has been previously demonstrated with the GEOS-03DAS using SBUV Version 6 ozone profiles. Now, the DAS has been updated to use the newly released SBUV Version 8 data. We will compare pre]lmlnarv results of SBUV radiance assimilation with the assimilation of retrieved ozone profiles, discuss methods to deal with the increased computational load, and try to assess the error characteristics and future potential of the new approach.

Is Combat Exposure Predictive of Higher Preoperative Stress in Military Members?

DTIC Science & Technology

2015-01-26

Bopp, Eric, Joseph USU Project Number: N12-P16 4 TSNRP Research Priorities that Study or Project Addresses Primary Priority Force Health...of the caregiver Other: Principal Investigator: Bopp, Eric, Joseph USU Project Number: N12-P16 5 Background The preoperative...e.g., diabetes, thyroid disorders), and (c) autoimmune disorders (e.g., Sjogren’s syndrome ). Patients arriving to the Preoperative Teaching Unit

Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability

NASA Astrophysics Data System (ADS)

Park, Jong-Yeon; Stock, Charles A.; Yang, Xiaosong; Dunne, John P.; Rosati, Anthony; John, Jasmin; Zhang, Shaoqing

2018-03-01

Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

NASA Astrophysics Data System (ADS)

Fillion, Anthony; Bocquet, Marc; Gratton, Serge

2018-04-01

The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss-Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder to satisfy. This is unfortunate because the assimilation performance also increases with this temporal extent. However, a quasi-static (QS) minimization may overcome these local extrema. It accomplishes this by gradually injecting the observations in the cost function. This method was introduced by Pires et al. (1996) in a 4D-Var context. We generalize this approach to four-dimensional strong-constraint nonlinear ensemble variational (EnVar) methods, which are based on both a nonlinear variational analysis and the propagation of dynamical error statistics via an ensemble. This forces one to consider the cost function minimizations in the broader context of cycled data assimilation algorithms. We adapt this QS approach to the iterative ensemble Kalman smoother (IEnKS), an exemplar of nonlinear deterministic four-dimensional EnVar methods. Using low-order models, we quantify the positive impact of the QS approach on the IEnKS, especially for long data assimilation windows. We also examine the computational cost of QS implementations and suggest cheaper algorithms.

A Decadal Inversion of CO2 Using the Global Eulerian-Lagrangian Coupled Atmospheric Model (GELCA): Sensitivity to the Ground-Based Observation Network

NASA Technical Reports Server (NTRS)

Shirai, T.; Ishizawa, M.; Zhuravlev, R.; Ganshin, A.; Belikov, D.; Saito, M.; Oda, T.; Valsala, V.; Gomez-Pelaez, A. J.; Langenfelds, R.;

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

NASA Technical Reports Server (NTRS)

Atlas, Robert; Hou, Arthur; Reale, Oreste

2004-01-01

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

The Impact of AMSR-E Soil Moisture Assimilation on Evapotranspiration Estimation

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Kumar, Sujay; Mocko, David; Tian, Yudong

2012-01-01

An assessment ofETestimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 CNLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

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.

Contribution Of The SWOT Mission To Large-Scale Hydrological Modeling Using Data Assimilation

NASA Astrophysics Data System (ADS)

Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Rochoux, M. C.; Garambois, P. A.; Paris, A.; Calmant, S.

2016-12-01

The purpose of this work is to improve water fluxes estimation on the continental surfaces, at interanual and interseasonal scale (from few years to decennial time period). More specifically, it studies contribution of the incoming SWOT satellite mission to improve hydrology model at global scale, and using the land surface model ISBA-TRIP. This model corresponds to the continental component of the CNRM (French meteorological research center)'s climatic model. This study explores the potential of satellite data to correct either input parameters of the river routing scheme TRIP or its state variables. To do so, a data assimilation platform (using an Ensemble Kalman Filter, EnKF) has been implemented to assimilate SWOT virtual observations as well as discharges estimated from real nadir altimetry data. A series of twin experiments is used to test and validate the parameter estimation module of the platform. SWOT virtual-observations of water heights along SWOT tracks (with a 10 cm white noise model error) are assimilated to correct the river routing model parameters. To begin with, we chose to focus exclusively on the river manning coefficient, with the possibility to easily extend to other parameters such as the river widths. First results show that the platform is able to recover the "true" Manning distribution assimilating SWOT-like water heights. The error on the coefficients goes from 35 % before assimilation to 9 % after four SWOT orbit repeat period of 21 days. In the state estimation mode, daily assimilation cycles are realized to correct TRIP river water storage initial state by assimilating ENVISAT-based discharge. Those observations are derived from ENVISAT water elevation measures, using rating curves from the MGB-IPH hydrological model (calibrated over the Amazon using in situ gages discharge). Using such kind of observation allows going beyond idealized twin experiments and also to test contribution of a remotely-sensed discharge product, which could prefigure the SWOT discharge product. The results show that discharge after assimilation are globally improved : the root-mean-square error between the analysis discharge ensemble mean and in situ discharges is reduced by 30 %, compared to the root-mean-square error between the free run and in situ discharges.

Further Exploring the Potential for Assimilation of Unmanned Aircraft Observations to Benefit Hurricane Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Sippel, Jason A.; Zhang, Fuqing; Weng, Yonghui; Braun, Scott A.; Cecil, Daniel J.

2015-01-01

This study explores the potential of assimilating data from multiple instruments onboard high-altitude, long-endurance unmanned aircraft to improve hurricane analyses and forecasts. A recent study found a significant positive impact on analyses and forecasts of Hurricane Karl when an ensemble Kalman filter was used to assimilate data from the High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), a new Doppler radar onboard the NASA Global Hawk (GH) unmanned airborne system. The GH can also carry other useful instruments, including dropsondes and the Hurricane Imaging Radiometer (HIRAD), which is a new radiometer that estimates large swaths of wind speeds and rainfall at the ocean surface. The primary finding is that simultaneously assimilating data from HIWRAP and the other GH-compatible instruments results in further analysis and forecast improvement for Karl. The greatest improvement comes when HIWRAP, HIRAD, and dropsonde data are simultaneously assimilated.

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

2014-01-01

Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

Data Assimilation Results from PLASMON

NASA Astrophysics Data System (ADS)

Jorgensen, A. M.; Lichtenberger, J.; Duffy, J.; Friedel, R. H.; Clilverd, M.; Heilig, B.; Vellante, M.; Manninen, J. K.; Raita, T.; Rodger, C. J.; Collier, A.; Reda, J.; Holzworth, R. H.; Ober, D. M.; Boudouridis, A.; Zesta, E.; Chi, P. J.

2013-12-01

VLF and magnetometer observations can be used to remotely sense the plasmasphere. VLF whistler waves can be used to measure the electron density and magnetic Field Line Resonance (FLR) measurements can be used to measure the mass density. In principle it is then possible to remotely map the plasmasphere with a network of ground-based stations which are also less expensive and more permanent than satellites. The PLASMON project, funded by the EU FP-7 program, is in the process of doing just this. A large number of ground-based observations will be input into a data assimilative framework which models the plasmasphere structure and dynamics. The data assimilation framework combines the Ensemble Kalman Filter with the Dynamic Global Core Plasma Model. In this presentation we will describe the plasmasphere model, the data assimilation approach that we have taken, PLASMON data and data assimilation results for specific events.

US Navy Operational Global Ocean and Arctic Ice Prediction Systems

DTIC Science & Technology

2014-09-01

meridional overturning circulation (Figure 29 in Hurlburt et al., 2011), when comparing a non-assimilative simulation with a data assimilative...boundary current regions of the Gulf Stream ( Atlantic ), the Kuroshio (Pacific), and the Agulhas and Somali Currents (both Indian). Consequently...Oceanic and Atmospheric Administration at the National Centers for Environmental Prediction (NCEP), initially for the North Atlantic (Mehra and

Ocean Data Impacts in Global HYCOM

DTIC Science & Technology

2014-08-01

The purpose of assimilation is to reduce the model initial condition error. Improved initial con- ditions should lead to an improved forecast...the determination of locations where forecast errors are sensitive to the initial conditions are essential for improving the data assimilation system...longwave radiation, total (large scale plus convective) precipitation, ground/sea temperature, zonal and me- ridional wind velocities at 10m, mean sea

The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

NASA Astrophysics Data System (ADS)

Zakeri, Zeinab; Azadi, Majid; Ghader, Sarmad

2018-01-01

Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.

Assimilation of seasonal chlorophyll and nutrient data into an adjoint three-dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization

NASA Astrophysics Data System (ADS)

Tjiputra, Jerry F.; Polzin, Dierk; Winguth, Arne M. E.

2007-03-01

An adjoint method is applied to a three-dimensional global ocean biogeochemical cycle model to optimize the ecosystem parameters on the basis of SeaWiFS surface chlorophyll observation. We showed with identical twin experiments that the model simulated chlorophyll concentration is sensitive to perturbation of phytoplankton and zooplankton exudation, herbivore egestion as fecal pellets, zooplankton grazing, and the assimilation efficiency parameters. The assimilation of SeaWiFS chlorophyll data significantly improved the prediction of chlorophyll concentration, especially in the high-latitude regions. Experiments that considered regional variations of parameters yielded a high seasonal variance of ecosystem parameters in the high latitudes, but a low variance in the tropical regions. These experiments indicate that the adjoint model is, despite the many uncertainties, generally capable to optimize sensitive parameters and carbon fluxes in the euphotic zone. The best fit regional parameters predict a global net primary production of 36 Pg C yr-1, which lies within the range suggested by Antoine et al. (1996). Additional constraints of nutrient data from the World Ocean Atlas showed further reduction in the model-data misfit and that assimilation with extensive data sets is necessary.

High-Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

NASA Astrophysics Data System (ADS)

Zhang, Qi; Pan, Yinong; Wang, Shuyu; Xu, Jianjun; Tang, Jianping

2017-10-01

Globally, reanalysis data sets are widely used in assessing climate change, validating numerical models, and understanding the interactions between the components of a climate system. However, due to the relatively coarse resolution, most global reanalysis data sets are not suitable to apply at the local and regional scales directly with the inadequate descriptions of mesoscale systems and climatic extreme incidents such as mesoscale convective systems, squall lines, tropical cyclones, regional droughts, and heat waves. In this study, by using a data assimilation system of Gridpoint Statistical Interpolation, and a mesoscale atmospheric model of Weather Research and Forecast model, we build a regional reanalysis system. This is preliminary and the first experimental attempt to construct a high-resolution reanalysis for China main land. Four regional test bed data sets are generated for year 2013 via three widely used methods (classical dynamical downscaling, spectral nudging, and data assimilation) and a hybrid method with data assimilation coupled with spectral nudging. Temperature at 2 m, precipitation, and upper level atmospheric variables are evaluated by comparing against observations for one-year-long tests. It can be concluded that the regional reanalysis with assimilation and nudging methods can better produce the atmospheric variables from surface to upper levels, and regional extreme events such as heat waves, than the classical dynamical downscaling. Compared to the ERA-Interim global reanalysis, the hybrid nudging method performs slightly better in reproducing upper level temperature and low-level moisture over China, which improves regional reanalysis data quality.

Regional Climate Simulation and Data Assimilation with Variable-Resolution GCMs

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.

2002-01-01

Variable resolution GCMs using a global stretched grid (SG) with enhanced regional resolution over one or multiple areas of interest represents a viable new approach to regional climateklimate change and data assimilation studies and applications. The multiple areas of interest, at least one within each global quadrant, include the major global mountains and major global monsoonal circulations over North America, South America, India-China, and Australia. They also can include the polar domains, and the European and African regions. The SG-approach provides an efficient regional downscaling to mesoscales, and it is an ideal tool for representing consistent interactions of globaYlarge- and regionallmeso- scales while preserving the high quality of global circulation. Basically, the SG-GCM simulations are no different from those of the traditional uniform-grid GCM simulations besides using a variable-resolution grid. Several existing SG-GCMs developed by major centers and groups are briefly described. The major discussion is based on the GEOS (Goddard Earth Observing System) SG-GCM regional climate simulations.

A Global Rapid Integrated Monitoring System for Water Cycle and Water Resource Assessment (Global-RIMS)

NASA Technical Reports Server (NTRS)

Roads, John; Voeroesmarty, Charles

2005-01-01

The main focus of our work was to solidify underlying data sets, the data processing tools and the modeling environment needed to perform a series of long-term global and regional hydrological simulations leading eventually to routine hydrometeorological predictions. A water and energy budget synthesis was developed for the Mississippi River Basin (Roads et al. 2003), in order to understand better what kinds of errors exist in current hydrometeorological data sets. This study is now being extended globally with a larger number of observations and model based data sets under the new NASA NEWS program. A global comparison of a number of precipitation data sets was subsequently carried out (Fekete et al. 2004) in which it was further shown that reanalysis precipitation has substantial problems, which subsequently led us to the development of a precipitation assimilation effort (Nunes and Roads 2005). We believe that with current levels of model skill in predicting precipitation that precipitation assimilation is necessary to get the appropriate land surface forcing.

Uniformed Services University of the Health Sciences. Journal 2001 Edition

DTIC Science & Technology

2002-05-15

faculty are actively working with other government and civilian institutions to share their internationally recognized expertise in weapons of mass...leadership are essential components of readiness. Today, USU SOM alumni represent 21 percent of the 11,833 physicians on active duty in the MHS; approximately...and Off-Campus Members of USU Departments and Programs and Department Activities Receiving Special Recognition During 2001 TABLE OF CONTENTS 1. THE

Identifying strengths and weaknesses of Quality Management Unit University of Sumatera Utara software using SCAMPI C

NASA Astrophysics Data System (ADS)

Gunawan, D.; Amalia, A.; Rahmat, R. F.; Muchtar, M. A.; Siregar, I.

2018-02-01

Identification of software maturity level is a technique to determine the quality of the software. By identifying the software maturity level, the weaknesses of the software can be observed. As a result, the recommendations might be a reference for future software maintenance and development. This paper discusses the software Capability Level (CL) with case studies on Quality Management Unit (Unit Manajemen Mutu) University of Sumatera Utara (UMM-USU). This research utilized Standard CMMI Appraisal Method for Process Improvement class C (SCAMPI C) model with continuous representation. This model focuses on activities for developing quality products and services. The observation is done in three process areas, such as Project Planning (PP), Project Monitoring and Control (PMC), and Requirements Management (REQM). According to the measurement of software capability level for UMM-USU software, turns out that the capability level for the observed process area is in the range of CL1 and CL2. Planning Project (PP) is the only process area which reaches capability level 2, meanwhile, PMC and REQM are still in CL 1 or in performed level. This research reveals several weaknesses of existing UMM-USU software. Therefore, this study proposes several recommendations for UMM-USU to improve capability level for observed process areas.

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.

Longitudinal Variations of Low-Latitude Ionospheric Irregularities during the 2015 St. Patrick's Day Storm

NASA Astrophysics Data System (ADS)

Pi, X.; Vergados, P.

2017-12-01

GPS data from more than 2000 globally distributed ground-based stations are processed to generate Global Map of Ionospheric Irregularities and Scintillation (GMIIS) at 5-minite cadence for the 2015 St. Patrick's Day Storm. The time sequence of GMIIS provides global snapshots of evolving ionospheric irregularities that are helpful in investigations of small-scale ionospheric perturbations globally. Such data from selected stations at longitudes distributed around the globe are also analyzed to investigate longitudinal variations of low-latitude ionospheric irregularities (LLII) during the storm. Prior to the storm day, The GPS data show typical seasonal (March equinox) activities of LLII during evening hours in different longitude regions, i.e., active in American through Asian longitudes but relatively inactive in the Pacific sector. The data also reveal dramatic changes in LLII during the storm main phase (17 March 2015) and recovery phase (18-19 March 2015). While remaining inactive in the Pacific region, LLII have gone through complicated variations in the longitude regions of high scintillation season. The variations include active, weakened or suppressed, or post-midnight triggering during the storm main phase and recovery phase depending on specific longitude. To understand possible responsible causes of these variations in different longitudes, the Global Assimilative Ionospheric Model (GAIM) is used to reproduce ambient ionospheric state and its disturbances. For this storm study, GAIM assimilates GPS data from about 650 globally distributed stations and from spaceborne receivers onboard the COSMIC satellites. The global assimilative modeling enables us to investigate the changes of the equatorial ionospheric anomaly (EIA) and corresponding ionospheric dynamical processes in the concerned longitudes. This presentation will combine pictures of small- and large-scale ionospheric perturbations and attempt to obtain insight into mechanisms that drive LLII changes during the major storm.

Extending the reanalysis to the ionosphere based on ground and LEO based GNSS observations

NASA Astrophysics Data System (ADS)

Yue, X.; Schreiner, W. S.; Kuo, Y.

2012-12-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis during 2002-2011 based on multi-source data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 hr in universal time, 5o in latitude, 10o in longitude, and ~ 30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content (GEC) derived from the international GNSS service (IGS) global ionospheric maps (GIM), the observed electron density profiles from the Poker Flat Incoherent Scatter Radar (PFISR) during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F-region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture

NASA Astrophysics Data System (ADS)

Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.

2016-06-01

Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data assimilation on the evaporation fields is very mild, and difficult to assess due to the limited availability of eddy-covariance data. Nonetheless, our continental-scale simulations indicate that the assimilation of soil moisture can have a substantial impact on the estimated dynamics of evaporation in water-limited regimes. Progressing towards our goal of using satellite soil moisture to increase understanding of global land evaporation, future research will focus on the global application of this methodology and the consideration of multiple evaporation models.

Heat balance statistics derived from four-dimensional assimilations with a global circulation model

NASA Technical Reports Server (NTRS)

Schubert, S. D.; Herman, G. F.

1981-01-01

The reported investigation was conducted to develop a reliable procedure for obtaining the diabatic and vertical terms required for atmospheric heat balance studies. The method developed employs a four-dimensional assimilation mode in connection with the general circulation model of NASA's Goddard Laboratory for Atmospheric Sciences. The initial analysis was conducted with data obtained in connection with the 1976 Data Systems Test. On the basis of the results of the investigation, it appears possible to use the model's observationally constrained diagnostics to provide estimates of the global distribution of virtually all of the quantities which are needed to compute the atmosphere's heat and energy balance.

The NASA Modern Era Reanalysis for Research and Applications, Version-2 (MERRA-2)

NASA Astrophysics Data System (ADS)

Gelaro, R.; McCarty, W.; Molod, A.; Suarez, M.; Takacs, L.; Todling, R.

2014-12-01

The NASA Modern Era Reanalysis for Research Applications Version-2 (MERRA-2) is a reanalysis for the satellite era using an updated version of the Goddard Earth Observing System Data Assimilation System Version-5 (GEOS-5) produced by the Global Modeling and Assimilation Office (GMAO). MERRA-2 will assimilate meteorological and aerosol observations not available to MERRA and includes improvements to the GEOS-5 model and analysis scheme so as to provide an ongoing climate analysis beyond MERRA's terminus. MERRA-2 will also serve as a development milestone for a future GMAO coupled Earth system analysis. Production of MERRA-2 began in June 2014 in four processing streams, with convergence to a single near-real time climate analysis expected by early 2015. This talk provides an overview of the MERRA-2 system developments and key science results. For example, compared with MERRA, MERRA-2 exhibits a well-balanced relationship between global precipitation and evaporation, with significantly reduced sensitivity to changes in the global observing system through time. Other notable improvements include reduced biases in the tropical middle- and upper-tropospheric wind and near-surface temperature over continents.

An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

NASA Astrophysics Data System (ADS)

Masina, Simona; Storto, Andrea; Ferry, Nicolas; Valdivieso, Maria; Haines, Keith; Balmaseda, Magdalena; Zuo, Hao; Drevillon, Marie; Parent, Laurent

2017-08-01

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993-2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

Impact of Flow-Dependent Error Correlations and Tropospheric Chemistry on Assimilated Ozone

NASA Technical Reports Server (NTRS)

Wargan, K.; Stajner, I.; Hayashi, H.; Pawson, S.; Jones, D. B. A.

2003-01-01

The presentation compares different versions of a global three-dimensional ozone data assimilation system developed at NASA's Data Assimilation Office. The Solar Backscatter Ultraviolet/2 (SBUV/2) total and partial ozone column retrievals are the sole data assimilated in all of the experiments presented. We study the impact of changing the forecast error covariance model from a version assuming static correlations with a one that captures a short-term Lagrangian evolution of those correlations. This is further combined with a study of the impact of neglecting the tropospheric ozone production, loss and dry deposition rates, which are obtained from the Harvard GEOS-CHEM model. We compare statistical characteristics of the assimilated data and the results of validation against independent observations, obtained from WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres. On the other hand, the main sensitivity to tropospheric chemistry is in the Tropics and sub-Tropics. The best agreement between the assimilated ozone and the in-situ sonde data is in the experiment using both flow-dependent error covariances and tropospheric chemistry.

Application of an Ensemble Smoother to Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Zhang, Sara; Zupanski, Dusanka; Hou, Arthur; Zupanski, Milija

2008-01-01

Assimilation of precipitation in a global modeling system poses a special challenge in that the observation operators for precipitation processes are highly nonlinear. In the variational approach, substantial development work and model simplifications are required to include precipitation-related physical processes in the tangent linear model and its adjoint. An ensemble based data assimilation algorithm "Maximum Likelihood Ensemble Smoother (MLES)" has been developed to explore the ensemble representation of the precipitation observation operator with nonlinear convection and large-scale moist physics. An ensemble assimilation system based on the NASA GEOS-5 GCM has been constructed to assimilate satellite precipitation data within the MLES framework. The configuration of the smoother takes the time dimension into account for the relationship between state variables and observable rainfall. The full nonlinear forward model ensembles are used to represent components involving the observation operator and its transpose. Several assimilation experiments using satellite precipitation observations have been carried out to investigate the effectiveness of the ensemble representation of the nonlinear observation operator and the data impact of assimilating rain retrievals from the TMI and SSM/I sensors. Preliminary results show that this ensemble assimilation approach is capable of extracting information from nonlinear observations to improve the analysis and forecast if ensemble size is adequate, and a suitable localization scheme is applied. In addition to a dynamically consistent precipitation analysis, the assimilation system produces a statistical estimate of the analysis uncertainty.

The Role of Scale and Model Bias in ADAPT's Photospheric Eatimation

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

Godinez Vazquez, Humberto C.; Hickmann, Kyle Scott; Arge, Charles Nicholas

2015-05-20

The Air Force Assimilative Photospheric flux Transport model (ADAPT), is a magnetic flux propagation based on Worden-Harvey (WH) model. ADAPT would be used to provide a global photospheric map of the Earth. A data assimilation method based on the Ensemble Kalman Filter (EnKF), a method of Monte Carlo approximation tied with Kalman filtering, is used in calculating the ADAPT models.

Real-Time IRI driven by GIRO data

NASA Astrophysics Data System (ADS)

Galkin, Ivan; Huang, Xueqin; Reinisch, Bodo; Bilitza, Dieter; Vesnin, Artem

Real-time extensions of the empirical International Reference Ionosphere (IRI) model are based on assimilative techniques that preserve the IRI formalism which is optimized for the description of climatological ionospheric features. The Global Ionosphere Radio Observatory (GIRO) team has developed critical parts of an IRI Real Time Assimilative Model (IRTAM) for the global ionospheric plasma distribution using measured data available in real time from ~50 ionosondes of the GIRO network, The current assimilation results present global assimilative maps of foF2 and hmF2 that reproduce available data at the sensor sites and smoothly return to the climatological specifications when and where the data are missing, and are free from artificial sharp gradients and short-lived artifacts when viewed in time progression. Animated real-time maps of foF2 and hmF2 are published with a few minutes latency at http://giro.uml.edu/IRTAM/. Our real-time IRI modeling uses morphing, a technique that transforms the climatological ionospheric specifications to match the observations by iteratively computing corrections to the original coefficients of the diurnal/spatial expansions, used in IRI to map the key ionospheric characteristics, while keeping the IRI expansion basis formalism intact. Computation of the updated coefficient set for a given point in time includes analysis of the latest 24-hour history of observations, which allows the morphing technique to sense evolving ionospheric dynamics even with a sparse sensor network. A Non-linear Error Compensation Technique for Associative Restoration (NECTAR), one of the features in our morphing approach, has been in operation at the Lowell GIRO Data Center since 2013. The cornerstone of NECTAR is a recurrent neural network optimizer that is responsible for smoothing the transitions between the grid cells where observations are available. NECTAR has proved suitable for real-time operations that require the assimilation code to be considerate of data uncertainties (noise) and immune to data errors. Future IRTAM work is directed toward accepting a greater diversity of near-real-time sensor data, and the paper discusses potential new data sources and challenges associated with their assimilation.

Impact of CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

NASA Astrophysics Data System (ADS)

Annane, B.; McNoldy, B. D.; Leidner, S. M.; Atlas, R. M.; Hoffman, R.; Majumdar, S.

2016-12-01

The Cyclone Global Navigation Satellite System, or CYGNSS, is a planned constellation of micro-satellites that will utilize reflected Global Positioning System (GPS) satellite signals to retrieve ocean surface wind speed along the satellites' ground tracks. The orbits are designed so that there is excellent coverage of the tropics and subtropics, resulting in more thorough spatial sampling and improved sampling intervals over tropical cyclones than is possible with current spaceborne scatterometer and passive microwave sensor platforms. Furthermore, CYGNSS will be able to retrieve winds under all precipitating conditions, and over a large range of wind speeds.A regional Observing System Simulation Experiment (OSSE) framework was developed at NOAA/AOML and University of Miami that features a high-resolution regional nature run (27-km regional domain with 9/3/1 km storm-following nests; Nolan et al., 2013) embedded within a lower-resolution global nature run . Simulated observations are generated by sampling from the nature run and are provided to a data assimilation scheme, which produces analyses for a high-resolution regional forecast model, the 2014 operational Hurricane-WRF model. For data assimilation, NOAA's GSI and EnKF systems are used. Analyses are performed on the parent domain at 9-km resolution. The forecast model uses a single storm-following 3-km resolution nest. Synthetic CYGNSS wind speed data have also been created, and the impacts of the assimilation of these data on the forecasts of tropical cyclone track and intensity will be discussed.In addition to the choice of assimilation scheme, we have also examined a number of other factors/parameters that effect the impact of simulated CYGNSS observations, including frequency of data assimilation cycling (e.g., hourly, 3-hourly and 6-hourly) and the assimilation of scalar versus vector synthetic CYGNSS winds.We have found sensitivity to all of the factors tested and will summarize the methods used for testing as well as results. Generally, we have found that more frequent cycling is better than less; and flow-dependent background error covariances (e.g., EnKF) are better than static or climatological assumptions about the background error covariance.

JRAero: the Japanese Reanalysis for Aerosol v1.0

NASA Astrophysics Data System (ADS)

Yumimoto, Keiya; Tanaka, Taichu Y.; Oshima, Naga; Maki, Takashi

2017-09-01

A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0. 90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

Satellite-Enhanced Dynamical Downscaling of Extreme Events

NASA Astrophysics Data System (ADS)

Nunes, A.

2015-12-01

Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical downscaling of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical downscaling of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to downscale extreme weather occurrences. Scale-selective bias correction is a method employed in the downscaling, similar to the spectral nudging technique, in which the downscaled solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the downscaling of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.

Data Assimilation using Artificial Neural Networks for the global FSU atmospheric model

NASA Astrophysics Data System (ADS)

Cintra, Rosangela; Cocke, Steven; Campos Velho, Haroldo

2015-04-01

Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. Uncertainty is the characteristic of the atmosphere, coupled with inevitable inadequacies in observations and computer models and increase errors in weather forecasts. Data assimilation is a technique to generate an initial condition to a weather or climate forecasts. This paper shows the results of a data assimilation technique using artificial neural networks (ANN) to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University in USA. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). The ANN data assimilation is made to emulate the initial condition from LETKF to run the FSUGSM. LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. The model FSUGSM is a multilevel (27 vertical levels) spectral primitive equation model with a vertical sigma coordinate. All variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The LETKF data assimilation experiments are based in synthetic observations data (surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity). For the ANN data assimilation scheme, we use Multilayer Perceptron (MLP-DA) with supervised training algorithm where ANN receives input vectors with their corresponding response or target output from LETKF scheme. An automatic tool that finds the optimal representation to these ANNs configures the MLP-DA in this experiment. After the training process, the scheme MLP-DA is seen as a function of data assimilation where the inputs are observations and a short-range forecast to each model grid point. The ANNs were trained with data from each month of 2001, 2002, 2003, and 2004. A hind-casting experiment for data assimilation cycle using MLP-DA was performed with synthetic observations for January 2005. The numerical results demonstrate the effectiveness of the ANN technique for atmospheric data assimilation, since the analyses (initial conditions) have similar quality to LETKF analyses. The major advantage of using MLP-DA is the computational performance, which is faster than LETKF. The reduced computational cost allows the inclusion of greater number of observations and new data sources and the use of high resolution of models, which ensures the accuracy of analysis and of its weather prediction

The Global Structure of UTLS Ozone in GEOS-5: A Multi-Year Assimilation of EOS Aura Data

NASA Technical Reports Server (NTRS)

Wargan, Krzysztof; Pawson, Steven; Olsen, Mark A.; Witte, Jacquelyn C.; Douglass, Anne R.; Ziemke, Jerald R.; Strahan, Susan E.; Nielsen, J. Eric

2015-01-01

Eight years of ozone measurements retrieved from the Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder, both on the EOS Aura satellite, have been assimilated into the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. This study thoroughly evaluates this assimilated product, highlighting its potential for science. The impact of observations on the GEOS-5 system is explored by examining the spatial distribution of the observation-minus-forecast statistics. Independent data are used for product validation. The correlation coefficient of the lower-stratospheric ozone column with ozonesondes is 0.99 and the bias is 0.5%, indicating the success of the assimilation in reproducing the ozone variability in that layer. The upper-tropospheric assimilated ozone column is about 10% lower than the ozonesonde column but the correlation is still high (0.87). The assimilation is shown to realistically capture the sharp cross-tropopause gradient in ozone mixing ratio. Occurrence of transport-driven low ozone laminae in the assimilation system is similar to that obtained from the High Resolution Dynamics Limb Sounder (HIRDLS) above the 400 K potential temperature surface but the assimilation produces fewer laminae than seen by HIRDLS below that surface. Although the assimilation produces 5 - 8 fewer occurrences per day (up to approximately 20%) during the three years of HIRDLS data, the interannual variability is captured correctly. This data-driven assimilated product is complementary to ozone fields generated from chemistry and transport models. Applications include study of the radiative forcing by ozone and tracer transport near the tropopause.

The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003-2015

NASA Astrophysics Data System (ADS)

Flemming, Johannes; Benedetti, Angela; Inness, Antje; Engelen, Richard J.; Jones, Luke; Huijnen, Vincent; Remy, Samuel; Parrington, Mark; Suttie, Martin; Bozzo, Alessio; Peuch, Vincent-Henri; Akritidis, Dimitris; Katragkou, Eleni

2017-02-01

A new global reanalysis data set of atmospheric composition (AC) for the period 2003-2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

Development of KIAPS Observation Processing Package for Data Assimilation System

NASA Astrophysics Data System (ADS)

Kang, Jeon-Ho; Chun, Hyoung-Wook; Lee, Sihye; Han, Hyun-Jun; Ha, Su-Jin

2015-04-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) was founded in 2011 by the Korea Meteorological Administration (KMA) to develop Korea's own global Numerical Weather Prediction (NWP) system as nine year (2011-2019) project. Data assimilation team at KIAPS has been developing the observation processing system (KIAPS Package for Observation Processing: KPOP) to provide optimal observations to the data assimilation system for the KIAPS Global Model (KIAPS Integrated Model - Spectral Element method based on HOMME: KIM-SH). Currently, the KPOP is capable of processing the satellite radiance data (AMSU-A, IASI), GPS Radio Occultation (GPS-RO), AIRCRAFT (AMDAR, AIREP, and etc…), and synoptic observation (SONDE and SURFACE). KPOP adopted Radiative Transfer for TOVS version 10 (RTTOV_v10) to get brightness temperature (TB) for each channel at top of the atmosphere (TOA), and Radio Occultation Processing Package (ROPP) 1-dimensional forward module to get bending angle (BA) at each tangent point. The observation data are obtained from the KMA which has been composited with BUFR format to be converted with ODB that are used for operational data assimilation and monitoring at the KMA. The Unified Model (UM), Community Atmosphere - Spectral Element (CAM-SE) and KIM-SH model outputs are used for the bias correction (BC) and quality control (QC) of the observations, respectively. KPOP provides radiance and RO data for Local Ensemble Transform Kalman Filter (LETKF) and also provides SONDE, SURFACE and AIRCRAFT data for Three-Dimensional Variational Assimilation (3DVAR). We are expecting all of the observation type which processed in KPOP could be combined with both of the data assimilation method as soon as possible. The preliminary results from each observation type will be introduced with the current development status of the KPOP.

HBCU Summer Undergraduate Training Program in Prostate Cancer: A Partnership Between USU-CPDR and UDC

DTIC Science & Technology

2015-10-02

conferences. 3 BODY Task 1: Selection Process: USU-CPDR summer internship program announcements for 2015 were made at the UDC through...AD_________________ Award Number: W81XWH-14-2-0142 TITLE: HBCU Summer Undergraduate Training Program in Prostate Cancer: A Partnership Between...DATES COVERED (From - To) 30 Sep 2014 - 29 Sep 2015 4. TITLE AND SUBTITLE: HBCU Summer Undergraduate Training Program in Prostate Cancer: A Partnership

The Online System for Lidar Data Handling and Real Time Monitoring of Lidar Operations at ALO-USU

NASA Astrophysics Data System (ADS)

Navarro, Luis A.; Wickwar, Vincent B.; Gamboa, Jose; Milla, Marco

2016-06-01

It is no longer sufficient to use lidar, such as the Rayleigh lidar at the Atmospheric Lidar Observatory (ALO) at Utah State University (USU), to observe the middle atmosphere and reduce the data to geophysical parameters. Extended operations, with inevitable equipment, data reduction, and analysis improvements, require us to keep careful track of all these changes and how they affect the scientific products. Furthermore, many of the funding agencies and the journals now require us to do, at least, some of this. We have built three interconnected data structures to organize and manage the different hardware and software setups from the Internet as well as to keep track of the products generated by these. These structures were implemented as a database, which is particularly important for groups with a large volume of information like the Rayleigh Group at ALO-USU.

Precursory changes in well water level prior to the March, 2000 eruption of Usu Volcano, Japan

NASA Astrophysics Data System (ADS)

Shibata, Tomo; Akita, Fujio

The height of water levels in two wells located near Usu volcano, Japan, changed in a systematic fashion for several months prior to the eruption of Usu volcano on 31 March 2000. In one well, water-level decrease relative to normal levels was first observed at the beginning of October 1999. The decreasing water-level is postulated to result from groundwater flow into cracks widened by intruding magma during dike formation. From the beginning of January 2000, the rate of decrease became higher. During this time, the water level of the second well increased by 0.05 m and then gradually decreased. The water-level changes are consistent with volumetric expansion of magma inside the magma chamber, followed by intrusion of magma into the fracture system associated with widening of cracks. We conclude that water-level observations can provide information that may potentially be used to predict further volcanic eruptions.

A Wavelet based Suboptimal Kalman Filter for Assimilation of Stratospheric Chemical Tracer Observations

NASA Technical Reports Server (NTRS)

Tangborn, Andrew; Auger, Ludovic

2003-01-01

A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. This scheme projects the discretized covariance propagation equations and covariance matrix onto an orthogonal set of compactly supported wavelets. Wavelet representation is localized in both location and scale, which allows for efficient representation of the inherently anisotropic structure of the error covariances. The truncation is carried out in such a way that the resolution of the error covariance is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance size by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the constituent field. This results indicate that propagation of error covariances for a global two-dimensional data assimilation system are currently feasible. Recommendations for further reduction in computational cost are made with the goal of extending this technique to three-dimensional global assimilation systems.

Estimating Evapotranspiration with Land Data Assimilation Systems

NASA Technical Reports Server (NTRS)

Peters-Lidard, C. D.; Kumar, S. V.; Mocko, D. M.; Tian, Y.

2011-01-01

Advancements in both land surface models (LSM) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDAS) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDAS systems, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 (NLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

Improving Estimates and Forecasts of Lake Carbon Pools and Fluxes Using Data Assimilation

NASA Astrophysics Data System (ADS)

Zwart, J. A.; Hararuk, O.; Prairie, Y.; Solomon, C.; Jones, S.

2017-12-01

Lakes are biogeochemical hotspots on the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. Observations and models of lake carbon pools and fluxes are rarely explicitly combined through data assimilation despite significant use of this technique in other fields with great success. Data assimilation adds value to both observations and models by constraining models with observations of the system and by leveraging knowledge of the system formalized by the model to objectively fill information gaps. In this analysis, we highlight the utility of data assimilation in lake carbon cycling research by using the Ensemble Kalman Filter to combine simple lake carbon models with observations of lake carbon pools. We demonstrate the use of data assimilation to improve a model's representation of lake carbon dynamics, to reduce uncertainty in estimates of lake carbon pools and fluxes, and to improve the accuracy of carbon pool size estimates relative to estimates derived from observations alone. Data assimilation techniques should be embraced as valuable tools for lake biogeochemists interested in learning about ecosystem dynamics and forecasting ecosystem states and processes.

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

NASA Astrophysics Data System (ADS)

Kumar, Prashant; Gopalan, Kaushik; Shukla, Bipasha Paul; Shyam, Abhineet

2017-11-01

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November-December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ˜10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

Final Technical Report for Department of Energy Award DE-SC0006625, “Predictability of the carbon-climate system on seasonal to decadal time scales.”

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

Fung, Inez

The project aims to investigate the feasibility of advancing our understanding of the carbon cycle, using a carbon-weather data assimilation system that updates the modeled carbon dioxide concentration and atmospheric circulation every six hours using CO 2 data (from the OCO 2 satellite) and weather data. At the core of the system is the DOE-NCAR-CAM5fv global circulation model coupled to the National Center for Atmospheric Research's Data Assimilation Testbed, running an ensemble of 30 models. This combination provides realistic vertical carbon dioxide gradients and conservation of dry air mass. A global four-dimensional distribution of atmospheric CO 2 concentration is produced.more » Our results show (1) that OCO 2 total precipitable water data are reliable and provide valuable uncertainty information for the OCO 2 data assimilation; and (2) that our approach is a promising method for monitoring national carbon dioxide emissions.« less

Multi-Scale Ionospheric Responses to the St. Patrick's Day Storm (2015) Studied Using a Multimodel Ensemble Prediction System and GPS Data

NASA Astrophysics Data System (ADS)

Pi, X.; Butala, M.; Vergados, P.; Mannucci, A. J.; Komjathy, A.; Wang, C.; Rosen, G.; Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.

2015-12-01

Under the U.S. NASA and NSF collaborative space weather modeling initiative, a Multimodel Ensemble Prediction System (MEPS) for ionosphere-thermosphere-electrodynamics is being developed. The system includes several Global Assimilative Ionospheric Models (GAIMs) developed by the investigators from Utah State University, Jet Propulsion Laboratory, and University of Southern California. In this study, four GAIMs are applied to a study of ionospheric response to the 17 March 2015 St. Patrick's Day storm. It is the most severe geomagnetic storm in the current solar cycle so far. The daily planetary magnetic Ap index and magnetic Kp, Dst, as well as AE indices reached their very high values, i.e., 108, 8, -202 nT, and 2269 nT, respectively. In the assimilative modeling, GPS data from hundreds of globally-distributed ground stations and a number of COSMIC satellites are assimilated into GAIMs to reproduce ionospheric 3-D volume densities and 2-D total electron content (TEC) during the severe storm. Evolution of strong, latitudinally-dependent, and hemispherically asymmetric ionospheric disturbances is revealed through the assimilative modeling. Using the same GPS data, Global Maps of Ionospheric Irregularities and Scintillation (GMIIS) have also been produced. Comparisons of the modeled large-scale ionospheric disturbances and measured small-scale ionospheric irregularities offer additional insight into the M-I-T coupling processes in different regions during varying storm phases. This presentation will provide a picture of distinguished multi-scale ionospheric response to the coronal mass ejection (CME) event during the major geomagnetic storm.

Global Real-Time Nowcasting of Ionosphere with Giro-Driven Assimilative IRI

NASA Astrophysics Data System (ADS)

Galkin, I. A.; Reinisch, B. W.; Huang, X. A.; Vesnin, A.; Bilitza, D.; Song, P.

2014-12-01

Real-time prediction of the ionosphere beyond its quiet-time median behavior has proved to be a great challenge: low-latency sensor data streams are scarce, and early comparisons conducted within the CEDAR ETI Assessment framework showed that, on average, the assimilative physics-based models perform on par with the long-term empirical predictions. This rather surprising result led to the formation of the Real-Time Task Force of the International Reference Ionosphere (IRI) science team in 2011, with a simple objective to develop a method for correcting the IRI long-term climatology definitions on the fly, i.e., in near real-time, using suitable observations. Three years later, a pilot version of the IRI-based Real-Time Assimilative Model "IRTAM" started its continuous operations at the Global Ionosphere Radio Observatory (GIRO) Data Center, using online feeds from the ionosondes contributing data to GIRO. The IRTAM version 0.1B builds and publishes every 15-minutes an updated "global weather" map of the peak density and height in the ionosphere, as well as a map of deviations from the classic IRI climate. Incidentally, the IRTAM verification and validation efforts shed light on the forecasting capabilities of the assimilative IRI extension, even though it has not yet involved external activity indicators. At the core of the assimilative computations, a Non-linear Error Compensation Technique for Associative Restoration (NECTAR) seeks agreement between IRI prediction and the 24-hour history of latest observations at GIRO sensor sites to produce the one map frame. The NECTAR first evaluates the diurnal harmonics of the observed deviations from the IRI climatology at each GIRO site to then independently compute the spatial maps for each diurnal harmonic. Thus obtained "corrective" coefficients of the spatial-diurnal expansion are added to the original IRI set of coefficients to obtain the IRTAM specification. We are intrigued by the IRTAM capability to glean ionospheric dynamics over no-data areas, and the potential for short-term forecasting.

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Treesearch

R. Quinn Thomas; Evan B. Brooks; Annika L. Jersild; Eric J. Ward; Randolph H. Wynne; Timothy J. Albaugh; Heather Dinon-Aldridge; Harold E. Burkhart; Jean-Christophe Domec; Timothy R. Fox; Carlos A. Gonzalez-Benecke; Timothy A. Martin; Asko Noormets; David A. Sampson; Robert O. Teskey

2017-01-01

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of...

Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content

NASA Astrophysics Data System (ADS)

Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.; Scholze, Marko

2018-04-01

The synthesis of model and observational information using data assimilation can improve our understanding of the terrestrial carbon cycle, a key component of the Earth's climate-carbon system. Here we provide a data assimilation framework for combining observations of solar-induced chlorophyll fluorescence (SIF) and a process-based model to improve estimates of terrestrial carbon uptake or gross primary production (GPP). We then quantify and assess the constraint SIF provides on the uncertainty in global GPP through model process parameters in an error propagation study. By incorporating 1 year of SIF observations from the GOSAT satellite, we find that the parametric uncertainty in global annual GPP is reduced by 73 % from ±19.0 to ±5.2 Pg C yr-1. This improvement is achieved through strong constraint of leaf growth processes and weak to moderate constraint of physiological parameters. We also find that the inclusion of uncertainty in shortwave down-radiation forcing has a net-zero effect on uncertainty in GPP when incorporated into the SIF assimilation framework. This study demonstrates the powerful capacity of SIF to reduce uncertainties in process-based model estimates of GPP and the potential for improving our predictive capability of this uncertain carbon flux.

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

NASA Technical Reports Server (NTRS)

Girotto, Manuela

2018-01-01

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Data Assimilation in the ADAPT Photospheric Flux Transport Model

DOE PAGES

Hickmann, Kyle S.; Godinez, Humberto C.; Henney, Carl J.; ...

2015-03-17

Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF)more » to accomplish data assimilation, allowing the covariance structure of the flux-transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.« less

Incorporating Satellite Time-Series Data into Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

Toward the assimilation of biogeochemical data in the CMEMS BIOMER coupled physical-biogeochemical operational system

NASA Astrophysics Data System (ADS)

Lamouroux, Julien; Testut, Charles-Emmanuel; Lellouche, Jean-Michel; Perruche, Coralie; Paul, Julien

2017-04-01

The operational production of data-assimilated biogeochemical state of the ocean is one of the challenging core projects of the Copernicus Marine Environment Monitoring Service. In that framework - and with the April 2018 CMEMS V4 release as a target - Mercator Ocean is in charge of improving the realism of its global ¼° BIOMER coupled physical-biogeochemical (NEMO/PISCES) simulations, analyses and re-analyses, and to develop an effective capacity to routinely estimate the biogeochemical state of the ocean, through the implementation of biogeochemical data assimilation. Primary objectives are to enhance the time representation of the seasonal cycle in the real time and reanalysis systems, and to provide a better control of the production in the equatorial regions. The assimilation of BGC data will rely on a simplified version of the SEEK filter, where the error statistics do not evolve with the model dynamics. The associated forecast error covariances are based on the statistics of a collection of 3D ocean state anomalies. The anomalies are computed from a multi-year numerical experiment (free run without assimilation) with respect to a running mean in order to estimate the 7-day scale error on the ocean state at a given period of the year. These forecast error covariances rely thus on a fixed-basis seasonally variable ensemble of anomalies. This methodology, which is currently implemented in the "blue" component of the CMEMS operational forecast system, is now under adaptation to be applied to the biogeochemical part of the operational system. Regarding observations - and as a first step - the system shall rely on the CMEMS GlobColour Global Ocean surface chlorophyll concentration products, delivered in NRT. The objective of this poster is to provide a detailed overview of the implementation of the aforementioned data assimilation methodology in the CMEMS BIOMER forecasting system. Focus shall be put on (1) the assessment of the capabilities of this data assimilation methodology to provide satisfying statistics of the model variability errors (through space-time analysis of dedicated representers of satellite surface Chla observations), (2) the dedicated features of the data assimilation configuration that have been implemented so far (e.g. log-transformation of the analysis state, multivariate Chlorophyll-Nutrient control vector, etc.) and (3) the assessment of the performances of this future operational data assimilation configuration.

Variational Continuous Assimilation of TMI and SSM/I Rain Rates: Impact on GEOS-3 Hurricane Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; Reale, Oreste

2003-01-01

We describe a variational continuous assimilation (VCA) algorithm for assimilating tropical rainfall data using moisture and temperature tendency corrections as the control variable to offset model deficiencies. For rainfall assimilation, model errors are of special concern since model-predicted precipitation is based on parameterized moist physics, which can have substantial systematic errors. This study examines whether a VCA scheme using the forecast model as a weak constraint offers an effective pathway to precipitation assimilation. The particular scheme we exarnine employs a '1+1' dimension precipitation observation operator based on a 6-h integration of a column model of moist physics from the Goddard Earth Observing System (GEOS) global data assimilation system DAS). In earlier studies, we tested a simplified version of this scheme and obtained improved monthly-mean analyses and better short-range forecast skills. This paper describes the full implementation ofthe 1+1D VCA scheme using background and observation error statistics, and examines how it may improve GEOS analyses and forecasts of prominent tropical weather systems such as hurricanes. Parallel assimilation experiments with and without rainfall data for Hurricanes Bonnie and Floyd show that assimilating 6-h TMI and SSM/I surfice rain rates leads to more realistic storm features in the analysis, which, in turn, provide better initial conditions for 5-day storm track prediction and precipitation forecast. These results provide evidence that addressing model deficiencies in moisture tendency may be crucial to making effective use of precipitation information in data assimilation.

Polar motion excitation analysis due to global continental water redistribution

NASA Astrophysics Data System (ADS)

Fernandez, L.; Schuh, H.

2006-10-01

We present the results obtained when studying the hydrological excitation of the Earth‘s wobble due to global redistribution of continental water storage. This work was performed in two steps. First, we computed the hydrological angular momentum (HAM) time series based on the global hydrological model LaD (Land Dynamics model) for the period 1980 till 2004. Then, we compared the effectiveness of this excitation by analysing the residuals of the geodetic time series after removing atmospheric and oceanic contributions with the respective hydrological ones. The emphasis was put on low frequency variations. We also present a comparison of HAM time series from LaD with respect to that one from a global model based on the assimilated soil moisture and snow accumulation data from NCEP/NCAR (The National Center for Environmental Prediction/The National Center for Atmospheric Research) reanalysis. Finally, we evaluate the performance of LaD model in closing the polar motion budget at seasonal periods in comparison with the NCEP and the Land Data Assimilation System (LDAS) models.

Preliminary Results from an Assimilation of TOMS Aerosol Observations Into the GOCART Model

NASA Technical Reports Server (NTRS)

daSilva, Arlindo; Weaver, Clark J.; Ginoux, Paul; Torres, Omar; Einaudi, Franco (Technical Monitor)

2000-01-01

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions that compare well with TOMS satellite observations. Surface, mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from.08-10 microns and only simulates Saharan dust. TOMS radiance observations in the ultra violet provide information on the mineral and carbonaceous aerosol fields. We use two main observables in this study: the TOMS aerosol index (AI) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance. These are sensitive to the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer. The Goddard Aerosol Assimilation System (GAAS) uses the Data Assimilation Office's Physical-space Statistical Analysis System (PSAS) to combine TOMS observations and GOCART model first guess fields. At this initial phase we only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols dominant and carbonaceous aerosols are minimal, Our preliminary results during summer show that the assimilation with TOMS data modifies both the aerosol mass loading and the single scattering albedo. Assimilated aerosol fields will be compared with assimilated aerosol fields from GOCART and AERONET observations over Cape Verde.

ASTER Images Mt. Usu Volcano

NASA Technical Reports Server (NTRS)

2000-01-01

On April 3, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra Satellite captured this image of the erupting Mt. Usu volcano in Hokkaido, Japan. With its 14 spectral bands from the visible to the thermal infrared wavelength region, and its high spatial resolution of 15 to 90 meters (about 50 to 300 feet), ASTER will image the Earth for the next 6 years to map and monitor the changing surface of our planet.

This false color infrared image of Mt Usu volcano is dominated by Lake Toya, an ancient volcanic caldera. On the south shore is the active Usu volcano. On Friday, March 31, more than 11,000 people were evacuated by helicopter, truck and boat from the foot of Usu, that began erupting from the northwest flank, shooting debris and plumes of smoke streaked with blue lightning thousands of feet in the air. Although no lava gushed from the mountain, rocks and ash continued to fall after the eruption. The region was shaken by thousands of tremors before the eruption. People said they could taste grit from the ash that was spewed as high as 2,700 meters (8,850 ft) into the sky and fell to coat surrounding towns with ash. 'Mount Usu has had seven significant eruptions that we know of, and at no time has it ended quickly with only a small scale eruption,' said Yoshio Katsui, a professor at Hokkaido University. This was the seventh major eruption of Mount Usu in the past 300 years. Fifty people died when the volcano erupted in 1822, its worst known eruption.

In the image, most of the land is covered by snow. Vegetation, appearing red in the false color composite, can be seen in the agricultural fields, and forests in the mountains. Mt. Usu is crossed by three dark streaks. These are the paths of ash deposits that rained out from eruption plumes two days earlier. The prevailing wind was from the northwest, carrying the ash away from the main city of Date. Ash deposited can be traced on the image as far away as 10 kilometers (16 miles) from the volcano.

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of five Earth-observing instruments launched December 18, 1999, on NASA's Terra satellite. The instrument was built by Japan's Ministry of International Trade and Industry. A joint U.S./Japan science team is responsible for validation and calibration of the instrument and the data products. Dr. Anne Kahle at NASA's Jet Propulsion Laboratory, Pasadena, California, is the U.S. Science team leader; Moshe Pniel of JPL is the project manager. ASTER is the only high resolution imaging sensor on Terra. The primary goal of the ASTER mission is to obtain high-resolution image data in 14 channels over the entire land surface, as well as black and white stereo images. With revisit time of between 4 and 16 days, ASTER will provide the capability for repeat coverage of changing areas on Earth's surface.

The broad spectral coverage and high spectral resolution of ASTER will provide scientists in numerous disciplines with critical information for surface mapping, and monitoring dynamic conditions and temporal change. Example applications are: monitoring glacial advances and retreats; monitoring potentially active volcanoes; identifying crop stress; determining cloud morphology and physical properties; wetlands evaluation; thermal pollution monitoring; coral reef degradation; surface temperature mapping of soils and geology; and measuring surface heat balance.

Potential of space-borne GNSS reflectometry to constrain simulations of the ocean circulation. A case study for the South African current system

NASA Astrophysics Data System (ADS)

Saynisch, Jan; Semmling, Maximilian; Wickert, Jens; Thomas, Maik

2015-11-01

The Agulhas current system transports warm and salty water masses from the Indian Ocean into the Southern Ocean and into the Atlantic. The transports impact past, present, and future climate on local and global scales. The size and variability, however, of the respective transports are still much debated. In this study, an idealized model based twin experiment is used to study whether sea surface height (SSH) anomalies estimated from reflected signals of the Global Navigation Satellite System reflectometry (GNSS-R) can be used to determine the internal water mass properties and transports of the Agulhas region. A space-borne GNSS-R detector on the International Space Station (ISS) is assumed and simulated. The detector is able to observe daily SSH fields with a spatial resolution of 1-5∘. Depending on reflection geometry, the precision of a single SSH observation is estimated to reach 3 cm (20 cm) when the carrier phase (code delay) information of the reflected GNSS signal is used. The average precision over the Agulhas region is 7 cm (42 cm). The proposed GNSS-R measurements surpass the radar-based satellite altimetry missions in temporal and spatial resolution but are less precise. Using the estimated GNSS-R characteristics, measurements of SSH are generated by sampling a regional nested general circulation model of the South African oceans. The artificial observations are subsequently assimilated with a 4DVAR adjoint data assimilation method into the same ocean model but with a different initial state and forcing. The assimilated and the original, i.e., the sampled model state, are compared to systematically identify improvements and degradations in the model variables that arise due to the assimilation of GNSS-R based SSH observations. We show that SSH and the independent, i.e., not assimilated model variables velocity, temperature, and salinity improve by the assimilation of GNSS-R based SSH observations. After the assimilation of 90 days of SSH observations, improvements in the independent variables cover the whole water column. Locally, up to 39 % of the original model state are recovered. Shorter assimilation windows result in enhanced reproduction of the observed and assimilated SSH but are accompanied by an insufficient or wrong recovery of sub-surface water properties. The assimilation of real GNSS-R observations, when available, and consequently the estimation of Agulhas water mass properties and the leakage of heat and salt into the Atlantic will benefit from this model-based study.

Sea Ice in the NCEP Seasonal Forecast System

NASA Astrophysics Data System (ADS)

Wu, X.; Saha, S.; Grumbine, R. W.; Bailey, D. A.; Carton, J.; Penny, S. G.

2017-12-01

Sea ice is known to play a significant role in the global climate system. For a weather or climate forecast system (CFS), it is important that the realistic distribution of sea ice is represented. Sea ice prediction is challenging; sea ice can form or melt, it can move with wind and/or ocean current; sea ice interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and also carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global forecast system, a land model, an ocean model (GFDL MOM4), and a sea ice model. In this work, we present the NCEP coupled model, the CFSv2 sea ice component that includes a dynamic thermodynamic sea ice model and a simple "assimilation" scheme, how sea ice has been assimilated in CFSR, the characteristics of the sea ice from CFSR and CFSv2, and the improvements of sea ice needed for future seasonal prediction system, part of the Unified Global Coupled System (UGCS), which is being developed and under testing, including sea ice data assimilation with the Local Ensemble Transform Kalman Filter (LETKF). Preliminary results from the UGCS testing will also be presented.

Optimization of terrestrial ecosystem model parameters using atmospheric CO2 concentration data with a global carbon assimilation system (GCAS)

NASA Astrophysics Data System (ADS)

Chen, Z.; Chen, J.; Zhang, S.; Zheng, X.; Shangguan, W.

2016-12-01

A global carbon assimilation system (GCAS) that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25° (Vmax25 ), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1°resolution for the period from 2002 to 2008. Optimized multi-year average Vmax25 values range from 49 to 51 μmol m-2 s-1 over most regions of world. Vegetation from tropical zones has relatively lower values than vegetation in temperate regions. Optimized multi-year average Q10 values varied from 1.95 to 2.05 over most regions of the world. Relatively high values of Q10 are derived over high/mid latitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at mid-high latitudes. The maximum in occurs during the growing season, while the minima appear during non-growing seasons. Q10 values decreases with increasing temperature. The seasonal variabilities of and Q10 are larger at higher latitudes with tropical or low latitude regions showing little seasonal variabilities.

Characterizing Middle Atmospheric Dynamical Variability and its Impact on the Thermosphere/Ionosphere System During Recent Stratospheric Sudden Warmings

NASA Astrophysics Data System (ADS)

McCormack, J. P.; Sassi, F.; Hoppel, K.; Ma, J.; Eckermann, S. D.

2015-12-01

We investigate the evolution of neutral atmospheric dynamics in the 10-100 km altitude range before, during, and after recent stratospheric sudden warmings (SSWs) using a prototype high-altitude version of the Navy Global Environmental Model (NAVGEM), which combines a 4-dimensional variational (4DVAR) data assimilation system with a 3-time-level semi-Lagrangian semi-implicit global forecast model. In addition to assimilating conventional meteorological observations, NAVGEM also assimilates middle atmospheric temperature and constituent observations from both operational and research satellite platforms to provide global synoptic meteorological analyses of winds, temperatures, ozone, and water vapor from the surface to ~90 km. In this study, NAVGEM analyses are used to diagnose the spatial and temporal evolution of the main dynamical drivers in the mesosphere and lower thermosphere (MLT) before, during, and after specific SSW events during the 2009-2013 period when large disturbances were observed in the thermosphere/ionosphere (TI) region. Preliminary findings show strong modulation of the semidiurnal tide in the MLT during the onset of an SSW. To assess the impact of the neutral atmosphere dynamical variability on the TI system, NAVGEM analyses are used to constrain simulations of select SSW events using the specified dynamics (SD) configuration of the extended Whole Atmosphere Community Climate Model (WACCM-X).

A study of the Ionospheric electron density profile with FORMOSAT-3/COSMIC observation data

NASA Astrophysics Data System (ADS)

Chou, Min-Yang; Tsai, Ho-Fang; Lin, Chi-Yen; Lee, I.-Te; Lin, Charles; Liu, Jann-Yenq

2015-04-01

The GPS Occultation Experiment payload onboard FORMOSAT-3/COSMIC microsatellite constellation is capable of scanning the ionospheric structure by the radio occultation (RO) technique to retrieve precise electron density profiles since 2006. Due to the success of FORMOSAT-3/COSMIC, the follow-on mission, FORMOSAT-7/COSMIC-2, is to launch 12 microsatellites in 2016 and 2018, respectively, with　the Global Navigation Satellite Systems (GNSS) RO instrument onboard for tracking GPS, Galileo and/or GLONASS satellite signals and to provide more than 8,000 RO soundings per day globally. An overview of the validation of the FORMOSAT-3/COSMIC ionospheric profiling is given by means of the traditional Abel transform through bending angle and total electron content (TEC), while the ionospheric data assimilation is also applied, based on the Gauss-Markov Kalman filter with the International Reference Ionosphere model (IRI-2007) and global ionosphere map (GIM) as background model, to assimilate TEC observations from FORMOSAT-3/COSMIC. The results shows comparison of electron density profiles from Abel inversion and data assimilation. Furthermore, an observing system simulation experiment is also applied to determine the impact of FORMOSAT-7/COSMIC-2 on ionospheric weather monitoring, which reveals an opportunity on advanced study of small spatial and temporal variations in the ionosphere.

Assessing the impacts of assimilating IASI and MOPITT CO retrievals using CESM-CAM-chem and DART

NASA Astrophysics Data System (ADS)

Barré, Jérôme; Gaubert, Benjamin; Arellano, Avelino F. J.; Worden, Helen M.; Edwards, David P.; Deeter, Merritt N.; Anderson, Jeffrey L.; Raeder, Kevin; Collins, Nancy; Tilmes, Simone; Francis, Gene; Clerbaux, Cathy; Emmons, Louisa K.; Pfister, Gabriele G.; Coheur, Pierre-François; Hurtmans, Daniel

2015-10-01

We show the results and evaluation with independent measurements from assimilating both MOPITT (Measurements Of Pollution In The Troposphere) and IASI (Infrared Atmospheric Sounding Interferometer) retrieved profiles into the Community Earth System Model (CESM). We used the Data Assimilation Research Testbed ensemble Kalman filter technique, with the full atmospheric chemistry CESM component Community Atmospheric Model with Chemistry. We first discuss the methodology and evaluation of the current data assimilation system with coupled meteorology and chemistry data assimilation. The different capabilities of MOPITT and IASI retrievals are highlighted, with particular attention to instrument vertical sensitivity and coverage and how these impact the analyses. MOPITT and IASI CO retrievals mostly constrain the CO fields close to the main anthropogenic, biogenic, and biomass burning CO sources. In the case of IASI CO assimilation, we also observe constraints on CO far from the sources. During the simulation time period (June and July 2008), CO assimilation of both instruments strongly improves the atmospheric CO state as compared to independent observations, with the higher spatial coverage of IASI providing better results on the global scale. However, the enhanced sensitivity of multispectral MOPITT observations to near surface CO over the main source regions provides synergistic effects at regional scales.

EMPIRE and pyenda: Two ensemble-based data assimilation systems written in Fortran and Python

NASA Astrophysics Data System (ADS)

Geppert, Gernot; Browne, Phil; van Leeuwen, Peter Jan; Merker, Claire

2017-04-01

We present and compare the features of two ensemble-based data assimilation frameworks, EMPIRE and pyenda. Both frameworks allow to couple models to the assimilation codes using the Message Passing Interface (MPI), leading to extremely efficient and fast coupling between models and the data-assimilation codes. The Fortran-based system EMPIRE (Employing Message Passing Interface for Researching Ensembles) is optimized for parallel, high-performance computing. It currently includes a suite of data assimilation algorithms including variants of the ensemble Kalman and several the particle filters. EMPIRE is targeted at models of all kinds of complexity and has been coupled to several geoscience models, eg. the Lorenz-63 model, a barotropic vorticity model, the general circulation model HadCM3, the ocean model NEMO, and the land-surface model JULES. The Python-based system pyenda (Python Ensemble Data Assimilation) allows Fortran- and Python-based models to be used for data assimilation. Models can be coupled either using MPI or by using a Python interface. Using Python allows quick prototyping and pyenda is aimed at small to medium scale models. pyenda currently includes variants of the ensemble Kalman filter and has been coupled to the Lorenz-63 model, an advection-based precipitation nowcasting scheme, and the dynamic global vegetation model JSBACH.

Improving GEOS-5 seven day forecast skill by assimilation of quality controlled AIRS temperature profiles

NASA Astrophysics Data System (ADS)

Susskind, J.; Rosenberg, R. I.

2016-12-01

The GEOS-5 Data Assimilation System (DAS) generates a global analysis every six hours by combining the previous six hour forecast for that time period with contemporaneous observations. These observations include in-situ observations as well as those taken by satellite borne instruments, such as AIRS/AMSU on EOS Aqua and CrIS/ATMS on S-NPP. Operational data assimilation methodology assimilates observed channel radiances Ri for IR sounding instruments such as AIRS and CrIS, but only for those channels i in a given scene whose radiances are thought to be unaffected by clouds. A limitation of this approach is that radiances in most tropospheric sounding channels are affected by clouds under partial cloud cover conditions, which occurs most of the time. The AIRS Science Team Version-6 retrieval algorithm generates cloud cleared radiances (CCR's) for each channel in a given scene, which represent the radiances AIRS would have observed if the scene were cloud free, and then uses them to determine quality controlled (QC'd) temperature profiles T(p) under all cloud conditions. There are potential advantages to assimilate either AIRS QC'd CCR's or QC'd T(p) instead of Ri in that the spatial coverage of observations is greater under partial cloud cover. We tested these two alternate data assimilation approaches by running three parallel data assimilation experiments over different time periods using GEOS-5. Experiment 1 assimilated all observations as done operationally, Experiment 2 assimilated QC'd values of AIRS CCRs in place of AIRS radiances, and Experiment 3 assimilated QC'd values of T(p) in place of observed radiances. Assimilation of QC'd AIRS T(p) resulted in significant improvement in seven day forecast skill compared to assimilation of CCR's or assimilation of observed radiances, especially in the Southern Hemisphere Extra-tropics.

Carbon Monoxide Data Assimilation for Atmospheric Composition and Climate Science: Evaluating Performance with Current and Future Observations

NASA Astrophysics Data System (ADS)

Barre, J.; Edwards, D. P.; Gaubert, B.; Worden, H. M.; Arellano, A. F.; Anderson, J. L.

2015-12-01

Current satellite observations of tropospheric composition made from low Earth orbit provide at best one or two measurements each day at any given location. Comparisons of Terra/MOPITT carbon monoxide (CO) and IASI/Metop CO observation assimilations will be presented. We use the DART Ensemble Adjustment Kalman Filter to assimilate observations in the CAM-Chem global chemistry-climate model. Data assimilation impacts due to both different instrument capabilities (i.e. vertical sensitivity and global coverage) will be discussed. Coverage is global but sparse, often with large uncertainties in individual measurements that limit examination of local and regional atmospheric composition over short time periods. This has hindered the operational uptake of these data for monitoring air quality and population exposure, and for initializing and evaluating chemical weather forecasts. By the end of the current decade there are planned geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations of continental domains for mapping pollutant sources and variability on diurnal and local scales. We describe Observing System Simulation Experiments (OSSEs) to evaluate the contributions of these GEO missions to improve knowledge of near-surface air pollution due to intercontinental long-range transport and quantify chemical precursor emissions. Our approach uses an efficient computational method to sample a high-resolution global GEOS-5 chemistry Nature Run over each geographical region of the GEO constellation. The demonstration carbon monoxide (CO) observation simulator, which will be expanded to other chemical pollutants, currently produces multispectral retrievals (MOPITT-like) and captures realistic scene-dependent variation in measurement vertical sensitivity and cloud cover. The impact of observing over each region is evaluated independently. Winter and summer cases studies are investigated i.e. where emissions, cloud cover and CO lifetime significantly change.

An Update on Experimental Climate Prediction and Analysis Products Being Developed at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2011-01-01

The Global Modeling and Assimilation Office at NASA's Goddard Space Flight Center is developing a number of experimental prediction and analysis products suitable for research and applications. The prediction products include a large suite of subseasonal and seasonal hindcasts and forecasts (as a contribution to the US National MME), a suite of decadal (10-year) hindcasts (as a contribution to the IPCC decadal prediction project), and a series of large ensemble and high resolution simulations of selected extreme events, including the 2010 Russian and 2011 US heat waves. The analysis products include an experimental atlas of climate (in particular drought) and weather extremes. This talk will provide an update on those activities, and discuss recent efforts by WCRP to leverage off these and similar efforts at other institutions throughout the world to develop an experimental global drought early warning system.

USU Center of Excellence in Theory and Analysis of the Geo-Plasma Environment

DTIC Science & Technology

1993-02-01

h4cgt) 4 V 5,’/c OJi- o PROGRAM PRO............ .. Bolling AFB, D.C. 20332-0o0/ ELEMENT NO. NO. N NO. 11. TITLE (Incad. Security Cla"aificaai-USU...and orbiting space structures. The overall goal of tht. research was to obtain a better understanding of the basic chemical and physical processes...Force systems, including OTH radars, communications, and orbiting space structures. The overall goal of the research was to obtain a better

Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation

DTIC Science & Technology

2015-01-01

tropical North Atlantic from the Argo float data (Chu et al. 2007 ), and temporal and spatial variability of global upper-ocean heat content (Chu 2011...O. V. Melnichenko, and N. C. Wells, 2007 : Long baro- clinic Rossby waves in the tropical North Atlantic observed fromprofiling floats. J...Harrison, and D. Stammer , D., Eds., Vol. 2, ESA Publ. WPP- 306, doi:10.5270/OceanObs09.cwp.86. Tang, Y., and R. Kleeman, 2004: SST assimilation

Comparison of global sst analyses for atmospheric data assimilation

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

Phoebus, P.A.; Cummings, J.A.

1995-03-17

Traditionally, atmospheric models were executed using a climatological estimate of the sea surface temperature (SST) to define the marine boundary layer. More recently, particularly since the deployment of remote sensing instruments and the advent of multichannel SST observations atmospheric models have been improved by using more timely estimates of the actual state of the ocean. Typically, some type of objective analysis is performed using the data from satellites along with ship, buoy, and bathythermograph observations, and perhaps even climatology, to produce a weekly or daily analysis of global SST. Some of the earlier efforts to produce real-time global temperature analysesmore » have been described by Clancy and Pollak (1983) and Reynolds (1988). However, just as new techniques have been developed for atmospheric data assimilation, improvements have been made to ocean data assimilation systems as well. In 1988, the U.S. Navy`s Fleet Numerical Meteorology and Oceanography Center (FNMOC) implemented a global three-dimensional ocean temperature analysis that was based on the optimum interpolation methodology (Clancy et al., 1990). This system, the Optimum Thermal Interpolation System (OTIS 1.0), was initially distributed on a 2.50 resolution grid, and was later modified to generate fields on a 1.250 grid (OTIS 1.1; Clancy et al., 1992). Other optimum interpolation-based analyses (OTIS 3.0) were developed by FNMOC to perform high-resolution three-dimensional ocean thermal analyses in areas with strong frontal gradients and clearly defined water mass characteristics.« less

Observing System Evaluations Using GODAE Systems

DTIC Science & Technology

2009-09-01

DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release, distribution is unlimite 13. SUPPLEMENTARY NOTES 20091228151 14. ABSTRACT Global ocean...forecast systems, developed under the Global Ocean Data Assimilation Experiment (GODAE), are a powerful means of assessing the impact of different...components of the Global Ocean Observing System (GOOS). Using a range of analysis tools and approaches, GODAE systems are useful for quantifying the

Evaluation and Assimilation of Cloud Cleared Radiances for AIRS in GEOS-5

NASA Technical Reports Server (NTRS)

Liu, Hui-chun

2008-01-01

The use of clear (cloud-free) channels for AIRS in GEOS-5 had shown positive impact on forecast skills in both hemispheres. However, improvements in forecast skills due to the assimilation of AIRS data are less impressive since the number of assimilated channels from AIRS is much larger than that from other Infrared sounders such as HIRS-3 onboard NOAA 15-17 satellites. This limitation of AIRS radiance data to improve the forecast skill is mainly due to the fact that channels capable of peaking below clouds are not used in the assimilation and yet those have highest vertical resolving capability of AIRS instrument are concentrated in the lower troposphere. On average, the percentage of AIRS footprints completely clear for all channels is less than 10%. The percentage of assimilated AIRS channel radiances however ranges from 100% for channels peaking in the upper stratosphere, above the cloud, to no more that 5% in the lower atmosphere due to cloud contamination. Our current ability to model and predict clouds accurately in global model, and to fully characterize and parameterize optical properties of cloud particles in radiative transfer model are the two major obstacles prohibiting us to use cloudy radiance directly in the assimilation. To further improve forecast skill using AIRS data, we ought to use the channels peaking below the clouds in the troposphere, which can be accomplished by assimilating cloud-cleared radiance. The cloud-cleared radiance data for AIRS used in this study were obtained from optimal cloud clearing procedures developed by researchers at CIMSS of University of Wisconsin at Madison to retrieve clear column radiances for all AIRS channels by collocating multi-band MODIS IR clear radiance observations with the AIRS cloudy radiances on a single footprint basis. Two adjacent AIRS cloudy footprints are used to retrieve one AIRS cloud-cleared radiance spectrum and no background information (first guess) is needed. To assimilate the cloud-cleared radiance data, the errors of the cloud-cleared radiances need to be addressed. The details of convolving AIRS radiances with MODIS spectral response function and comparison with MODIS-measured cloud-free radiance will be presented. The range of errors of cloud-cleared radiances for AIRS using collocated MODIS clear and near-by AIRS clear data will be shown. The NASA. global data assimilation model, GEOS-5, is used to evaluate and assimilate the cloud-cleared radiance for AIRS. The residues between the cloud-cleared brightness temperature and the simulated brightness temperature from background (i.e., OMFs) will be investigated. The quality control procedures will be documented based on error estimation and the OMFs. Finally, the impacts between assimilation of clear channel radiances and cloud-cleared radiances will be addressed.

Multi-site assimilation of a terrestrial biosphere model (BETHY) using satellite derived soil moisture data

NASA Astrophysics Data System (ADS)

Wu, Mousong; Sholze, Marko

2017-04-01

We investigated the importance of soil moisture data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, soil respiration, as well as soil texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). Soil moisture data from Soil Moisture and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS soil moisture data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of soil moisture to the system improved soil moisture as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with soil moisture derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS soil moisture data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

2011-01-01

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

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.

Current Status and Challenges of Atmospheric Data Assimilation

NASA Astrophysics Data System (ADS)

Atlas, R. M.; Gelaro, R.

2016-12-01

The issues of modern atmospheric data assimilation are fairly simple to comprehend but difficult to address, involving the combination of literally billions of model variables and tens of millions of observations daily. In addition to traditional meteorological variables such as wind, temperature pressure and humidity, model state vectors are being expanded to include explicit representation of precipitation, clouds, aerosols and atmospheric trace gases. At the same time, model resolutions are approaching single-kilometer scales globally and new observation types have error characteristics that are increasingly non-Gaussian. This talk describes the current status and challenges of atmospheric data assimilation, including an overview of current methodologies, the difficulty of estimating error statistics, and progress toward coupled earth system analyses.

GPS=A Good Candidate for Data Assimilation?

NASA Technical Reports Server (NTRS)

Poli, P.; Joiner, J.; Kursinski, R.; Einaudi, Franco (Technical Monitor)

2000-01-01

The Global Positioning System (GPS) enables positioning anywhere about our planet. The microwave signals sent by the 24 transmitters are sensitive to the atmosphere. Using the radio occultation technique, it is possible to perform soundings, with a Low Earth Orbiter (700 km) GPS receiver. The insensitiveness to clouds and aerosols, the relatively high vertical resolution (1.5 km), the self-calibration and stability of the GPS make it a priori a potentially good observing system candidate for data assimilation. A low-computing cost simple method to retrieve both temperature and humidity will be presented. Comparisons with radiosonde show the capability of the GPS to resolve the tropopause. Options for using GPS for data assimilation and remaining issues will be discussed.

The Mercator Océan operational and reanalysis systems: overview of recent improvements and scientific key issues

NASA Astrophysics Data System (ADS)

Hernandez, F.; Benkiran, M.; Bourdalle-Badie, R.; Bricaud, C.; Cailleau, S.; Chanut, J.; Desportes, C.; Dombrowsky, E.; drevillon, M.; Drillet, Y.; Elmoussaoui, A.; Ferry, N.; Garric, G.; Greiner, E.; Le Galloudec, O.; Lellouche, J.; Levier, B.; Parent, L.; Perruche, C.; Reffray, G.; Regnier, C.; Rémy, E.; Testut, C.; Tranchant, B.

2011-12-01

In the framework of the European project GMES/MyOcean, Mercator Océan has designed a hierarchy of ocean analysis, forecasting and reanalysis systems based on numerical models of the ocean/sea-ice, data assimilation methods and biogeochemistry model. Operational weekly analysis provide initial conditions for daily predictions. All ocean model configurations are based on NEMO. The 1/4° global and 1/12° Atlantic and Mediterranean configurations are improved with (i) the use of high frequency (3h) atmospheric forcings including the diurnal cycle, (ii) the use of the CORE bulk formulation, (iii) the use of a new TKE vertical mixing scheme, (iv) the use of the LIM2-EVP ice model. Leading to a better representation of the diurnal cycle, the stratification in upper layers, the sea-ice interannual extensions, or mesoscale features and WBC in the 1/12°. The 1/36° IBI regional configuration, adds non linear free surface, atmospheric pressure and tidal forcing, barotropic/baroclinic time splitting, and specific boundary conditions for operational nesting with the global system. At regional scale, a number of improvements (bathymetry and bottom friction) make it suitable for coastal modelling, validated state-of-art coastal ocean models. Data assimilation is based on reduced order Kalman filter using 3D multivariate modal decomposition of the forecast error. It assimilates jointly satellite altimetry, SST and in situ observations (temperature and salinity profiles, including ARGO data). Among difficulties, the assimilation has to be operated in real time, with limited and less accurate set of observations. Recent improvements in the global systems include (v) the insertion of the zonal/meridional velocity components into the control vector, (vi) the use of the IAU procedure, (vii) the insertion of new observational operators, (viii) the use of a new MDT, (ix) the introduction of pseudo-observations, (x) the use of a bias correction method based on a variational approach to estimate large scale biases. These improvements limit noise introduced by sequential assimilation, like in in the vertical dynamics. Water masses, on the shelves or near major run-off, like the Amazon discharge, are preserved. A biogeochemistry prediction system has been added, based on PISCES model. it uses spatial degradation of the real time physic provides by the 1/4° global system. A reanalysis covering the "altimetric era" (1992-2009) has been carried out in collaboration with the Drakkar community. GLORYS reanalyses describe the evolution of the ocean and sea-ice states; It is based on Mercator operational 1/4° global system, but with 75 levels vertical grid, ERA-Interim atmospheric forcing fields (corrected with satellite-based fluxes) and the assimilation of delayed time reprocessed and quality controlled observations. First studies show the reanalysis usefulness for interannual assessment of the mesoscale dynamics, but also the thermohaline circulations changes like MOC.

Evaluation of the tropical variability from the Beijing Climate Center's real-time operational global Ocean Data Assimilation System

NASA Astrophysics Data System (ADS)

Zhou, Wei; Chen, Mengyan; Zhuang, Wei; Xu, Fanghua; Zheng, Fei; Wu, Tongwen; Wang, Xin

2016-02-01

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC GODAS2.0) has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC GODAS2.0 for the user community. BCC GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational (3DVAR) data assimilation, and global ocean circulation model [Modular Ocean Model 4 (MOM4)]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC GODAS2.0 reanalysis are compared and assessed with observations for 1990-2011. The climatology of the mixed layer depth of BCC GODAS2.0 is generally in agreement with that ofWorld Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC GODAS2.0 are evaluated. The standard deviation of the SST in BCC GODAS2.0 agrees well with observations in the tropical Pacific. BCC GODAS2.0 is able to capture the main features of El Ni˜no Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of El Ni˜no Modoki are also reproduced.

Multi-year assimilation of IASI and MLS ozone retrievals: variability of tropospheric ozone over the tropics in response to ENSO

NASA Astrophysics Data System (ADS)

Peiro, Hélène; Emili, Emanuele; Cariolle, Daniel; Barret, Brice; Le Flochmoën, Eric

2018-05-01

The Infrared Atmospheric Sounder Instrument (IASI) allows global coverage with very high spatial resolution and its measurements are promising for long-term ozone monitoring. In this study, Microwave Limb Sounder (MLS) O3 profiles and IASI O3 partial columns (1013.25-345 hPa) are assimilated in a chemistry transport model to produce 6-hourly analyses of tropospheric ozone for 6 years (2008-2013). We have compared and evaluated the IASI-MLS analysis and the MLS analysis to assess the added value of IASI measurements. The global chemical transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) has been used with a linear ozone chemistry scheme and meteorological forcing fields from ERA-Interim (ECMWF global reanalysis) with a horizontal resolution of 2° × 2° and 60 vertical levels. The MLS and IASI O3 retrievals have been assimilated with a 4-D variational algorithm to constrain stratospheric and tropospheric ozone respectively. The ozone analyses are validated against ozone soundings and tropospheric column ozone (TCO) from the OMI-MLS residual method. In addition, an Ozone ENSO Index (OEI) is computed from the analysis to validate the TCO variability during the ENSO events. We show that the assimilation of IASI reproduces the variability of tropospheric ozone well during the period under study. The variability deduced from the IASI-MLS analysis and the OMI-MLS measurements are similar for the period of study. The IASI-MLS analysis can reproduce the extreme oscillation of tropospheric ozone caused by ENSO events over the tropical Pacific Ocean, although a correction is required to reduce a constant bias present in the IASI-MLS analysis.

Impact of Assimilated and Interactive Aerosol on Tropical Cyclogenesis

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, K. M.; daSilva, A.; Matsui, T.

2014-01-01

This article investigates the impact 3 of Saharan dust on the development of tropical cyclones in the Atlantic. A global data assimilation and forecast system, the NASA GEOS-5, is used to assimilate all satellite and conventional data sets used operationally for numerical weather prediction. In addition, this new GEOS-5 version includes assimilation of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS). The analysis so obtained comprises atmospheric quantities and a realistic 3-d aerosol and cloud distribution, consistent with the meteorology and validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data. These improved analyses are used to initialize GEOS-5 forecasts, explicitly accounting for aerosol direct radiative effects and their impact on the atmospheric dynamics. Parallel simulations with/without aerosol radiative effects show that effects of dust on static stability increase with time, becoming highly significant after day 5 and producing an environment less favorable to tropical cyclogenesis.

Empowering Geoscience with Improved Data Assimilation Using the Data Assimilation Research Testbed "Manhattan" Release.

NASA Astrophysics Data System (ADS)

Raeder, K.; Hoar, T. J.; Anderson, J. L.; Collins, N.; Hendricks, J.; Kershaw, H.; Ha, S.; Snyder, C.; Skamarock, W. C.; Mizzi, A. P.; Liu, H.; Liu, J.; Pedatella, N. M.; Karspeck, A. R.; Karol, S. I.; Bitz, C. M.; Zhang, Y.

2017-12-01

The capabilities of the Data Assimilation Research Testbed (DART) at NCAR have been significantly expanded with the recent "Manhattan" release. DART is an ensemble Kalman filter based suite of tools, which enables researchers to use data assimilation (DA) without first becoming DA experts. Highlights: significant improvement in efficient ensemble DA for very large models on thousands of processors, direct read and write of model state files in parallel, more control of the DA output for finer-grained analysis, new model interfaces which are useful to a variety of geophysical researchers, new observation forward operators and the ability to use precomputed forward operators from the forecast model. The new model interfaces and example applications include the following: MPAS-A; Model for Prediction Across Scales - Atmosphere is a global, nonhydrostatic, variable-resolution mesh atmospheric model, which facilitates multi-scale analysis and forecasting. The absence of distinct subdomains eliminates problems associated with subdomain boundaries. It demonstrates the ability to consistently produce higher-quality analyses than coarse, uniform meshes do. WRF-Chem; Weather Research and Forecasting + (MOZART) Chemistry model assimilates observations from FRAPPÉ (Front Range Air Pollution and Photochemistry Experiment). WACCM-X; Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension assimilates observations of electron density to investigate sudden stratospheric warming. CESM (weakly) coupled assimilation; NCAR's Community Earth System Model is used for assimilation of atmospheric and oceanic observations into their respective components using coupled atmosphere+land+ocean+sea+ice forecasts. CESM2.0; Assimilation in the atmospheric component (CAM, WACCM) of the newly released version is supported. This version contains new and extensively updated components and software environment. CICE; Los Alamos sea ice model (in CESM) is used to assimilate multivariate sea ice concentration observations to constrain the model's ice thickness, concentration, and parameters.

Global analyses of water vapor, cloud and precipitation derived from a diagnostic assimilation of SSM/I geophysical retrievals

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Cohen, Charles

1990-01-01

An analytical approach is described for diagnostically assimilating moisture data from Special Sensor Microwave Imager (SSM/I) into a global analysis of water vapor, cloud content, and precipitation. In this method, 3D fields of wind and temperature values taken from ECMWF gridded analysis are used to drive moisture conservation equations with parameterized microphysical treatment of vapor, liquid, and ice; the evolving field of water vapor is periodically updated or constrained by SSM/I retrievals of precipitable water. Initial results indicate that this diagnostic model can produce realistic large-scale fields of cloud and precipitation. The resulting water vapor analyses agree well with SSM/I and have an additional advantage of being synoptic.

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Modeling and Assimilating Ocean Color Radiances

NASA Technical Reports Server (NTRS)

Gregg, Watson

2012-01-01

Radiances are the source of information from ocean color sensors to produce estimates of biological and geochemical constituents. They potentially provide information on various other aspects of global biological and chemical systems, and there is considerable work involved in deriving new information from these signals. Each derived product, however, contains errors that are derived from the application of the radiances, above and beyond the radiance errors. A global biogeochemical model with an explicit spectral radiative transfer model is used to investigate the potential of assimilating radiances. The results indicate gaps in our understanding of radiative processes in the oceans and their relationships with biogeochemical variables. Most important, detritus optical properties are not well characterized and produce important effects of the simulated radiances. Specifically, there does not appear to be a relationship between detrital biomass and its optical properties, as there is for chlorophyll. Approximations are necessary to get beyond this problem. In this reprt we will discuss the challenges in modeling and assimilation water-leaving radiances and the prospects for improving our understanding of biogeochemical process by utilizing these signals.

The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0

NASA Technical Reports Server (NTRS)

Suarez, Max J.; Rienecker, M. M.; Todling, R.; Bacmeister, J.; Takacs, L.; Liu, H. C.; Gu, W.; Sienkiewicz, M.; Koster, R. D.; Gelaro, R.;

A regional ionospheric TEC mapping technique over China and adjacent areas on the basis of data assimilation

NASA Astrophysics Data System (ADS)

Aa, Ercha; Huang, Wengeng; Yu, Shimei; Liu, Siqing; Shi, Liqin; Gong, Jiancun; Chen, Yanhong; Shen, Hua

2015-06-01

In this paper, a regional total electron content (TEC) mapping technique over China and adjacent areas (70°E-140°E and 15°N-55°N) is developed on the basis of a Kalman filter data assimilation scheme driven by Global Navigation Satellite Systems (GNSS) data from the Crustal Movement Observation Network of China and International GNSS Service. The regional TEC maps can be generated accordingly with the spatial and temporal resolution being 1°×1° and 5 min, respectively. The accuracy and quality of the TEC mapping technique have been validated through the comparison with GNSS observations, the International Reference Ionosphere model values, the global ionosphere maps from Center for Orbit Determination of Europe, and the Massachusetts Institute of Technology Automated Processing of GPS TEC data from Madrigal database. The verification results indicate that great systematic improvements can be obtained when data are assimilated into the background model, which demonstrates the effectiveness of this technique in providing accurate regional specification of the ionospheric TEC over China and adjacent areas.

Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances

2006-01-01

In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

NASA Astrophysics Data System (ADS)

Zhang, Shupeng; Yi, Xue; Zheng, Xiaogu; Chen, Zhuoqi; Dan, Bo; Zhang, Xuanze

2014-11-01

In this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie-Ames-Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002-2008 period is 2.97 ± 1.1 PgC yr-1, and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr-1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

NASA Astrophysics Data System (ADS)

Chen, Zhuoqi; Chen, Jing M.; Zhang, Shupeng; Zheng, Xiaogu; Ju, Weiming; Mo, Gang; Lu, Xiaoliang

2017-12-01

The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

The HYCOM (HYbrid Coordinate Ocean Model) Data Assimilative System

DTIC Science & Technology

2007-06-01

Systems Inc., Stennis Space Center. MS, USA d SHOM/CMO, Toulouse. France € Los Alamos National Laboratory, Los Alamos, NM. USA Received 1 October 2004...Global Ocean Data Assimilation ’U. of Miami, NRL, Los Alamos, NOAA/NCEP, NOAA/AOML, Experiment (GODAE). GODAE is a coordinated inter- NOAA/PMEL, PSI...of Miami, the Naval all three approaches and the optimal distribution is Research Laboratory (NRL), and the Los Alamos chosen at every time step. The

Operational Implementation Design for the Earth System Prediction Capability (ESPC): A First-Look

DTIC Science & Technology

2014-02-20

Hybrid NAVDAS-AR data assimilation system assisting by providing dynamic estimates of the error in the background forecasts. 2.1.2 NAVDAS-AR – the...directly assimilates radiances from microwave radiometers and from interferometers and spectrometers in the infrared, and bending angle from Global...real-time analysis (at +3:00). Late in the 12-hr watch (around +8:00), a post-time NAVGEM/NAVDAS-AR run generates the background fields for the next

Towards a satellite driven land surface model using SURFEX modelling platform Offline Data Assimilation: an assessment of the method over Europe and the Mediterranean basin

NASA Astrophysics Data System (ADS)

Albergel, Clément; Munier, Simon; Leroux, Delphine; Fairbairn, David; Dorigo, Wouter; Decharme, Bertrand; Calvet, Jean-Christophe

2017-04-01

Modelling platforms including Land Surface Models (LSMs), forced by gridded atmospheric variables and coupled to river routing models are necessary to increase our understanding of the terrestrial water cycle. These LSMs need to simulate biogeophysical variables like Surface and Root Zone Soil Moisture (SSM, RZSM), Leaf Area Index (LAI) in a way that is fully consistent with the representation of surface/energy fluxes and river discharge simulations. Global SSM and LAI products are now operationally available from spaceborne instruments and they can be used to constrain LSMs through Data Assimilation (DA) techniques. In this study, an offline data assimilation system implemented in Météo-France's modelling platform (SURFEX) is tested over Europe and the Mediterranean basin to increase prediction accuracy for land surface variables. The resulting Land Data Assimilation System (LDAS) makes use of a simplified Extended Kalman Filter (SEKF). It is able to ingests information from satellite derived (i) SSM from the latest version of the ESA Climate Change Initiative as well as (ii) LAI from the Copernicus GLS project to constrain the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (ISBA-CTRIP). ERA-Interim observations based atmospheric forcing with precipitations corrected from Global Precipitation Climatology Centre observations (GPCC) is used to force ISBA-CTRIP at a resolution of 0.5 degree over 2000-2015. The model sensitivity to the assimilated observations is presented and a set of statistical diagnostics used to evaluate the impact of assimilating SSM and LAI on different model biogeophysical variables are provided. It is demonstrated that the assimilation scheme works effectively. The SEKF is able to extract useful information from the data signal at the grid scale and distribute the RZSM and LAI increments throughout the model impacting soil moisture, terrestrial vegetation and water cycle, surface carbon and energy fluxes.

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.

A preliminary experiment for the long-term regional reanalysis over Japan assimilating conventional observations with NHM-LETKF

NASA Astrophysics Data System (ADS)

Fukui, Shin; Iwasaki, Toshiki; Saito, Kazuo; Seko, Hiromu; Kunii, Masaru

2016-04-01

Several long-term global reanalyses have been produced by major operational centres and have contributed to the advance of weather and climate researches considerably. Although the horizontal resolutions of these global reanalyses are getting higher partly due to the development of computing technology, they are still too coarse to reproduce local circulations and precipitation realistically. To solve this problem, dynamical downscaling is often employed. However, the forcing from lateral boundaries only cannot necessarily control the inner fields especially in long-term dynamical downscaling. Regional reanalysis is expected to overcome the difficulty. To maintain the long-term consistency of the analysis quality, it is better to assimilate only the conventional observations that are available in long period. To confirm the effectiveness of the regional reanalysis, some assimilation experiments are performed. In the experiments, only conventional observations (SYNOP, SHIP, BUOY, TEMP, PILOT, TC-Bogus) are assimilated with the NHM-LETKF system, which consists of the nonhydrostatic model (NHM) of the Japan Meteorological Agency (JMA) and the local ensemble transform Kalman filter (LETKF). The horizontal resolution is 25 km and the domain covers Japan and its surroundings. Japanese 55-year reanalysis (JRA-55) is adopted as the initial and lateral boundary conditions for the NHM-LETKF forecast-analysis cycles. The ensemble size is 10. The experimental period is August 2014 as a representative of warm season for the region. The results are verified against the JMA's operational Meso-scale Analysis, which is produced with assimilating observation data including various remote sensing observations using a 4D-Var scheme, and compared with those of the simple dynamical downscaling experiment without data assimilation. Effects of implementation of lateral boundary perturbations derived from an EOF analysis of JRA-55 over the targeted domain are also examined. The comparison proposes that the assimilation system can reproduce more accurate fields than dynamical downscaling. The implementation of the lateral boundary perturbations implies that the perturbations contribute to providing more appropriate ensemble spreads, though the perturbations are not necessarily consistent to those of the inner fields given by NHM-LETKF.

Radiative Effect of Clouds on Tropospheric Chemistry in a Global Three-Dimensional Chemical Transport Model

NASA Technical Reports Server (NTRS)

Liu, Hongyu; Crawford, James H.; Pierce, Robert B.; Norris, Peter; Platnick, Steven E.; Chen, Gao; Logan, Jennifer A.; Yantosca, Robert M.; Evans, Mat J.; Kittaka, Chieko;

Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

NASA Astrophysics Data System (ADS)

Albergel, Clément; Munier, Simon; Leroux, Delphine Jennifer; Dewaele, Hélène; Fairbairn, David; Lavinia Barbu, Alina; Gelati, Emiliano; Dorigo, Wouter; Faroux, Stéphanie; Meurey, Catherine; Le Moigne, Patrick; Decharme, Bertrand; Mahfouf, Jean-Francois; Calvet, Jean-Christophe

2017-10-01

In this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil-biosphere-atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth). A sensitivity test of the Jacobians over 2000-2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

2009-05-01

A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Evaluating the Impact of the Summit Station, Greenland Radiosonde Program on Science and Forecast Services

NASA Astrophysics Data System (ADS)

Martinez, C. J.; Starkweather, S.; Cox, C. J.; Solomon, A.; Shupe, M.

2015-12-01

Radiosondes are balloon-borne meteorological sensors used to acquire profiles of temperature and humidity. Radiosonde data are essential inputs for numerical weather prediction models and are used for climate research, particularly in the creation of reanalysis products. However, radiosonde programs are costly to maintain, in particular in the remote regions of the Arctic (e.g., $440,000/yr at Summit, Greenland), where only 40 of approximately 1000 routine global launches are made. The climate of this data-sparse region is poorly understood and forecast data assimilation procedures are designed for global applications. Thus, observations may be rejected from the data assimilation because they are too far from the model expectations. For the most cost-efficient deployment of resources and to improve forecasting methods, analyses of the effectiveness of individual radiosonde programs are necessary. Here, we evaluate how radiosondes launched twice daily (0 and 12 UTC) from Summit Station, Greenland, (72.58⁰N, 38.48⁰W, 3210 masl) influence the European Centre for Medium Range Weather Forecasting (ECMWF) operational forecasts from June 2013 through May of 2015. A statistical analysis is conducted to determine the impact of the observations on the forecast model and the meteorological regimes that the model fails to reproduce are identified. Assimilation rates in the inversion layer are lower than any other part of the troposphere. Above the inversion, assimilation rates range from 85%-100%, 60%-98%, and > 99% for temperature, humidity, and wind, respectively. The lowest assimilation rates are found near the surface, possibly associated with biases in the representation of the temperature inversion by the ECMWF model at Summit. Consequently, assimilation rates are lower near the surface during winter when strong temperature inversions are frequently observed. Our findings benefit the scientific community who uses this information for climatological analysis of the Greenland Ice Sheet, and thus further analysis is warranted.

Technical Report Series on Global Modeling and Data Assimilation. Volume 7: Proceedings of the Workshop on the GEOS-1 Five-year Assimilation

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Schubert, Siegfried; Rood, Richard

1995-01-01

The primary objective of the three-day workshop on results from the Data Assimilation Office (DAO) five-year assimilation was to provide timely feedback from the data users concerning the strengths and weaknesses of version 1 of the Goddard Earth Observing System (GEOS-1) assimilated products. A second objective was to assess user satisfaction with the current methods of data access and retrieval. There were a total of 49 presentations, with about half (23) of the presentations from scientists from outside of Goddard. The first two days were devoted to applications of data: studies of the energy diagnostics, precipitation and diabatic heating, hydrological modeling and moisture transport, cloud forcing and validation, various aspects of intraseasonal, seasonal, and interannual variability, ocean wind stress applications, and validation of surface fluxes. The last day included talks from the National Meteorological Center (NMC), the National Center for Atmospheric Research (NCAR), the Center for Ocean-Land-Atmosphere Studies (COLA), the United States Navy, and the European Center for Medium Range Weather Forecasts (ECMWF).

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zhang, X.; Zavodsky, B. T.; Heinrichs, T.; Broderson, D.

2014-01-01

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

Naming game with biased assimilation over adaptive networks

NASA Astrophysics Data System (ADS)

Fu, Guiyuan; Zhang, Weidong

2018-01-01

The dynamics of two-word naming game incorporating the influence of biased assimilation over adaptive network is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he may refuse to accept the conveyed word from the speaker with a predefined probability, if the conveyed word is different from his current memory. Secondly, the adaptive network is formulated by rewiring the links. Theoretical analysis is developed to show that the population in NGBA will eventually reach global consensus on either A or B. Numerical simulation results show that the larger strength of biased assimilation on both words, the slower convergence speed, while larger strength of biased assimilation on only one word can slightly accelerate the convergence; larger population size can make the rate of convergence slower to a large extent when it increases from a relatively small size, while such effect becomes minor when the population size is large; the behavior of adaptively reconnecting the existing links can greatly accelerate the rate of convergence especially on the sparse connected network.

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

NASA Technical Reports Server (NTRS)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Pfaendtner, James; Bloom, Stephen; Lamich, David; Seablom, Michael; Sienkiewicz, Meta; Stobie, James; Dasilva, Arlindo

1995-01-01

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Data assimilation of GNSS zenith total delays from a Nordic processing centre

NASA Astrophysics Data System (ADS)

Lindskog, Magnus; Ridal, Martin; Thorsteinsson, Sigurdur; Ning, Tong

2017-11-01

Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

Research highlights of the global modeling and simulation branch for 1986-1987

NASA Technical Reports Server (NTRS)

Baker, Wayman (Editor); Susskind, Joel (Editor); Pfaendtner, James (Editor); Randall, David (Editor); Atlas, Robert (Editor)

1988-01-01

This document provides a summary of the research conducted in the Global Modeling and Simulation Branch and highlights the most significant accomplishments in 1986 to 1987. The Branch has been the focal point for global weather and climate prediction research in the Laboratory for Atmospheres through the retrieval and use of satellite data, the development of global models and data assimilation techniques, the simulation of future observing systems, and the performance of atmospheric diagnostic studies.

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana M.; Vollmer, Bruce E.; Kempler, Steven J.

2016-01-01

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, Ronald M.

2015-01-01

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

The Global Modeling and Assimilation Office (GMAO) 4d-Var and its Adjoint-based Tools

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Tremolet, Yannick

2008-01-01

The fifth generation of the Goddard Earth Observing System (GEOS-5) Data Assimilation System (DAS) is a 3d-var system that uses the Grid-point Statistical Interpolation (GSI) system developed in collaboration with NCEP, and a general circulation model developed at Goddard, that includes the finite-volume hydrodynamics of GEOS-4 wrapped in the Earth System Modeling Framework and physical packages tuned to provide a reliable hydrological cycle for the integration of the Modern Era Retrospective-analysis for Research and Applications (MERRA). This MERRA system is essentially complete and the next generation GEOS is under intense development. A prototype next generation system is now complete and has been producing preliminary results. This prototype system replaces the GSI-based Incremental Analysis Update procedure with a GSI-based 4d-var which uses the adjoint of the finite-volume hydrodynamics of GEOS-4 together with a vertical diffusing scheme for simplified physics. As part of this development we have kept the GEOS-5 IAU procedure as an option and have added the capability to experiment with a First Guess at the Appropriate Time (FGAT) procedure, thus allowing for at least three modes of running the data assimilation experiments. The prototype system is a large extension of GEOS-5 as it also includes various adjoint-based tools, namely, a forecast sensitivity tool, a singular vector tool, and an observation impact tool, that combines the model sensitivity tool with a GSI-based adjoint tool. These features bring the global data assimilation effort at Goddard up to date with technologies used in data assimilation systems at major meteorological centers elsewhere. Various aspects of the next generation GEOS will be discussed during the presentation at the Workshop, and preliminary results will illustrate the discussion.

Simulations of Tropospheric NO2 by the Global Modeling Initiative (GMI) Model Utilizing Assimilated and Forecast Meteorological Fields: Comparison to Ozone Monitoring Instrument (OMI) Measurements

NASA Technical Reports Server (NTRS)

Rodriquez, J. M.; Yoshida, Y.; Duncan, B. N.; Bucsela, E. J.; Gleason, J. F.; Allen, D.; Pickering, K. E.

2007-01-01

We present simulations of the tropospheric composition for the years 2004 and 2005, carried out by the GMI Combined Stratosphere-Troposphere (Combo) model, at a resolution of 2degx2.5deg. The model includes a new parameterization of lightning sources of NO(x) which is coupled to the cloud mass fluxes in the adopted meteorological fields. These simulations use two different sets of input meteorological fields: a)late-look assimilated fields from the Global Modeling and Assimilation Office (GMAO), GEOS-4 system and b) 12-hour forecast fields initialized with the assimilated data. Comparison of the forecast to the assimilated fields indicates that the forecast fields exhibit less vigorous convection, and yield tropical precipitation fields in better agreement with observations. Since these simulations include a complete representation of the stratosphere, they provide realistic stratosphere-tropospheric fluxes of O3 and NO(y). Furthermore, the stratospheric contribution to total columns of different troposheric species can be subtracted in a consistent fashion, and the lightning production of NO(y) will depend on the adopted meteorological field. We concentrate here on the simulated tropospheric columns of NO2, and compare them to observations by the OM1 instrument for the years 2004 and 2005. The comparison is used to address these questions: a) is there a significant difference in the agreement/disagreement between simulations for these two different meteorological fields, and if so, what causes these differences?; b) how do the simulations compare to OMI observations, and does this comparison indicate an improvement in simulations with the forecast fields? c) what are the implications of these simulations for our understanding of the NO2 emissions over continental polluted regions?

Continental-Scale Evaluation of Assimilated Soil Moisture Retrievals From the Advanced Microwave Scanning Radiometer

USDA-ARS?s Scientific Manuscript database

Soil moisture is a fundamental data source used in crop growth stage and crop stress models developed by the USDA Foreign Agriculture Service for global crop estimation. USDA’s International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA). Currently, the PECAD DSS utiliz...

Toward a multivariate reanalysis of the North Atlantic ocean biogeochemistry during 1998-2006 based on the assimilation of SeaWiFS chlorophyll data

NASA Astrophysics Data System (ADS)

Fontana, C.; Brasseur, P.; Brankart, J.-M.

2012-04-01

Today, the routine assimilation of satellite data into operational models of the ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical-biogeochemical model of the North-Atlantic. The aim is on one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical-biogeochemical modelling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9 year-long period. In this frame, two experiences are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, the nitrate World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface chlorophyll concentrations analysis and forecast, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related litterature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentration deeper than 50 m. The assessement of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalysing the ocean biogeochemistry based on ocean color data.

Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998-2006 based on the assimilation of SeaWiFS chlorophyll data

NASA Astrophysics Data System (ADS)

Fontana, C.; Brasseur, P.; Brankart, J.-M.

2013-01-01

Today, the routine assimilation of satellite data into operational models of ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical-biogeochemical model of the North Atlantic. The aim is on the one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical-biogeochemical modeling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9-year period. In this frame, two experiments are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, nitrate of the World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface analysis and forecast chlorophyll concentrations, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related literature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentrations deeper than 50 meters. The assessment of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalyzing the ocean biogeochemistry based on ocean color data.

Assessment of SMAP soil moisture for global simulation of gross primary production

NASA Astrophysics Data System (ADS)

He, Liming; Chen, Jing M.; Liu, Jane; Bélair, Stéphane; Luo, Xiangzhong

2017-07-01

In this study, high-quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process-based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun-induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment-2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single-cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater-stressed areas are included.

Assessment and Applications of NASA Ozone Data Products Derived from Aura OMI-MLS Satellite Measurements in Context of the GMI Chemical Transport Model

NASA Technical Reports Server (NTRS)

Ziemke, J. R.; Olsen, M. A.; Witte, J. C.; Douglass, A. R.; Strahan, S. E.; Wargan, K.; Liu, X.; Schoeberl, M. R.; Yang, K.; Kaplan, T. B.;

Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing ratio, air temperature and overhead column ozone) used for the calculation of the linearized photochemical production and loss of ozone. Furthermore the radiative budget in the tropopause region is strongly affected by water vapor cooling, which impact requires further evaluation for the use in chemically coupled operational NWP systems.

Coordination, Data Management and Enhancement of the International Arctic Buoy Programme (IABP) a US Interagency Arctic Buoy Programme \\201USIABP\\202 contribution to the IABP

DTIC Science & Technology

2013-09-30

data from the IABP ); 2.) Forecasting weather and sea ice conditions; 3.) Forcing, assimilation and validation of global weather and climate models ...International Arctic Buoy Programme ( IABP ) A US Interagency Arctic Buoy Programme (USIABP) contribution to the IABP Dr. Ignatius G. Rigor Polar...ice motion. These observations are assimilated into Numerical Weather Prediction (NWP) models that are used to forecast weather on synoptic time

Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

NASA Technical Reports Server (NTRS)

Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph;

Evaluation of Assimilative SST Forecasts in the Okinawa Trough and Gulf of Mexico

DTIC Science & Technology

2012-12-06

Ruth H. Preller, 7300 Socurily, Code -- 1231 Office of 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs (Unclassified/ Unlimited Only...E. R. Franchi , 7000 Pubii<: Aifaini iiJneia5s;ii$(iF. · - Unlimited Only), Code 7030•4 0 ·-5 -J·z_...operational global ocean model GOFS 2.6. The SST level 2 data assimilated in these studies is provided by NAVOCEANO and introduced into NCODA via its OCNQC

Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter

DTIC Science & Technology

2013-09-30

accuracy of the analysis . Root mean square difference ( RMSD ) is much smaller for RIP than for either Simple Ocean Data Assimilation or Incremental... Analysis Update globally for temperature as well as salinity. Regionally the same results were found, with only one exception in which the salinity RMSD ...short-term forecast using a numerical model with the observations taken within the forecast time window. The resulting state is the so-called “ analysis

Technical report series on global modeling and data assimilation. Volume 1: Documentation of the Goddard Earth Observing System (GEOS) General Circulation Model, version 1

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Takacs, Lawrence L.; Molod, Andrea; Wang, Tina

1994-01-01

This technical report documents Version 1 of the Goddard Earth Observing System (GEOS) General Circulation Model (GCM). The GEOS-1 GCM is being used by NASA's Data Assimilation Office (DAO) to produce multiyear data sets for climate research. This report provides a documentation of the model components used in the GEOS-1 GCM, a complete description of model diagnostics available, and a User's Guide to facilitate GEOS-1 GCM experiments.

An Introduction to Observing System Simulation Experiments

NASA Technical Reports Server (NTRS)

Prive, Nikki C.

2017-01-01

Observing System Simulation Experiments (OSSEs) are used to estimate the potential impact of proposed new instruments and data on numerical weather prediction. OSSEs can also be used to help design new observing platforms and to investigate the behavior of data assimilation systems. A basic overview of how to design and perform an OSSE will be given, as well as best practices and pitfalls. Some examples using the OSSE framework developed at the NASA Global Modeling and Assimilation Office will be shown.

Assimilation of Precipitation Measurement Missions Microwave Radiance Observations With GEOS-5

NASA Technical Reports Server (NTRS)

Jin, Jianjun; Kim, Min-Jeong; McCarty, Will; Akella, Santha; Gu, Wei

2015-01-01

The Global Precipitation Mission (GPM) Core Observatory satellite was launched in February, 2014. The GPM Microwave Imager (GMI) is a conically scanning radiometer measuring 13 channels ranging from 10 to 183 GHz and sampling between 65 S 65 N. This instrument is a successor to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), which has observed 9 channels at frequencies ranging 10 to 85 GHz between 40 S 40 N since 1997. This presentation outlines the base procedures developed to assimilate GMI and TMI radiances in clear-sky conditions, including quality control methods, thinning decisions, and the estimation of, observation errors. This presentation also shows the impact of these observations when they are incorporated into the GEOS-5 atmospheric data assimilation system.

A case study of GWE satellite data impact on GLA assimilation analyses of two ocean cyclones

NASA Technical Reports Server (NTRS)

Gallimore, R. G.; Johnson, D. R.

1986-01-01

The effects of the Global Weather Experiment (GWE) data obtained on January 18-20, 1979 on Goddard Laboratory for Atmospheres assimilation analyses of simultaneous cyclones in the western Pacific and Atlantic oceans are examined. The ability of satellite data within assimilation models to determine the baroclinic structures of developing extratropical cyclones is evaluated. The impact of the satellite data on the amplitude and phase of the temperature structure within the storm domain, potential energy, and baroclinic growth rate is studied. The GWE data are compared with Data Systems Test results. It is noted that it is necessary to characterize satellite effects on the baroclinic structure of cyclone waves which degrade numerical weather predictions of cyclogenesis.

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

PubMed Central

Sivareddy, S.; Paul, Arya; Sluka, Travis; Ravichandran, M.; Kalnay, Eugenia

2017-01-01

Assimilation methods, meant to constrain divergence of model trajectory from reality using observations, do not exactly satisfy the physical laws governing the model state variables. This allows mismatches in the analysis in the vicinity of observation locations where the effect of assimilation is most prominent. These mismatches are usually mitigated either by the model dynamics in between the analysis cycles and/or by assimilation at the next analysis cycle. However, if the observations coverage is limited in space, as it was in the ocean before the Argo era, these mechanisms may be insufficient to dampen the mismatches, which we call shocks, and they may remain and grow. Here we show through controlled experiments, using real and simulated observations in two different ocean models and assimilation systems, that such shocks are generated in the ocean at the lateral boundaries of the moored buoy network. They thrive and propagate westward as Rossby waves along these boundaries. However, these shocks are essentially eliminated by the assimilation of near-homogenous global Argo distribution. These findings question the fidelity of ocean reanalysis products in the pre-Argo era. For example, a reanalysis that ignores Argo floats and assimilates only moored buoys, wrongly represents 2008 as a negative Indian Ocean Dipole year. PMID:28429748

Dynamic Responses of the Earth's Outer Core to Assimilation of Observed Geomagnetic Secular Variation

NASA Technical Reports Server (NTRS)

Kuang, Weijia; Tangborn, Andrew

2014-01-01

Assimilation of surface geomagnetic observations and geodynamo models has advanced very quickly in recent years. However, compared to advanced data assimilation systems in meteorology, geomagnetic data assimilation (GDAS) is still in an early stage. Among many challenges ranging from data to models is the disparity between the short observation records and the long time scales of the core dynamics. To better utilize available observational information, we have made an effort in this study to directly assimilate the Gauss coefficients of both the core field and its secular variation (SV) obtained via global geomagnetic field modeling, aiming at understanding the dynamical responses of the core fluid to these additional observational constraints. Our studies show that the SV assimilation helps significantly to shorten the dynamo model spin-up process. The flow beneath the core-mantle boundary (CMB) responds significantly to the observed field and its SV. The strongest responses occur in the relatively small scale flow (of the degrees L is approx. 30 in spherical harmonic expansions). This part of the flow includes the axisymmetric toroidal flow (of order m = 0) and non-axisymmetric poloidal flow with m (is) greater than 5. These responses can be used to better understand the core flow and, in particular, to improve accuracies of predicting geomagnetic variability in future.

Application of data assimilation methods for analysis and integration of observed and modeled Arctic Sea ice motions

NASA Astrophysics Data System (ADS)

Meier, Walter Neil

This thesis demonstrates the applicability of data assimilation methods to improve observed and modeled ice motion fields and to demonstrate the effects of assimilated motion on Arctic processes important to the global climate and of practical concern to human activities. Ice motions derived from 85 GHz and 37 GHz SSM/I imagery and estimated from two-dimensional dynamic-thermodynamic sea ice models are compared to buoy observations. Mean error, error standard deviation, and correlation with buoys are computed for the model domain. SSM/I motions generally have a lower bias, but higher error standard deviations and lower correlation with buoys than model motions. There are notable variations in the statistics depending on the region of the Arctic, season, and ice characteristics. Assimilation methods are investigated and blending and optimal interpolation strategies are implemented. Blending assimilation improves error statistics slightly, but the effect of the assimilation is reduced due to noise in the SSM/I motions and is thus not an effective method to improve ice motion estimates. However, optimal interpolation assimilation reduces motion errors by 25--30% over modeled motions and 40--45% over SSM/I motions. Optimal interpolation assimilation is beneficial in all regions, seasons and ice conditions, and is particularly effective in regimes where modeled and SSM/I errors are high. Assimilation alters annual average motion fields. Modeled ice products of ice thickness, ice divergence, Fram Strait ice volume export, transport across the Arctic and interannual basin averages are also influenced by assimilated motions. Assimilation improves estimates of pollutant transport and corrects synoptic-scale errors in the motion fields caused by incorrect forcings or errors in model physics. The portability of the optimal interpolation assimilation method is demonstrated by implementing the strategy in an ice thickness distribution (ITD) model. This research presents an innovative method of combining a new data set of SSM/I-derived ice motions with three different sea ice models via two data assimilation methods. The work described here is the first example of assimilating remotely-sensed data within high-resolution and detailed dynamic-thermodynamic sea ice models. The results demonstrate that assimilation is a valuable resource for determining accurate ice motion in the Arctic.

Global Modeling of Tropospheric Chemistry with Assimilated Meteorology: Model Description and Evaluation

NASA Technical Reports Server (NTRS)

Bey, Isabelle; Jacob, Daniel J.; Yantosca, Robert M.; Logan, Jennifer A.; Field, Brendan D.; Fiore, Arlene M.; Li, Qin-Bin; Liu, Hong-Yu; Mickley, Loretta J.; Schultz, Martin G.

2001-01-01

We present a first description and evaluation of GEOS-CHEM, a global three-dimensional (3-D) model of tropospheric chemistry driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Data Assimilation Office (DAO). The model is applied to a 1-year simulation of tropospheric ozone-NOx-hydrocarbon chemistry for 1994, and is evaluated with observations both for 1994 and for other years. It reproduces usually to within 10 ppb the concentrations of ozone observed from the worldwide ozonesonde data network. It simulates correctly the seasonal phases and amplitudes of ozone concentrations for different regions and altitudes, but tends to underestimate the seasonal amplitude at northern midlatitudes. Observed concentrations of NO and peroxyacetylnitrate (PAN) observed in aircraft campaigns are generally reproduced to within a factor of 2 and often much better. Concentrations of HNO3 in the remote troposphere are overestimated typically by a factor of 2-3, a common problem in global models that may reflect a combination of insufficient precipitation scavenging and gas-aerosol partitioning not resolved by the model. The model yields an atmospheric lifetime of methylchloroform (proxy for global OH) of 5.1 years, as compared to a best estimate from observations of 5.5 plus or minus 0.8 years, and simulates H2O2 concentrations observed from aircraft with significant regional disagreements but no global bias. The OH concentrations are approximately 20% higher than in our previous global 3-D model which included an UV-absorbing aerosol. Concentrations of CO tend to be underestimated by the model, often by 10-30 ppb, which could reflect a combination of excessive OH (a 20% decrease in model OH could be accommodated by the methylchloroform constraint) and an underestimate of CO sources (particularly biogenic). The model underestimates observed acetone concentrations over the South Pacific in fall by a factor of 3; a missing source from the ocean may be implicated.

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

DOE PAGES

Safta, C.; Ricciuto, Daniel M.; Sargsyan, Khachik; ...

2015-07-01

In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employedmore » in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.« less

An Overview of Atmospheric Composition OSSE Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

daSilva, Arlinda

2012-01-01

A model-based Observing System Simulation Experiment (OSSE) is a framework for numerical experimentation in which observables are simulated from fields generated by an earth system model, including a parameterized description of observational error characteristics. Simulated observations can be used for sampling studies, quantifying errors in analysis or retrieval algorithms, and ultimately being a planning tool for designing new observing missions. While this framework has traditionally been used to assess the impact of observations on numerical weather prediction, it has a much broader applicability, in particular to aerosols and chemical constituents. In this talk we will give a general overview of Observing System Simulation Experiments (OSSE) activities at NASA's Global Modeling and Assimilation Office, with focus on its emerging atmospheric composition component.

The 1969–1985 Pozzuoli event and active volcanisms

PubMed Central

Yokoyama, Izumi

2006-01-01

Pozzuoli is located at the center of the Campi Flegrei caldera, near Naples and is famous for its anomalous subsidence and upheaval documented since the Roman period. Its secular and gradual subsidence can be interpreted as self-loading compaction of the caldera fills while abrupt upheavals are geologically suspected to be caused by magmagenic movements or steam forces. In order to interpret the origin and the process of the Pozzuoli upheavals, they are compared with active volcanisms represented by the 1977–1982 eruption of Usu volcano in Hokkaido. Usu volcano outburst in 1977 in major pumice eruptions and repeated magmatic and phreatomagmatic eruptions, and manifested remarkable ground deformations accompanying earthquake swarms. In 1969, the ground of Pozzuoli began to upheave with increases in seismicity but finally failed to cause any eruptive phenomena at the surface; nevertheless there are common characteristics of their motives and processes between the two events. The motive of the Usu deformation is clearly due to magma movements while that of the Pozzuoli upheaval has not been completely settled. A quantitative relationship between seismicity and deformation gives a clue for discussing the motive of the Pozzuoli deformations. The discharge rates of seismic energy and the deformation rates are compared between the two events and a certain similarity is found. This suggests that the origin of the Pozzuoli event may be partly magmatic as well as the Usu eruption, but its behavior largely depends on the property of the caldera deposits. When their deformation volumes are taken into consideration, their characteristics become quantitatively conspicuous. The ground at Pozzuoli is much more easily deformed by the upward motive force than Usu volcano. This is due to the rheological property of the caldera deposits of Campi Flegrei, and agrees to the theory that interprets the secular subsidence observed in historical times, as self-loading compaction. It is interesting that there is a point of contact between anomalous movements of the ground along the seashore in Italy and remarkable magmatic movements at the active volcano in Japan. PMID:25873752

A Dynamic Approach to Addressing Observation-Minus-Forecast Mean Differences in a Land Surface Skin Temperature Data Assimilation System

NASA Technical Reports Server (NTRS)

Draper, Clara; Reichle, Rolf; De Lannoy, Gabrielle; Scarino, Benjamin

2015-01-01

In land data assimilation, bias in the observation-minus-forecast (O-F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates, and an assumption that the O-F mean differences are stationary. A two-stage observation bias and state estimation filter is presented, as an alternative to observation rescaling that does not require a long data record or assume stationary O-F mean differences. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal scale O-F mean difference from the assimilated observations, allowing the assimilation to correct the model for synoptic-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature (Tsk) observations into the Catchment land surface model. Global maps of the O-F mean differences are presented, and the two-stage filter is evaluated for one year over the Americas. The two-stage filter effectively removed the Tsk O-F mean differences, for example the GOES-West O-F mean difference at 21:00 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed Tsk observations, the two-stage assimilation reduced the unbiased Root Mean Square Difference (ubRMSD) of the modeled Tsk by 10 of the open-loop values.

Comparison of Flow-Dependent and Static Error Correlation Models in the DAO Ozone Data Assimilation System

NASA Technical Reports Server (NTRS)

Wargan, K.; Stajner, I.; Pawson, S.

2003-01-01

In a data assimilation system the forecast error covariance matrix governs the way in which the data information is spread throughout the model grid. Implementation of a correct method of assigning covariances is expected to have an impact on the analysis results. The simplest models assume that correlations are constant in time and isotropic or nearly isotropic. In such models the analysis depends on the dynamics only through assumed error standard deviations. In applications to atmospheric tracer data assimilation this may lead to inaccuracies, especially in regions with strong wind shears or high gradient of potential vorticity, as well as in areas where no data are available. In order to overcome this problem we have developed a flow-dependent covariance model that is based on short term evolution of error correlations. The presentation compares performance of a static and a flow-dependent model applied to a global three- dimensional ozone data assimilation system developed at NASA s Data Assimilation Office. We will present some results of validation against WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres and in the troposphere. We will also discuss statistical characteristics of both models; in particular we will argue that including evolution of error correlations leads to stronger internal consistency of a data assimilation ,

Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

NASA Technical Reports Server (NTRS)

Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson

2013-01-01

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2004-01-01

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

40 years of military medical education: an overview of the Long-Term Career Outcome Study (LTCOS).

PubMed

Durning, Steven J; Artino, Anthony R; Dong, Ting; Cruess, David F; Gilliland, William R; DeZee, Kent J; Saguil, Aaron; Waechter, Donna M; McManigle, John E

2012-09-01

In 2005, the Long-Term Career Outcome Study (LTCOS) was established by the Dean, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences (USU). The original charge to the LTCOS team was to establish an electronic database of current and past students at USU. Since its inception, however, the LTCOS team has broadened its mission and started collecting and analyzing data on a continuous basis for the purposes of program evaluation and, in some cases, research. The purpose of this commentary is to review the history of the LTCOS, including details about USU, a brief review of prior LTCOS work, and progress made since our last essay on LTCOS efforts. This commentary also provides an introduction to the special issue, which is arranged as a series of articles that span the medical education continuum (i.e., before, during, and after medical school). The relative balance of articles in each phase of training represents the LTCOS team's efforts to address the entire continuum of medical education.

Data Assimilation at FLUXNET to Improve Models towards Ecological Forecasting (Invited)

NASA Astrophysics Data System (ADS)

Luo, Y.

2009-12-01

Dramatically increased volumes of data from observational and experimental networks such as FLUXNET call for transformation of ecological research to increase its emphasis on quantitative forecasting. Ecological forecasting will also meet the societal need to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-based models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today’s ecological models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting is data assimilation, which uses data to inform initial conditions and to help constrain a model during simulation to yield results that approximate reality as closely as possible. In an era with dramatically increased availability of data from observational and experimental networks, data assimilation is a key technique that helps convert the raw data into ecologically meaningful products so as to accelerate our understanding of ecological processes, test ecological theory, forecast changes in ecological services, and better serve the society. This talk will use examples to illustrate how data from FLUXNET have been assimilated with process-based models to improve estimates of model parameters and state variables; to quantify uncertainties in ecological forecasting arising from observations, models and their interactions; and to evaluate information contributions of data and model toward short- and long-term forecasting of ecosystem responses to global change.

A Unified Data Assimilation Strategy for Regional Coupled Atmosphere-Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xie, Lian; Liu, Bin; Zhang, Fuqing; Weng, Yonghui

2014-05-01

Improving tropical cyclone (TC) forecasts is a top priority in weather forecasting. Assimilating various observational data to produce better initial conditions for numerical models using advanced data assimilation techniques has been shown to benefit TC intensity forecasts, whereas assimilating large-scale environmental circulation into regional models by spectral nudging or Scale-Selective Data Assimilation (SSDA) has been demonstrated to improve TC track forecasts. Meanwhile, taking into account various air-sea interaction processes by high-resolution coupled air-sea modelling systems has also been shown to improve TC intensity forecasts. Despite the advances in data assimilation and air-sea coupled models, large errors in TC intensity and track forecasting remain. For example, Hurricane Nate (2011) has brought considerable challenge for the TC operational forecasting community, with very large intensity forecast errors (27, 25, and 40 kts for 48, 72, and 96 h, respectively) for the official forecasts. Considering the slow-moving nature of Hurricane Nate, it is reasonable to hypothesize that air-sea interaction processes played a critical role in the intensity change of the storm, and accurate representation of the upper ocean dynamics and thermodynamics is necessary to quantitatively describe the air-sea interaction processes. Currently, data assimilation techniques are generally only applied to hurricane forecasting in stand-alone atmospheric or oceanic model. In fact, most of the regional hurricane forecasting models only included data assimilation techniques for improving the initial condition of the atmospheric model. In such a situation, the benefit of adjustments in one model (atmospheric or oceanic) by assimilating observational data can be compromised by errors from the other model. Thus, unified data assimilation techniques for coupled air-sea modelling systems, which not only simultaneously assimilate atmospheric and oceanic observations into the coupled air-sea modelling system, but also nudging the large-scale environmental flow in the regional model towards global model forecasts are of increasing necessity. In this presentation, we will outline a strategy for an integrated approach in air-sea coupled data assimilation and discuss its benefits and feasibility from incremental results for select historical hurricane cases.

Assimilation of Radio Occultation Data From the Chinese Fengyun Meterological Satellite at GRAPES

NASA Astrophysics Data System (ADS)

LIU, Y.

2016-12-01

GNOS (GNSS Occultation Sounder) is a new radio occultation payload onboard the Chinese FY-3 series satellites, which probes the Earth's neutral atmosphere and the ionosphere. GNOS is capable of tracking the signals of both the Beidou (the Chinese navigation satellite system) and the GPS navigation satellite systems. The first FY-3C satellite with GNOS launch on 23 September 2013 successfully, and has more than 500 RO events daily, including approximately 400 GPS and 100 Beidou RO events. In this paper the data quality from FY3C GNOS, including GPS and Beidou radio accultation data, will be presented. The impact experiments of assimilating GNOS radio accultation refractivity profiles in GRAPES (Global and Regional Assimilation Prediction System) a new generation numerical model system of China Meteorological Administration, are also presented. Results show that the lowest probing height of 90% GNOS profile can reach 4KM away from the surface. The bias of GNOS refractivity profiles compared to reanalysis and radiosonde data is greater than those of COSMIC and GRAS, but after data quality control the standard deviation of GNOS refractivity is approximately 2%. The results of the GNOS assimilation experiments show that GNOS data can improve the analysis in the upper troposphere and lower stratosphere, particularly in the southern hemisphere and the ocean, which produce the neutral and positive impacts in GRAPES assimilation system. The combined impact of assimilating both GPS and Beidou GNOS radio occultation is greater than assimilating either instrument individually.

Insights on the impact of systematic model errors on data assimilation performance in changing catchments

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

2018-03-01

The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Specification and Prediction of the Radiation Environment Using Data Assimilative VERB code

NASA Astrophysics Data System (ADS)

Shprits, Yuri; Kellerman, Adam

2016-07-01

We discuss how data assimilation can be used for the reconstruction of long-term evolution, bench-marking of the physics based codes and used to improve the now-casting and focusing of the radiation belts and ring current. We also discuss advanced data assimilation methods such as parameter estimation and smoothing. We present a number of data assimilation applications using the VERB 3D code. The 3D data assimilative VERB allows us to blend together data from GOES, RBSP A and RBSP B. 1) Model with data assimilation allows us to propagate data to different pitch angles, energies, and L-shells and blends them together with the physics-based VERB code in an optimal way. We illustrate how to use this capability for the analysis of the previous events and for obtaining a global and statistical view of the system. 2) The model predictions strongly depend on initial conditions that are set up for the model. Therefore, the model is as good as the initial conditions that it uses. To produce the best possible initial conditions, data from different sources (GOES, RBSP A, B, our empirical model predictions based on ACE) are all blended together in an optimal way by means of data assimilation, as described above. The resulting initial conditions do not have gaps. This allows us to make more accurate predictions. Real-time prediction framework operating on our website, based on GOES, RBSP A, B and ACE data, and 3D VERB, is presented and discussed.

Can Aerosol Direct Radiative Effects Account for Analysis Increments of Temperature in the Tropical Atlantic?

NASA Technical Reports Server (NTRS)

da Silva, Arlindo M.; Alpert, Pinhas

2016-01-01

In the late 1990's, prior to the launch of the Terra satellite, atmospheric general circulation models (GCMs) did not include aerosol processes because aerosols were not properly monitored on a global scale and their spatial distributions were not known well enough for their incorporation in operational GCMs. At the time of the first GEOS Reanalysis (Schubert et al. 1993), long time series of analysis increments (the corrections to the atmospheric state by all available meteorological observations) became readily available, enabling detailed analysis of the GEOS-1 errors on a global scale. Such analysis revealed that temperature biases were particularly pronounced in the Tropical Atlantic region, with patterns depicting a remarkable similarity to dust plumes emanating from the African continent as evidenced by TOMS aerosol index maps. Yoram Kaufman was instrumental encouraging us to pursue this issue further, resulting in the study reported in Alpert et al. (1998) where we attempted to assess aerosol forcing by studying the errors of a the GEOS-1 GCM without aerosol physics within a data assimilation system. Based on this analysis, Alpert et al. (1998) put forward that dust aerosols are an important source of inaccuracies in numerical weather-prediction models in the Tropical Atlantic region, although a direct verification of this hypothesis was not possible back then. Nearly 20 years later, numerical prediction models have increased in resolution and complexity of physical parameterizations, including the representation of aerosols and their interactions with the circulation. Moreover, with the advent of NASA's EOS program and subsequent satellites, atmospheric aerosols are now monitored globally on a routine basis, and their assimilation in global models are becoming well established. In this talk we will reexamine the Alpert et al. (1998) hypothesis using the most recent version of the GEOS-5 Data Assimilation System with assimilation of aerosols. We will explicitly calculate the impact of aerosols on the temperature analysis increments in the tropical Atlantic and assess the extent to which inclusion of atmospheric aerosols have reduced these increments.

4D-Var Developement at GMAO

NASA Technical Reports Server (NTRS)

Pelc, Joanna S.; Todling, Ricardo; Akkraoui, Amal El

2014-01-01

The Global Modeling and Assimilation Offce (GMAO) is currently using an IAU-based 3D-Var data assimilation system. GMAO has been experimenting with a 3D-Var-hybrid version of its data assimilation system (DAS) for over a year now, which will soon become operational and it will rapidly progress toward a 4D-EnVar. Concurrently, the machinery to exercise traditional 4DVar is in place and it is desirable to have a comparison of the traditional 4D approach with the other available options, and evaluate their performance in the Goddard Earth Observing System (GEOS) DAS. This work will also explore the possibility for constructing a reduced order model (ROM) to make traditional 4D-Var computationally attractive for increasing model resolutions. Part of the research on ROM will be to search for a suitably acceptable space to carry on the corresponding reduction. This poster illustrates how the IAU-based 4D-Var assimilation compares with our currently used IAU-based 3D-Var.

Variational Assimilation of GOME Total-Column Ozone Satellite Data in a 2D Latitude-Longitude Tracer-Transport Model.

NASA Astrophysics Data System (ADS)

Eskes, H. J.; Piters, A. J. M.; Levelt, P. F.; Allaart, M. A. F.; Kelder, H. M.

1999-10-01

A four-dimensional data-assimilation method is described to derive synoptic ozone fields from total-column ozone satellite measurements. The ozone columns are advected by a 2D tracer-transport model, using ECMWF wind fields at a single pressure level. Special attention is paid to the modeling of the forecast error covariance and quality control. The temporal and spatial dependence of the forecast error is taken into account, resulting in a global error field at any instant in time that provides a local estimate of the accuracy of the assimilated field. The authors discuss the advantages of the 4D-variational (4D-Var) approach over sequential assimilation schemes. One of the attractive features of the 4D-Var technique is its ability to incorporate measurements at later times t > t0 in the analysis at time t0, in a way consistent with the time evolution as described by the model. This significantly improves the offline analyzed ozone fields.

Paleo Data Assimilation of Pseudo-Tree-Ring-Width Chronologies in a Climate Model

NASA Astrophysics Data System (ADS)

Fallah Hassanabadi, B.; Acevedo, W.; Reich, S.; Cubasch, U.

2016-12-01

Using the Time-Averaged Ensemble Kalman Filter (EnKF) and a forward model, we assimilate the pseudo Tree-Ring-Width (TRW) chronologies into an Atmospheric Global Circulation model. This study investigates several aspects of Paleo-Data Assimilation (PDA) within a perfect-model set-up: (i) we test the performance of several forward operators in the framework of a PDA-based climate reconstruction, (ii) compare the PDA-based simulations' skill against the free ensemble runs and (iii) inverstigate the skill of the "online" (with cycling) DA and the "off-line" (no-cycling) DA. In our experiments, the "online" (with cycling) PDA approach did not outperform the "off-line" (no-cycling) one, despite its considerable additional implementation complexity. On the other hand, it was observed that the error reduction achieved by assimilating a particular pseudo-TRW chronology is modulated by the strength of the yearly internal variability of the model at the chronology site. This result might help the dendrochronology community to optimize their sampling efforts.

Can Assimilation of Satellite Ozone Data Contribute to the Understanding of the Lower Stratospheric Ozone?

NASA Technical Reports Server (NTRS)

Stajner, I.; Wargan, K.; Pawson, S.; Hayashi, H.; Chang, L.-P.; Rood, R.

2004-01-01

We study the quality of lower stratospheric ozone fields from a three- dimensional global ozone assimilation system. Ozone in this region is important for the forcing of climate, but its global distribution is not fully known because of its large temporal and vertical variability. Modeled fields often have biases due to the inaccurate representation of transport processes in this region with strong gradients. Accurate ozonesonde or satellite occultation measurements have very limited coverage. Nadir measurements, such as those from the Solar Backscatter Ultraviolet/2 (SBUV/2) instrument that provide wide latitudinal coverage, lack the vertical resolution needed to represent sharp vertical features. Limb measurements, such as those from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), provide a finer vertical resolution. We show that assimilation of MIPAS data in addition to SBUV/2 data leads to better estimates of ozone in comparison with independent high quality satellite, aircraft, and ozone sonde measurements. Other modifications to the statistical analysis that have an impact on the lower stratospheric ozone will be mentioned: error covariance modeling and data selection. Direct and indirect impacts of transport and chemistry models will be discussed. Implications for multi-year analyses and short-tern prediction will be addressed.

The Impact of British Airways Wind Observations on the Goddard Earth Observing System Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid; Sienkiewicz, M.; Tenenbaum, J.; Kondratyeva, Y.; Owens, T.; Oztunali, M.; Atlas, Robert (Technical Monitor)

2001-01-01

British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication System. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing System Data Assimilation System (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation System (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and forecasts was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day forecast anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 forecasts from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on forecasts beyond 2-3 days for all levels at the most areas.

The Path from Large Earth Science Datasets to Information

NASA Astrophysics Data System (ADS)

Vicente, G. A.

2013-12-01

The NASA Goddard Earth Sciences Data (GES) and Information Services Center (DISC) is one of the major Science Mission Directorate (SMD) for archiving and distribution of Earth Science remote sensing data, products and services. This virtual portal provides convenient access to Atmospheric Composition and Dynamics, Hydrology, Precipitation, Ozone, and model derived datasets (generated by GSFC's Global Modeling and Assimilation Office), the North American Land Data Assimilation System (NLDAS) and the Global Land Data Assimilation System (GLDAS) data products (both generated by GSFC's Hydrological Sciences Branch). This presentation demonstrates various tools and computational technologies developed in the GES DISC to manage the huge volume of data and products acquired from various missions and programs over the years. It explores approaches to archive, document, distribute, access and analyze Earth Science data and information as well as addresses the technical and scientific issues, governance and user support problem faced by scientists in need of multi-disciplinary datasets. It also discusses data and product metrics, user distribution profiles and lessons learned through interactions with the science communities around the world. Finally it demonstrates some of the most used data and product visualization and analyses tools developed and maintained by the GES DISC.

Comparing WSA coronal and solar wind model predictions driven by line-of-sight and vector HMI ADAPT maps

NASA Astrophysics Data System (ADS)

Arge, C. N.; Henney, C. J.; Shurkin, K.; Wallace, S.

2017-12-01

As the primary input to nearly all coronal models, reliable estimates of the global solar photospheric magnetic field distribution are critical for accurate modeling and understanding of solar and heliospheric magnetic fields. The Air Force Data Assimilative Photospheric flux Transport (ADAPT) model generates synchronic (i.e., globally instantaneous) maps by evolving observed solar magnetic flux using relatively well understood transport processes when measurements are not available and then updating modeled flux with new observations (available from both the Earth and the far-side of the Sun) using data assimilation methods that rigorously take into account model and observational uncertainties. ADAPT is capable of assimilating line-of-sight and vector magnetic field data from all observatory sources including the expected photospheric vector magnetograms from the Polarimetric and Helioseismic Imager (PHI) on the Solar Orbiter, as well as those generated using helioseismic methods. This paper compares Wang-Sheeley-Arge (WSA) coronal and solar wind modeling results at Earth and STEREO A & B using ADAPT input model maps derived from both line-of-site and vector SDO/HMI magnetograms that include methods for incorporating observations of a large, newly emerged (July 2010) far-side active region (AR11087).

On the Issue of Excess Lower Stratospheric Subtropical Transport in GEOS-DAS

NASA Technical Reports Server (NTRS)

Tan, Wei-Wu; Geller, Marvin; Pawson, Steven

2002-01-01

In recent years, data assimilation has become an indispensable tool for our understanding of the global features of meteorological variables. However, assessments of transport characteristics using trajectory related methods as well as chemical transport models (CTMs) show that results derived from assimilated (or analyzed) winds exhibit significantly larger mixing and entrainment rates compared to results derived from GCM winds, which are closer to results derived from observations (e.g., Douglass et al., 2002; Schoeberl et al., 2002). This discrepancy presents a serious challenge to our ability to understand and model global trace gas transport and distribution. We use the GEOS-DAS to explore this issue by examining how the process of data assimilation alters the dynamics of the underlying GCM and how this leads to the excess of lower stratospheric mixing and transport in the subtropics. In particular, we show that significant model biases in tropical winds necessitate large analysis increments. These increments directly force large subtropical regions of instability with negative PV gradient on the one hand, and generate excessive noise in the tropical wind fields on the other. The result is an excess of transport in the lower stratospheric subtropics.

Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI

NASA Astrophysics Data System (ADS)

Massart, S.; Agusti-Panareda, A.; Aben, I.; Butz, A.; Chevallier, F.; Crevosier, C.; Engelen, R.; Frankenberg, C.; Hasekamp, O.

2014-06-01

The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and by 15 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 3 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, their impact is mainly positive but more pronounced and effective when combined with version v.2.0 of the TANSO products. The resulting analysis globally underestimates the column-averaged dry-air mole fractions of methane (xCH4) just under 1 ppb on average compared to the TCCON data, whereas in the tropics it overestimates xCH4 by about 3 ppb. The random error is estimated to be less than 7 ppb when compared to TCCON data.

The USU medical PDA initiative: the PDA as an educational tool.

PubMed

Moore, Leon; Richardson, Bradley R; Williams, Robert W

2002-01-01

A medical personal digital assistant (PDA) initiative for healthcare students began in 2000 at the Uniformed Services University of the Health Sciences (USU). The University issued PDAs to Graduate School of Nursing (GSN) and School of Medicine (SOM) students. These devices were used to provide clinical reference material to the students, to facilitate clinical experience log collection, and the normal organizer functions of a PDA. Both medical and graduate nursing students were surveyed both before and during clinical training to determine the perceived usefulness of the PDA. A quantitative approach was utilized to emphasize the measurable variables.

Results of the Simulation and Assimilation of Doppler Wind Lidar Observations in Preparation for European Space Agency's Aeolus Mission

NASA Technical Reports Server (NTRS)

McCarty, Will

2011-01-01

With the launch of the European Space Agency's Aeolus Mission in 2013, direct spaceborne measurements of vertical wind profiles are imminent via Doppler wind lidar technology. Part of the preparedness for such missions is the development of the proper data assimilation methodology for handling such observations. Since no heritage measurements exist in space, the Joint Observing System Simulation Experiment (Joint OSSE) framework has been utilized to generate a realistic proxy dataset as a precursor to flight. These data are being used for the development of the Gridpoint Statistical Interpolation (GSI) data assimilation system utilized at a number of centers through the United States including the Global Modeling and Assimilation Office (GMAO) at NASA/Goddard Space Flight Center and at the National Centers for Environmental Prediction (NOAA/NWS/NCEP) as an activity through the Joint Center for Satellite Data Assimilation. An update of this ongoing effort will be presented, including the methodology of proxy data generation, the limitations of the proxy data, the handling of line-of-sight wind measurements within the GSI, and the impact on both analyses and forecasts with the addition of the new data type.

Technical Report Series on Global Modeling and Data Assimilation. Volume 14; A Comparison of GEOS Assimilated Data with FIFE Observations

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Suarez, Max J. (Editor); Schubert, Siegfried D.

1998-01-01

First ISLSCP Field Experiment (FIFE) observations have been used to validate the near-surface proper- ties of various versions of the Goddard Earth Observing System (GEOS) Data Assimilation System. The site- averaged FIFE data set extends from May 1987 through November 1989, allowing the investigation of several time scales, including the annual cycle, daily means and diurnal cycles. Furthermore, the development of the daytime convective planetary boundary layer is presented for several days. Monthly variations of the surface energy budget during the summer of 1988 demonstrate the affect of the prescribed surface soil wetness boundary conditions. GEOS data comes from the first frozen version of the assimilation system (GEOS-1 DAS) and two experimental versions of GEOS (v. 2.0 and 2.1) with substantially greater vertical resolution and other changes that influence the boundary layer. This report provides a baseline for future versions of the GEOS data assimilation system that will incorporate a state-of-the-art land surface parameterization. Several suggestions are proposed to improve the generality of future comparisons. These include the use of more diverse field experiment observations and an estimate of gridpoint heterogeneity from the new land surface parameterization.

Considerations for Using Hybrid Ensemble-Variational Data Assimilation in NASA-GMAO's Next Reanalysis System

NASA Technical Reports Server (NTRS)

El Akkraoui, Amal; Todling, Ricardo

2017-01-01

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is the latest reanalysis produced by GMAO, and provides global data spanning the period 1980-present. The atmospheric data assimilation component of MERRA-2 used a 3D-Var scheme, which was operational at the time of its design. Since then, a Hybrid 3D-Var, then a Hybrid 4D-EnVar were implemented, adding an ensemble component to the data assimilation scheme. In this work, we will be examining the benefits of using hybrid ensemble flow-dependent covariances to represent errors and uncertainties in historic periods. Specifically, periods of pre- and post-satellites, as well as periods of active tropical cyclone seasons. Finally, we will also be exploring the use of adaptive localization scales.

Benchmarking the mesoscale variability in global ocean eddy-permitting numerical systems

NASA Astrophysics Data System (ADS)

Cipollone, Andrea; Masina, Simona; Storto, Andrea; Iovino, Doroteaciro

2017-10-01

The role of data assimilation procedures on representing ocean mesoscale variability is assessed by applying eddy statistics to a state-of-the-art global ocean reanalysis (C-GLORS), a free global ocean simulation (performed with the NEMO system) and an observation-based dataset (ARMOR3D) used as an independent benchmark. Numerical results are computed on a 1/4 ∘ horizontal grid (ORCA025) and share the same resolution with ARMOR3D dataset. This "eddy-permitting" resolution is sufficient to allow ocean eddies to form. Further to assessing the eddy statistics from three different datasets, a global three-dimensional eddy detection system is implemented in order to bypass the need of regional-dependent definition of thresholds, typical of commonly adopted eddy detection algorithms. It thus provides full three-dimensional eddy statistics segmenting vertical profiles from local rotational velocities. This criterion is crucial for discerning real eddies from transient surface noise that inevitably affects any two-dimensional algorithm. Data assimilation enhances and corrects mesoscale variability on a wide range of features that cannot be well reproduced otherwise. The free simulation fairly reproduces eddies emerging from western boundary currents and deep baroclinic instabilities, while underestimates shallower vortexes that populate the full basin. The ocean reanalysis recovers most of the missing turbulence, shown by satellite products , that is not generated by the model itself and consistently projects surface variability deep into the water column. The comparison with the statistically reconstructed vertical profiles from ARMOR3D show that ocean data assimilation is able to embed variability into the model dynamics, constraining eddies with in situ and altimetry observation and generating them consistently with local environment.

Assimilation of the ESA CCI Soil Moisture ACTIVE and PASSIVE Product into the SURFEX Land Surface Model using the Ensemble Transform Kalman Filter

NASA Astrophysics Data System (ADS)

Blyverket, J.; Hamer, P.; Bertino, L.; Lahoz, W. A.

2017-12-01

The European Space Agency Climate Change Initiative for soil moisture (ESA CCI SM) was initiated in 2012 for a period of six years, the objective for this period was to produce the most complete and consistent global soil moisture data record based on both active and passive sensors. The ESA CCI SM products consist of three surface soil moisture datasets: The ACTIVE product and the PASSIVE product were created by fusing scatterometer and radiometer soil moisture data, respectively. The COMBINED product is a blended product based on the former two datasets. In this study we assimilate globally both the ACTIVE and PASSIVE product at a 25 km spatial resolution. The different satellite platforms have different overpass times, an observation is mapped to the hours 00.00, 06.00, 12.00 or 18.00 if it falls within a 3 hour window centred at these times. We use the SURFEX land surface model with the ISBA diffusion scheme for the soil hydrology. For the assimilation routine we apply the Ensemble Transform Kalman Filter (ETKF). The land surface model is driven by perturbed MERRA-2 atmospheric forcing data, which has a temporal resolution of one hour and is mapped to the SURFEX model grid. Bias between the land surface model and the ESA CCI product is removed by cumulative distribution function (CDF) matching. This work is a step towards creating a global root zone soil moisture product from the most comprehensive satellite surface soil moisture product available. As a first step we consider the period from 2010 - 2016. This allows for comparison against other global root zone soil moisture products (SMAP Level 4, which is independent of the ESA CCI SM product).

Assimilation of all-weather GMI and ATMS observations into HWRF

NASA Astrophysics Data System (ADS)

Moradi, I.; Evans, F.; McCarty, W.; Marks, F.; Eriksson, P.

2017-12-01

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume; Koster, Randal D. (Editor)

2014-01-01

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Modeling ionospheric pre-reversal enhancement and plasma bubble growth rate using data assimilation

NASA Astrophysics Data System (ADS)

Rajesh, P. K.; Lin, C. C. H.; Chen, C. H.; Matsuo, T.

2017-12-01

We report that assimilating total electron content (TEC) into a coupled thermosphere-ionosphere model by using the ensemble Kalman filter results in improved specification and forecast of eastward pre-reversal enhancement (PRE) electric field (E-field). Through data assimilation, the ionospheric plasma density, thermospheric winds, temperature and compositions are adjusted simultaneously. The improvement of dusk-side PRE E-field over the prior state is achieved primarily by intensification of eastward neutral wind. The improved E-field promotes a stronger plasma fountain and deepens the equatorial trough. As a result, the horizontal gradients of Pedersen conductivity and eastward wind are increased due to greater zonal electron density gradient and smaller ion drag at dusk, respectively. Such modifications provide preferable conditions and obtain a strengthened PRE magnitude closer to the observation. The adjustment of PRE E-field is enabled through self-consistent thermosphere and ionosphere coupling processes captured in the model. The assimilative outputs are further utilized to calculate the flux tube integrated Rayleigh-Taylor instability growth rate during March 2015 for investigation of global plasma bubble occurrence. Significant improvements in the calculated growth rates could be achieved because of the improved update of zonal electric field in the data assimilation forecast. The results suggest that realistic estimate or prediction of plasma bubble occurrence could be feasible by taking advantage of the data assimilation approach adopted in this work.

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele; Kovach, Robin M.; Vernieres, Guillaume

2014-01-01

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Diagnostics of sources of tropospheric ozone using data assimilation during the KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Gaubert, B.; Emmons, L. K.; Miyazaki, K.; Buchholz, R. R.; Tang, W.; Arellano, A. F., Jr.; Tilmes, S.; Barré, J.; Worden, H. M.; Raeder, K.; Anderson, J. L.; Edwards, D. P.

2017-12-01

Atmospheric oxidative capacity plays a crucial role in the fate of greenhouse gases and air pollutants as well as in the formation of secondary pollutants such as tropospheric ozone. The attribution of sources of tropospheric ozone is a difficult task because of biases in input parameters and forcings such as emissions and meteorology in addition to errors in chemical schemes. We assimilate satellite remote sensing observations of ozone precursors such as carbon monoxide (CO) and nitrogen dioxide (NO2) in the global coupled chemistry-transport model: Community Atmosphere Model with Chemistry (CAM-Chem). The assimilation is completed using an Ensemble Adjustment Kalman Filter (EAKF) in the Data Assimilation Research Testbed (DART) framework which allows estimates of unobserved parameters and potential constraints on secondary pollutants and emissions. The ensemble will be constructed using perturbations in chemical kinetics, different emission fields and by assimilating meteorological observations to fully assess uncertainties in the chemical fields of targeted species. We present a set of tools such as emission tags (CO and propane), combined with diagnostic analysis of chemical regimes and perturbation of emissions ratios to estimate a regional budget of primary and secondary pollutants in East Asia and their sensitivity to data assimilation. This study benefits from the large set of aircraft and ozonesonde in-situ observations from the Korea-United States Air Quality (KORUS-AQ) campaign that occurred in South Korea in May-June 2016.

Assimilation of Ocean-Color Plankton Functional Types to Improve Marine Ecosystem Simulations

NASA Astrophysics Data System (ADS)

Ciavatta, S.; Brewin, R. J. W.; Skákala, J.; Polimene, L.; de Mora, L.; Artioli, Y.; Allen, J. I.

2018-02-01

We assimilated phytoplankton functional types (PFTs) derived from ocean color into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton, and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998-2003. The skill of the reference and reanalysis simulations in estimating ocean color and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean-color PFTs (except nanoplankton), as well as the not-assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not-assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air-sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf-sea ecosystems.

Supporting Operational Data Assimilation Capabilities to the Research Community

NASA Astrophysics Data System (ADS)

Shao, H.; Hu, M.; Stark, D. R.; Zhou, C.; Beck, J.; Ge, G.

2017-12-01

The Developmental Testbed Center (DTC), in partnership with the National Centers for Environmental Prediction (NCEP) and other operational and research institutions, provides operational data assimilation capabilities to the research community and helps transition research advances to operations. The primary data assimilation system supported currently by the DTC is the Gridpoint Statistical Interpolation (GSI) system and the National Oceanic and Atmospheric Administration (NOAA) Ensemble Kalman Filter (EnKF) system. GSI is a variational based system being used for daily operations at NOAA, NCEP, the National Aeronautics and Space Administration, and other operational agencies. Recently, GSI has evolved into a four-dimensional EnVar system. Since 2009, the DTC has been releasing the GSI code to the research community annually and providing user support. In addition to GSI, the DTC, in 2015, began supporting the ensemble based EnKF data assimilation system. EnKF shares the observation operator with GSI and therefore, just as GSI, can assimilate both conventional and non-conventional data (e.g., satellite radiance). Currently, EnKF is being implemented as part of the GSI based hybrid EnVar system for NCEP Global Forecast System operations. This paper will summarize the current code management and support framework for these two systems. Following that is a description of available community services and facilities. Also presented is the pathway for researchers to contribute their development to the daily operations of these data assimilation systems.

Comparison of Surface Ground Temperature from Satellite Observations and the Off-Line Land Surface GEOS Assimilation System

NASA Technical Reports Server (NTRS)

Yang, R.; Houser, P.; Joiner, J.

1998-01-01

The surface ground temperature (Tg) is an important meteorological variable, because it represents an integrated thermal state of the land surface determined by a complex surface energy budget. Furthermore, Tg affects both the surface sensible and latent heat fluxes. Through these fluxes. the surface budget is coupled with the atmosphere above. Accurate Tg data are useful for estimating the surface radiation budget and fluxes, as well as soil moisture. Tg is not included in conventional synoptical weather station reports. Currently, satellites provide Tg estimates globally. It is necessary to carefully consider appropriate methods of using these satellite data in a data assimilation system. Recently, an Off-line Land surface GEOS Assimilation (OLGA) system was implemented at the Data Assimilation Office at NASA-GSFC. One of the goals of OLGA is to assimilate satellite-derived Tg data. Prior to the Tg assimilation, a thorough investigation of satellite- and model-derived Tg, including error estimates, is required. In this study we examine the Tg from the n Project (ISCCP DI) data and the OLGA simulations. The ISCCP data used here are 3-hourly DI data (2.5x2.5 degree resolution) for 1992 summer months (June, July, and August) and winter months (January and February). The model Tg for the same periods were generated by OLGA. The forcing data for this OLGA 1992 simulation were generated from the GEOS-1 Data Assimilation System (DAS) at Data Assimilation Office NASA-GSFC. We examine the discrepancies between ISCCP and OLGA Tg with a focus on its spatial and temporal characteristics, particularly on the diurnal cycle. The error statistics in both data sets, including bias, will be estimated. The impact of surface properties, including vegetation cover and type, topography, etc, on the discrepancies will be addressed.

Status and Plans for Reanalysis at NASA/GMAO

NASA Technical Reports Server (NTRS)

Gelaro, Ron

2017-01-01

Reanalysis plays a critical role in GMAOs goal to enhance NASA's program of Earth observations, providing vital data sets for climate research and the development of future missions. As the breadth of NASAs observations expands to include multiple components of the Earth system, so does the need to assimilate observations from currently uncoupled components of the system in a more physically consistent manner. GMAOs most recent reanalysis of the satellite era, MERRA-2, has completed the period 1980-present, and is now running as a continuing global climate analysis with two- to three-week latency. MERRA-2 assimilates meteorological and aerosol observations as a weakly coupled assimilation system as a first step toward GMAOs longer term goal of developing an integrated Earth system analysis (IESA) capability that will couple assimilation systems for the atmosphere, ocean, land and chemistry. The GMAO strategy is to progress incrementally toward an IESA through an evolving combination of coupled systems and offline component reanalyses driven by, for example, MERRA-2 atmospheric forcing. Most recently, the GMAO has implemented a weakly coupled assimilation scheme for analyzing ocean skin temperature within the existing atmospheric analysis. The scheme uses background fields from a near-surface ocean diurnal layer model to assimilate surface-sensitive radiances plus in-situ observations along with all other observations in the atmospheric assimilation system. In addition, MERRA-2-driven simulations of the ocean (plus sea ice) and atmospheric chemistry (for the EOS period) are currently underway, as is the development of a coupled atmosphere-ocean assimilation system. This talk will describe the status of these ongoing efforts and the planned steps toward an IESA capability for climate research.

Continuous data assimilation experiments with the NMC eta model: A GALE IOP 1 case study. [NMC (National Meteorological Center); GALE IOP (Genesis of Atlantic Lows Experiment intensive observing period)

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

Ramamurthy, M.K.; Xu, T.Y.

1993-11-01

The current major expansion in observational capability of the National Weather Service is principally in the volume of asynchronous data rather than synchronous observations at the standard synoptic times. Hence, the National Meteorological Center is considering a continuous data assimilation system to replace at some time the intermittent system now used by its regional and global operational models. We describe this system, based on the Newtonian relaxation technique, as developed for the eta model. Experiments are performed for the first intensive observing period of the Genesis of Atlantic Lows Experiment (GALE) in January 1986, when strong upper-level cyclogenesis occurred, withmore » a pronounced tropopause fold but only modest surface development. The GALE level IIIb dataset was used for initializing and updating the model. Issues addressed in the experiments include choice of update variable, number, and length of update segments; need for updating moisture and surface pressure information; nudging along boundaries; and noise control. Assimilation of data from a single level was also studied. Use of a preforecast assimilation cycle was found to eliminate the spinup problem almost entirely. Multiple, shorter assimilation segments produced better forecasts than a single, longer cycle. Updating the mass field was less effective than nudging the wind field but assimilating both was best. Assimilation of moisture data, surprisingly, affected the spinup adversely, but nudging the surface pressure information reduced the spurious pillow effect. Assimilation of single-level information was ineffective unless accompanied by increased vertical coupling, obtained from a control integration. 52 refs., 19 figs., 1 tab.« less

Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

2018-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Calibration of Ocean Forcing with satellite Flux Estimates (COFFEE)

NASA Astrophysics Data System (ADS)

Barron, Charlie; Jan, Dastugue; Jackie, May; Rowley, Clark; Smith, Scott; Spence, Peter; Gremes-Cordero, Silvia

2016-04-01

Predicting the evolution of ocean temperature in regional ocean models depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. Within the COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates, real-time satellite observations are used to estimate shortwave, longwave, sensible, and latent air-sea heat flux corrections to a background estimate from the prior day's regional or global model forecast. These satellite-corrected fluxes are used to prepare a corrected ocean hindcast and to estimate flux error covariances to project the heat flux corrections for a 3-5 day forecast. In this way, satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. While traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle, COFFEE endeavors to appropriately partition and reduce among various surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using operational global or regional atmospheric forcing. Experiment cases combine different levels of flux calibration with assimilation alternatives. The cases use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger regional domains where a full 4DVAR methodology may be cost-prohibitive.

Evaluation of a Soil Moisture Data Assimilation System Over the Conterminous United States

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W. T.; Zhan, X.; Reynolds, C. A.; Jackson, T. J.

2008-12-01

A data assimilation system has been designed to integrate surface soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online soil moisture model used by the USDA Foreign Agriculture Service for global crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) ingests global soil moisture within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer soil moisture model employed by IPAD can potentially enhance the reliability of the CADRE soil moisture estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E soil moisture estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing soil moisture results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each model simulation over the conterminous United States, as well as statistical assessments for each simulation over various land cover types.

Downscaling, 2-way Nesting, and Data Assimilative Modeling in Coastal and Shelf Waters of the U.S. Mid-Atlantic Bight and Gulf of Maine

NASA Astrophysics Data System (ADS)

Wilkin, J.; Levin, J.; Lopez, A.; Arango, H.

2016-02-01

Coastal ocean models that downscale output from basin and global scale models are widely used to study regional circulation at enhanced resolution and locally important ecosystem, biogeochemical, and geomorphologic processes. When operated as now-cast or forecast systems, these models offer predictions that assist decision-making for numerous maritime applications. We describe such a system for shelf waters of the Mid-Atlantic Bight (MAB) and Gulf of Maine (GoM) where the MARACOOS and NERACOOS associations of U.S. IOOS operate coastal ocean observing systems that deliver a dense observation set using CODAR HF-radar, autonomous underwater glider vehicles (AUGV), telemetering moorings, and drifting buoys. Other U.S. national and global observing systems deliver further sustained observations from moorings, ships, profiling floats, and a constellation of satellites. Our MAB and GoM re-analysis and forecast system uses the Regional Ocean Modeling System (ROMS; myroms.org) with 4-dimensional Variational (4D-Var) data assimilation to adjust initial conditions, boundary conditions, and surface forcing in each analysis cycle. Data routinely assimilated include CODAR velocities, altimeter satellite sea surface height (with coastal corrections), satellite temperature, in situ CTD data from AUGV and ships (NMFS Ecosystem Monitoring voyages), and all in situ data reported via the WMO GTS network. A climatological data assimilative analysis of hydrographic and long-term mean velocity observations specifies the regional Mean Dynamic Topography that augments altimeter sea level anomaly data and is also used to adjust boundary condition biases that would otherwise be introduced in the process of downscaling from global models. System performance is described with respect to the impact of satellite, CODAR and in situ observations on analysis skill. Results from a 2-way nested modeling system that adds enhanced resolution over the NSF OOI Pioneer Array in the central MAB are also shown.

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

NASA Technical Reports Server (NTRS)

Zhang, Yong-Fei; Hoar, Tim J.; Yang, Zong-Liang; Anderson, Jeffrey L.; Toure, Ally M.; Rodell, Matthew

2014-01-01

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

Tree die-off in response to global change-type drought: Mortality insights from a decade of plant water potential measurements

USGS Publications Warehouse

Breshears, D.D.; Myers, O.B.; Meyer, Clifton W.; Barnes, F.J.; Zou, C.B.; Allen, Craig D.; McDowell, N.G.; Pockman, W. T.

2009-01-01

Global climate change is projected to produce warmer, longer, and more frequent droughts, referred to here as “global change-type droughts”, which have the potential to trigger widespread tree die-off. However, drought-induced tree mortality cannot be predicted with confidence, because long-term field observations of plant water stress prior to, and culminating in, mortality are rare, precluding the development and testing of mechanisms. Here, we document plant water stress in two widely distributed, co-occurring species, piñon pine (Pinus edulis) and juniper (Juniperus monosperma), over more than a decade, leading up to regional-scale die-off of piñon pine trees in response to global change-related drought. Piñon leaf water potentials remained substantially below their zero carbon assimilation point for at least 10 months prior to dying, in contrast to those of juniper, which rarely dropped below their zero-assimilation point. These data suggest that piñon mortality was driven by protracted water stress, leading to carbon starvation and associated increases in susceptibility to other disturbances (eg bark beetles), a finding that should help to improve predictions of mortality during drought.

Indices and Dynamics of Global Hydroclimate Over the Past Millennium from Data Assimilation

NASA Astrophysics Data System (ADS)

Steiger, N. J.; Smerdon, J. E.

2017-12-01

Reconstructions based on data assimilation (DA) are at the forefront of model-data syntheses in that such reconstructions optimally fuse proxy data with climate models. DA-based paleoclimate reconstructions have the benefit of being physically-consistent across the reconstructed climate variables and are capable of providing dynamical information about past climate phenomena. Here we use a new implementation of DA, that includes updated proxy system models and climate model bias correction procedures, to reconstruct global hydroclimate on seasonal and annual timescales over the last millennium. This new global hydroclimate product includes reconstructions of the Palmer Drought Severity Index, the Standardized Precipitation Evapotranspiration Index, and global surface temperature along with dynamical variables including the Nino 3.4 index, the latitudinal location of the intertropical convergence zone, and an index of the Atlantic Multidecadal Oscillation. Here we present a validation of the reconstruction product and also elucidate the causes of severe drought in North America and in equatorial Africa. Specifically, we explore the connection between droughts in North America and modes of ocean variability in the Pacific and Atlantic oceans. We also link drought over equatorial Africa to shifts of the intertropical convergence zone and modes of ocean variability.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-02-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in-situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols and greenhouse gases, and is based on the Integrated Forecast System of the ECMWF. The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past three years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Initializing carbon cycle predictions from the Community Land Model by assimilating global biomass observations

NASA Astrophysics Data System (ADS)

Fox, A. M.; Hoar, T. J.; Smith, W. K.; Moore, D. J.

2017-12-01

The locations and longevity of terrestrial carbon sinks remain uncertain, however it is clear that in order to predict long-term climate changes the role of the biosphere in surface energy and carbon balance must be understood and incorporated into earth system models (ESMs). Aboveground biomass, the amount of carbon stored in vegetation, is a key component of the terrestrial carbon cycle, representing the balance of uptake through gross primary productivity (GPP), losses from respiration, senescence and mortality over hundreds of years. The best predictions of current and future land-atmosphere fluxes are likely from the integration of process-based knowledge contained in models and information from observations of changes in carbon stocks using data assimilation (DA). By exploiting long times series, it is possible to accurately detect variability and change in carbon cycle dynamics through monitoring ecosystem states, for example biomass derived from vegetation optical depth (VOD), and use this information to initialize models before making predictions. To make maximum use of information about the current state of global ecosystems when using models we have developed a system that combines the Community Land Model (CLM) with the Data Assimilation Research Testbed (DART), a community tool for ensemble DA. This DA system is highly innovative in its complexity, completeness and capabilities. Here we described a series of activities, using both Observation System Simulation Experiments (OSSEs) and real observations, that have allowed us to quantify the potential impact of assimilating VOD data into CLM-DART on future land-atmosphere fluxes. VOD data are particularly suitable to use in this activity due to their long temporal coverage and appropriate scale when combined with CLM, but their absolute values rely on many assumptions. Therefore, we have had to assess the implications of the VOD retrieval algorithms, with an emphasis on detecting uncertainty due to assumptions and inputs in the algorithms that are incompatible with those encoded within CLM. It is probable that VOD describes changes in biomass more accurately than absolute values, so in additional to sequential assimilation of observations, we have tested alternative filter algorithms, and assimilating VOD anomalies.

Assimilation of passive and active CCI soil moisture products into hydrological modelling: an intercomparison study in Europe

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Camici, S.; Brocca, L.; Marchesini, I.

2017-12-01

Soil moisture (SM) is a key variable in rainfall-runoff partitioning since it acts on the main hydrological processes taking part within a catchment. Modeling SM is often a difficult task due to its large variability at different temporal and spatial scales. Ground soil moisture measurements are a valuable tool for improving runoff prediction but are often limited and suffer from spatial representativeness issues. Remotely sensed observations offer a new source of data able to cope the latter issues thus opening new possibilities for improving flood simulations worldwide. Today, several different SM products are available at increased accuracy with respect to the past. Some interesting products are those derived from the Climate Change Initiative (CCI) which offer the most complete and most consistent global SM data record based on active and passive microwave sensors.Thanks to the combination of multiple sensors within an active, a passive and an active+passive products, the CCI SM is expected to provide a significant benefit for the improvement of rainfall-runoff simulations through data assimilation. However, previous studies have shown that the success of the assimilation is not only related to the accuracy of the observations but also to the specific climate and the catchment physical and hydrological characteristics as well as to many necessary choices related to the assimilation technique. These choices along with the type of SM observations (i.e. passive or active) might play an important role for the success or the failure of the assimilation exercise which is not still clear. In this study, based on a large dataset of catchments covering large part of the Europe, we assimilated satellite SM observations from the passive and the active CCI SM products into Modello Idrologico Semiditribuito in Continuo (MISDc, Brocca et al. 2011). Rainfall and temperature data were collected from the European Climate Assessment & Dataset (E-OBS) while discharge data were obtained from the Global Runoff Data Centre (GRDC). Preliminary results show a general improvement of the hydrological simulations for catchments located in the Mediterranean areas specifically for the active product while lower performance is obtained at northern latitudes due to the presence of snow and ice.

Data Assimilation and Adjusted Spherical Harmonic Model of VTEC Map over Thailand

NASA Astrophysics Data System (ADS)

Klinngam, Somjai; Maruyama, Takashi; Tsugawa, Takuya; Ishii, Mamoru; Supnithi, Pornchai; Chiablaem, Athiwat

2016-07-01

The global navigation satellite system (GNSS) and high frequency (HF) communication are vulnerable to the ionospheric irregularities, especially when the signal travels through the low-latitude region and around the magnetic equator known as equatorial ionization anomaly (EIA) region. In order to study the ionospheric effects to the communications performance in this region, the regional map of the observed total electron content (TEC) can show the characteristic and irregularities of the ionosphere. In this work, we develop the two-dimensional (2D) map of vertical TEC (VTEC) over Thailand using the adjusted spherical harmonic model (ASHM) and the data assimilation technique. We calculate the VTEC from the receiver independent exchange (RINEX) files recorded by the dual-frequency global positioning system (GPS) receivers on July 8th, 2012 (quiet day) at 12 stations around Thailand: 0° to 25°E and 95°N to 110°N. These stations are managed by Department of Public Works and Town & Country Planning (DPT), Thailand, and the South East Asia Low-latitude ionospheric Network (SEALION) project operated by National Institute of Information and Communications Technology (NICT), Japan, and King Mongkut's Institute of Technology Ladkrabang (KMITL). We compute the median observed VTEC (OBS-VTEC) in the grids with the spatial resolution of 2.5°x5° in latitude and longitude and time resolution of 2 hours. We assimilate the OBS-VTEC with the estimated VTEC from the International Reference Ionosphere model (IRI-VTEC) as well as the ionosphere map exchange (IONEX) files provided by the International GNSS Service (IGS-VTEC). The results show that the estimation of the 15-degree ASHM can be improved when both of IRI-VTEC and IGS-VTEC are weighted by the latitude-dependent factors before assimilating with the OBS-VTEC. However, the IRI-VTEC assimilation can improve the ASHM estimation more than the IGS-VTEC assimilation. Acknowledgment: This work is partially funded by the Ministry of Science and Technology of Thailand and King Mongkut's Institute of Technology Ladkrabang under grant no. A118-59-011.

Source Attribution of Near-surface Ozone in the Western US: Improved Estimates by TF HTAP2 Multi-model Experiment and Multi-scale Chemical Data Assimilation

NASA Astrophysics Data System (ADS)

Huang, M.; Bowman, K. W.; Carmichael, G. R.; Lee, M.; Park, R.; Henze, D. K.; Chai, T.; Flemming, J.; Lin, M.; Weinheimer, A. J.; Wisthaler, A.; Jaffe, D. A.

2014-12-01

Near-surface ozone in the western US can be sensitive to transported background pollutants from the free troposphere over the eastern Pacific, as well as various local emissions sources. Accurately estimating ozone source contributions in this region has strong policy-relevant significance as the air quality standards tend to go down. Here we improve modeled contributions from local and non-local sources to western US ozone base on the HTAP2 (Task Force on Hemispheric Transport of Air Pollution) multi-model experiment, along with multi-scale chemical data assimilation. We simulate western US air quality using the STEM regional model on a 12 km horizontal resolution grid, during the NASA ARCTAS field campaign period in June 2008. STEM simulations use time-varying boundary conditions downscaled from global GEOS-Chem model simulations. Standard GEOS-Chem simulation overall underpredicted ozone at 1-5 km in the eastern Pacific, resulting in underestimated contributions from the transported background pollutants to surface ozone inland. These negative biases can be reduced by using the output from several global models that support the HTAP2 experiment, which all ran with the HTAP2 harmonized emission inventory and also calculated the contributions from east Asian anthropogenic emissions. We demonstrate that the biases in GEOS-Chem boundary conditions can be more efficiently reduced via assimilating satellite ozone profiles from the Tropospheric Emission Spectrometer (TES) instrument using the three dimensional variational (3D-Var) approach. Base upon these TES-constrained GEOS-Chem boundary conditions, we then update regional nitrogen dioxide and isoprene emissions in STEM through the four dimensional variational (4D-Var) assimilation of the Ozone Monitoring Instrument (OMI) nitrogen dioxide columns and the NASA DC-8 aircraft isoprene measurements. The 4D-Var assimilation spatially redistributed the emissions of nitrogen oxides and isoprene from various US sources, and in the meantime updated the modeled ozone and its US source contributions. Compared with available independent measurements (e.g., ozone observed on the DC-8 aircraft, and at EPA and Mt. Bachelor monitoring stations) during this period, modeled ozone fields after the multi-scale assimilation show overall improvement.

Data assimialation for real-time prediction and reanalysis

NASA Astrophysics Data System (ADS)

Shprits, Y.; Kellerman, A. C.; Podladchikova, T.; Kondrashov, D. A.; Ghil, M.

2015-12-01

We discuss the how data assimilation can be used for the analysis of individual satellite anomalies, development of long-term evolution reconstruction that can be used for the specification models, and use of data assimilation to improve the now-casting and focusing of the radiation belts. We also discuss advanced data assimilation methods such as parameter estimation and smoothing.The 3D data assimilative VERB allows us to blend together data from GOES, RBSP A and RBSP B. Real-time prediction framework operating on our web site based on GOES, RBSP A, B and ACE data and 3D VERB is presented and discussed. In this paper we present a number of application of the data assimilation with the VERB 3D code. 1) Model with data assimilation allows to propagate data to different pitch angles, energies, and L-shells and blends them together with the physics based VERB code in an optimal way. We illustrate how we use this capability for the analysis of the previous events and for obtaining a global and statistical view of the system. 2) The model predictions strongly depend on initial conditions that are set up for the model. Therefore the model is as good as the initial conditions that it uses. To produce the best possible initial condition data from different sources ( GOES, RBSP A, B, our empirical model predictions based on ACE) are all blended together in an optimal way by means of data assimilation as described above. The resulting initial condition does not have gaps. That allows us to make a more accurate predictions.

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

NASA Technical Reports Server (NTRS)

Lyster, Peter M.; Guo, J.; Clune, T.; Larson, J. W.; Atlas, Robert (Technical Monitor)

2001-01-01

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

Mantle circulation models with variational data assimilation: Inferring past mantle flow and structure from plate motion histories and seismic tomography

NASA Astrophysics Data System (ADS)

Bunge, Hans-Peter

2002-08-01

Earth's mantle overturns itself about once every 200 Million years (myrs). Prima facie evidence for this overturn is the motion of tectonic plates at the surface of the Earth driving the geologic activity of our planet. Supporting evidence also comes from seismic tomograms of the Earth's interior that reveal the convective currents in remarkable clarity. Much has been learned about the physics of solid state mantle convection over the past two decades aided primarily by sophisticated computer simulations. Such simulations are reaching the threshold of fully resolving the convective system globally. In this talk we will review recent progress in mantle dynamics studies. We will then turn our attention to the fundamental question of whether it is possible to explicitly reconstruct mantle flow back in time. This is a classic problem of history matching, amenable to control theory and data assimilation. The technical advances that make such approach feasible are dramatically increasing compute resources, represented for example through Beowulf clusters, and new observational initiatives, represented for example through the US-Array effort that should lead to an order-of-magnitude improvement in our ability to resolve Earth structure seismically below North America. In fact, new observational constraints on deep Earth structure illustrate the growing importance of of improving our data assimilation skills in deep Earth models. We will explore data assimilation through high resolution global adjoint models of mantle circulation and conclude that it is feasible to reconstruct mantle flow back in time for at least the past 100 myrs.

Cross Comparison of Electron Density and Electron Temperature Observations from the DICE CubeSat Langmuir Probes and the Millstone Hill Incoherent Scatter Radar.

NASA Astrophysics Data System (ADS)

Swenson, C.; Erickson, P. J.; Crowley, G.; Pilinski, M.; Barjatya, A.; Fish, C. S.

2014-12-01

The Dynamic Ionosphere CubeSat Experiment (DICE) consists of two identical 1.5U CubeSats deployed simultaneously from a single P-POD (Poly Picosatellite Orbital Deployer) into the same orbit. Several observational campaigns were planned between the DICE CubeSats and the mid-latitude Millstone Hill Incoherent Scatter Radar (ISR) in order to calibrate the DICE measurements of electron density and electron temperature. In this presentation, we compare in-situ observations from the Dynamic Ionosphere CubeSat Experiment (DICE) and from the Millstone Hill ISR. Both measurements are cross-calibrated against an assimilative model of the global ionospheric electron density. The electron density and electron temperature were obtained for three Millstone Hill DICE overflights (2013-03-12, 2013-03-15, 2013-03-17). We compare the data during quiet and geomagnetically disturbed conditions and find evidence of an storm enhanced density (SED) plume in the topside ionosphere on 2013-03-17 at 19? UTC. During this disturbed interval, American longitude sector high density plasma was convected near 15 SLT towards the noontime cusp. DICE was selected for flight under the NSF "CubeSat-based Science Mission for Space Weather and Atmospheric Research" program. The DICE twin satellites were launched on a Delta II rocket on October 28, 2011. The satellites are flying in a "leader-follower" formation in an elliptical orbit which ranges from 820 to 400 km in altitude. Each satellite carries a fixed-bias DC Langmuir Probe (DCP) to measure in-situ ionospheric plasma densities and a science grade magnetometer to measure DC and AC geomagnetic fields. The purpose of these measurements was to permit accurate identification of storm-time features such as the SED bulge and plume. The mission team combines expertise from ASTRA, Utah State University/Space Dynamics Laboratory (USU/SDL), and Embry-Riddle Aeronautical University. In this paper we present a comparison of data from DICE and Millstone Hill ISR during quiet and magnetically disturbed conditions.

Mathematical foundations of hybrid data assimilation from a synchronization perspective

NASA Astrophysics Data System (ADS)

Penny, Stephen G.

2017-12-01

The state-of-the-art data assimilation methods used today in operational weather prediction centers around the world can be classified as generalized one-way coupled impulsive synchronization. This classification permits the investigation of hybrid data assimilation methods, which combine dynamic error estimates of the system state with long time-averaged (climatological) error estimates, from a synchronization perspective. Illustrative results show how dynamically informed formulations of the coupling matrix (via an Ensemble Kalman Filter, EnKF) can lead to synchronization when observing networks are sparse and how hybrid methods can lead to synchronization when those dynamic formulations are inadequate (due to small ensemble sizes). A large-scale application with a global ocean general circulation model is also presented. Results indicate that the hybrid methods also have useful applications in generalized synchronization, in particular, for correcting systematic model errors.

Mathematical foundations of hybrid data assimilation from a synchronization perspective.

PubMed

Penny, Stephen G

2017-12-01

The state-of-the-art data assimilation methods used today in operational weather prediction centers around the world can be classified as generalized one-way coupled impulsive synchronization. This classification permits the investigation of hybrid data assimilation methods, which combine dynamic error estimates of the system state with long time-averaged (climatological) error estimates, from a synchronization perspective. Illustrative results show how dynamically informed formulations of the coupling matrix (via an Ensemble Kalman Filter, EnKF) can lead to synchronization when observing networks are sparse and how hybrid methods can lead to synchronization when those dynamic formulations are inadequate (due to small ensemble sizes). A large-scale application with a global ocean general circulation model is also presented. Results indicate that the hybrid methods also have useful applications in generalized synchronization, in particular, for correcting systematic model errors.

Carbon assimilation and transfer through kelp forests in the NE Atlantic is diminished under a warmer ocean climate.

PubMed

Pessarrodona, Albert; Moore, Pippa J; Sayer, Martin D J; Smale, Dan A

2018-06-03

Global climate change is affecting carbon cycling by driving changes in primary productivity and rates of carbon fixation, release and storage within Earth's vegetated systems. There is, however, limited understanding of how carbon flow between donor and recipient habitats will respond to climatic changes. Macroalgal-dominated habitats, such as kelp forests, are gaining recognition as important carbon donors within coastal carbon cycles, yet rates of carbon assimilation and transfer through these habitats are poorly resolved. Here, we investigated the likely impacts of ocean warming on coastal carbon cycling by quantifying rates of carbon assimilation and transfer in Laminaria hyperborea kelp forests-one of the most extensive coastal vegetated habitat types in the NE Atlantic-along a latitudinal temperature gradient. Kelp forests within warm climatic regimes assimilated, on average, more than three times less carbon and donated less than half the amount of particulate carbon compared to those from cold regimes. These patterns were not related to variability in other environmental parameters. Across their wider geographical distribution, plants exhibited reduced sizes toward their warm-water equatorward range edge, further suggesting that carbon flow is reduced under warmer climates. Overall, we estimated that Laminaria hyperborea forests stored ~11.49 Tg C in living biomass and released particulate carbon at a rate of ~5.71 Tg C year -1 . This estimated flow of carbon was markedly higher than reported values for most other marine and terrestrial vegetated habitat types in Europe. Together, our observations suggest that continued warming will diminish the amount of carbon that is assimilated and transported through temperate kelp forests in NE Atlantic, with potential consequences for the coastal carbon cycle. Our findings underline the need to consider climate-driven changes in the capacity of ecosystems to fix and donate carbon when assessing the impacts of climate change on carbon cycling. © 2018 The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Global Gross Primary Productivity for 2015 Inferred from OCO-2 SIF and a Carbon-Cycle Data Assimilation System

NASA Astrophysics Data System (ADS)

Norton, A.; Rayner, P. J.; Scholze, M.; Koffi, E. N. D.

2016-12-01

The intercomparison study CMIP5 among other studies (e.g. Bodman et al., 2013) has shown that the land carbon flux contributes significantly to the uncertainty in projections of future CO2 concentration and climate (Friedlingstein et al., 2014)). The main challenge lies in disaggregating the relatively well-known net land carbon flux into its component fluxes, gross primary production (GPP) and respiration. Model simulations of these processes disagree considerably, and accurate observations of photosynthetic activity have proved a hindrance. Here we build upon the Carbon Cycle Data Assimilation System (CCDAS) (Rayner et al., 2005) to constrain estimates of one of these uncertain fluxes, GPP, using satellite observations of Solar Induced Fluorescence (SIF). SIF has considerable benefits over other proxy observations as it tracks not just the presence of vegetation but actual photosynthetic activity (Walther et al., 2016; Yang et al., 2015). To combine these observations with process-based simulations of GPP we have coupled the model SCOPE with the CCDAS model BETHY. This provides a mechanistic relationship between SIF and GPP, and the means to constrain the processes relevant to SIF and GPP via model parameters in a data assimilation system. We ingest SIF observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) for 2015 into the data assimilation system to constrain estimates of GPP in space and time, while allowing for explicit consideration of uncertainties in parameters and observations. Here we present first results of the assimilation with SIF. Preliminary results indicate a constraint on global annual GPP of at least 75% when using SIF observations, reducing the uncertainty to < 3 PgC yr-1. A large portion of the constraint is propagated via parameters that describe leaf phenology. These results help to bring together state-of-the-art observations and model to improve understanding and predictive capability of GPP.

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.

High Performance Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System at NASA/GSFC

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Kumar, S. V.; Santanello, J. A.; Tian, Y.; Rodell, M.; Mocko, D.; Reichle, R.

2008-12-01

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters-Lidard et al., 2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. The LIS software was the co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts - North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell et al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of these systems, now use specific configurations of the LIS software in their current implementations. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through 'plugins'. In addition to these capabilities, LIS has also been demonstrated for parameter estimation (Peters-Lidard et al., 2008; Santanello et al., 2007) and data assimilation (Kumar et al., 2008). Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, land data assimilation and parameter estimation will be presented.

Improving Global Modeling and Data Analysis Using Remotely-Sensed Rainfall Data: Lessons From TRMM and Plans for GPM

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Einaudi, Franco (Technical Monitor)

2001-01-01

I will discuss the need for accurate rainfall observations to improve our ability to model the earth's climate and improve short-range weather forecasts. I will give an overview of the recent progress in using of rainfall data provided by TRMM and other microwave instruments in data assimilation to improve global analyses and diagnose state-dependent systematic errors in physical parameterizations. I will outline the current and future research strategies in preparation for the Global Precipitation Mission.

A reconstruction of global hydroclimate and dynamical variables over the Common Era.

PubMed

Steiger, Nathan J; Smerdon, Jason E; Cook, Edward R; Cook, Benjamin I

2018-05-22

Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere-ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Niño indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

Online Simulations and Forecasts of the Global Aerosol Distribution in the NASA GEOS-5 Model

NASA Technical Reports Server (NTRS)

Colarco, Peter

2006-01-01

We present an analysis of simulations of the global aerosol system in the NASA GEOS-5 transport, radiation, and chemistry model. The model includes representations of all major tropospheric aerosol species, including dust, sea salt, black carbon, particulate organic matter, and sulfates. The aerosols are run online for the period 2000 through 2005 in a simulation driven by assimilated meteorology from the NASA Goddard Data Assimilation System. Aerosol surface mass concentrations are compared with existing long-term surface measurement networks. Aerosol optical thickness is compared with ground-based AERONET sun photometry and space-based retrievals from MODIS, MISR, and OMI. Particular emphasis is placed here on consistent sampling of model and satellite aerosol optical thickness to account for diurnal variations in aerosol optical properties. Additionally, we illustrate the use of this system for providing chemical weather forecasts in support of various NASA and community field missions.

Integrating Data from GRACE and Other Observing Systems for Hydrological Research and Applications

NASA Technical Reports Server (NTRS)

Rodell, M.; Famiglietti, J. S.; McWilliams, E.; Beaudoing, H. K.; Li, B.; Zaitchik, B.; Reichle, R.; Bolten, J.

2011-01-01

The Gravity Recovery and Climate Experiment (GRACE) mission provides a unique view of water cycle dynamics, enabling the only space based observations of water on and beneath the land surface that are not limited by depth. GRACE data are immediately useful for large scale applications such as ice sheet ablation monitoring, but they are even more valuable when combined with other types of observations, either directly or within a data assimilation system. Here we describe recent results of hydrological research and applications projects enabled by GRACE. These include the following: 1) global monitoring of interannual variability of terrestrial water storage and groundwater; 2) water balance estimates of evapotranspiration over several large river basins; 3) NASA's Energy and Water Cycle Study (NEWS) state of the global water budget project; 4) drought indicator products now being incorporated into the U.S. Drought Monitor; 5) GRACE data assimilation over several regions.

HYCOM High-resolution Eddying Simulations

DTIC Science & Technology

2014-07-01

Meteorological Organization,the International Council for Science and the Intergovernmental Oceanographic Commission of UNESCO . * 2 ExciJa IJQes I I I I I...forecasting systems (Metzger et al., 2014a ). Within the framework of the multinational Global Ocean Data Assimilation Experiment (GODAE) and under the...10.1016/j.ocemod.2011.02.011. Metzger, E. J., and Coauthors, 2014a : US Navy operational global ocean and Arctic ice prediction systems. Oceanography

Implementation of Coupled Skin Temperature Analysis and Bias Correction in a Global Atmospheric Data Assimilation System

NASA Technical Reports Server (NTRS)

Radakovich, Jon; Bosilovich, M.; Chern, Jiun-dar; daSilva, Arlindo

2004-01-01

The NASA/NCAR Finite Volume GCM (fvGCM) with the NCAR CLM (Community Land Model) version 2.0 was integrated into the NASA/GMAO Finite Volume Data Assimilation System (fvDAS). A new method was developed for coupled skin temperature assimilation and bias correction where the analysis increment and bias correction term is passed into the CLM2 and considered a forcing term in the solution to the energy balance. For our purposes, the fvDAS CLM2 was run at 1 deg. x 1.25 deg. horizontal resolution with 55 vertical levels. We assimilate the ISCCP-DX (30 km resolution) surface temperature product. The atmospheric analysis was performed 6-hourly, while the skin temperature analysis was performed 3-hourly. The bias correction term, which was updated at the analysis times, was added to the skin temperature tendency equation at every timestep. In this presentation, we focus on the validation of the surface energy budget at the in situ reference sites for the Coordinated Enhanced Observation Period (CEOP). We will concentrate on sites that include independent skin temperature measurements and complete energy budget observations for the month of July 2001. In addition, MODIS skin temperature will be used for validation. Several assimilations were conducted and preliminary results will be presented.

The role of data assimilation in maximizing the utility of geospace observations (Invited)

NASA Astrophysics Data System (ADS)

Matsuo, T.

2013-12-01

Data assimilation can facilitate maximizing the utility of existing geospace observations by offering an ultimate marriage of inductive (data-driven) and deductive (first-principles based) approaches to addressing critical questions in space weather. Assimilative approaches that incorporate dynamical models are, in particular, capable of making a diverse set of observations consistent with physical processes included in a first-principles model, and allowing unobserved physical states to be inferred from observations. These points will be demonstrated in the context of the application of an ensemble Kalman filter (EnKF) to a thermosphere and ionosphere general circulation model. An important attribute of this approach is that the feedback between plasma and neutral variables is self-consistently treated both in the forecast model as well as in the assimilation scheme. This takes advantage of the intimate coupling between the thermosphere and ionosphere described in general circulation models to enable the inference of unobserved thermospheric states from the relatively plentiful observations of the ionosphere. Given the ever-growing infrastructure for the global navigation satellite system, this is indeed a promising prospect for geospace data assimilation. In principle, similar approaches can be applied to any geospace observing systems to extract more geophysical information from a given set of observations than would otherwise be possible.

Sampling Biases in MODIS and SeaWiFS Ocean Chlorophyll Data

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Casey, Nancy W.

2007-01-01

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (<3%) in the midlatitudes (between -40deg and 40deg). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid. High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is <75deg are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and consequently low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll. The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.

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.

Assimilation of Satellite Sea Surface Salinity Fields: Validating Ocean Analyses and Identifying Errors in Surface Buoyancy Fluxes

NASA Astrophysics Data System (ADS)

Mehra, A.; Nadiga, S.; Bayler, E. J.; Behringer, D.

2014-12-01

Recently available satellite sea-surface salinity (SSS) fields provide an important new global data stream for assimilation into ocean forecast systems. In this study, we present results from assimilating satellite SSS fields from NASA's Aquarius mission into the National Oceanic and Atmospheric Administration's (NOAA) operational Modular Ocean Model version 4 (MOM4), the oceanic component of NOAA's operational seasonal-interannual Climate Forecast System (CFS). Experiments on the sensitivity of the ocean's overall state to different relaxation time periods were run to evaluate the importance of assimilating high-frequency (daily to mesoscale) and low-frequency (seasonal) SSS variability. Aquarius SSS data (Aquarius Data Processing System (ADPS) version 3.0), mapped daily fields at 1-degree spatial resolution, were used. Four model simulations were started from the same initial ocean condition and forced with NOAA's daily Climate Forecast System Reanalysis (CFSR) fluxes, using a relaxation technique to assimilate daily satellite sea surface temperature (SST) fields and selected SSS fields, where, except as noted, a 30-day relaxation period is used. The simulations are: (1) WOAMC, the reference case and similar to the operational setup, assimilating monthly climatological SSS from the 2009 NOAA World Ocean Atlas; (2) AQ_D, assimilating daily Aquarius SSS; (3) AQ_M, assimilating monthly Aquarius SSS; and (4) AQ_D10, assimilating daily Aquarius SSS, but using a 10-day relaxation period. The analysis focuses on the tropical Pacific Ocean, where the salinity dynamics are intense and dominated by El Niño interannual variability in the cold tongue region and by high-frequency precipitation events in the western Pacific warm pool region. To assess the robustness of results and conclusions, we also examine the results for the tropical Atlantic and Indian Oceans. Preliminary validation studies are conducted using observations, such as satellite sea-surface height (SSH) fields and in situ Argo buoy vertical profiles of temperature and salinity, to demonstrate that SSS data assimilation improves ocean state representation of the following variables: ocean heat content (0-300m), dynamic height (0-1000m), mixed-layer depth, sea surface heigh, and surface buoyancy fluxes.

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

NASA Astrophysics Data System (ADS)

Jones, Emlyn M.; Baird, Mark E.; Mongin, Mathieu; Parslow, John; Skerratt, Jenny; Lovell, Jenny; Margvelashvili, Nugzar; Matear, Richard J.; Wild-Allen, Karen; Robson, Barbara; Rizwi, Farhan; Oke, Peter; King, Edward; Schroeder, Thomas; Steven, Andy; Taylor, John

2016-12-01

Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

Assimilating GRACE terrestrial water storage data into a conceptual hydrology model for the River Rhine

NASA Astrophysics Data System (ADS)

Widiastuti, E.; Steele-Dunne, S. C.; Gunter, B.; Weerts, A.; van de Giesen, N.

2009-12-01

Terrestrial water storage (TWS) is a key component of the terrestrial and global hydrological cycles, and plays a major role in the Earth’s climate. The Gravity Recovery and Climate Experiment (GRACE) twin satellite mission provided the first space-based dataset of TWS variations, albeit with coarse resolution and limited accuracy. Here, we examine the value of assimilating GRACE observations into a well-calibrated conceptual hydrology model of the Rhine river basin. In this study, the ensemble Kalman filter (EnKF) and smoother (EnKS) were applied to assimilate the GRACE TWS variation data into the HBV-96 rainfall run-off model, from February 2003 to December 2006. Two GRACE datasets were used, the DMT-1 models produced at TU Delft, and the CSR-RL04 models produced by UT-Austin . Each center uses its own data processing and filtering methods, yielding two different estimates of TWS variations and therefore two sets of assimilated TWS estimates. To validate the results, the model estimated discharge after the data assimilation was compared with measured discharge at several stations. As expected, the updated TWS was generally somewhere between the modeled and observed TWS in both experiments and the variance was also lower than both the prior error covariance and the assumed GRACE observation error. However, the impact on the discharge was found to depend heavily on the assimilation strategy used, in particular on how the TWS increments were applied to the individual storage terms of the hydrology model.

An Observing System Simulation Experiment of assimilating leaf area index and soil moisture over cropland

NASA Astrophysics Data System (ADS)

Lafont, Sebastien; Barbu, Alina; Calvet, Jean-Christophe

2013-04-01

A Land Data Assimilation System (LDAS) is an off-line data assimilation system featuring uncoupled land surface model which is driven by observation-based atmospheric forcing. In this study the experiments were conducted with a surface externalized (SURFEX) modelling platform developed at Météo-France. It encompasses the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The photosynthetic activity depends on the vegetation types. The input soil and vegetation parameters are provided by the ECOCLIMAP II global database which assigns the ecosystem classes in several plant functional types as grassland, crops, deciduous forest and coniferous forest. New versions of the model have been recently developed in order to better describe the agricultural plant functional types. We present a set of observing system simulation experiments (OSSE) which asses leaf area index (LAI) and soil moisture assimilation for improving the land surface estimates in a controlled synthetic environment. Synthetic data were assimilated into ISBA-A-gs using an Extended Kalman Filter (EKF). This allows for an understanding of model responses to an augmentation of the number of crop types and different parameters associated to this modification. In addition, the interactions between uncertainties in the model and in the observations were investigated. This study represents the first step of a process that envisages the extension of LDAS to the new versions of the ISBA-A-gs model in order to assimilate remote sensing observations.

The Tangent Linear and Adjoint of the FV3 Dynamical Core: Development and Applications

NASA Technical Reports Server (NTRS)

Holdaway, Daniel

2018-01-01

GMAO (NASA's Global Modeling and Assimilation Office) has developed a highly sophisticated adjoint modeling system based on the most recent version of the finite volume cubed sphere (FV3) dynamical core. This provides a mechanism for investigating sensitivity to initial conditions and examining observation impacts. It also allows for the computation of singular vectors and for the implementation of hybrid 4DVAR (4-Dimensional Variational Assimilation). In this work we will present the scientific assessment of the new adjoint system and show results from a number of research application of the adjoint system.

(The effects at ozone on CO sub 2 assimilation by bean plants)

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

Norby, R.J.

The traveler participated in ongoing laboratory experiments on the effects of ozone on Co{sub 2} assimilation by bean plants, providing advice on experimental procedures, assistance with data analysis, and access to current literature. He presented a seminar in which the importance of research on elevated CO{sub 2} and global climate change was emphasized because these are areas of major emphasis at Oak Ridge National Laboratory in which international collaboration would be especially beneficial. The most promising area for future collaboration was identified as the integration of experimental research with a tree growth model developed at Tartu University.

Proceedings of the First National Workshop on the Global Weather Experiment: Current Achievements and Future Directions, volume 2, part 1

NASA Technical Reports Server (NTRS)

1985-01-01

Topics covered include: data systems and quality; analysis and assimilation techniques; impacts on forecasts; tropical forecasts; analysis intercomparisons; improvements in predictability; and heat sources and sinks.

Global Assimilative Ionospheric Model

DTIC Science & Technology

2002-09-30

CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C), as well as from the Ionospheric Occultation Experiment (IOX) instrument developed by...strong interest in future collaborative research. TRANSITIONS Our project is still in its initial stage. No software has been transitioned to

The development of global GRAPES 4DVAR

NASA Astrophysics Data System (ADS)

Liu, Yongzhu

2017-04-01

Four-dimensional variation data assimilation (4DVAR) has given a great contribution to the improvement of NWP system over the past twenty years. Therefore, our strategy is to develop an operational global 4D-Var system from the outset. The aim at the paper is to introduce the development of the global GRAPES four-dimensional variation data assimilation (4DVAR) using incremental analysis schemes and to presents results of a comparison between 4DVAR using 6-hour assimilation window and simplified physics during the minimization with three-dimensional variation data assimilation (3DVAR). The dynamical cores of the tangent-linear and adjoint models are developed directly based on the non-hydrostatic forecast model. In addition, the standard correctness checks have been performed. As well as the development adjoint codes, most of our work is focused on improving the computational efficiency since the bulk of the computational cost of 4D-Var is in the integration of the tangent-linear and adjoint models. In terms of tangent-linear model, the wall-clock time is reduced to about 1.2 times as much as one of nonlinear model through the optimizing of the software framework. The significant computational cost savings on adjoint model result from the removing the redundant recompilations of model trajectories. It is encouraging that the wall-clock time of adjoint model is less than 1.5 times as much as one of nonlinear model. The current difficulty is that the numerical scheme used within the linear model is based on strategically on the numeric of the corresponding nonlinear model. Further computational acceleration should be expected from the improvement on nonlinear numerical algorithm. A series of linearized physical parameterization schemes has been developed to improve the representation of perturbed fields in the linear model. It consists of horizontal and vertical diffusion, sub-grid scale orographic gravity wave drag, large-scale condensation and cumulus convection schemes. We also found the straightforward linearization based on the nonlinear physical scheme might lead to significant growing of spurious unstable perturbations. It is essential to simplify the linear physics with respect to the non-linear schemes. The improvement on the perturbed fields in the tangent-linear model is visible with the linear physics included, especially at the low level. GRAPES variation data assimilation system adopts the incremental approach. The work is ongoing to develop a pre-operational 4DVAR suite with 0.25° outer loop resolution and multiple outer-loops configurations. One 4DVAR analysis using 6-hour assimilation windows can be finished within 40-minutes when using the available conventional and satellite data. In summary, it was found that the analysis over the northern, southern hemispheres, tropical region and East Asian area of GRAPES 4DVAR performed better than GRAPES 3DVAR for one month experiments. Moreover, the forecast results show that northern and southern extra-tropical scores for GRAPES 4DVAR are already better than GRAPES 3DVAR, but the tropical performance needs further investigations. Therefore, the subsequent main improvements will aim to enhance its computational efficiency and accuracy in 2017. The global GRAPES 4DVAR is planned for operation in 2018.

Graphics Processing Unit (GPU) Acceleration of the Goddard Earth Observing System Atmospheric Model

NASA Technical Reports Server (NTRS)

Putnam, Williama

2011-01-01

The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

USGS Publications Warehouse

Ji, Lei; Senay, Gabriel B.; Verdin, James P.

2015-01-01

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

Preliminary Observing System Simulation Experiments for Doppler Wind Lidars Deployed on the International Space Station

NASA Technical Reports Server (NTRS)

Kemp, E.; Jacob, J.; Rosenberg, R.; Jusem, J. C.; Emmitt, G. D.; Wood, S.; Greco, L. P.; Riishojgaard, L. P.; Masutani, M.; Ma, Z.;

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2014-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution and life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

The GEOS-5 Neural Network Retrieval for AOD

NASA Astrophysics Data System (ADS)

Castellanos, P.; da Silva, A. M., Jr.

2017-12-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

The GEOS-5 Neural Network Retrieval (NNR) for AOD

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo

2017-01-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2013-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution & life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

The Hyperspectral Infrared Imager (HyspIRI) Public Health & Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2013-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution & life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

Technical Report Series on Global Modeling and Data Assimilation. Volume 20; The Climate of the FVCCM-3 Model

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Chang, Yehui; Schubert, Siegfried D.; Lin, Shian-Jiann; Nebuda, Sharon; Shen, Bo-Wen

2001-01-01

This document describes the climate of version 1 of the NASA-NCAR model developed at the Data Assimilation Office (DAO). The model consists of a new finite-volume dynamical core and an implementation of the NCAR climate community model (CCM-3) physical parameterizations. The version of the model examined here was integrated at a resolution of 2 degrees latitude by 2.5 degrees longitude and 32 levels. The results are based on assimilation that was forced with observed sea surface temperature and sea ice for the period 1979-1995, and are compared with NCEP/NCAR reanalyses and various other observational data sets. The results include an assessment of seasonal means, subseasonal transients including the Madden Julian Oscillation, and interannual variability. The quantities include zonal and meridional winds, temperature, specific humidity, geopotential height, stream function, velocity potential, precipitation, sea level pressure, and cloud radiative forcing.

JPL/USC GAIM: Validating COSMIC and Ground-Based GPS Assimilation Results to Estimate Ionospheric Electron Densities

NASA Astrophysics Data System (ADS)

Komjathy, A.; Wilson, B.; Akopian, V.; Pi, X.; Mannucci, A.; Wang, C.

2008-12-01

We seem to be in the midst of a revolution in ionospheric remote sensing driven by the abundance of ground and space-based GPS receivers, new UV remote sensing satellites, and the advent of data assimilation techniques for space weather. In particular, the COSMIC 6-satellite constellation was launched in April 2006. COSMIC now provides unprecedented global coverage of GPS occultations measurements, each of which yields electron density information with unprecedented ~1 km vertical resolution. Calibrated measurements of ionospheric delay (total electron content or TEC) suitable for input into assimilation models is currently made available in near real-time (NRT) from the COSMIC with a latency of 30 to 120 minutes. The University of Southern California (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed a real-time Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for DoD customers and NASA flight projects. JPL/USC GAIM is a physics- based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion and electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. Daily (delayed) GAIM runs can accept as input ground GPS TEC data from 1200+ sites, occultation links from CHAMP, SAC-C, and the COSMIC constellation, UV limb and nadir scans from the TIMED and DMSP satellites, and in situ data from a variety of satellites (DMSP and C/NOFS). Real-Time GAIM (RTGAIM) ingests multiple data sources in real time, updates the 3D electron density grid every 5 minutes, and solves for improved drivers every 1-2 hours. Since our forward physics model and the adjoint model were expressly designed for data assimilation and computational efficiency, all of this can be accomplished on a single dual- processor Unix workstation. Customers are currently evaluating the accuracy of JPL/USC GAIM 'nowcasts' for ray tracing applications and trans-ionospheric path delay calibration. In the presentation, we will discuss the expected impact of NRT COSMIC occultation and NRT ground-based measurements and present validation results for ingest of COSMIC data into GAIM using measurements from World Days. We will quality check our COSMIC-derived products by comparing Abel profiles and JPL- processed results. Furthermore, we will validate GAIM assimilation results using Incoherent Scatter Radar measurements from Arecibo, Jicamarca and Millstone Hill datasets. We will conclude by characterizing the improved electron density states using dual-frequency altimeter-derived Jason vertical TEC measurements.

A new GNSS-enabled floating device as a means for retrieving river bathymetry by assimilation into a hydrodynamic model

NASA Astrophysics Data System (ADS)

Hostache, R.; Matgen, P.; Giustarini, L.

2012-04-01

Hydrodynamic models form an important component in flood forecasting systems. Model predictions with reduced uncertainty critically depend on the availability of detailed information about floodplain topography and riverbed bathymetry. While digital elevation models with varying spatial resolutions and accuracy levels are readily available at a global scale and can be used to infer floodplain geometry, bathymetric data is often not available and ground surveys are time and resource intensive. In this general context, our study aims at evaluating the hydrometric value of the Global Navigation Satellite System (GNSS) for bathymetry retrieval. Integrated with satellite telecommunication systems, drifting or anchored floaters equipped with navigation systems such as GPS and Galileo, enable the quasi-continuous measurement and near real-time transmission of water levels and flow velocities, virtually from any point in the world. The presented study investigates the potential of assimilating GNSS-derived water level measurements into a hydraulic model in order to estimate river bathymetry. First, an ensemble of possible bathymetries and roughness parameters was randomly generated using a Monte-Carlo sampling approach. Next, water level measurements provided by a drifting GNSS-equipped buoy were assimilated into a hydrodynamic model using as input a recorded discharge hydrograph and as geometry the generated bathymetry ensemble. Synthetic experiments were carried out with a one-dimensional hydraulic model implemented over a 19 km reach of the Alzette River. A Particle Filter was used as an assimilation algorithm for integrating observation data into the hydraulic model. The synthetic observation, simulating the data obtained from GNSS measurements, was generated using a perturbed forward run of the hydrodynamic model using the true bathymetry (ground survey). The scenario adopted in the data assimilation experiment assumed that during a flood event, a buoy was launched into the water every ten hours. This frequency was considered plausible as the time needed for the buoy to drift from the upstream to the downstream end of the study area is estimated to be less than 6 h. Consequently, a time window of 10 h would allow an operator to launch the buoy at the upstream end, recover it at the downstream end and finally drive back to the upstream end and launch it again into the river channel.This synthetic observation was then assimilated into the hydraulic model. The first results were promising as sequentially assimilating the water level values provided by the synthetic GNSS-equipped buoy allowed gradually rejecting wrong bathymetries and converging toward bathymetries that are consistent with the ground surveyed one.

A study of regional-scale aerosol assimilation using a Stretch-NICAM

NASA Astrophysics Data System (ADS)

Misawa, S.; Dai, T.; Schutgens, N.; Nakajima, T.

2013-12-01

Although aerosol is considered to be harmful to human health and it became a social issue, aerosol models and emission inventories include large uncertainties. In recent studies, data assimilation is applied to aerosol simulation to get more accurate aerosol field and emission inventory. Most of these studies, however, are carried out only on global scale, and there are only a few researches about regional scale aerosol assimilation. In this study, we have created and verified an aerosol assimilation system on regional scale, in hopes to reduce an error associated with the aerosol emission inventory. Our aerosol assimilation system has been developed using an atmospheric climate model, NICAM (Non-hydrostaric ICosahedral Atmospheric Model; Satoh et al., 2008) with a stretch grid system and coupled with an aerosol transport model, SPRINTARS (Takemura et al., 2000). Also, this assimilation system is based on local ensemble transform Kalman filter (LETKF). To validate this system, we used a simulated observational data by adding some artificial errors to the surface aerosol fields constructed by Stretch-NICAM-SPRINTARS. We also included a small perturbation in original emission inventory. This assimilation with modified observational data and emission inventory was performed in Kanto-plane region around Tokyo, Japan, and the result indicates the system reducing a relative error of aerosol concentration by 20%. Furthermore, we examined a sensitivity of the aerosol assimilation system by varying the number of total ensemble (5, 10 and 15 ensembles) and local patch (domain) size (radius of 50km, 100km and 200km), both of which are the tuning parameters in LETKF. The result of the assimilation with different ensemble number 5, 10 and 15 shows that the larger the number of ensemble is, the smaller the relative error become. This is consistent with ensemble Kalman filter theory and imply that this assimilation system works properly. Also we found that assimilation system does not work well in a case of 200km radius, while a domain of 50km radius is less efficient than when domain of 100km radius is used.Therefore, we expect that the optimized size lies somewhere between 50km to 200km. We will show a real analysis of real data from suspended particle matter (SPM) network in the Kanto-plane region.

A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations

NASA Astrophysics Data System (ADS)

Tian, X.; Xie, Z.; Liu, Y.; Cai, Z.; Fu, Y.; Zhang, H.; Feng, L.

2014-12-01

We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

Observations and High-Resolution Numerical Simulations of a Non-Developing Tropical Disturbance in the Western North Pacific

DTIC Science & Technology

2013-09-01

potential energy CFSR Climate Forecast System Reanalysis COAMPS Coupled Ocean / Atmosphere Mesoscale Prediction System DA data assimilation DART Data...developing (TCS025) tropical disturbance using the adjoint and tangent linear models for the Coupled Ocean – Atmosphere Mesoscale Prediction System (COAMPS...for Medium-range Weather Forecasts ELDORA ELectra DOppler RAdar EOL Earth Observing Laboratory GPS global positioning system GTS Global

Validation and Intercomparison Studies Within GODAE

DTIC Science & Technology

2009-09-01

unlimited. 13. SUPPLEMENTARY NOTES 20091228154 14. ABSTRACT During the Global Ocean Data Assimilation Experiment (GODAE), seven international... global -ocean and basin-scale forecasting systems of different countries in routine interaction and continuous operation, (2) to assess the quality and... Franchi , 7000 Public Affairs (Unclassified/ Unlimited Only), Code 7o30 4 Division, Code ^VtcV Vs-Jc \\ -Vi<-’/c ••>’ 3^v’.-:5, w. 3Uo|eri 1

Surface Hydrology in Global River Basins in the Off-Line Land-Surface GEOS Assimilation (OLGA) System

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Yang, Runhua; Houser, Paul R.

1998-01-01

Land surface hydrology for the Off-line Land-surface GEOS Analysis (OLGA) system and Goddard Earth Observing System (GEOS-1) Data Assimilation System (DAS) has been examined using a river routing model. The GEOS-1 DAS land-surface parameterization is very simple, using an energy balance prediction of surface temperature and prescribed soil water. OLGA uses near-surface atmospheric data from the GEOS-1 DAS to drive a more comprehensive parameterization of the land-surface physics. The two global systems are evaluated using a global river routing model. The river routing model uses climatologic surface runoff from each system to simulate the river discharge from global river basins, which can be compared to climatologic river discharge. Due to the soil hydrology, the OLGA system shows a general improvement in the simulation of river discharge compared to the GEOS-1 DAS. Snowmelt processes included in OLGA also have a positive effect on the annual cycle of river discharge and source runoff. Preliminary tests of a coupled land-atmosphere model indicate improvements to the hydrologic cycle compared to the uncoupled system. The river routing model has provided a useful tool in the evaluation of the GCM hydrologic cycle, and has helped quantify the influence of the more advanced land surface model.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-11-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Assimilation of GPM GMI Rainfall Product with WRF GSI

NASA Technical Reports Server (NTRS)

Li, Xuanli; Mecikalski, John; Zavodsky, Bradley

2015-01-01

The Global Precipitation Measurement (GPM) is an international mission to provide next-generation observations of rain and snow worldwide. The GPM built on Tropical Rainfall Measuring Mission (TRMM) legacy, while the core observatory will extend the observations to higher latitudes. The GPM observations can help advance our understanding of precipitation microphysics and storm structures. Launched on February 27th, 2014, the GPM core observatory is carrying advanced instruments that can be used to quantify when, where, and how much it rains or snows around the world. Therefore, the use of GPM data in numerical modeling work is a new area and will have a broad impact in both research and operational communities. The goal of this research is to examine the methodology of assimilation of the GPM retrieved products. The data assimilation system used in this study is the community Gridpoint Statistical Interpolation (GSI) system for the Weather Research and Forecasting (WRF) model developed by the Development Testbed Center (DTC). The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this research explores regional assimilation of the GPM products with case studies. Our presentation will highlight our recent effort on the assimilation of the GPM product 2AGPROFGMI, the retrieved Microwave Imager (GMI) rainfall rate data for initializing a real convective storm. WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to other GPM products. Further details of the methodology of data assimilation, preliminary result and test on the impact of GPM data and the influence on precipitation forecast will be presented at the conference.

Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system

NASA Astrophysics Data System (ADS)

Oigawa, Masanori; Tsuda, Toshitaka; Seko, Hiromu; Shoji, Yoshinori; Realini, Eugenio

2018-05-01

We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13-14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.[Figure not available: see fulltext.

Land Surface Data Assimilation

NASA Astrophysics Data System (ADS)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land surface observation, modeling and data assimilation, followed by a discussion of various hydrologic data assimilation challenges, and finally conclude with several land surface data assimilation case studies.

Mechanisms and Control of Phloem Transport in Trees: Fast and Slow - Sink and Source

NASA Astrophysics Data System (ADS)

Gessler, Arthur; Hagedorn, Frank; Galiano, Lucia; Schaub, Marcus; Joseph, Jobin; Arend, Matthias; Hommel, Robert; Kayler, Zachary

2017-04-01

Trees are large global stores of carbon that will be affected by increased carbon dioxide levels and climate change in the future. However, at present we cannot properly predict the carbon balance of forests as we lack knowledge on how plant physiological processes and especially the transport of carbon within the plant interact with environmental drivers and ecosystem-scale processes. The central conveyor belt for C allocation and distribution within the tree is the phloem and its functionality under environmental stress (esp. drought) is important for the avoidance of C starvation. This paper addresses the distribution of new assimilates within the plant, and sheds light on phloem transport mechanisms and transport control using 13C pulse labelling techniques. We provide experimental evidence that at least two mechanisms are employed to couple C sink processes to assimilation. We observed a fast increase of belowground respiration with the onset of photosynthesis, which we assume is induced by pressure concentration waves travelling through the phloem. A second, much later occurring peak in respiration is fueled by new 13C labeled assimilates. Moreover, we relate phloem transport velocity and intensity of labelled 13C assimilates to drought stress intensity and give indication how sink rather than source control might affect phloem transport in trees. During drought, net photosynthesis, soil respiration and the allocation of recent assimilates below ground were reduced. Carbohydrates accumulated in metabolically resting roots but not in leaves, indicating sink control of the tree carbon balance. After drought release, soil respiration recovered faster than assimilation and CO2 fluxes exceeded those in continuously watered trees for months. This stimulation was related to greater assimilate allocation to and metabolization in the rhizosphere. These findings show that trees prioritize the investment of assimilates below ground, probably to regain root functions after drought and indicate that sink activity governs carbon allocation not only during drought stress but also after stress release.

Towards uncertainty estimates in global operational forecasts of trace gases in the Copernicus Atmosphere Monitoring System

NASA Astrophysics Data System (ADS)

Huijnen, V.; Bouarar, I.; Chabrillat, S. H.; Christophe, Y.; Thierno, D.; Karydis, V.; Marecal, V.; Pozzer, A.; Flemming, J.

2017-12-01

Operational atmospheric composition analyses and forecasts such as developed in the Copernicus Atmosphere Monitoring Service (CAMS) rely on modules describing emissions, chemical conversion, transport and removal processing, as well as data assimilation methods. The CAMS forecasts can be used to drive regional air quality models across the world. Critical analyses of uncertainties in any of these processes are continuously needed to advance the quality of such systems on a global scale, ranging from the surface up to the stratosphere. With regard to the atmospheric chemistry to describe the fate of trace gases, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere combined with the Cariolle scheme to describe stratospheric ozone, as integrated in ECMWF's Integrated Forecasting System (IFS). It is further constrained by assimilation of satellite observations of CO, O3 and NO2. As part of CAMS we have recently developed three fully independent schemes to describe the chemical conversion throughout the atmosphere. These parameterizations originate from parent model codes in MOZART, MOCAGE and a combination of TM5/BASCOE. In this contribution we evaluate the correspondence and elemental differences in the performance of the three schemes in an otherwise identical model configuration (excluding data-assimilation) against a large range of in-situ and satellite-based observations of ozone, CO, VOC's and chlorine-containing trace gases for both troposphere and stratosphere. This analysis aims to provide a measure of model uncertainty in the operational system for tracers that are not, or poorly, constrained by data assimilation. It aims also to provide guidance on the directions for further model improvement with regard to the chemical conversion module.

An effort to improve track and intensity prediction of tropical cyclones through vortex initialization in NCUM-global model

NASA Astrophysics Data System (ADS)

Singh, Vivek; Routray, A.; Mallick, Swapan; George, John P.; Rajagopal, E. N.

2016-05-01

Tropical cyclones (TCs) have strong impact on socio-economic conditions of the countries like India, Bangladesh and Myanmar owing to its awful devastating power. This brings in the need of precise forecasting system to predict the tracks and intensities of TCs accurately well in advance. However, it has been a great challenge for major operational meteorological centers over the years. Genesis of TCs over data sparse warm Tropical Ocean adds more difficulty to this. Weak and misplaced vortices at initial time are one of the prime sources of track and intensity errors in the Numerical Weather Prediction (NWP) models. Many previous studies have reported the forecast skill of track and intensity of TC improved due to the assimilation of satellite data along with vortex initialization (VI). Keeping this in mind, an attempt has been made to investigate the impact of vortex initialization for simulation of TC using UK-Met office global model, operational at NCMRWF (NCUM). This assessment is carried out by taking the case of a extremely severe cyclonic storm "Chapala" that occurred over Arabian Sea (AS) from 28th October to 3rd November 2015. Two numerical experiments viz. Vort-GTS (Assimilation of GTS observations with VI) and Vort-RAD (Same as Vort-GTS with assimilation of satellite data) are carried out. This vortex initialization study in NCUM model is first of its type over North Indian Ocean (NIO). The model simulation of TC is carried out with five different initial conditions through 24 hour cycles for both the experiments. The results indicate that the vortex initialization with assimilation of satellite data has a positive impact on the track and intensity forecast, landfall time and position error of the TCs.

Development of an OSSE Framework for a Global Atmospheric Data Assimilation System

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Errico, Ronald M.; Prive, N.

2012-01-01

Observing system simulation experiments (OSSEs) are powerful tools for estimating the usefulness of various configurations of envisioned observing systems and data assimilation techniques. Their utility stems from their being conducted in an entirely simulated context, utilizing simulated observations having simulated errors and drawn from a simulation of the earth's environment. Observations are generated by applying physically based algorithms to the simulated state, such as performed during data assimilation or using other appropriate algorithms. Adding realistic instrument plus representativeness errors, including their biases and correlations, can be critical for obtaining realistic assessments of the impact of a proposed observing system or analysis technique. If estimates of the expected accuracy of proposed observations are realistic, then the OSSE can be also used to learn how best to utilize the new information, accelerating its transition to operations once the real data are available. As with any inferences from simulations, however, it is first imperative that some baseline OSSEs are performed and well validated against corresponding results obtained with a real observing system. This talk provides an overview of, and highlights critical issues related to, the development of an OSSE framework for the tropospheric weather prediction component of the NASA GEOS-5 global atmospheric data assimilation system. The framework includes all existing observations having significant impact on short-term forecast skill. Its validity has been carefully assessed using a range of metrics that can be evaluated in both the OSSE and real contexts, including adjoint-based estimates of observation impact. A preliminary application to the Aeolus Doppler wind lidar mission, scheduled for launch by the European Space Agency in 2014, has also been investigated.

Generation of a Realistic Soil Moisture Initialization System and its Potential Impact on Short-to-Seasonal Forecasting of Near Surface Variables

NASA Astrophysics Data System (ADS)

Boisserie, M.; Cocke, S.; O'Brien, J. J.

2009-12-01

Although the amount of water contained in the soil seems insignificant when compared to the total amount of water on a global-scale, soil moisture is widely recognized as a crucial variable for climate studies. It plays a key role in regulating the interaction between the atmosphere and the land-surface by controlling the repartition between the surface latent and sensible heat fluxes. In addition, the persistence of soil moisture anomalies provides one of the most important components of memory for the climate system. Several studies have shown that, during the boreal summer in mid-latitudes, the soil moisture role in controlling the continental precipitation variability may be more important than that of the sea surface temperature (Koster et al. 2000, Hong and Kalnay 2000, Koster et al. 2000, Kumar and Hoerling 1995, Trenberth et al. 1998, Shukla 1998). Although all of the above studies have demonstrated the strong sensitivity of seasonal forecasts to the soil moisture initial conditions, they relied on extreme or idealized soil moisture levels. The question of whether realistic soil moisture initial conditions lead to improved seasonal predictions has not been adequately addressed. Progress in addressing this question has been hampered by the lack of long-term reliable observation-based global soil moisture data sets. Since precipitation strongly affects the soil moisture characteristics at the surface and in depth, an alternative to this issue is to assimilate precipitation. Because precipitation is a diagnostic variable, most of the current reanalyses do not directly assimilate it into their models (M. Bosilovitch, 2008). In this study, an effective technique that directly assimilates the precipitation is used. We examine two experiments. In the first experiment, the model is initialized by directly assimilating a global, 3-hourly, 1.0° precipitation dataset, provided by Sheffield et al. (2006), in a continuous assimilation period of a couple of months. For this, we use a technique named the Precipitation Assimilation Reanalysis (PAR) described in Nunes and Cocke (2004). This technique consists of modifying the vertical profile of humidity as a function of the observed and predicted model rain rates. In the second experiment, the model is initialized without precipitation assimilation. For each experiment, ten sets of seasonal forecasts of the coupled land-atmosphere Florida State University/Center for Ocean and Atmosphere Predictions Studies (FSU/COAPS) model were generated, starting from the boreal summer of each year between 1986 and 1995. For each forecast, ten ensembles are produced by starting the forecast from the 1st and the 15th of each month from April to August. The results of these experiments show, first, that the PAR technique greatly improves the temporal and spatial variability of out model soil moisture estimate. Second, using these realistic soil moisture initial conditions, we found a significant increase in the air temperature seasonal forecasting skills. However, not significant increase has been found in the precipitation seasonal forecasting skills. The results of this study are involved in the GLACE-2 international multi-model experiment.

Muon radiography and deformation analysis of the lava dome formed by the 1944 eruption of Usu, Hokkaido —Contact between high-energy physics and volcano physics—

PubMed Central

TANAKA, Hiroyuki K. M.; YOKOYAMA, Izumi

2008-01-01

Lava domes are one of the conspicuous topographic features on volcanoes. The subsurface structure of the lava dome is important to discuss its formation mechanism. In the 1944 eruption of Volcano Usu, Hokkaido, a new lava dome was formed at its eastern foot. After the completion of the lava dome, various geophysical methods were applied to the dome to study its subsurface structure, but resulted in a rather ambiguous conclusion. Recently, from the results of the levelings, which were repeated during the eruption, “pseudo growth curves” of the lava dome were obtained. The curves suggest that the lava dome has a bulbous shape. In the present work, muon radiography, which previously proved effective in imaging the internal structure of Volcano Asama, has been applied to the Usu lava dome. The muon radiography measures the distribution of the “density length” of volcanic bodies when detectors are arranged properly. The result obtained is consistent with the model deduced from the pseudo growth curves. The measurement appears to afford useful method to clarify the subsurface structure of volcanoes and its temporal changes, and in its turn to discuss volcanic processes. This is a point of contact between high-energy physics and volcano physics. PMID:18941290

Muon radiography and deformation analysis of the lava dome formed by the 1944 eruption of Usu, Hokkaido--contact between high-energy physics and volcano physics--.

PubMed

K M Tanaka, Hiroyuki; Yokoyama, Izumi

2008-01-01

Lava domes are one of the conspicuous topographic features on volcanoes. The subsurface structure of the lava dome is important to discuss its formation mechanism. In the 1944 eruption of Volcano Usu, Hokkaido, a new lava dome was formed at its eastern foot. After the completion of the lava dome, various geophysical methods were applied to the dome to study its subsurface structure, but resulted in a rather ambiguous conclusion. Recently, from the results of the levelings, which were repeated during the eruption, "pseudo growth curves" of the lava dome were obtained. The curves suggest that the lava dome has a bulbous shape. In the present work, muon radiography, which previously proved effective in imaging the internal structure of Volcano Asama, has been applied to the Usu lava dome. The muon radiography measures the distribution of the "density length" of volcanic bodies when detectors are arranged properly. The result obtained is consistent with the model deduced from the pseudo growth curves. The measurement appears to afford useful method to clarify the subsurface structure of volcanoes and its temporal changes, and in its turn to discuss volcanic processes. This is a point of contact between high-energy physics and volcano physics.

The Mars Analysis Correction Data Assimilation (MACDA): A reference atmospheric reanalysis

NASA Astrophysics Data System (ADS)

Montabone, Luca; Lewis, Stephen R.; Steele, Liam J.; Holmes, James; Read, Peter L.; Valeanu, Alexandru; Smith, Michael D.; Kass, David; Kleinboehl, Armin; LMD Team, MGS/TES Team, MRO/MCS Team

2016-10-01

The Mars Analysis Correction Data Assimilation (MACDA) dataset version 1.0 contains the reanalysis of fundamental atmospheric and surface variables for the planet Mars covering a period of about three Martian years (late MY 24 to early MY 27). This four-dimensional dataset has been produced by data assimilation of retrieved thermal profiles and column dust optical depths from NASA's Mars Global Surveyor/Thermal Emission Spectrometer (MGS/TES), which have been assimilated into a Mars global climate model (MGCM) using the Analysis Correction scheme developed at the UK Meteorological Office.The MACDA v1.0 reanalysis is publicly available, and the NetCDF files can be downloaded from the archive at the Centre for Environmental Data Analysis/British Atmospheric Data Centre (CEDA/BADC). The variables included in the dataset can be visualised using an ad-hoc graphical user interface (the "MACDA Plotter") located at the following URL: http://macdap.physics.ox.ac.uk/The first paper about MACDA reanalysis of TES retrievals appeared in 2006, although the acronym MACDA was not yet used at that time. Ten years later, MACDA v1.0 has been used by several researchers worldwide and has contributed to the advancement of the knowledge about the martian atmosphere in critical areas such as the radiative impact of water ice clouds, the solsticial pause in baroclinic wave activity, and the climatology and dynamics of polar vortices, to cite only a few. It is therefore timely to review the scientific results obtained by using such Mars reference atmospheric reanalysis, in order to understand what priorities the user community should focus on in the next decade.MACDA is an ongoing collaborative project, and work funded by NASA MDAP Programme is currently undertaken to produce version 2.0 of the Mars atmospheric reanalysis. One of the key improvements is the extension of the reanalysis period to nine martian years (MY 24 through MY 32), with the assimilation of NASA's Mars Reconnaissance Orbiter/Mars Climate Sounder (MRO/MCS) retrievals of thermal and dust opacity profiles. MACDA 2.0 is also going to be based on an improved version of the underlying MGCM and an updated scheme to fully assimilate (radiative active) tracers, such as dust.

The analysis sensitivity to tropical winds from the Global Weather Experiment

NASA Technical Reports Server (NTRS)

Paegle, J.; Paegle, J. N.; Baker, W. E.

1986-01-01

The global scale divergent and rotational flow components of the Global Weather Experiment (GWE) are diagnosed from three different analyses of the data. The rotational flow shows closer agreement between the analyses than does the divergent flow. Although the major outflow and inflow centers are similarly placed in all analyses, the global kinetic energy of the divergent wind varies by about a factor of 2 between different analyses while the global kinetic energy of the rotational wind varies by only about 10 percent between the analyses. A series of real data assimilation experiments has been performed with the GLA general circulation model using different amounts of tropical wind data during the First Special Observing Period of the Global Weather Experiment. In exeriment 1, all available tropical wind data were used; in the second experiment, tropical wind data were suppressed; while, in the third and fourth experiments, only tropical wind data with westerly and easterly components, respectively, were assimilated. The rotational wind appears to be more sensitive to the presence or absence of tropical wind data than the divergent wind. It appears that the model, given only extratropical observations, generates excessively strong upper tropospheric westerlies. These biases are sufficiently pronounced to amplify the globally integrated rotational flow kinetic energy by about 10 percent and the global divergent flow kinetic energy by about a factor of 2. Including only easterly wind data in the tropics is more effective in controlling the model error than including only westerly wind data. This conclusion is especially noteworthy because approximately twice as many upper tropospheric westerly winds were available in these cases as easterly winds.

Improving Arctic Sea Ice Edge Forecasts by Assimilating High Horizontal Resolution Sea Ice Concentration Data into the US Navy’s Ice Forecast Systems

DTIC Science & Technology

2016-06-13

Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product... Global Ocean Fore- cast System (GOFS 3.1). Prior to 2 February 2015, the ice concentration fields from both ACNFS and GOFS 3.1 had been updated with...Scanning Radiometer (AMSR2) on the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission – Water (GCOM-W) platform became available

Memória fonológica em crianças bilíngues bimodais e crianças com implante coclear

PubMed Central

de Quadros, Ronice Müller; Cruz, Carina Rebello; Pizzio, Aline Lemos

2014-01-01

RESUMO Este estudo comparou o desempenho de crianças bilíngues bimodais ouvintes (filhas de pais surdos) e crianças surdas usuárias de implante coclear (filhas de pais surdos e de pais ouvintes), com diferentes contextos de acesso à Língua Brasileira de Sinais (Libras), em tarefas que envolvem memória fonologica. Os testes utilizados foram: Teste de Pseudopalavras (Santos e Bueno, 2003) e Teste de Pseudosinais (desenvolvido pelos pesquisadores responsáveis pelo Projeto ‘Desenvolvimento Bilíngue Bimoda’). Além disso, foram incluídos dois grupos de controle, formados por crianças surdas (usuarias de Libras), e adultos bilíngues bimodais ouvintes. Na análise dos resultados, em relação ao desempenho entre os dois grupos testados foi constatado que o grupo de crianças bilíngues bimodais ouvintes apresentou desempenho superior, nos dois testes. No entanto, ao ser analisado o desempenho da criança surda usuaria de implante coclear, filha de pais surdos, que possui acesso irrestrito à Libras e comparado com o das crianças surdas usuárias de implante coclear, que possuem acesso restrito à Libras, foi constatado que o seu desempenho foi semelhante ao do grupo de crianças bilíngues bimodais ouvintes. As crianças surdas usuárias de implante coclear com acesso restrito à Libras e, portanto, com acesso maior ao Português apresentaram escores mais baixos nas tarefas, principalmente do teste em Português. Os resultados sugerem que as crianças surdas usuárias de implante coclear em processo de aquisição da línguagem podem se beneficiar com o acesso irrestrito à Libras, atingindo inclusive desempenho semelhante a de crianças bilíngues bimodais ouvintes. PMID:25110473

Technical Report Series on Global Modeling and Data Assimilation. Volume 40; Soil Moisture Active Passive (SMAP) Project Assessment Report for the Beta-Release L4_SM Data Product

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Liu, Qing; Colliander, Andreas; Conaty, Austin; Jackson, Thomas; Kimball, John

2015-01-01

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against sparse network measurements and the evaluation of the assimilation diagnostics address Stage 2 validation criteria by expanding the assessment to regional and global scales.

Potentials of satellite derived SIF products to constrain GPP simulated by the new ORCHIDEE-FluOR terrestrial model at the global scale

NASA Astrophysics Data System (ADS)

Bacour, C.; Maignan, F.; Porcar-Castell, A.; MacBean, N.; Goulas, Y.; Flexas, J.; Guanter, L.; Joiner, J.; Peylin, P.

2016-12-01

A new era for improving our knowledge of the terrestrial carbon cycle at the global scale has begun with recent studies on the relationships between remotely sensed Sun Induce Fluorescence (SIF) and plant photosynthetic activity (GPP), and the availability of such satellite-derived products now "routinely" produced from GOSAT, GOME-2, or OCO-2 observations. Assimilating SIF data into terrestrial ecosystem models (TEMs) represents a novel opportunity to reduce the uncertainty of their prediction with respect to carbon-climate feedbacks, in particular the uncertainties resulting from inaccurate parameter values. A prerequisite is a correct representation in TEMs of the several drivers of plant fluorescence from the leaf to the canopy scale, and in particular the competing processes of photochemistry and non photochemical quenching (NPQ).In this study, we present the first results of a global scale assimilation of GOME-2 SIF products within a new version of the ORCHIDEE land surface model including a physical module of plant fluorescence. At the leaf level, the regulation of fluorescence yield is simulated both by the photosynthesis module of ORCHIDEE to calculate the photochemical yield and by a parametric model to estimate NPQ. The latter has been calibrated on leaf fluorescence measurements performed for boreal coniferous and Mediterranean vegetation species. A parametric representation of the SCOPE radiative transfer model is used to model the plant fluorescence fluxes for PSI and PSII and the scaling up to the canopy level. The ORCHIDEE-FluOR model is firstly evaluated with respect to in situ measurements of plant fluorescence flux and photochemical yield for scots pine and wheat. The potentials of SIF data to constrain the modelled GPP are evaluated by assimilating one year of GOME-2-SIF products within ORCHIDEE-FluOR. We investigate in particular the changes in the spatial patterns of GPP following the optimization of the photosynthesis and phenology parameters. We analyze the differences obtained using a simpler fluorescence model in ORCHIDEE hypothesizing a linear relationship between SIF and GPP, and an independent simultaneous assimilation of three data-streams (in situ flux measurements, satellite derived NDVI and atmospheric CO2 concentrations).

A study on assimilating potential vorticity data

NASA Astrophysics Data System (ADS)

Li, Yong; Ménard, Richard; Riishøjgaard, Lars Peter; Cohn, Stephen E.; Rood, Richard B.

1998-08-01

The correlation that exists between the potential vorticity (PV) field and the distribution of chemical tracers such as ozone suggests the possibility of using tracer observations as proxy PV data in atmospheric data assimilation systems. Especially in the stratosphere, there are plentiful tracer observations but a general lack of reliable wind observations, and the correlation is most pronounced. The issue investigated in this study is how model dynamics would respond to the assimilation of PV data. First, numerical experiments of identical-twin type were conducted with a simple univariate nuding algorithm and a global shallow water model based on PV and divergence (PV-D model). All model fields are successfully reconstructed through the insertion of complete PV data alone if an appropriate value for the nudging coefficient is used. A simple linear analysis suggests that slow modes are recovered rapidly, at a rate nearly independent of spatial scale. In a more realistic experiment, appropriately scaled total ozone data from the NIMBUS-7 TOMS instrument were assimilated as proxy PV data into the PV-D model over a 10-day period. The resulting model PV field matches the observed total ozone field relatively well on large spatial scales, and the PV, geopotential and divergence fields are dynamically consistent. These results indicate the potential usefulness that tracer observations, as proxy PV data, may offer in a data assimilation system.

High-Resolution Assimilation of GRACE Terrestrial Water Storage Observations to Represent Local-Scale Water Table Depths

NASA Astrophysics Data System (ADS)

Stampoulis, D.; Reager, J. T., II; David, C. H.; Famiglietti, J. S.; Andreadis, K.

2017-12-01

Despite the numerous advances in hydrologic modeling and improvements in Land Surface Models, an accurate representation of the water table depth (WTD) still does not exist. Data assimilation of observations of the joint NASA and DLR mission, Gravity Recovery and Climate Experiment (GRACE) leads to statistically significant improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of water storage. However, the usually shallow groundwater compartment of the models presents a problem with GRACE assimilation techniques, as these satellite observations account for much deeper aquifers. To improve the accuracy of groundwater estimates and allow the representation of the WTD at fine spatial scales we implemented a novel approach that enables a large-scale data integration system to assimilate GRACE data. This was achieved by augmenting the Variable Infiltration Capacity (VIC) hydrologic model, which is the core component of the Regional Hydrologic Extremes Assessment System (RHEAS), a high-resolution modeling framework developed at the Jet Propulsion Laboratory (JPL) for hydrologic modeling and data assimilation. The model has insufficient subsurface characterization and therefore, to reproduce groundwater variability not only in shallow depths but also in deep aquifers, as well as to allow GRACE assimilation, a fourth soil layer of varying depth ( 1000 meters) was added in VIC as the bottom layer. To initialize a water table in the model we used gridded global WTD data at 1 km resolution which were spatially aggregated to match the model's resolution. Simulations were then performed to test the augmented model's ability to capture seasonal and inter-annual trends of groundwater. The 4-layer version of VIC was run with and without assimilating GRACE Total Water Storage anomalies (TWSA) over the Central Valley in California. This is the first-ever assimilation of GRACE TWSA for the determination of realistic water table depths, at fine scales that are required for local water management. In addition, Open Loop and GRACE-assimilation simulations of water table depth were compared to in-situ data over the state of California, derived from observation wells operated/maintained by the U.S. Geological Service.

Preliminary Results from an Assimilation of Saharan Dust Using TOMS Radiances and the GOCART Model

NASA Technical Reports Server (NTRS)

Weaver, C. J.; daSilva, Arlindo; Ginoux, Paul; Torres, Omar; Einaudi, Franco (Technical Monitor)

2000-01-01

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions. Surface mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from .08-10 microns and only simulates Saharan dust. We draw the assimilation to two observables in this study: the TOMS aerosol index (Al) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance alone. The forward model that simulates the observables requires the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer from the GOCART fields. The forward model also requires a refractive index for the dust. We test three index values to see which best fits the TOMS observables. These are 1) for Saharan dust reported by Patterson, 2) for a mixture of Saharan dust and a highly reflective material (sea salt or sulfate) and 3) for pure illite. The assimilation works best assuming either pure illite or the dust mixture. Our assimilation cycle first determines values of the aerosol index (Al) and the radiance at 380 nm based on the GOCART aerosol fields. Differences between the observed and GOCART model calculated Al and 380 nm radiance are first analyzed horizontally using the Physical-space Statistical Analysis System (PSAS). A quasi-Newton iteration is then performed to produce analyzed 3D aerosol fields according to parameterized background and observation error covariances. We only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols are dominant and carbonaceous aerosols are minimal.

Primary health care assessment from the users' perspectives: a systematic review.

PubMed

Paula, Weslla Karla Albuquerque Silva de; Samico, Isabella Chagas; Caminha, Maria de Fátima Costa; Filho, Malaquias Batista; Silva, Suzana Lins da

2016-04-01

Analyze the evaluation of the attributes of primary care made by users of basic units of Brazilian health by using PCATool instrument adapted to Brazil. A systematic literature review conducted in the PubMed database, IBECS, LILACS, SciELO and BDTD. 4,405 documents were found, selected 23 full texts. After Full reading and application of eligibility criteria, 14 articles were evaluated. The studies showed that primary care performs well in longitudinality attributes, completeness and coordination and worse performance on attributes access first contact, family counseling and community orientation, even in the basic units with the Family Health. The users of basic health units assessed as unsatisfactory attributes considered essential for a health care more equitable and competing for user autonomy and social control. It is inferred that there are still obstacles hindering user access to basic health services and care actions are still being developed without favoring user participation and the community context in which they live. Analisar a avaliação dos atributos da Atenção Básica feita por usuários de unidades básicas de saúde brasileiras, mediante utilização de adaptações do instrumento PCATool para o Brasil. Revisão sistemática da literatura realizada nas bases de dados Medline/PubMed, LILACS, IBECS, SciELO e BDTD. Foram encontrados 4.405 documentos, sendo selecionados 23 textos completos. Após leitura integral e aplicação dos critérios de elegibilidade, 14 artigos foram avaliados. Os estudos apontaram que a Atenção Básica tem bom desempenho nos atributos longitudinalidade, integralidade e coordenação e pior desempenho nos atributos acesso de primeiro contato, orientação familiar e a orientação comunitária, mesmo nas unidades básicas com Saúde da Família. Os usuários das unidades básicas de saúde avaliaram como insatisfatórios atributos considerados fundamentais para uma atenção à saúde mais equânime e concorrente para autonomia do usuário e controle social. Infere-se que ainda existem obstáculos que dificultam o acesso dos usuários aos serviços básicos de saúde e que ações do cuidado ainda estejam sendo desenvolvidas sem privilegiar a participação do usuário e o contexto comunitário onde vivem.

Leaf nitrogen assimilation and partitioning differ among subtropical forest plants in response to canopy addition of nitrogen treatments.

PubMed

Liu, Nan; Wu, Shuhua; Guo, Qinfeng; Wang, Jiaxin; Cao, Ce; Wang, Jun

2018-05-12

Global increases in nitrogen deposition may alter forest structure and function by interfering with plant nitrogen metabolism (e.g., assimilation and partitioning) and subsequent carbon assimilation, but it is unclear how these responses to nitrogen deposition differ among species. In this study, we conducted a 2-year experiment to investigate the effects of canopy addition of nitrogen (CAN) on leaf nitrogen assimilation and partitioning in three subtropical forest plants (Castanea henryi, Ardisia quinquegona, and Blastus cochinchinensis). We hypothesized that responses of leaf nitrogen assimilation and partitioning to CAN differ among subtropical forest plants. CAN increased leaf nitrate reductase (NR) activity, and leaf nitrogen and chlorophyll contents but reduced leaf maximum photosynthetic rate (A max ), photosynthetic nitrogen use efficiency (PNUE), ribulose-1,5-bisphosphate carboxylase (Rubisco) activity, and metabolic protein content of an overstory tree species C. henryi. In an understory tree A. quinquegona, CAN increased NR activity and glutamine synthetase activity and therefore increased metabolic protein synthesis (e.g., Rubisco) in leaves. In the shrub B. cochinchinensis, CAN increased A max , PNUE, Rubisco content, metabolic protein content, and Rubisco activity in leaves. Leaf nitrogen assimilation and partitioning results indicated that A. quinquegona and B. cochinchinensis may better acclimate to CAN than C. henryi and that the acclimation mechanism differs among the species. Results from this study suggest that long-term elevated atmospheric nitrogen deposition has contributed to the ongoing transformation of subtropical forests into communities dominated by small trees and shrubs. Copyright © 2018 Elsevier B.V. All rights reserved.

Using Satellite-derived Ice Concentration to Represent Antarctic Coastal Polynyas in Ocean Climate Models

NASA Technical Reports Server (NTRS)

Stoessel, Achim; Markus, Thorsten

2003-01-01

The focus of this paper is on the representation of Antarctic coastal polynyas in global ice-ocean general circulation models (OGCMs), in particular their local, regional, and high-frequency behavior. This is verified with the aid of daily ice concentration derived from satellite passive microwave data using the NASATeam 2 (NT2) and the bootstrap (BS) algorithms. Large systematic regional and temporal discrepancies arise, some of which are related to the type of convection parameterization used in the model. An attempt is made to improve the fresh-water flux associated with melting and freezing in Antarctic coastal polynyas by ingesting (assimilating) satellite ice concentration where it comes to determining the thermodynamics of the open-water fraction of a model grid cell. Since the NT2 coastal open-water fraction (polynyas) tends to be less extensive than the simulated one in the decisive season and region, assimilating NT2 coastal ice concentration yields overall reduced net freezing rates, smaller formation rates of Antarctic Bottom Water, and a stronger southward flow of North Atlantic Deep Water across 30 S. Enhanced net freezing rates occur regionally when NT2 coastal ice concentration is assimilated, concomitant with a more realistic ice thickness distribution and accumulation of High-Salinity Shelf Water. Assimilating BS rather than NT2 coastal ice concentration, the differences to the non-assimilated simulation are generally smaller and of opposite sign. This suggests that the model reproduces coastal ice concentration in closer agreement with the BS data than with the NT2 data, while more realistic features emerge when NT2 data are assimilated.

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.

2014-01-01

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar (CFS) HURRICANE WEATHER RESEARCH and FORECASTING (HWRF) GLOBAL ENSEMBLE FORECAST SYSTEM (GEFS) NATIONAL Climate Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 Page Author: EMC

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar conducts a program of research and development in support of the National Centers for Environmental Prediction (NCEP) operational forecasting mission for global prediction. This research and development in

Retrieval Assimilation and Modeling of Atmospheric Water Vapor from Ground- and Space-Based GPS Networks: Investigation of the Global and Regional Hydrological Cycles

NASA Technical Reports Server (NTRS)

Dickey, Jean O.

1999-01-01

Uncertainty over the response of the atmospheric hydrological cycle (particularly the distribution of water vapor and cloudiness) to anthropogenic forcing is a primary source of doubt in current estimates of global climate sensitivity, which raises severe difficulties in evaluating its likely societal impact. Fortunately, a variety of advanced techniques and sensors are beginning to shed new light on the atmospheric hydrological cycle. One of the most promising makes use of the sensitivity of the Global Positioning System (GPS) to the thermodynamic state, and in particular the water vapor content, of the atmosphere through which the radio signals propagate. Our strategy to derive the maximum benefit for hydrological studies from the rapidly increasing GPS data stream will proceed in three stages: (1) systematically analyze and archive quality-controlled retrievals using state-of-the-art techniques; (2) employ both currently available and innovative assimilation procedures to incorporate these determinations into advanced regional and global atmospheric models and assess their effects; and (3) apply the results to investigate selected scientific issues of relevance to regional and global hydrological studies. An archive of GPS-based estimation of total zenith delay (TZD) data and water vapor where applicable has been established with expanded automated quality control. The accuracy of the GPS estimates is being monitored; the investigation of systematic errors is ongoing using comparisons with water vapor radiometers. Meteorological packages have been implemented. The accuracy and utilization of the TZD estimates has been improved by implementing a troposphere gradient model. GPS-based gradients have been validated as real atmospheric moisture gradients, establishing a link between the estimated gradients and the passage of weather fronts. We have developed a generalized ray tracing inversion scheme that can be used to analyze occultation data acquired from space- or land-based receivers. The National Center for Atmospheric Research mesoscale model (version MM5) has been adapted for Southern California, and assimilation studies are underway. Additional information is contained in the original.

NASA Giovanni Portals for NLDAS/GLDAS Online Visualization, Analysis, and Intercomparison

NASA Technical Reports Server (NTRS)

Rui, Hualan; Teng, William L.; Vollmer, Bruce; Mocko, David M.; Beaudoing, Hiroko Kato; Rodell, Matthew

2011-01-01

The North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) are generating a series of land surface forcing (e.g., precipitation, surface meteorology, and radiation), state (e.g., soil moisture and temperature, and snow), and flux (e.g., evaporation and sensible heat flux) products, simulated by several land surface models. To date, NLDAS and GLDAS have generated more than 30 (1979 - present) and 60 (1948 - present) years of data, respectively. To further facilitate data accessibility and utilization, three new portals in the NASA Giovanni system have been made available for NLDAS and GLDAS online visualization, analysis, and intercomparison.

Land Surface Modeling and Data Assimilation to Support Physical Precipitation Retrievals for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Tian. Yudong; Kumar, Sujay; Geiger, James; Choudhury, Bhaskar

2010-01-01

Objective: The objective of this proposal is to provide a routine land surface modeling and data assimilation capability for GPM in order to provide global land surface states that are necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in GPM, is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land.

Technical report series on global modeling and data assimilation. Volume 5: Documentation of the AIRES/GEOS dynamical core, version 2

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Takacs, Lawrence L.

1995-01-01

A detailed description of the numerical formulation of Version 2 of the ARIES/GEOS 'dynamical core' is presented. This code is a nearly 'plug-compatible' dynamics for use in atmospheric general circulation models (GCMs). It is a finite difference model on a staggered latitude-longitude C-grid. It uses second-order differences for all terms except the advection of vorticity by the rotation part of the flow, which is done at fourth-order accuracy. This dynamical core is currently being used in the climate (ARIES) and data assimilation (GEOS) GCMs at Goddard.

Ring Current Pressure Estimation withRAM-SCB using Data Assimilation and VanAllen Probe Flux Data

NASA Astrophysics Data System (ADS)

Godinez, H. C.; Yu, Y.; Henderson, M. G.; Larsen, B.; Jordanova, V.

2015-12-01

Capturing and subsequently modeling the influence of tail plasma injections on the inner magnetosphere is particularly important for understanding the formation and evolution of Earth's ring current. In this study, the ring current distribution is estimated with the Ring Current-Atmosphere Interactions Model with Self-Consistent Magnetic field (RAM-SCB) using, for the first time, data assimilation techniques and particle flux data from the Van Allen Probes. The state of the ring current within the RAM-SCB is corrected via an ensemble based data assimilation technique by using proton flux from one of the Van Allen Probes, to capture the enhancement of ring current following an isolated substorm event on July 18 2013. The results show significant improvement in the estimation of the ring current particle distributions in the RAM-SCB model, leading to better agreement with observations. This newly implemented data assimilation technique in the global modeling of the ring current thus provides a promising tool to better characterize the effect of substorm injections in the near-Earth regions. The work is part of the Space Hazards Induced near Earth by Large, Dynamic Storms (SHIELDS) project in Los Alamos National Laboratory.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation

NASA Astrophysics Data System (ADS)

Benedetti, A.; Morcrette, J.-J.; Boucher, O.; Dethof, A.; Engelen, R. J.; Fisher, M.; Flentje, H.; Huneeus, N.; Jones, L.; Kaiser, J. W.; Kinne, S.; Mangold, A.; Razinger, M.; Simmons, A. J.; Suttie, M.

2009-07-01

This study presents the new aerosol assimilation system, developed at the European Centre for Medium-Range Weather Forecasts, for the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The aerosol modeling and analysis system is fully integrated in the operational four-dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalyses of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort. It focuses on the theoretical architecture and practical implementation of the aerosol assimilation system. It also provides a discussion of the background errors and observations errors for the aerosol fields, and presents a subset of results from the 2-year reanalysis which has been run for 2003 and 2004 using data from the Moderate Resolution Imaging Spectroradiometer on the Aqua and Terra satellites. Independent data sets are used to show that despite some compromises that have been made for feasibility reasons in regards to the choice of control variable and error characteristics, the analysis is very skillful in drawing to the observations and in improving the forecasts of aerosol optical depth.

Assimilation of river altimetry data for effective bed elevation and roughness coefficient

NASA Astrophysics Data System (ADS)

Brêda, João Paulo L. F.; Paiva, Rodrigo C. D.; Bravo, Juan Martin; Passaia, Otávio

2017-04-01

Hydrodynamic models of large rivers are important prediction tools of river discharge, height and floods. However, these techniques still carry considerable errors; part of them related to parameters uncertainties related to river bathymetry and roughness coefficient. Data from recent spatial altimetry missions offers an opportunity to reduce parameters uncertainty through inverse methods. This study aims to develop and access different methods of altimetry data assimilation to improve river bottom levels and Manning roughness estimations in a 1-D hydrodynamic model. The case study was a 1,100 km reach of the Madeira River, a tributary of the Amazon. The tested assimilation methods are direct insertion, linear interpolation, SCE-UA global optimization algorithm and a Kalman Filter adaptation. The Kalman Filter method is composed by new physically based covariance functions developed from steady-flow and backwater equations. It is accessed the benefits of altimetry missions with different spatio-temporal resolutions, such as ICESAT-1, Envisat and Jason 2. Level time series of 5 gauging stations and 5 GPS river height profiles are used to assess and validate the assimilation methods. Finally, the potential of future missions are discussed, such as ICESAT-2 and SWOT satellites.

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

NASA Astrophysics Data System (ADS)

Acevedo, Walter; Fallah, Bijan; Reich, Sebastian; Cubasch, Ulrich

2017-05-01

Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a process-based tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the time-averaged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in the model. This result might help the dendrochronology community to optimize their sampling efforts.

Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models?

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

Tan, Zeli; Zhuang, Qianlai; Henze, Daven K.

Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range ofmore » 496.4–511.5 Tg yr −1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr −1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr −1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations.« less

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.

Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models?

DOE PAGES

Tan, Zeli; Zhuang, Qianlai; Henze, Daven K.; ...

2016-10-12

Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range ofmore » 496.4–511.5 Tg yr −1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr −1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr −1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations.« less

Discharge estimation in ungauged basins through variational data assimilation : The potential of the SWOT mission.

NASA Astrophysics Data System (ADS)

Oubanas, H.; Gejadze, I.; Malaterre, P. O.; Durand, M. T.; Wei, R.; Frasson, R. P. M.; Domeneghetti, A.

2017-12-01

This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, using a variant of the standard variational data assimilation method `4D-Var'. The hydrology SWOT simulator, developed at the Jet Propulsion Laboratory (JPL) has been used to simulate the expected performance of the KaRIn instrument onboard the satellite, producing synthetic SWOT observations of height and width, at each satellite overpass. SWOT data products were synthesized at the spatial scale of 200 m along the river centerline. Using a 1.5D full Saint-Venant hydraulic model, variational data assimilation simultaneously estimates the inflow discharge, river bathymetry and bed roughness. The proposed method has been designed for an application to fully ungauged basins; therefore, the prior information is derived from the SWOT observations only and the globally available ancillary information. Two reaches of the Po and Sacramento Rivers of about 130 km and 150 km, respectively, have been considered in this study. Discharge was successfully recovered at the overpass time with a relative-root-mean-square error of 16% and 12.3% for the Po and Sacramento Rivers, respectively. The estimates of the bed level and the roughness coefficient demonstrate a local improvement; however they may not provide reliable global information of the river bathymetry and roughness.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Smith, William L., Jr.; Bedka, Kristopher M.; Nguyen, Louis; Palikonda, Rabindra; Hong, Gang; Trepte, Qing Z.; Chee, Thad; Scarino, Benjamin; Spangenberg, Douglas A.;

Scientific advances of the MyOcean projects underpinning the transition towards the Marine Copernicus service

NASA Astrophysics Data System (ADS)

Brasseur, Pierre

2015-04-01

The MyOcean projects supported by the European Commission period have been developed during the 2008-2015 period to build an operational service of ocean physical state and ecosystem information to intermediate and downstream users in the areas of marine safety, marine resources, marine and coastal environment and weather, climate and seasonal forecasting. The "core" information provided to users is obtained through the combination of satellite and in situ observations, eddy-resolving modelling of the global ocean and regional european seas, biochemistry, ecosystem and sea-ice modelling, and data assimilation for global to basin scale circulation. A comprehensive R&D plan was established in 2010 to ensure the collection and provision of information of best possible quality for daily estimates of the ocean state (real-time), its short-term evolution, and its history over the past (reanalyses). A service validation methodology was further developed to ensure proper scientific evaluation and routine monitoring of the accuracy of MyOcean products. In this presentation, we will present an overview of the main scientific advances achieved in MyOcean using the NEMO modelling platform, ensemble-based assimilation schemes, coupled circulation-ecosystem, sea-ice assimilative models and probabilistic methodologies for ensemble validation. We will further highlight the key areas that will require additional innovation effort to support the Marine Copernicus service evolution.

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

NASA Technical Reports Server (NTRS)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

NASA Astrophysics Data System (ADS)

Minnis, P.; Smith, W., Jr.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Hong, G.; Trepte, Q.; Chee, T.; Scarino, B. R.; Spangenberg, D.; Sun-Mack, S.; Fleeger, C.; Ayers, J. K.; Chang, F. L.; Heck, P. W.

2014-12-01

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

New drought indices from the assimilation of satellite data

NASA Astrophysics Data System (ADS)

Calvet, Jean-Christophe; Barbu, Alina; Fairbairn, David

2016-04-01

The current agricultural drought indicators produced by Meteo-France are derived from digital simulations of soil moisture produced by the SURFEX modelling platform. In the framework of the IMAGINES European project, a research was conducted in order to assess the impact on the monitoring of agricultural droughts of the integration of satellite data into SURFEX. A data assimilation system was implemented to this end. It provides simulations of the biomass and leaf area index of straw cereals and grasslands over France. It is shown that these simulations can be improved through the assimilation of satellite products distributed in near-real-time by the Copernicus Global Land service (http://land.copernicus.eu/global/). Reference in situ observations of the agricultural yields show that using satellite data, a significant correlation between the maximum annual above-ground biomass simulated by SURFEX and the agricultural yield at the scale of administrative units (départements) can be achieved. Without satellite data, very low correlations are observed. It is also shown that new 10-day drought indicators, complementary to soil moisture, can be derived from the leaf area index and from the above-ground biomass of vegetation. These demonstration drought monitoring products for the 2008-2013 period are freely available on the project web site (http://fp7-imagines.eu/) for 45 administrative units for cereals and for 48 administrative units for grasslands.

Explore GPM IMERG and Other Global Precipitation Products with GES DISC GIOVANNI

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana M.; Vollmer, Bruce; MacRitchie, Kyle; Kempler, Steven

2015-01-01

New features and capabilities in the newly released GIOVANNI allow exploring GPM IMERG (Integrated Multi-satelliE Retrievals for GPM) Early, Late and Final Run global half-hourly and monthly precipitation products as well as other precipitation products distributed by the GES DISC such as TRMM Multi-Satellite Precipitation Analysis (TMPA), MERRA (Modern Era Retrospective-Analysis for Research and Applications), NLDAS (North American Land Data Assimilation Systems), GLDAS (Global Land Data Assimilation Systems), etc. GIOVANNI is a web-based tool developed by the GES DISC (Goddard Earth Sciences and Data Information Services Center) to visualize and analyze Earth science data without having to download data and software. The new interface in GIOVANNI allows searching and filtering precipitation products from different NASA missions and projects and expands the capabilities to inter-compare different precipitation products in one interface. Knowing differences in precipitation products is important to identify issues in retrieval algorithms, biases, uncertainties, etc. Due to different formats, data structures, units and so on, it is not easy to inter-compare precipitation products. Newly added features and capabilities (unit conversion, regridding, etc.) in GIOVANNI make inter-comparisons possible. In this presentation, we will describe these new features and capabilities along with examples.

New Developments in the Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Hoar, T. J.; Anderson, J. L.; Raeder, K.; Karspeck, A. R.; Romine, G.; Liu, H.; Collins, N.

2011-12-01

NCAR's Data Assimilation Research Testbed (DART) is a community facility that provides ensemble data assimilation tools for geophysical applications. DART works with an expanding set of models and a wide range of conventional and novel observations, and provides a variety of assimilation algorithms and diagnostic tools. The Kodiak release of DART became available in July 2011 and includes more than 20 major feature enhancements, support for 24 models, support for (at least) 14 observation formats, expanded documentation and diagnostic tools, and 12 new utilities. A few examples of research projects that demonstrate the effectiveness and flexibility of the DART are described. The Community Atmosphere Model (CAM) and DART assimilated all the observations that were used in the NCEP/NCAR Reanalysis to produce a global, 6-hourly, 80-member ensemble reanalysis for 1998 through the present. The dataset is ideal for research applications that would benefit from an ensemble of equally-likely atmospheric states that are consistent with observations. Individual ensemble members may be used as a "data atmosphere" in any Community Earth System Model (CESM) experiment. The CESM interfaces for the Parallel Ocean Program (POP) and the Community Land Model (CLM) also support multiple instances, allowing data assimilation experiments exploiting unique atmospheric forcing for each POP or CLM model instance. A multi-year DART ocean assimilation has been completed and provides valuable insight into the successes and challenges of oceanic data assimilation. The DART/CLM research focuses on snow cover fraction and snow depth. The Weather Research and Forecasting (WRF) model was used with DART to perform a real-time CONUS domain mesoscale ensemble analysis with continuous cycling for 47 days. A member was selected once daily for high-resolution convective forecasts supporting a test phase of the Deep Convective Clouds and Chemistry experiment and the Storm Prediction Center spring experiment. The impacts of Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer (AMSR) microwave total precipitable water (TPW) observations on analyses and forecasts of tropical cyclone Sinlaku (2008) are investigated by performing assimilations with a 45km resolution WRF model over the Western Pacific domain for 8-14 Septmber, 2008. Particular emphasis is on the performance of the assimilation algorithms in the hurricane core and the impact of novel observations in the hurricane core.

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

DOE PAGES

Peylin, Philippe; Bacour, Cédric; MacBean, Natasha; ...

2016-09-20

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely themore » Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

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

Peylin, Philippe; Bacour, Cédric; MacBean, Natasha

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely themore » Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less

Global Energy and Water Cycle Experiment (GEWEX) and the Continental-scale International Project (GCIP)

NASA Technical Reports Server (NTRS)

Vane, Deborah

1993-01-01

A discussion of the objectives of the Global Energy and Water Cycle Experiment (GEWEX) and the Continental-scale International Project (GCIP) is presented in vugraph form. The objectives of GEWEX are as follows: determine the hydrological cycle by global measurements; model the global hydrological cycle; improve observations and data assimilation; and predict response to environmental change. The objectives of GCIP are as follows: determine the time/space variability of the hydrological cycle over a continental-scale region; develop macro-scale hydrologic models that are coupled to atmospheric models; develop information retrieval schemes; and support regional climate change impact assessment.

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar Documentation for operational and research users Operational Models All of the secondary bulleted items will be climate MOM4 HYCOM-Wavewatch Modeling Research Global and regional Institutionally supported components

An operational global-scale ocean thermal analysis system

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

Clancy, R. M.; Pollak, K.D.; Phoebus, P.A.

1990-04-01

The Optimum Thermal Interpolation System (OTIS) is an ocean thermal analysis system designed for operational use at FNOC. It is based on the optimum interpolation of the assimilation technique and functions in an analysis-prediction-analysis data assimilation cycle with the TOPS mixed-layer model. OTIS provides a rigorous framework for combining real-time data, climatology, and predictions from numerical ocean prediction models to produce a large-scale synoptic representation of ocean thermal structure. The techniques and assumptions used in OTIS are documented and results of operational tests of global scale OTIS at FNOC are presented. The tests involved comparisons of OTIS against an existingmore » operational ocean thermal structure model and were conducted during February, March, and April 1988. Qualitative comparison of the two products suggests that OTIS gives a more realistic representation of subsurface anomalies and horizontal gradients and that it also gives a more accurate analysis of the thermal structure, with improvements largest below the mixed layer. 37 refs.« less

Energetics of the martian atmosphere using the Mars Analysis Correction Data Assimilation (MACDA) dataset

NASA Astrophysics Data System (ADS)

Battalio, Michael; Szunyogh, Istvan; Lemmon, Mark

2016-09-01

The energetics of the atmosphere of the northern hemisphere of Mars during the pre-winter solstice period are explored using the Mars Analysis Correction Data Assimilation (MACDA) dataset (v1.0) and the eddy kinetic energy equation, with the quasi-geostrophic omega equation providing vertical velocities. Traveling waves are typically triggered by geopotential flux convergence. The effect of dust on baroclinic instability is examined by comparing a year with a global-scale dust storm (GDS) to two years without a global-scale dust storm. During the non-GDS years, results agree with that of a previous study using a general circulation model simulation. In the GDS year, waves develop a mixed baroclinic/barotropic growth phase before decaying barotropically. Though the total amount of eddy kinetic energy generated by baroclinic energy conversion is lower during the GDS year, the maximum eddy intensity is not diminished. Instead, the number of intense eddies is reduced by about 50%.

A 3-hydroxypropionate/4-hydroxybutyrate autotrophic carbon dioxide assimilation pathway in Archaea.

PubMed

Berg, Ivan A; Kockelkorn, Daniel; Buckel, Wolfgang; Fuchs, Georg

2007-12-14

The assimilation of carbon dioxide (CO2) into organic material is quantitatively the most important biosynthetic process. We discovered that an autotrophic member of the archaeal order Sulfolobales, Metallosphaera sedula, fixed CO2 with acetyl-coenzyme A (acetyl-CoA)/propionyl-CoA carboxylase as the key carboxylating enzyme. In this system, one acetyl-CoA and two bicarbonate molecules were reductively converted via 3-hydroxypropionate to succinyl-CoA. This intermediate was reduced to 4-hydroxybutyrate and converted into two acetyl-CoA molecules via 4-hydroxybutyryl-CoA dehydratase. The key genes of this pathway were found not only in Metallosphaera but also in Sulfolobus, Archaeoglobus, and Cenarchaeum species. Moreover, the Global Ocean Sampling database contains half as many 4-hydroxybutyryl-CoA dehydratase sequences as compared with those found for another key photosynthetic CO2-fixing enzyme, ribulose-1,5-bisphosphate carboxylase-oxygenase. This indicates the importance of this enzyme in global carbon cycling.

A global carbon assimilation system based on a dual optimization method

NASA Astrophysics Data System (ADS)

Zheng, H.; Li, Y.; Chen, J. M.; Wang, T.; Huang, Q.; Huang, W. X.; Wang, L. H.; Li, S. M.; Yuan, W. P.; Zheng, X.; Zhang, S. P.; Chen, Z. Q.; Jiang, F.

2015-02-01

Ecological models are effective tools for simulating the distribution of global carbon sources and sinks. However, these models often suffer from substantial biases due to inaccurate simulations of complex ecological processes. We introduce a set of scaling factors (parameters) to an ecological model on the basis of plant functional type (PFT) and latitudes. A global carbon assimilation system (GCAS-DOM) is developed by employing a dual optimization method (DOM) to invert the time-dependent ecological model parameter state and the net carbon flux state simultaneously. We use GCAS-DOM to estimate the global distribution of the CO2 flux on 1° × 1° grid cells for the period from 2001 to 2007. Results show that land and ocean absorb -3.63 ± 0.50 and -1.82 ± 0.16 Pg C yr-1, respectively. North America, Europe and China contribute -0.98 ± 0.15, -0.42 ± 0.08 and -0.20 ± 0.29 Pg C yr-1, respectively. The uncertainties in the flux after optimization by GCAS-DOM have been remarkably reduced by more than 60%. Through parameter optimization, GCAS-DOM can provide improved estimates of the carbon flux for each PFT. Coniferous forest (-0.97 ± 0.27 Pg C yr-1) is the largest contributor to the global carbon sink. Fluxes of once-dominant deciduous forest generated by the Boreal Ecosystems Productivity Simulator (BEPS) are reduced to -0.78 ± 0.23 Pg C yr-1, the third largest carbon sink.

A critical review on the improvement of photosynthetic carbon assimilation in C3 plants using genetic engineering.

PubMed

Ruan, Cheng-Jiang; Shao, Hong-Bo; Teixeira da Silva, Jaime A

2012-03-01

Global warming is one of the most serious challenges facing us today. It may be linked to the increase in atmospheric CO2 and other greenhouse gases (GHGs), leading to a rise in sea level, notable shifts in ecosystems, and in the frequency and intensity of wild fires. There is a strong interest in stabilizing the atmospheric concentration of CO2 and other GHGs by decreasing carbon emission and/or increasing carbon sequestration. Biotic sequestration is an important and effective strategy to mitigate the effects of rising atmospheric CO2 concentrations by increasing carbon sequestration and storage capacity of ecosystems using plant photosynthesis and by decreasing carbon emission using biofuel rather than fossil fuel. Improvement of photosynthetic carbon assimilation, using transgenic engineering, potentially provides a set of available and effective tools for enhancing plant carbon sequestration. In this review, firstly different biological methods of CO2 assimilation in C3, C4 and CAM plants are introduced and three types of C4 pathways which have high photosynthetic performance and have evolved as CO2 pumps are briefly summarized. Then (i) the improvement of photosynthetic carbon assimilation of C3 plants by transgenic engineering using non-C4 genes, and (ii) the overexpression of individual or multiple C4 cycle photosynthetic genes (PEPC, PPDK, PCK, NADP-ME and NADP-MDH) in transgenic C3 plants (e.g. tobacco, potato, rice and Arabidopsis) are highlighted. Some transgenic C3 plants (e.g. tobacco, rice and Arabidopsis) overexpressing the FBP/SBPase, ictB and cytochrome c6 genes showed positive effects on photosynthetic efficiency and growth characteristics. However, over the last 28 years, efforts to overexpress individual, double or multiple C4 enzymes in C3 plants like tobacco, potato, rice, and Arabidopsis have produced mixed results that do not confirm or eliminate the possibility of improving photosynthesis of C3 plants by this approach. Finally, a prospect is provided on the challenges of enhancing carbon assimilation of C3 plants using transgenic engineering in the face of global warming, and the trends of the most promising approaches to improving the photosynthetic performance of C3 plants.

JPL/USC GAIM: Using COSMIC Occultations in a Real-Time Global Ionospheric Data Assimilation Model

NASA Astrophysics Data System (ADS)

Mandrake, L.; Komjathy, A.; Wilson, B. D.; Pi, X.; Hajj, G.; Iijima, B.; Wang, C.

2006-12-01

We are in the midst of a revolution in ionospheric remote sensing driven by the illuminating powers of ground and space-based GPS receivers, new UV remote sensing satellites, and the advent of data assimilation techniques for space weather. In particular, the COSMIC 6-satellite constellation launched in April 2006. COSMIC will provide unprecedented global coverage of GPS occultations (~5000 per day), each of which yields electron density information with unprecedented ~1 km vertical resolution. Calibrated measurements of ionospheric delay (total electron content or TEC) suitable for input into assimilation models will be available in near real-time (NRT) from the COSMIC project with a latency of 30 to 120 minutes. Similarly, NRT TEC data are available from two worldwide NRT networks of ground GPS receivers (~75 5-minute sites and ~125 more hourly sites, operated by JPL and others). The combined NRT ground and space-based GPS datasets provide a new opportunity to more accurately specify the 3-dimensional ionospheric density with a time lag of only 15 to 120 minutes. With the addition of the vertically-resolved NRT occultation data, the retrieved profile shapes will model the hour-to-hour ionospheric "weather" much more accurately. The University of Southern California (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed a real-time Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for DoD customers and NASA flight projects. JPL/USC GAIM is a physics- based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion & electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. Daily (delayed) GAIM runs can accept as input ground GPS TEC data from 1000+ sites, occultation links from CHAMP, SAC-C, and the COSMIC constellation, UV limb and nadir scans from the TIMED and DMSP satellites, and in situ data from a variety of satellites (DMSP and C/NOFS). RTGAIM ingests multiple data sources in real time, updates the 3D electron density grid every 5 minutes, and solves for improved drivers every 1-2 hours. Since our forward physics model and the adjoint model were expressly designed for data assimilation and computational efficiency, all of this can be accomplished on a single dual-processor Unix workstation. Customers are currently evaluating the accuracy of JPL/USC GAIM "nowcasts" for ray tracing applications and trans-ionospheric path delay calibration. In the talk, we will discuss the expected impact of COSMIC occultation data; show first results for ingest of COSMIC data using the GAIM Kalman filter; present validation of the GAIM electron density grid by comparisons to Abel profiles and independent datasets; describe recent improvements to the JPL/USC GAIM model; and describe our plans for NRT ingest of COSMIC data into RTGAIM.

Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

NASA Astrophysics Data System (ADS)

Aberson, Sim David

In 1997, the National Hurricane Center and the Hurricane Research Division began conducting operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve operational forecast models. During the first two years, twenty-four missions were conducted around tropical cyclones threatening the continental United States, Puerto Rico, and the Virgin Islands. Global Positioning System dropwindsondes were released from the aircraft at 150--200 km intervals along the flight track in the tropical cyclone environment to obtain wind, temperature, and humidity profiles from flight level (around 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and transmitted to the National Centers for Environmental Prediction (NCEP). There, they were ingested into the Global Data Assimilation System that subsequently provides initial and time-dependent boundary conditions for numerical models that forecast tropical cyclone track and intensity. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Neither the forecasts from the Aviation run of the Global Spectral Model nor the shallow-water VICBAR model were improved with the assimilation of the dropwindsonde data. This mediocre result is found to be due mainly to the difficulty in operationally quantifying the storm-motion vector used to create accurate synthetic data to represent the tropical cyclone vortex in the models. A secondary limit on forecast improvements from the surveillance missions is the limited amount of data provided by the one surveillance aircraft in regular missions. The inability of some surveillance missions to surround the tropical cyclone with dropwindsonde observations is a possible third limit, though the results are inconclusive. Due to limited aircraft resources, optimal observing strategies for these missions must be developed. Since observations in areas of decaying error modes are unlikely to have large impact on subsequent forecasts, such strategies should be based on taking observations in those geographic locations corresponding to the most rapidly growing error modes in the numerical models and on known deficiencies in current data assimilation systems. Here, the most rapidly growing modes are represented by areas of large forecast spread in the NCEP bred-mode global ensemble forecasting system. The sampling strategy requires sampling the entire target region at approximately the same resolution as the North American rawinsonde network to limit the possibly spurious spread of information from dropwindsonde observations into data-sparse regions where errors are likely to grow. When only the subset of data in these fully-sampled target regions is assimilated into the numerical models, statistically significant reduction of the track forecast errors of up to 25% within the critical first two days of the forecast are seen. These model improvements are comparable with the cumulative business-as-usual track forecast model improvements expected over eighteen years.

Validation of a Global Ionospheric Data Assimilation Model

NASA Astrophysics Data System (ADS)

Wilson, B.; Hajj, G.; Wang, C.; Pi, X.; Rosen, I.

2003-04-01

As the number of ground and space-based receivers tracking the global positioning system (GPS) steadily increases, and the quantity of other ionospheric remote sensing data such as measurements of airglow also increases, it is becoming possible to monitor changes in the ionosphere continuously and on a global scale with unprecedented accuracy and reliability. However, in order to make effective use of such a large volume of data for both ionospheric specification and forecast, it is important to develop a data- driven ionospheric model that is consistent with the underlying physical principles governing ionosphere dynamics. A fully 3-dimensional Global Assimilative Ionosphere Model (GAIM) is currently being developed by a joint University of Southern California and Jet Propulsion Laboratory team. GAIM uses a first-principles ionospheric physics model (“forward” model) and Kalman filtering and 4DVAR techniques to not only solve for densities on a 3D grid but also estimate key driving forces which are inputs to the theoretical model, such as the ExB drift, neutral wind, and production terms. The driving forces are estimated using an “adjoint equation” to compute the required partial derivatives, thereby greatly reducing the computational demands compared to other techniques. For estimation of the grid densities, GAIM uses an approximate Kalman filter implementation in which the portions of the covariance matrix that are retained (the off-diagonal elements) are determined by assumed but physical correlation lengths in the ionosphere. By selecting how sparse or full the covariance matrix is over repeated Kalman filter runs, one can fully investigate the tradeoff between estimation accuracy and computational speed. Although GAIM will ultimately use multiple datatypes and many data sources, we have performed a first study of quantitative accuracy by ingesting GPS-derived TEC observations from ground and space-based receivers and nighttime UV radiance data from the LORAAS limb scanner on ARGOS, and then comparing the retrieved density field to independent ionospheric observations. A series of such GAIM retrievals will be presented and validated by comparisons to: vertical TEC data from the TOPEX altimeter, slant TEC data from ground GPS sites that were not included in the assimilation runs, and global ionosonde data (F0F2, HMF2, and bottom-side profiles where available). By presenting animated movies of the GAIM densities and vertical TEC maps, and their errors computed as differences from the independent observations, we will demonstrate the reasonableness and physicality of the climatology derived from the GAIM forward model, examine the consistency of the GPS and UV datatypes, and characterize the quantitative accuracy of the ionospheric “weather” specification provided by the assimilation retrievals.

OCO-2 Column Carbon Dioxide and Biometric Data Jointly Constrain Parameterization and Projection of a Global Land Model

NASA Astrophysics Data System (ADS)

Shi, Z.; Crowell, S.; Luo, Y.; Rayner, P. J.; Moore, B., III

2015-12-01

Uncertainty in predicted carbon-climate feedback largely stems from poor parameterization of global land models. However, calibration of global land models with observations has been extremely challenging at least for two reasons. First we lack global data products from systematical measurements of land surface processes. Second, computational demand is insurmountable for estimation of model parameter due to complexity of global land models. In this project, we will use OCO-2 retrievals of dry air mole fraction XCO2 and solar induced fluorescence (SIF) to independently constrain estimation of net ecosystem exchange (NEE) and gross primary production (GPP). The constrained NEE and GPP will be combined with data products of global standing biomass, soil organic carbon and soil respiration to improve the community land model version 4.5 (CLM4.5). Specifically, we will first develop a high fidelity emulator of CLM4.5 according to the matrix representation of the terrestrial carbon cycle. It has been shown that the emulator fully represents the original model and can be effectively used for data assimilation to constrain parameter estimation. We will focus on calibrating those key model parameters (e.g., maximum carboxylation rate, turnover time and transfer coefficients of soil carbon pools, and temperature sensitivity of respiration) for carbon cycle. The Bayesian Markov chain Monte Carlo method (MCMC) will be used to assimilate the global databases into the high fidelity emulator to constrain the model parameters, which will be incorporated back to the original CLM4.5. The calibrated CLM4.5 will be used to make scenario-based projections. In addition, we will conduct observing system simulation experiments (OSSEs) to evaluate how the sampling frequency and length could affect the model constraining and prediction.

Improving Reanalyses Using TRMM and SSM/I-Derived Precipitation and Total Precipitable Water Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.

1999-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of rainfall and total precipitable water (TPW) observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve these fields in reanalyses. The DAO has developed a "1+1"D procedure to assimilate 6-hr averaged rainfall and TPW into the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS. The "1+1" designation refers to one spatial dimension plus one temporal dimension. The scheme minimizes the least-square differences between the satellite-retrieved rain rates and those produced by the column model over the 6-hr analysis window. The control variables are analysis increments of moisture within the Incremental Analysis Update (IAU) framework of the GEOS DAS. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but differs in its choice of the control variable. Instead of estimating the initial condition at the beginning of the assimilation cycle, it estimates the constant IAU forcing applied over a 6-hr assimilation cycle. In doing so, it imposes the forecast model as a weak constraint in a manner similar to the variational continuous assimilation techniques. We present results from an experiment in which the observed rain rate and TPW are assumed to be "perfect". They show that assimilating the TMI and SSM/I-derived surface precipitation and TPW observations improves not only the precipitation and moisture fields but also key climate parameters directly linked to convective activities such as clouds, the outgoing longwave radiation, and the large-scale circulation in the tropics. In particular, assimilating these data types reduce the state-dependent systematic errors in the assimilated products. The improved analysis also leads to a better short-range forecast, but the impact is modest compared with improvements in the time-averaged fields. These results suggest that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged "climate content" in the assimilated data without comparable improvements in the short-range forecast skill. Results of this experiment provide a useful benchmark for evaluating error covariance models for optimal use of these data types.

Interannual Variation in Phytoplankton Class-specific Primary Production at a Global Scale

NASA Technical Reports Server (NTRS)

Rousseaux, Cecile; Gregg, Watson

2014-01-01

Phytoplankton is responsible for over half of the net primary production on earth. The knowledge on the contribution of various phytoplankton groups to the total primary production is still poorly understood. Data from satellite observations suggest that for upwelling regions, photosynthetic rates by microplankton is higher than that of nanoplankton but that when the spatial extent is considered, the production by nanoplankton is comparable or even larger than microplankton. Here, we used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of 4 phytoplankton groups to the total primary production. Globally, diatoms were the group that contributed the most to the total phytoplankton production (approx. 50%) followed by coccolithophores and chlorophytes. Primary production by diatoms was highest in high latitude (>45 deg) and in major upwelling systems (Equatorial Pacific and Benguela system). We assessed the effects of climate variability on the class-specific primary production using global (i.e. Multivariate El Nino Index, MEI) and 'regional' climate indices (e.g. Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

The Tropical Rainfall Measuring (TRMM) - What Have We Learned and What Does the Future Hold?

NASA Technical Reports Server (NTRS)

Kummerow, C.; Hong, Y.; Olsen, W. S.

2000-01-01

Rainfall is important in the hydrological cycle and to the lives and welfare of humans. In addition to being a life-giving resource, rainfall processes also plays a crucial role in the dynamics of the global atmospheric circulation. Three-fourths of the energy that drives the atmospheric wind circulation comes from the latent heat released by tropical precipitation. It varies greatly in space and time. The rain-producing cloud systems may last several hours or days. Their dimensions range from 10 km to several hundred km. This makes it difficult to incorporate rainfall directly large-scale weather and climate models. Until the end of 1997, precipitation in the global tropics was not known to within a factor of two. Regarding "global warming", the various large-scale models differed among themselves in the predicted magnitude of the warming and in the expected regional effects of these temperature and moisture changes. The Tropical Rainfall Measuring Mission (TRMM) satellite has yielded important interim results related to rainfall observations, data assimilation and model forecast skills when rainfall data is assimilated. This talk will summarize where the TRMM science team is with regards to answering some of these important scientific challenges, as well as discuss the future Global Precipitation Mission which will provide 3 hourly rainfall coverage and offers some unique collaborative potential for NOAA and NASA.

Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals

NASA Astrophysics Data System (ADS)

Bolten, J.; Crow, W.; Zhan, X.; Reynolds, C.

2008-08-01

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is 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 Model Configuration Collaborators Documentation and Code FAQ Operational Change Log Parallel Experiment

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.