A FAST BAYESIAN METHOD FOR UPDATING AND FORECASTING HOURLY OZONE LEVELS

EPA Science Inventory

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows...

Economic Models for Projecting Industrial Capacity for Defense Production: A Review

DTIC Science & Technology

1983-02-01

macroeconomic forecast to establish the level of civilian final demand; all use the DoD Bridge Table to allocate budget category outlays to industries. Civilian...output table.’ 3. Macroeconomic Assumptions and the Prediction of Final Demand All input-output models require as a starting point a prediction of final... macroeconomic fore- cast of GNP and its components and (2) a methodology to transform these forecast values of consumption, investment, exports, etc. into

Integrating predictive information into an agro-economic model to guide agricultural management

NASA Astrophysics Data System (ADS)

Zhang, Y.; Block, P.

2016-12-01

Skillful season-ahead climate predictions linked with responsive agricultural planning and management have the potential to reduce losses, if adopted by farmers, particularly for rainfed-dominated agriculture such as in Ethiopia. Precipitation predictions during the growing season in major agricultural regions of Ethiopia are used to generate predicted climate yield factors, which reflect the influence of precipitation amounts on crop yields and serve as inputs into an agro-economic model. The adapted model, originally developed by the International Food Policy Research Institute, produces outputs of economic indices (GDP, poverty rates, etc.) at zonal and national levels. Forecast-based approaches, in which farmers' actions are in response to forecasted conditions, are compared with no-forecast approaches in which farmers follow business as usual practices, expecting "average" climate conditions. The effects of farmer adoption rates, including the potential for reduced uptake due to poor predictions, and increasing forecast lead-time on economic outputs are also explored. Preliminary results indicate superior gains under forecast-based approaches.

Configuring a Graphical User Interface for Managing Local HYSPLIT Model Runs Through AWIPS

NASA Technical Reports Server (NTRS)

Wheeler, mark M.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; VanSpeybroeck, Kurt M.

2009-01-01

Responding to incidents involving the release of harmful airborne pollutants is a continual challenge for Weather Forecast Offices in the National Weather Service. When such incidents occur, current protocol recommends forecaster-initiated requests of NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model output through the National Centers of Environmental Prediction to obtain critical dispersion guidance. Individual requests are submitted manually through a secured web site, with desired multiple requests submitted in sequence, for the purpose of obtaining useful trajectory and concentration forecasts associated with the significant release of harmful chemical gases, radiation, wildfire smoke, etc., into local the atmosphere. To help manage the local HYSPLIT for both routine and emergency use, a graphical user interface was designed for operational efficiency. The interface allows forecasters to quickly determine the current HYSPLIT configuration for the list of predefined sites (e.g., fixed sites and floating sites), and to make any necessary adjustments to key parameters such as Input Model. Number of Forecast Hours, etc. When using the interface, forecasters will obtain desired output more confidently and without the danger of corrupting essential configuration files.

Three models intercomparison for Quantitative Precipitation Forecast over Calabria

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Lavagnini, A.; Accadia, C.; Mariani, S.; Casaioli, M.

2004-11-01

In the framework of the National Project “Sviluppo di distretti industriali per le Osservazioni della Terra” (Development of Industrial Districts for Earth Observations) funded by MIUR (Ministero dell'Università e della Ricerca Scientifica --Italian Ministry of the University and Scientific Research) two operational mesoscale models were set-up for Calabria, the southernmost tip of the Italian peninsula. Models are RAMS (Regional Atmospheric Modeling System) and MM5 (Mesoscale Modeling 5) that are run every day at Crati scrl to produce weather forecast over Calabria (http://www.crati.it). This paper reports model intercomparison for Quantitative Precipitation Forecast evaluated for a 20 month period from 1th October 2000 to 31th May 2002. In addition to RAMS and MM5 outputs, QBOLAM rainfall fields are available for the period selected and included in the comparison. This model runs operationally at “Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici”. Forecasts are verified comparing models outputs with raingauge data recorded by the regional meteorological network, which has 75 raingauges. Large-scale forcing is the same for all models considered and differences are due to physical/numerical parameterizations and horizontal resolutions. QPFs show differences between models. Largest differences are for BIA compared to the other considered scores. Performances decrease with increasing forecast time for RAMS and MM5, whilst QBOLAM scores better for second day forecast.

A comparative verification of high resolution precipitation forecasts using model output statistics

NASA Astrophysics Data System (ADS)

van der Plas, Emiel; Schmeits, Maurice; Hooijman, Nicolien; Kok, Kees

2017-04-01

Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution meso-scale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this study a verification strategy based on model output statistics is applied that aims to address both double penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis and lead time. The ELR equations are derived for predictands based on areal calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the non-hydrostatic model Harmonie (2.5 km resolution), Hirlam (11 km resolution) and the ECMWF model (16 km resolution), overall yielding similar Brier skill scores for the 3 post-processed models, but larger differences for individual lead times. Besides, the Fractions Skill Score is computed using the 3 deterministic forecasts, showing somewhat better skill for the Harmonie model. In other words, despite the realism of Harmonie precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the 2 lower resolution models, at least in the Netherlands.

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.

How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

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

FAA Aviation Forecast Conference Proceedings (16th)

DTIC Science & Technology

1991-02-01

FORECASTS The FAA forecasting process is a continuous one which involves FAA Forecast Branch’s interaction with various FAA Offices and Services... process uses various economic and aviation data bases, the outputs of several econometric models and equations, and other analytical techniques. The FAA...workload measures, summarized numerically in the table on page 8, are the resultant forecasts of this process and are used annually by the agency for

Value of Forecaster in the Loop

DTIC Science & Technology

2014-09-01

forecast system IFR instrument flight rules IMC instrument meteorological conditions LAMP Localized Aviation Model Output Statistics Program METOC...obtaining valuable experience. Additional factors have impacted the Navy weather forecast process. There has been a the realignment of the meteorology...forecasts that are assessed, it may be a relatively small number that have direct impact on the decision-making process. Whether the value is minimal or

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2017-04-01

Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes distance, stream-connectivity and size of the catchment areas into account. This can in some cases have a slight negative impact on the calibration error, but avoids large differences between parameters of nearby locations, whether stream connected or not. The spatial calibration also makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-04-01

Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative impact on the calibration error, but makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005.

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

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

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Cardiac surgery productivity and throughput improvements.

PubMed

Lehtonen, Juha-Matti; Kujala, Jaakko; Kouri, Juhani; Hippeläinen, Mikko

2007-01-01

The high variability in cardiac surgery length--is one of the main challenges for staff managing productivity. This study aims to evaluate the impact of six interventions on open-heart surgery operating theatre productivity. A discrete operating theatre event simulation model with empirical operation time input data from 2603 patients is used to evaluate the effect that these process interventions have on the surgery output and overtime work. A linear regression model was used to get operation time forecasts for surgery scheduling while it also could be used to explain operation time. A forecasting model based on the linear regression of variables available before the surgery explains 46 per cent operating time variance. The main factors influencing operation length were type of operation, redoing the operation and the head surgeon. Reduction of changeover time between surgeries by inducing anaesthesia outside an operating theatre and by reducing slack time at the end of day after a second surgery have the strongest effects on surgery output and productivity. A more accurate operation time forecast did not have any effect on output, although improved operation time forecast did decrease overtime work. A reduction in the operation time itself is not studied in this article. However, the forecasting model can also be applied to discover which factors are most significant in explaining variation in the length of open-heart surgery. The challenge in scheduling two open-heart surgeries in one day can be partly resolved by increasing the length of the day, decreasing the time between two surgeries or by improving patient scheduling procedures so that two short surgeries can be paired. A linear regression model is created in the paper to increase the accuracy of operation time forecasting and to identify factors that have the most influence on operation time. A simulation model is used to analyse the impact of improved surgical length forecasting and five selected process interventions on productivity in cardiac surgery.

Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

An application of a multi model approach for solar energy prediction in Southern Italy

NASA Astrophysics Data System (ADS)

Avolio, Elenio; Lo Feudo, Teresa; Calidonna, Claudia Roberta; Contini, Daniele; Torcasio, Rosa Claudia; Tiriolo, Luca; Montesanti, Stefania; Transerici, Claudio; Federico, Stefano

2015-04-01

The accuracy of the short and medium range forecast of solar irradiance is very important for solar energy integration into the grid. This issue is particularly important for Southern Italy where a significant availability of solar energy is associated with a poor development of the grid. In this work we analyse the performance of two deterministic models for the prediction of surface temperature and short-wavelength radiance for two sites in southern Italy. Both parameters are needed to forecast the power production from solar power plants, so the performance of the forecast for these meteorological parameters is of paramount importance. The models considered in this work are the RAMS (Regional Atmospheric Modeling System) and the WRF (Weather Research and Forecasting Model) and they were run for the summer 2013 at 4 km horizontal resolution over Italy. The forecast lasts three days. Initial and dynamic boundary conditions are given by the 12 UTC deterministic forecast of the ECMWF-IFS (European Centre for Medium Weather Range Forecast - Integrated Forecasting System) model, and were available every 6 hours. Verification is given against two surface stations located in Southern Italy, Lamezia Terme and Lecce, and are based on hourly output of models forecast. Results for the whole period for temperature show a positive bias for the RAMS model and a negative bias for the WRF model. RMSE is between 1 and 2 °C for both models. Results for the whole period for the short-wavelength radiance show a positive bias for both models (about 30 W/m2 for both models) and a RMSE of 100 W/m2. To reduce the model errors, a statistical post-processing technique, i.e the multi-model, is adopted. In this approach the two model's outputs are weighted with an adequate set of weights computed for a training period. In general, the performance is improved by the application of the technique, and the RMSE is reduced by a sizeable fraction (i.e. larger than 10% of the initial RMSE) depending on the forecasting time and parameter. The performance of the multi model is discussed as a function of the length of the training period and is compared with the performance of the MOS (Model Output Statistics) approach. ACKNOWLEDGMENTS This work is partially supported by projects PON04a2E Sinergreen-ResNovae - "Smart Energy Master for the energetic government of the territory" and PONa3_00363 "High Technology Infrastructure for Climate and Environment Monitoring" (I-AMICA) founded by Italian Ministry of University and Research (MIUR) PON 2007-2013. The ECMWF and CNMCA (Centro Nazionale di Meteorologia e Climatologia Aeronautica) are acknowledged for the use of the MARS (Meteorological Archive and Retrieval System).

Use of High-Resolution WRF Simulations to Forecast Lightning Threat

NASA Technical Reports Server (NTRS)

McCaul, E. W., Jr.; LaCasse, K.; Goodman, S. J.; Cecil, D. J.

2008-01-01

Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors aloft in storms. This relationship is exploited, in conjunction with the capabilities of cloud-resolving forecast models such as WRF, to forecast explicitly the threat of lightning from convective storms using selected output fields from the model forecasts. The simulated vertical flux of graupel at -15C and the shape of the simulated reflectivity profile are tested in this study as proxies for charge separation processes and their associated lightning risk. Our lightning forecast method differs from others in that it is entirely based on high-resolution simulation output, without reliance on any climatological data. short [6-8 h) simulations are conducted for a number of case studies for which three-dmmensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity fields, and METAR and ACARS data y&eld satisfactory simulations. __nalyses of the lightning threat fields suggests that both the graupel flux and reflectivity profile approaches, when properly calibrated, can yield reasonable lightning threat forecasts, although an ensemble approach is probably desirable in order to reduce the tendency for misplacement of modeled storms to hurt the accuracy of the forecasts. Our lightning threat forecasts are also compared to other more traditional means of forecasting thunderstorms, such as those based on inspection of the convective available potential energy field.

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Lin; Gupta, Hoshin V.; Gao, Xiaogang; Sorooshian, Soroosh; Imam, Bisher

2002-12-01

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

A perturbative approach for enhancing the performance of time series forecasting.

PubMed

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications products of aviation forecast models

NASA Technical Reports Server (NTRS)

Garthner, John P.

1988-01-01

A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

NASA Technical Reports Server (NTRS)

Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire

2008-01-01

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

Forecasting Global Rainfall for Points Using ECMWF's Global Ensemble and Its Applications in Flood Forecasting

NASA Astrophysics Data System (ADS)

Pillosu, F. M.; Hewson, T.; Mazzetti, C.

2017-12-01

Prediction of local extreme rainfall has historically been the remit of nowcasting and high resolution limited area modelling, which represent only limited areas, may not be spatially accurate, give reasonable results only for limited lead times (<2 days) and become prohibitively expensive at global scale. ECMWF/EFAS/GLOFAS have developed a novel, cost-effective and physically-based statistical post-processing software ("ecPoint-Rainfall, ecPR", operational in 2017) that uses ECMWF Ensemble (ENS) output to deliver global probabilistic rainfall forecasts for points up to day 10. Firstly, ecPR applies a new notion of "remote calibration", which 1) allows us to replicate a multi-centennial training period using only one year of data, and 2) provides forecasts for anywhere in the world. Secondly, the software applies an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals, and of where biases in the model can be improved upon. A long-term verification has shown that the post-processed rainfall has better reliability and resolution at every lead time if compared with ENS, and for large totals, ecPR outputs have the same skill at day 5 that the raw ENS has at day 1 (ROC area metric). ecPR could be used as input for hydrological models if its probabilistic output is modified accordingly to the inputs requirements for hydrological models. Indeed, ecPR does not provide information on where the highest total is likely to occur inside the gridbox, nor on the spatial distribution of rainfall values nearby. "Scenario forecasts" could be a solution. They are derived from locating the rainfall peak in sensitive positions (e.g. urban areas), and then redistributing the remaining quantities in the gridbox modifying traditional spatial correlation characterization methodologies (e.g. variogram analysis) in order to take account, for instance, of the type of rainfall forecast (stratiform, convective). Such an approach could be a turning point in the field of medium-range global real-time riverine flood forecasts. This presentation will illustrate for ecPR 1) system calibration, 2) operational implementation, 3) long-term verification, 4) future developments, and 5) early ideas for the application of ecPR outputs in hydrological models.

Influence of Forecast Accuracy of Photovoltaic Power Output on Capacity Optimization of Microgrid Composition under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Sone, Akihito; Kato, Takeyoshi; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). If a number of MGs are controlled to maintain the predetermined electricity demand including RE-based DGs as negative demand, they would contribute to supply-demand balancing of whole electric power system. For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on a demonstrative study on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization. Three forecast cases with different accuracy are compared. The main results are as follows. Even with no forecast error during every 30 min. as the ideal forecast method, the required capacity of NaS battery reaches about 40% of PVS capacity for mitigating the instantaneous forecast error within 30 min. The required capacity to compensate for the forecast error is doubled with the actual forecast method. The influence of forecast error can be reduced by adjusting the scheduled power output of controllable DGs according to the weather forecast. Besides, the required capacity can be reduced significantly if the error of balancing control in a MG is acceptable for a few percentages of periods, because the total periods of large forecast error is not so often.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

DOE PAGES

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia; ...

2016-01-01

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

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

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.

Draft Forecasts from Real-Time Runs of Physics-Based Models - A Road to the Future

NASA Technical Reports Server (NTRS)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2008-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. After consultations with NOAA/SEC and with AFWA, CCMC has developed a set of tools as a first step to make real-time model output useful to forecast centers. In this presentation, we will discuss the motivation for this activity, the actions taken so far, and options for future tools from model output.

Aviation Terminal Forecasts

Science.gov Websites

News of the Day... view past news Central Pacific Hurricane Season Outlook for 2018 2017-18 Hawaii Wet Local Graphics National Graphics Model Output River and Lakes Climate and Past Weather Local National Model Output Climate and Past Weather Local National More... Hawaii Climate Portal Local Programs

Identifying, Tracking, and Prioritizing Parts Unavailability

DTIC Science & Technology

2013-03-01

of RFM with MRP Equivalents in Parentheses ................ 13 Figure 2. Flow Chart of Repair Process...by the Air Force is the Reparability Forecast Model (RFM). The RFM operates similar to a Material Requirements Planning ( MRP ) system, where it...Outputs of RFM with MRP Equivalents in Parentheses (Gaudette et al, 2002: 7) The Reparability Forecast Model does not determine stock levels or

Forecasting timber, biomass, and tree carbon pools with the output of state and transition models

Treesearch

Xiaoping Zhou; Miles A. Hemstrom

2012-01-01

The Integrated Landscape Assessment Project (ILAP) uses spatial vegetation data and state and transition models (STM) to forecast future vegetation conditions and the interacting effects of natural disturbances and management activities. Results from ILAP will help land managers, planners, and policymakers evaluate management strategies that reduce fire risk, improve...

A Comparison of the Forecast Skills among Three Numerical Models

NASA Astrophysics Data System (ADS)

Lu, D.; Reddy, S. R.; White, L. J.

2003-12-01

Three numerical weather forecast models, MM5, COAMPS and WRF, operating with a joint effort of NOAA HU-NCAS and Jackson State University (JSU) during summer 2003 have been chosen to study their forecast skills against observations. The models forecast over the same region with the same initialization, boundary condition, forecast length and spatial resolution. AVN global dataset have been ingested as initial conditions. Grib resolution of 27 km is chosen to represent the current mesoscale model. The forecasts with the length of 36h are performed to output the result with 12h interval. The key parameters used to evaluate the forecast skill include 12h accumulated precipitation, sea level pressure, wind, surface temperature and dew point. Precipitation is evaluated statistically using conventional skill scores, Threat Score (TS) and Bias Score (BS), for different threshold values based on 12h rainfall observations whereas other statistical methods such as Mean Error (ME), Mean Absolute Error(MAE) and Root Mean Square Error (RMSE) are applied to other forecast parameters.

Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

2015-12-01

Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

Hybrid Stochastic Forecasting Model for Management of Large Open Water Reservoir with Storage Function

NASA Astrophysics Data System (ADS)

Kozel, Tomas; Stary, Milos

2017-12-01

The main advantage of stochastic forecasting is fan of possible value whose deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. Discharge in measurement profile could be categorized as random process. Content of article is construction and application of forecasting model for managed large open water reservoir with supply function. Model is based on neural networks (NS) and zone models, which forecasting values of average monthly flow from inputs values of average monthly flow, learned neural network and random numbers. Part of data was sorted to one moving zone. The zone is created around last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to zone. The model was compiled for forecast of 1 to 12 month with using backward month flows (NS inputs) from 2 to 11 months for model construction. Data was got ridded of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. The data were with monthly step and forecast is not recurring. 90 years long real flow series was used for compile of the model. First 75 years were used for calibration of model (matrix input-output relationship), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, was used application to management of artificially made reservoir. Course of water reservoir management using Genetic algorithm (GE) + real flow series was compared with Fuzzy model (Fuzzy) + forecast made by Moving zone model. During evaluation process was founding the best size of zone. Results show that the highest number of input did not give the best results and ideal size of zone is in interval from 25 to 35, when course of management was almost same for all numbers from interval. Resulted course of management was compared with course, which was obtained from using GE + real flow series. Comparing results showed that fuzzy model with forecasted values has been able to manage main malfunction and artificially disorders made by model were founded essential, after values of water volume during management were evaluated. Forecasting model in combination with fuzzy model provide very good results in management of water reservoir with storage function and can be recommended for this purpose.

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Gallego, C.; Costa, A.; Cuerva, A.

2010-09-01

Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single-ANN model (without regime classification) is adopted as a reference model. Both models are evaluated in terms of Improvement over Persistence on the Mean Square Error basis (IoP%) when predicting horizons form 1 time-step to 5. The case of a wind farm located in the complex terrain of Alaiz (north of Spain) has been considered. Three years of available power output data with a hourly resolution have been employed: two years for training and validation of the model and the last year for assessing the accuracy. Results showed that the RS-ANN overcame the single-ANN model for one step-ahead forecasts: the overall IoP% was up to 8.66% for the RS-ANN model (depending on the gradient criterion selected to consider the ramp regime triggered) and 6.16% for the single-ANN. However, both models showed similar accuracy for larger horizons. A locally-weighted evaluation during ramp events for one-step ahead was also performed. It was found that the IoP% during ramps-up increased from 17.60% (case of single-ANN) to 22.25% (case of RS-ANN); however, during the ramps-down events this improvement increased from 18.55% to 19.55%. Three main conclusions are derived from this case study: It highlights the importance of considering statistical models capable of differentiate several regimes showed by the output power time series in order to improve the forecasting during extreme events like ramps. On-line regime classification based on available power output data didn't seem to contribute to improve forecasts for horizons beyond one-step ahead. Tacking into account other explanatory variables (local wind measurements, NWP outputs) could lead to a better understanding of ramp events, improving the regime assessment also for further horizons. The RS-ANN model slightly overcame the single-ANN during ramp-down events. If further research reinforce this effect, special attention should be addressed to understand the underlying processes during ramp-down events.

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn; Watson, Leela R.

2015-01-01

NASA's Launch Services Program, Ground Systems Development and Operations, Space Launch System and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). Examples include determining if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 km Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the high-resolution WRF Environmental Modeling System (EMS) model configured by the AMU (Watson 2013) in real time. Implementing a real-time version of the ER WRF-EMS would generate a larger database of model output than in the previous AMU task for determining model performance, and allows the AMU more control over and access to the model output archive. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The AMU also calculated verification statistics to determine model performance compared to observational data. Finally, the AMU made the model output available on the AMU Advanced Weather Interactive Processing System II (AWIPS II) servers, which allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations (RWO) AWIPS II client computers and conduct real-time subjective analyses.

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

NASA Astrophysics Data System (ADS)

Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

2018-03-01

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

An Operational Short-Term Forecasting System for Regional Hydropower Management

NASA Astrophysics Data System (ADS)

Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

2017-12-01

The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

Space market model space industry input-output model

NASA Technical Reports Server (NTRS)

Hodgin, Robert F.; Marchesini, Roberto

1987-01-01

The goal of the Space Market Model (SMM) is to develop an information resource for the space industry. The SMM is intended to contain information appropriate for decision making in the space industry. The objectives of the SMM are to: (1) assemble information related to the development of the space business; (2) construct an adequate description of the emerging space market; (3) disseminate the information on the space market to forecasts and planners in government agencies and private corporations; and (4) provide timely analyses and forecasts of critical elements of the space market. An Input-Output model of market activity is proposed which are capable of transforming raw data into useful information for decision makers and policy makers dealing with the space sector.

Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2014-05-01

When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a daily basis over a period of three years. For the two catchments considered, this resulted in well calibrated and sharp forecast distributions over all lead-times from 1 to 114 h. Training observations tended to be better indicators for the dependence structure than the raw ensemble.

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

NASA Technical Reports Server (NTRS)

Dreher, Joseph G.

2009-01-01

For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

Investigating Anomalies in the Output Generated by the Weather Research and Forecasting (WRF) Model

NASA Astrophysics Data System (ADS)

Decicco, Nicholas; Trout, Joseph; Manson, J. Russell; Rios, Manny; King, David

2015-04-01

The Weather Research and Forecasting (WRF) model is an advanced mesoscale numerical weather prediction (NWP) model comprised of two numerical cores, the Numerical Mesoscale Modeling (NMM) core, and the Advanced Research WRF (ARW) core. An investigation was done to determine the source of erroneous output generated by the NMM core. In particular were the appearance of zero values at regularly spaced grid cells in output fields and the NMM core's evident (mis)use of static geographic information at a resolution lower than the nesting level for which the core is performing computation. A brief discussion of the high-level modular architecture of the model is presented as well as methods utilized to identify the cause of these problems. Presented here are the initial results from a research grant, ``A Pilot Project to Investigate Wake Vortex Patterns and Weather Patterns at the Atlantic City Airport by the Richard Stockton College of NJ and the FAA''.

A comparative analysis of errors in long-term econometric forecasts

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

Tepel, R.

1986-04-01

The growing body of literature that documents forecast accuracy falls generally into two parts. The first is prescriptive and is carried out by modelers who use simulation analysis as a tool for model improvement. These studies are ex post, that is, they make use of known values for exogenous variables and generate an error measure wholly attributable to the model. The second type of analysis is descriptive and seeks to measure errors, identify patterns among errors and variables and compare forecasts from different sources. Most descriptive studies use an ex ante approach, that is, they evaluate model outputs based onmore » estimated (or forecasted) exogenous variables. In this case, it is the forecasting process, rather than the model, that is under scrutiny. This paper uses an ex ante approach to measure errors in forecast series prepared by Data Resources Incorporated (DRI), Wharton Econometric Forecasting Associates (Wharton), and Chase Econometrics (Chase) and to determine if systematic patterns of errors can be discerned between services, types of variables (by degree of aggregation), length of forecast and time at which the forecast is made. Errors are measured as the percent difference between actual and forecasted values for the historical period of 1971 to 1983.« less

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

Emulation for probabilistic weather forecasting

NASA Astrophysics Data System (ADS)

Cornford, Dan; Barillec, Remi

2010-05-01

Numerical weather prediction models are typically very expensive to run due to their complexity and resolution. Characterising the sensitivity of the model to its initial condition and/or to its parameters requires numerous runs of the model, which is impractical for all but the simplest models. To produce probabilistic forecasts requires knowledge of the distribution of the model outputs, given the distribution over the inputs, where the inputs include the initial conditions, boundary conditions and model parameters. Such uncertainty analysis for complex weather prediction models seems a long way off, given current computing power, with ensembles providing only a partial answer. One possible way forward that we develop in this work is the use of statistical emulators. Emulators provide an efficient statistical approximation to the model (or simulator) while quantifying the uncertainty introduced. In the emulator framework, a Gaussian process is fitted to the simulator response as a function of the simulator inputs using some training data. The emulator is essentially an interpolator of the simulator output and the response in unobserved areas is dictated by the choice of covariance structure and parameters in the Gaussian process. Suitable parameters are inferred from the data in a maximum likelihood, or Bayesian framework. Once trained, the emulator allows operations such as sensitivity analysis or uncertainty analysis to be performed at a much lower computational cost. The efficiency of emulators can be further improved by exploiting the redundancy in the simulator output through appropriate dimension reduction techniques. We demonstrate this using both Principal Component Analysis on the model output and a new reduced-rank emulator in which an optimal linear projection operator is estimated jointly with other parameters, in the context of simple low order models, such as the Lorenz 40D system. We present the application of emulators to probabilistic weather forecasting, where the construction of the emulator training set replaces the traditional ensemble model runs. Thus the actual forecast distributions are computed using the emulator conditioned on the ‘ensemble runs' which are chosen to explore the plausible input space using relatively crude experimental design methods. One benefit here is that the ensemble does not need to be a sample from the true distribution of the input space, rather it should cover that input space in some sense. The probabilistic forecasts are computed using Monte Carlo methods sampling from the input distribution and using the emulator to produce the output distribution. Finally we discuss the limitations of this approach and briefly mention how we might use similar methods to learn the model error within a framework that incorporates a data assimilation like aspect, using emulators and learning complex model error representations. We suggest future directions for research in the area that will be necessary to apply the method to more realistic numerical weather prediction models.

Applications of Machine Learning to Downscaling and Verification

NASA Astrophysics Data System (ADS)

Prudden, R.

2017-12-01

Downscaling, sometimes known as super-resolution, means converting model data into a more detailed local forecast. It is a problem which could be highly amenable to machine learning approaches, provided that sufficient historical forecast data and observations are available. It is also closely linked to the subject of verification, since improving a forecast requires a way to measure that improvement. This talk will describe some early work towards downscaling Met Office ensemble forecasts, and discuss how the output may be usefully evaluated.

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

NASA Astrophysics Data System (ADS)

Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

2018-02-01

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

Forecast first: An argument for groundwater modeling in reverse

USGS Publications Warehouse

White, Jeremy

2017-01-01

Numerical groundwater models are important compo-nents of groundwater analyses that are used for makingcritical decisions related to the management of ground-water resources. In this support role, models are oftenconstructed to serve a specific purpose that is to provideinsights, through simulation, related to a specific func-tion of a complex aquifer system that cannot be observeddirectly (Anderson et al. 2015).For any given modeling analysis, several modelinput datasets must be prepared. Herein, the datasetsrequired to simulate the historical conditions are referredto as the calibration model, and the datasets requiredto simulate the model’s purpose are referred to as theforecast model. Future groundwater conditions or otherunobserved aspects of the groundwater system may besimulated by the forecast model—the outputs of interestfrom the forecast model represent the purpose of themodeling analysis. Unfortunately, the forecast model,needed to simulate the purpose of the modeling analysis,is seemingly an afterthought—calibration is where themajority of time and effort are expended and calibrationis usually completed before the forecast model is evenconstructed. Herein, I am proposing a new groundwatermodeling workflow, referred to as the “forecast first”workflow, where the forecast model is constructed at anearlier stage in the modeling analysis and the outputsof interest from the forecast model are evaluated duringsubsequent tasks in the workflow.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

PubMed Central

Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

PubMed

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

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

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-10-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV

NASA Astrophysics Data System (ADS)

Ho, Y.; Weber, J.

2017-12-01

WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.

The total probabilities from high-resolution ensemble forecasting of floods

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2015-04-01

Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005. Skøien, J. O., Merz, R. and Blöschl, G.: Top-kriging - Geostatistics on stream networks, Hydrol. Earth Syst. Sci., 10(2), 277-287, 2006.

MesoNAM Verification Phase II

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2011-01-01

The 45th Weather Squadron Launch Weather Officers use the 12-km resolution North American Mesoscale model (MesoNAM) forecasts to support launch weather operations. In Phase I, the performance of the model at KSC/CCAFS was measured objectively by conducting a detailed statistical analysis of model output compared to observed values. The objective analysis compared the MesoNAM forecast winds, temperature, and dew point to the observed values from the sensors in the KSC/CCAFS wind tower network. In Phase II, the AMU modified the current tool by adding an additional 15 months of model output to the database and recalculating the verification statistics. The bias, standard deviation of bias, Root Mean Square Error, and Hypothesis test for bias were calculated to verify the performance of the model. The results indicated that the accuracy decreased as the forecast progressed, there was a diurnal signal in temperature with a cool bias during the late night and a warm bias during the afternoon, and there was a diurnal signal in dewpoint temperature with a low bias during the afternoon and a high bias during the late night.

Forecasting sea fog on the coast of southern China

NASA Astrophysics Data System (ADS)

Huang, H.; Huang, B.; Liu, C.; Tu, J.; Wen, G.; Mao, W.

2016-12-01

Forecast sea fog is still full of challenges. We have performed the numerical forecasting of sea fog on the coast of southern China by using the operational meso-scale regional model GRAPES (Global/Regional assimilation and prediction system). The GRAPES model horizontal resolution was 3km and with 66 vertical levels. A total of 72 hours forecasting of sea fog was conducted with hourly outputs over the sea fog event. The results show that the model system can predict reasonable characteristics of typical sea fog events on the coast of southern China. The scope of sea fog coincides with the observations of meteorological stations, the observations of the Marine Meteorological Science Experiment Base (MMSEB) at Bohe, Maoming and satellite products of sea fog. The goal of this study is to establish an operational numerical forecasting model system of sea fog on the coast of southern China.

A Preliminary Evaluation of the GFS Physics in the Navy Global Environmental Model

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Verification of Global Assimilation of Ionospheric Measurements Gauss Markov (GAIM-GM) Model Forecast Accuracy

DTIC Science & Technology

2011-09-01

m b e r o f O cc u rr e n ce s 50 ( a ) Kp 0-3 (b) Kp 4-9 Figure 25. Scatter plot of...dependent physics based model that uses the Ionospheric Forecast Model ( IFM ) as a background model upon which perturbations are imposed via a Kalman filter...vertical output resolution as the IFM . GAIM-GM can also be run in a regional mode with a finer resolution (Scherliess et al., 2006). GAIM-GM is

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

The use of a high resolution model in a private environment.

NASA Astrophysics Data System (ADS)

van Dijke, D.; Malda, D.

2009-09-01

The commercial organisation MeteoGroup uses high resolution modelling for multiple purposes. MeteoGroup uses the Weather Research and Forecasting Model (WRF®1). WRF is used in the operational environment of several MeteoGroup companies across Europe. It is also used in hindcast studies, for example hurricane tracking, wind climate computation and deriving boundary conditions for air quality models. A special operational service was set up for our tornado chasing team that uses high resolution flexible WRF data to chase for super cells and tornados in the USA during spring. Much effort is put into the development and improvement of the pre- and post-processing of the model. At MeteoGroup the static land-use data has been extended and adjusted to improve temperature and wind forecasts. The system has been modified such that sigma level input data from the global ECMWF model can be used for initialisation. By default only pressure level data could be used. During the spin-up of the model synoptical observations are nudged. A program to adjust possible initialisation errors of several surface parameters in coastal areas has been implemented. We developed an algorithm that computes cloud fractions using multiple direct model output variables. Forecasters prefer to use weather codes for their daily forecasts to detect severe weather. For this usage we developed model weather codes using a variety of direct model output and our own derived variables. 1 WRF® is a registered trademark of the University Corporation for Atmospheric Research (UCAR)

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

Aggregation of Environmental Model Data for Decision Support

NASA Astrophysics Data System (ADS)

Alpert, J. C.

2013-12-01

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

The Canadian Ozone Watch and UV-B advisory programs

NASA Technical Reports Server (NTRS)

Kerr, J. B.; Mcelroy, C. T.; Tarasick, D. W.; Wardle, D. I.

1994-01-01

The Ozone Watch, initiated in March, 1992, is a weekly bulletin describing the state of the ozone layer over Canada. The UV-B advisory program, which started in May, 1992, produces daily forecasts of clear-sky UV-B radiation. The forecast procedures use daily ozone measurements from the eight-station monitoring network, the output from the Canadian operational forecast model and a UV-B algorithm based on three years of spectral UV-B measurements with the Brewer spectrophotometer.

Technical note: Combining quantile forecasts and predictive distributions of streamflows

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

OceanNOMADS: Real-time and retrospective access to operational U.S. ocean prediction products

NASA Astrophysics Data System (ADS)

Harding, J. M.; Cross, S. L.; Bub, F.; Ji, M.

2011-12-01

The National Oceanic and Atmospheric Administration (NOAA) National Operational Model Archive and Distribution System (NOMADS) provides both real-time and archived atmospheric model output from servers at the National Centers for Environmental Prediction (NCEP) and National Climatic Data Center (NCDC) respectively (http://nomads.ncep.noaa.gov/txt_descriptions/marRutledge-1.pdf). The NOAA National Ocean Data Center (NODC) with NCEP is developing a complementary capability called OceanNOMADS for operational ocean prediction models. An NCEP ftp server currently provides real-time ocean forecast output (http://www.opc.ncep.noaa.gov/newNCOM/NCOM_currents.shtml) with retrospective access through NODC. A joint effort between the Northern Gulf Institute (NGI; a NOAA Cooperative Institute) and the NOAA National Coastal Data Development Center (NCDDC; a division of NODC) created the developmental version of the retrospective OceanNOMADS capability (http://www.northerngulfinstitute.org/edac/ocean_nomads.php) under the NGI Ecosystem Data Assembly Center (EDAC) project (http://www.northerngulfinstitute.org/edac/). Complementary funding support for the developmental OceanNOMADS from U.S. Integrated Ocean Observing System (IOOS) through the Southeastern University Research Association (SURA) Model Testbed (http://testbed.sura.org/) this past year provided NODC the analogue that facilitated the creation of an NCDDC production version of OceanNOMADS (http://www.ncddc.noaa.gov/ocean-nomads/). Access tool development and storage of initial archival data sets occur on the NGI/NCDDC developmental servers with transition to NODC/NCCDC production servers as the model archives mature and operational space and distribution capability grow. Navy operational global ocean forecast subsets for U.S waters comprise the initial ocean prediction fields resident on the NCDDC production server. The NGI/NCDDC developmental server currently includes the Naval Research Laboratory Inter-America Seas Nowcast/Forecast System over the Gulf of Mexico from 2004-Mar 2011, the operational Naval Oceanographic Office (NAVOCEANO) regional USEast ocean nowcast/forecast system from early 2009 to present, and the NAVOCEANO operational regional AMSEAS (Gulf of Mexico/Caribbean) ocean nowcast/forecast system from its inception 25 June 2010 to present. AMSEAS provided one of the real-time ocean forecast products accessed by NOAA's Office of Response and Restoration from the NGI/NCDDC developmental OceanNOMADS during the Deep Water Horizon oil spill last year. The developmental server also includes archived, real-time Navy coastal forecast products off coastal Japan in support of U.S./Japanese joint efforts following the 2011 tsunami. Real-time NAVOCEANO output from regional prediction systems off Southern California and around Hawaii, currently available on the NCEP ftp server, are scheduled for archival on the developmental OceanNOMADS by late 2011 along with the next generation Navy/NOAA global ocean prediction output. Accession and archival of additional regions is planned as server capacities increase.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

On the effect of model parameters on forecast objects

NASA Astrophysics Data System (ADS)

Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

2018-04-01

Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Rainfall forecast in the Upper Mahaweli basin in Sri Lanka using RegCM model

NASA Astrophysics Data System (ADS)

Muhammadh, K. M.; Mafas, M. M. M.; Weerakoon, S. B.

2017-04-01

The Upper Mahaweli basin is the upper most sub basin of 788 km2 in size above Polgolla barrage in the Mahaweli River, the longest river in Sri Lanka which starts from the central hills of the island and drains to the sea at the North-east coast. Rainfall forecast in the Upper Mahaweli basin is important for issuing flood warning in the river downstream of the reservoirs, landslide warning in the settlements in hilly areas. Anticipatory water management in the basin including reservoir operations, barrage gate operation for releasing water for irrigation and flood control also require reliable rainfall and runoff prediction in the sub basin. In this study, the Regional Climate Model (RegCM V4.4.5.11) is calibrated for the basin to dynamically downscale reanalysis weather data of Global Climate Model (GCM) to forecast the rainfall in the basin. Observed rainfalls at gauging stations within the basin were used for model calibration and validation. The observed rainfall data was analysed using ARC GIS and the output of RegCM was analysed using GrADS tool. The output of the model and the observed precipitation were obtained on grids of size 0.1 degrees and the accuracy of the predictions were analysed using RMSE and Mean Model Absolute Error percentage (MAME %). The predictions by the calibrated RegCM model for the basin is shown to be satisfactory. The model is a useful tool for rainfall forecast in the Upper Mahaweli River basin.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Simple statistical bias correction techniques greatly improve moderate resolution air quality forecast at station level

NASA Astrophysics Data System (ADS)

Curci, Gabriele; Falasca, Serena

2017-04-01

Deterministic air quality forecast is routinely carried out at many local Environmental Agencies in Europe and throughout the world by means of eulerian chemistry-transport models. The skill of these models in predicting the ground-level concentrations of relevant pollutants (ozone, nitrogen dioxide, particulate matter) a few days ahead has greatly improved in recent years, but it is not yet always compliant with the required quality level for decision making (e.g. the European Commission has set a maximum uncertainty of 50% on daily values of relevant pollutants). Post-processing of deterministic model output is thus still regarded as a useful tool to make the forecast more reliable. In this work, we test several bias correction techniques applied to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). The bias correction techniques adopted are, in order of complexity: (1) application of multiplicative factors calculated as the ratio of model-to-observed concentrations averaged over the previous days; (2) correction of the statistical distribution of model forecasts, in order to make it similar to that of the observations; (3) development and application of Model Output Statistics (MOS) regression equations. We illustrate differences and advantages/disadvantages of the three approaches. All the methods are relatively easy to implement for other modelling systems.

Projected Applications of a "Weather in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew; Zavodsky, Bradley T.; Case, Jonathan L.; LaFontaine, Frank J.; Srikishen, Jayanthi

2010-01-01

The NASA Short-term Prediction Research and Transition Center (SPoRT)'s new "Weather in a Box" resources will provide weather research and forecast modeling capabilities for real-time application. Model output will provide additional forecast guidance and research into the impacts of new NASA satellite data sets and software capabilities. By combining several research tools and satellite products, SPoRT can generate model guidance that is strongly influenced by unique NASA contributions.

Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Hemri, S.; Klein, B.

2017-11-01

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

PubMed Central

Zhao, Xiuli; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

PubMed

Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

How to improve an un-alterable model forecast? A sequential data assimilation based error updating approach

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K. T.

2012-12-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing value of water resources and influences operation of hydropower reservoirs significantly. Improving hourly reservoir inflow forecasts over a 24 hours lead-time is considered with the day-ahead (Elspot) market of the Nordic exchange market in perspectives. The procedure presented comprises of an error model added on top of an un-alterable constant parameter conceptual model, and a sequential data assimilation routine. The structure of the error model was investigated using freely available software for detecting mathematical relationships in a given dataset (EUREQA) and adopted to contain minimum complexity for computational reasons. As new streamflow data become available the extra information manifested in the discrepancies between measurements and conceptual model outputs are extracted and assimilated into the forecasting system recursively using Sequential Monte Carlo technique. Besides improving forecast skills significantly, the probabilistic inflow forecasts provided by the present approach entrains suitable information for reducing uncertainty in decision making processes related to hydropower systems operation. The potential of the current procedure for improving accuracy of inflow forecasts at lead-times unto 24 hours and its reliability in different seasons of the year will be illustrated and discussed thoroughly.

Probabilistic Predictions of PM2.5 Using a Novel Ensemble Design for the NAQFC

NASA Astrophysics Data System (ADS)

Kumar, R.; Lee, J. A.; Delle Monache, L.; Alessandrini, S.; Lee, P.

2017-12-01

Poor air quality (AQ) in the U.S. is estimated to cause about 60,000 premature deaths with costs of 100B-150B annually. To reduce such losses, the National AQ Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of ozone, particulate matter less than 2.5 mm in diameter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit the exposure and reduce air pollution-caused health problems. The current NAQFC, based on the U.S. Environmental Protection Agency Community Multi-scale AQ (CMAQ) modeling system, provides only deterministic AQ forecasts and does not quantify the uncertainty associated with the predictions, which could be large due to the chaotic nature of atmosphere and nonlinearity in atmospheric chemistry. This project aims to take NAQFC a step further in the direction of probabilistic AQ prediction by exploring and quantifying the potential value of ensemble predictions of PM2.5, and perturbing three key aspects of PM2.5 modeling: the meteorology, emissions, and CMAQ secondary organic aerosol formulation. This presentation focuses on the impact of meteorological variability, which is represented by three members of NOAA's Short-Range Ensemble Forecast (SREF) system that were down-selected by hierarchical cluster analysis. These three SREF members provide the physics configurations and initial/boundary conditions for the Weather Research and Forecasting (WRF) model runs that generate required output variables for driving CMAQ that are missing in operational SREF output. We conducted WRF runs for Jan, Apr, Jul, and Oct 2016 to capture seasonal changes in meteorology. Estimated emissions of trace gases and aerosols via the Sparse Matrix Operator Kernel (SMOKE) system were developed using the WRF output. WRF and SMOKE output drive a 3-member CMAQ mini-ensemble of once-daily, 48-h PM2.5 forecasts for the same four months. The CMAQ mini-ensemble is evaluated against both observations and the current operational deterministic NAQFC products, and analyzed to assess the impact of meteorological biases on PM2.5 variability. Quantification of the PM2.5 prediction uncertainty will prove a key factor to support cost-effective decision-making while protecting public health.

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2010-01-01

The 45th Weather Squadron (45 WS) Launch Weather Officers use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature and dew point, as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network. Objective statistics will give the forecasters knowledge of the model's strength and weaknesses, which will result in improved forecasts for operations.

Improvement of short-term numerical wind predictions

NASA Astrophysics Data System (ADS)

Bedard, Joel

Geophysic Model Output Statistics (GMOS) are developed to optimize the use of NWP for complex sites. GMOS differs from other MOS that are widely used by meteorological centers in the following aspects: it takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; it can be directly applied without any training, although training will further improve the results. The GMOS was applied to improve the Environment Canada GEM-LAM 2.5km forecasts at North Cape (PEI, Canada): It improves the predictions RMSE by 25-30% for all time horizons and almost all meteorological conditions; the topographic signature of the forecast error due to insufficient grid refinement is eliminated and the NWP combined with GMOS outperform the persistence from a 2h horizon, instead of 4h without GMOS. Finally, GMOS was applied at another site (Bouctouche, NB, Canada): similar improvements were observed, thus showing its general applicability. Keywords: wind energy, wind power forecast, numerical weather prediction, complex sites, model output statistics

Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Stock price prediction using geometric Brownian motion

NASA Astrophysics Data System (ADS)

Farida Agustini, W.; Restu Affianti, Ika; Putri, Endah RM

2018-03-01

Geometric Brownian motion is a mathematical model for predicting the future price of stock. The phase that done before stock price prediction is determine stock expected price formulation and determine the confidence level of 95%. On stock price prediction using geometric Brownian Motion model, the algorithm starts from calculating the value of return, followed by estimating value of volatility and drift, obtain the stock price forecast, calculating the forecast MAPE, calculating the stock expected price and calculating the confidence level of 95%. Based on the research, the output analysis shows that geometric Brownian motion model is the prediction technique with high rate of accuracy. It is proven with forecast MAPE value ≤ 20%.

NEMS Freight Transportation Module Improvement Study

EIA Publications

2015-01-01

The U.S. Energy Information Administration (EIA) contracted with IHS Global, Inc. (IHS) to analyze the relationship between the value of industrial output, physical output, and freight movement in the United States for use in updating analytic assumptions and modeling structure within the National Energy Modeling System (NEMS) freight transportation module, including forecasting methodologies and processes to identify possible alternative approaches that would improve multi-modal freight flow and fuel consumption estimation.

Models for forecasting energy use in the US farm sector

NASA Astrophysics Data System (ADS)

Christensen, L. R.

1981-07-01

Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.

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Study on optimization of the short-term operation of cascade hydropower stations by considering output error

NASA Astrophysics Data System (ADS)

Wang, Liping; Wang, Boquan; Zhang, Pu; Liu, Minghao; Li, Chuangang

2017-06-01

The study of reservoir deterministic optimal operation can improve the utilization rate of water resource and help the hydropower stations develop more reasonable power generation schedules. However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules. In this paper, output error generated by the uncertainty of the forecasting inflow was regarded as a variable to develop a short-term reservoir optimal operation model for reducing operation risk. To accomplish this, the concept of Value at Risk (VaR) was first applied to present the maximum possible loss of power generation schedules, and then an extreme value theory-genetic algorithm (EVT-GA) was proposed to solve the model. The cascade reservoirs of Yalong River Basin in China were selected as a case study to verify the model, according to the results, different assurance rates of schedules can be derived by the model which can present more flexible options for decision makers, and the highest assurance rate can reach 99%, which is much higher than that without considering output error, 48%. In addition, the model can greatly improve the power generation compared with the original reservoir operation scheme under the same confidence level and risk attitude. Therefore, the model proposed in this paper can significantly improve the effectiveness of power generation schedules and provide a more scientific reference for decision makers.

What might we learn from climate forecasts?

PubMed Central

Smith, Leonard A.

2002-01-01

Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them “better,” estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as “chaos” prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system. PMID:11875200

Application of Wavelet Filters in an Evaluation of ...

EPA Pesticide Factsheets

Air quality model evaluation can be enhanced with time-scale specific comparisons of outputs and observations. For example, high-frequency (hours to one day) time scale information in observed ozone is not well captured by deterministic models and its incorporation into model performance metrics lead one to devote resources to stochastic variations in model outputs. In this analysis, observations are compared with model outputs at seasonal, weekly, diurnal and intra-day time scales. Filters provide frequency specific information that can be used to compare the strength (amplitude) and timing (phase) of observations and model estimates. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollu

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

2012-01-01

Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

Comparative assessment of several post-processing methods for correcting evapotranspiration forecasts derived from TIGGE datasets.

NASA Astrophysics Data System (ADS)

Tian, D.; Medina, H.

2017-12-01

Post-processing of medium range reference evapotranspiration (ETo) forecasts based on numerical weather prediction (NWP) models has the potential of improving the quality and utility of these forecasts. This work compares the performance of several post-processing methods for correcting ETo forecasts over the continental U.S. generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database using data from Europe (EC), the United Kingdom (MO), and the United States (NCEP). The pondered post-processing techniques are: simple bias correction, the use of multimodels, the Ensemble Model Output Statistics (EMOS, Gneitting et al., 2005) and the Bayesian Model Averaging (BMA, Raftery et al., 2005). ETo estimates based on quality-controlled U.S. Regional Climate Reference Network measurements, and computed with the FAO 56 Penman Monteith equation, are adopted as baseline. EMOS and BMA are generally the most efficient post-processing techniques of the ETo forecasts. Nevertheless, the simple bias correction of the best model is commonly much more rewarding than using multimodel raw forecasts. Our results demonstrate the potential of different forecasting and post-processing frameworks in operational evapotranspiration and irrigation advisory systems at national scale.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Molthan, A.; Case, J.; Venner, J.; Moreno-Madriñán, M. J.; Delgado, F.

2012-12-01

Over the past two years, scientists in the Earth Science Office at NASA's Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real-time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA's Short-term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface- and satellite-based observations.

Applications of the gambling score in evaluating earthquake predictions and forecasts

NASA Astrophysics Data System (ADS)

Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

2010-05-01

This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.

Hydrologic Modeling at the National Water Center: Operational Implementation of the WRF-Hydro Model to support National Weather Service Hydrology

NASA Astrophysics Data System (ADS)

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

2015-12-01

The National Weather Service (NWS) National Water Center(NWC) is collaborating with the NWS National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) to implement a first-of-its-kind operational instance of the Weather Research and Forecasting (WRF)-Hydro model over the Continental United States (CONUS) and contributing drainage areas on the NWS Weather and Climate Operational Supercomputing System (WCOSS) supercomputer. The system will provide seamless, high-resolution, continuously cycling forecasts of streamflow and other hydrologic outputs of value from both deterministic- and ensemble-type runs. WRF-Hydro will form the core of the NWC national water modeling strategy, supporting NWS hydrologic forecast operations along with emergency response and water management efforts of partner agencies. Input and output from the system will be comprehensively verified via the NWC Water Resource Evaluation Service. Hydrologic events occur on a wide range of temporal scales, from fast acting flash floods, to long-term flow events impacting water supply. In order to capture this range of events, the initial operational WRF-Hydro configuration will feature 1) hourly analysis runs, 2) short-and medium-range deterministic forecasts out to two day and ten day horizons and 3) long-range ensemble forecasts out to 30 days. All three of these configurations are underpinned by a 1km execution of the NoahMP land surface model, with channel routing taking place on 2.67 million NHDPlusV2 catchments covering the CONUS and contributing areas. Additionally, the short- and medium-range forecasts runs will feature surface and sub-surface routing on a 250m grid, while the hourly analyses will feature this same 250m routing in addition to nudging-based assimilation of US Geological Survey (USGS) streamflow observations. A limited number of major reservoirs will be configured within the model to begin to represent the first-order impacts of streamflow regulation.

Consistency Between Convection Allowing Model Output and Passive Microwave Satellite Observations

NASA Astrophysics Data System (ADS)

Bytheway, J. L.; Kummerow, C. D.

2018-01-01

Observations from the Global Precipitation Measurement (GPM) core satellite were used along with precipitation forecasts from the High Resolution Rapid Refresh (HRRR) model to assess and interpret differences between observed and modeled storms. Using a feature-based approach, precipitating objects were identified in both the National Centers for Environmental Prediction Stage IV multisensor precipitation product and HRRR forecast at lead times of 1, 2, and 3 h at valid times corresponding to GPM overpasses. Precipitating objects were selected for further study if (a) the observed feature occurred entirely within the swath of the GPM Microwave Imager (GMI) and (b) the HRRR model predicted it at all three forecast lead times. Output from the HRRR model was used to simulate microwave brightness temperatures (Tbs), which were compared to those observed by the GMI. Simulated Tbs were found to have biases at both the warm and cold ends of the distribution, corresponding to the stratiform/anvil and convective areas of the storms, respectively. Several experiments altered both the simulation microphysics and hydrometeor classification in order to evaluate potential shortcomings in the model's representation of precipitating clouds. In general, inconsistencies between observed and simulated brightness temperatures were most improved when transferring snow water content to supercooled liquid hydrometeor classes.

Prototype methodology for obtaining cloud seeding guidance from HRRR model data

NASA Astrophysics Data System (ADS)

Dawson, N.; Blestrud, D.; Kunkel, M. L.; Waller, B.; Ceratto, J.

2017-12-01

Weather model data, along with real time observations, are critical to determine whether atmospheric conditions are prime for super-cooled liquid water during cloud seeding operations. Cloud seeding groups can either use operational forecast models, or run their own model on a computer cluster. A custom weather model provides the most flexibility, but is also expensive. For programs with smaller budgets, openly-available operational forecasting models are the de facto method for obtaining forecast data. The new High-Resolution Rapid Refresh (HRRR) model (3 x 3 km grid size), developed by the Earth System Research Laboratory (ESRL), provides hourly model runs with 18 forecast hours per run. While the model cannot be fine-tuned for a specific area or edited to provide cloud-seeding-specific output, model output is openly available on a near-real-time basis. This presentation focuses on a prototype methodology for using HRRR model data to create maps which aid in near-real-time cloud seeding decision making. The R programming language is utilized to run a script on a Windows® desktop/laptop computer either on a schedule (such as every half hour) or manually. The latest HRRR model run is downloaded from NOAA's Operational Model Archive and Distribution System (NOMADS). A GRIB-filter service, provided by NOMADS, is used to obtain surface and mandatory pressure level data for a subset domain which greatly cuts down on the amount of data transfer. Then, a set of criteria, identified by the Idaho Power Atmospheric Science Group, is used to create guidance maps. These criteria include atmospheric stability (lapse rates), dew point depression, air temperature, and wet bulb temperature. The maps highlight potential areas where super-cooled liquid water may exist, reasons as to why cloud seeding should not be attempted, and wind speed at flight level.

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

Weighting of NMME temperature and precipitation forecasts across Europe

NASA Astrophysics Data System (ADS)

Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

2017-09-01

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

Forecasting for a Remote Island: A Class Exercise.

NASA Astrophysics Data System (ADS)

Riordan, Allen J.

2003-06-01

Students enrolled in a satellite meteorology course at North Carolina State University, Raleigh, recently had an unusual opportunity to apply their forecast skills to predict wind and weather conditions for a remote site in the Southern Hemisphere. For about 40 days starting in early February 2001, students used satellite and model guidance to develop forecasts to support a research team stationed on Bouvet Island (54°26S, 3°24E). Internet products together with current output from NCEP's Aviation (AVN) model supported the activity. Wind forecasts were of particular interest to the Bouvet team because violent winds often developed unexpectedly and posed a safety hazard.Results were encouraging in that 24-h wind speed forecasts showed reasonable reliability over a wide range of wind speeds. Forecasts for 48 h showed only marginal skill, however. Two critical events were well forecasted-the major February storm with wind speeds of over 120 kt and a brief calm period following several days of strong winds in early March. The latter forecast proved instrumental in recovering the research team.

The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

NASA Astrophysics Data System (ADS)

Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths correspond approximately to a return period of 200 years. Most rivers in Umbria have been involved, exceeding hydrometric thresholds and causing flooding (e.g. Chiascio river). The flood event was continuously monitored at the Umbria Region CF and the possible evolution predicted and assessed on the basis of the model forecasts. The predictions provided by MISDc and STAFOM-RCM were found useful to support real-time decision-making addressed to flood risk management. Moreover, the quantification of the uncertainty affecting the deterministic forecast stages was found consistent with the level of confidence selected and had practical utility corroborating the need of coupling deterministic forecast and 'uncertainty' when the model output is used to support decisions about flood management. REFERENCES Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., Melone, F. (2014). Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3), 729-743. Brocca, L., Melone, F., Moramarco, T. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrological Processes, 25 (18), 2801-2813

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Validating the WRF-Chem model for wind energy applications using High Resolution Doppler Lidar data from a Utah 2012 field campaign

NASA Astrophysics Data System (ADS)

Mitchell, M. J.; Pichugina, Y. L.; Banta, R. M.

2015-12-01

Models are important tools for assessing potential of wind energy sites, but the accuracy of these projections has not been properly validated. In this study, High Resolution Doppler Lidar (HRDL) data obtained with high temporal and spatial resolution at heights of modern turbine rotors were compared to output from the WRF-chem model in order to help improve the performance of the model in producing accurate wind forecasts for the industry. HRDL data were collected from January 23-March 1, 2012 during the Uintah Basin Winter Ozone Study (UBWOS) field campaign. A model validation method was based on the qualitative comparison of the wind field images, time-series analysis and statistical analysis of the observed and modeled wind speed and direction, both for case studies and for the whole experiment. To compare the WRF-chem model output to the HRDL observations, the model heights and forecast times were interpolated to match the observed times and heights. Then, time-height cross-sections of the HRDL and WRF-Chem wind speed and directions were plotted to select case studies. Cross-sections of the differences between the observed and forecasted wind speed and directions were also plotted to visually analyze the model performance in different wind flow conditions. A statistical analysis includes the calculation of vertical profiles and time series of bias, correlation coefficient, root mean squared error, and coefficient of determination between two datasets. The results from this analysis reveals where and when the model typically struggles in forecasting winds at heights of modern turbine rotors so that in the future the model can be improved for the industry.

New insights on short-term solar irradiance forecast for space weather applications

NASA Astrophysics Data System (ADS)

Vieira, L. A.; Dudok de Wit, T.; Balmaceda, L. A.; Dal Lago, A.; Da Silva, L. A.; Gonzalez, W. D.

2013-12-01

The conditions of the thermosphere, the ionosphere, the neutral atmosphere, and the oceans on time scales from days to millennia are highly dependent on the solar electromagnetic output, the solar irradiance. The development of physics-based solar irradiance models during the last decade improved significantly our understanding of the solar forcing on Earth's climate. These models are based on the assumption that most of the solar irradiance variability is related to the magnetic field structure of the Sun. Recently, these models were extended to allow short-term forecast (1 to 15 days) of the total and spectral solar irradiance. The extension of the irradiance models is based on solar surface magnetic flux models and/or artificial neural network models. Here, we discuss in details the irradiance forecast models based on observations of the solar surface magnetic field realized by the HMI instrument on board of SDO spacecraft. We constrained and validated the models by comparing the output of the models and observations of the solar irradiance made by instruments onboard The SORCE spacecraft. This study received funding from the European Community's Seventh Framework Programme (FP7/2007-2013, FP7-SPACE-2010-1) under the grant agreement nrs. 218816 (SOTERIA project, www.soteria-space.eu) and 261948 (ATMOP,www.atmop.eu), and by the CNPq/Brazil under the grant number 312488/2012-2. We also gratefully thank the instrument teams for making their data available.

Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming

NASA Astrophysics Data System (ADS)

Jiang, Jiang; Huang, Yuanyuan; Ma, Shuang; Stacy, Mark; Shi, Zheng; Ricciuto, Daniel M.; Hanson, Paul J.; Luo, Yiqi

2018-03-01

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon-flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux- versus pool-based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data-model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux-related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool-related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast-turnover pools to various CO2 and warming treatments were observed sooner than slow-turnover pools. Our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

Future mission studies: Forecasting solar flux directly from its chaotic time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

The mathematical structure of the programs written to construct a nonlinear predictive model to forecast solar flux directly from its time series without reference to any underlying solar physics is presented. This method and the programs are written so that one could apply the same technique to forecast other chaotic time series, such as geomagnetic data, attitude and orbit data, and even financial indexes and stock market data. Perhaps the most important application of this technique to flight dynamics is to model Goddard Trajectory Determination System (GTDS) output of residues between observed position of spacecraft and calculated position with no drag (drag flag = off). This would result in a new model of drag working directly from observed data.

An application of ensemble/multi model approach for wind power production forecasting

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

2011-02-01

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

Modifications to the Objective Lightning Probability Forecast Tool at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

NASA Technical Reports Server (NTRS)

Crawford, Winifred; Roeder, William

2010-01-01

The 45th Weather Squadron (45 WS) at Cape Canaveral Air Force Station (CCAFS) includes the probability of lightning occurrence in their 24-Hour and Weekly Planning Forecasts, briefed at 0700 EDT for daily operations planning on Kennedy Space Center (KSC) and CCAFS. This forecast is based on subjective analyses of model and observational data and output from an objective tool developed by the Applied Meteorology Unit (AMU). This tool was developed over two phases (Lambert and Wheeler 2005, Lambert 2007). It consists of five equations, one for each warm season month (May-Sep), that calculate the probability of lightning occurrence for the day and a graphical user interface (GUI) to display the output. The Phase I and II equations outperformed previous operational tools by a total of 56%. Based on this success, the 45 WS tasked the AMU with Phase III to improve the tool further.

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2010-01-01

The 45th Weather Squadron (45 WS) Launch Weather Officers (LWO's) use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit (AMU; Bauman et ai, 2004) to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature (T) and dew pOint (T d), as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network shown in Table 1. These objective statistics give the forecasters knowledge of the model's strengths and weaknesses, which will result in improved forecasts for operations.

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn A.; Watson, Leela R.

2015-01-01

Customer: NASA's Launch Services Program (LSP), Ground Systems Development and Operations (GSDO), and Space Launch System (SLS) programs. NASA's LSP, GSDO, SLS and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). For example, to determine if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 kilometer Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the AMU high-resolution WRF Environmental Modeling System (EMS) model (Watson 2013) in real-time. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The model was set up with a triple-nested grid configuration over KSC/CCAFS based on previous AMU work (Watson 2013). The outer domain (D01) has 12-kilometer grid spacing, the middle domain (D02) has 4-kilometer grid spacing, and the inner domain (D03) has 1.33-kilometer grid spacing. The model runs a 12-hour forecast every hour, D01 and D02 domain outputs are available once an hour and D03 is every 15 minutes during the forecast period. The AMU assessed the WRF-EMS 1.33-kilometer domain model performance for the 2014 warm season (May-September). Verification statistics were computed using the Model Evaluation Tools, which compared the model forecasts to observations. The mean error values were close to 0 and the root mean square error values were less than 1.8 for mean sea-level pressure (millibars), temperature (degrees Kelvin), dewpoint temperature (degrees Kelvin), and wind speed (per millisecond), all very small differences between the forecast and observations considering the normal magnitudes of the parameters. The precipitation forecast verification results showed consistent under-forecasting of the precipitation object size. This could be an artifact of calculating the statistics for each hour rather than for the entire 12-hour period. The AMU will continue to generate verification statistics for the 1.33-kilometer WRF-EMS domain as data become available in future cool and warm seasons. More data will produce more robust statistics and reveal a more accurate assessment of model performance. Once the formal task was complete, the AMU conducted additional work to better understand the wind direction results. The results were stratified diurnally and by wind speed to determine what effects the stratifications would have on the model wind direction verification statistics. The results are summarized in the addendum at the end of this report. In addition to verifying the model's performance, the AMU also made the output available in the Advanced Weather Interactive Processing System II (AWIPS II). This allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations AWIPS II client computers and conduct real-time subjective analyses. In the future, the AMU will implement an updated version of the WRF-EMS model that incorporates local data assimilation. This model will also run in real-time and be made available in AWIPS II.

Linked population and economic models: some methodological issues in forecasting, analysis, and policy optimization.

PubMed

Madden, M; Batey Pwj

1983-05-01

Some problems associated with demographic-economic forecasting include finding models appropriate for a declining economy with unemployment, using a multiregional approach in an interregional model, finding a way to show differential consumption while endogenizing unemployment, and avoiding unemployment inconsistencies. The solution to these problems involves the construction of an activity-commodity framework, locating it within a group of forecasting models, and indicating possible ratios towards dynamization of the framework. The authors demonstrate the range of impact multipliers that can be derived from the framework and show how these multipliers relate to Leontief input-output multipliers. It is shown that desired population distribution may be obtained by selecting instruments from the economic sphere to produce, through the constraints vector of an activity-commodity framework, targets selected from demographic activities. The next step in this process, empirical exploitation, was carried out by the authors in the United Kingdom, linking an input-output model with a wide selection of demographic and demographic-economic variables. The generally tenuous control which government has over any variables in systems of this type, especially in market economies, makes application in the policy field of the optimization approach a partly conjectural exercise, although the analytic capacity of the approach can provide clear indications of policy directions.

Workstation-Based Real-Time Mesoscale Modeling Designed for Weather Support to Operations at the Kennedy Space Center and Cape Canaveral Air Station

NASA Technical Reports Server (NTRS)

Manobianco, John; Zack, John W.; Taylor, Gregory E.

1996-01-01

This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Deriving Tools from Real-time Runs: A New CCMC Support for SEC and AFWA

NASA Technical Reports Server (NTRS)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2008-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions. the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models. and on the transition of appropriate models to space weather forecast centers. As part of the latter activity. the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. After consultations with NOA/SEC and with AFWA, CCMC has developed a set of tools as a first step to make real-time model output useful to forecast centers. In this presentation, we will discuss the motivation for this activity, the actions taken so far, and options for future tools from model output.

Evaluation of Mesoscale Model Phenomenological Verification Techniques

NASA Technical Reports Server (NTRS)

Lambert, Winifred

2006-01-01

Forecasters at the Spaceflight Meteorology Group, 45th Weather Squadron, and National Weather Service in Melbourne, FL use mesoscale numerical weather prediction model output in creating their operational forecasts. These models aid in forecasting weather phenomena that could compromise the safety of launch, landing, and daily ground operations and must produce reasonable weather forecasts in order for their output to be useful in operations. Considering the importance of model forecasts to operations, their accuracy in forecasting critical weather phenomena must be verified to determine their usefulness. The currently-used traditional verification techniques involve an objective point-by-point comparison of model output and observations valid at the same time and location. The resulting statistics can unfairly penalize high-resolution models that make realistic forecasts of a certain phenomena, but are offset from the observations in small time and/or space increments. Manual subjective verification can provide a more valid representation of model performance, but is time-consuming and prone to personal biases. An objective technique that verifies specific meteorological phenomena, much in the way a human would in a subjective evaluation, would likely produce a more realistic assessment of model performance. Such techniques are being developed in the research community. The Applied Meteorology Unit (AMU) was tasked to conduct a literature search to identify phenomenological verification techniques being developed, determine if any are ready to use operationally, and outline the steps needed to implement any operationally-ready techniques into the Advanced Weather Information Processing System (AWIPS). The AMU conducted a search of all literature on the topic of phenomenological-based mesoscale model verification techniques and found 10 different techniques in various stages of development. Six of the techniques were developed to verify precipitation forecasts, one to verify sea breeze forecasts, and three were capable of verifying several phenomena. The AMU also determined the feasibility of transitioning each technique into operations and rated the operational capability of each technique on a subjective 1-10 scale: (1) 1 indicates that the technique is only in the initial stages of development, (2) 2-5 indicates that the technique is still undergoing modifications and is not ready for operations, (3) 6-8 indicates a higher probability of integrating the technique into AWIPS with code modifications, and (4) 9-10 indicates that the technique was created for AWIPS and is ready for implementation. Eight of the techniques were assigned a rating of 5 or below. The other two received ratings of 6 and 7, and none of the techniques a rating of 9-10. At the current time, there are no phenomenological model verification techniques ready for operational use. However, several of the techniques described in this report may become viable techniques in the future and should be monitored for updates in the literature. The desire to use a phenomenological verification technique is widespread in the modeling community, and it is likely that other techniques besides those described herein are being developed, but the work has not yet been published. Therefore, the AMIU recommends that the literature continue to be monitored for updates to the techniques described in this report and for new techniques being developed whose results have not yet been published. 111

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

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

Jacobs, John M.; Rhodes, M.; Brown, C. W.

The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity aremore » capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.« less

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

NASA Astrophysics Data System (ADS)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

Short time ahead wind power production forecast

NASA Astrophysics Data System (ADS)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-09-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

NASA Astrophysics Data System (ADS)

Bailey, Monika E.; Isaac, George A.; Gultepe, Ismail; Heckman, Ivan; Reid, Janti

2014-01-01

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.

Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2017-03-01

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

Biospheric Monitoring and Ecological Forecasting using EOS/MODIS data, ecosystem modeling, planning and scheduling technologies

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Votava, P.; Golden, K.; Hashimoto, H.; Jolly, M.; White, M.; Running, S.; Coughlan, J.

2003-12-01

The latest generation of NASA Earth Observing System satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the Biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, information with the highest economic value would be forecasting impending conditions of the biosphere that would allow advanced decision-making to mitigate dangers, or exploit positive trends. We have developed a software system called the Terrestrial Observation and Prediction System (TOPS) to facilitate rapid analysis of ecosystem states/functions by integrating EOS data with ecosystem models, surface weather observations and weather/climate forecasts. Land products from MODIS (Moderate Resolution Imaging Spectroradiometer) including land cover, albedo, snow, surface temperature, leaf area index are ingested into TOPS for parameterization of models and for verifying model outputs such as snow cover and vegetation phenology. TOPS is programmed to gather data from observing networks such as USDA soil moisture, AMERIFLUX, SNOWTEL to further enhance model predictions. Key technologies enabling TOPS implementation include the ability to understand and process heterogeneous-distributed data sets, automated planning and execution of ecosystem models, causation analysis for understanding model outputs. Current TOPS implementations at local (vineyard) to global scales (global net primary production) can be found at http://www.ntsg.umt.edu/tops.

Using Bayes Model Averaging for Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Preede Revheim, Pål; Beyer, Hans Georg

2014-05-01

For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data does not contain information, but it has the disadvantage of nearly doubling the number of model parameters to be estimated. Second, the BMA procedure is run with group mean wind power as the response variable instead of group mean wind speed. This also solves the problem with longer consecutive periods without information in the input data, but it leaves the power curve to also be estimated from the data. [1] Raftery, A. E., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174. [2]Revheim, P. P. and H. G. Beyer (2013). Using Bayesian Model Averaging for wind farm group forecasts. EWEA Wind Power Forecasting Technology Workshop,Rotterdam, 4-5 December 2013. [3]Sloughter, J. M., T. Gneiting and A. E. Raftery (2010). Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association, Vol. 105, No. 489, 25-35

Dynamical Downscaling of Seasonal Climate Prediction over Nordeste Brazil with ECHAM3 and NCEP's Regional Spectral Models at IRI.

NASA Astrophysics Data System (ADS)

Nobre, Paulo; Moura, Antonio D.; Sun, Liqiang

2001-12-01

This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January-April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution improves upon the AGCM rainfall forecast, reducing both seasonal bias and root-mean-square error. On the other hand, the RSM-20 forecasts presented larger errors, with spatial patterns that resemble those of local topography. The better forecast of the position and width of the intertropical convergence zone (ITCZ) over the tropical Atlantic by the RSM-80 model is one of the principal reasons for better-forecast scores of the RSM-80 relative to the AGCM. The regional model improved the spatial as well as the temporal details of rainfall distribution, and also presenting the minimum spread among the ensemble members. The statistics of synoptic-scale weather variability on seasonal timescales were best forecast with the regional 80-km model over the Nordeste. The possibility of forecasting the frequency distribution of dry and wet spells within the rainy season is encouraging.

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor-A Solution for Smoothing the Output Power of PV Power Plants.

PubMed

Sukič, Primož; Štumberger, Gorazd

2017-05-13

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants

PubMed Central

Sukič, Primož; Štumberger, Gorazd

2017-01-01

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly. PMID:28505078

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

PubMed

Armeanu, Daniel; Andrei, Jean Vasile; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

PubMed Central

Armeanu, Daniel; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. PMID:28742100

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Comparison of Four Precipitation Forcing Datasets in Land Information System Simulations over the Continental U.S.

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Kumar, Sujay V.; Kuliogwski, Robert J.; Langston, Carrie

2013-01-01

This paper and poster presented a description of the current real-time SPoRT-LIS run over the southeastern CONUS to provide high-resolution, land surface initialization grids for local numerical model forecasts at NWS forecast offices. The LIS hourly output also offers a supplemental dataset to aid in situational awareness for convective initiation forecasts, assessing flood potential, and monitoring drought at fine scales. It is a goal of SPoRT and several NWS forecast offices to expand the LIS to an entire CONUS domain, so that LIS output can be utilized by NWS Western Region offices, among others. To make this expansion cleanly so as to provide high-quality land surface output, SPoRT tested new precipitation datasets in LIS as an alternative forcing dataset to the current radar+gauge Stage IV product. Similar to the Stage IV product, the NMQ product showed comparable patterns of precipitation and soil moisture distribution, but suffered from radar gaps in the intermountain West, and incorrectly set values to zero instead of missing in the data-void regions of Mexico and Canada. The other dataset tested was the next-generation GOES-R QPE algorithm, which experienced a high bias in both coverage and intensity of accumulated precipitation relative to the control (NLDAS2), Stage IV, and NMQ simulations. The resulting root zone soil moisture was substantially higher in most areas.

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Coordinated Parameterization Development and Large-Eddy Simulation for Marine and Arctic Cloud-Topped Boundary Layers

NASA Technical Reports Server (NTRS)

Bretherton, Christopher S.

2002-01-01

The goal of this project was to compare observations of marine and arctic boundary layers with: (1) parameterization systems used in climate and weather forecast models; and (2) two and three dimensional eddy resolving (LES) models for turbulent fluid flow. Based on this comparison, we hoped to better understand, predict, and parameterize the boundary layer structure and cloud amount, type, and thickness as functions of large scale conditions that are predicted by global climate models. The principal achievements of the project were as follows: (1) Development of a novel boundary layer parameterization for large-scale models that better represents the physical processes in marine boundary layer clouds; and (2) Comparison of column output from the ECMWF global forecast model with observations from the SHEBA experiment. Overall the forecast model did predict most of the major precipitation events and synoptic variability observed over the year of observation of the SHEBA ice camp.

Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

NASA Astrophysics Data System (ADS)

Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

2014-05-01

While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

Forecast Method of Solar Irradiance with Just-In-Time Modeling

NASA Astrophysics Data System (ADS)

Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments.

PubMed

Dunea, Daniel; Pohoata, Alin; Iordache, Stefania

2015-07-01

The paper presents the screening of various feedforward neural networks (FANN) and wavelet-feedforward neural networks (WFANN) applied to time series of ground-level ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10 and PM2.5 fractions) recorded at four monitoring stations located in various urban areas of Romania, to identify common configurations with optimal generalization performance. Two distinct model runs were performed as follows: data processing using hourly-recorded time series of airborne pollutants during cold months (O3, NO2, and PM10), when residential heating increases the local emissions, and data processing using 24-h daily averaged concentrations (PM2.5) recorded between 2009 and 2012. Dataset variability was assessed using statistical analysis. Time series were passed through various FANNs. Each time series was decomposed in four time-scale components using three-level wavelets, which have been passed also through FANN, and recomposed into a single time series. The agreement between observed and modelled output was evaluated based on the statistical significance (r coefficient and correlation between errors and data). Daubechies db3 wavelet-Rprop FANN (6-4-1) utilization gave positive results for O3 time series optimizing the exclusive use of the FANN for hourly-recorded time series. NO2 was difficult to model due to time series specificity, but wavelet integration improved FANN performances. Daubechies db3 wavelet did not improve the FANN outputs for PM10 time series. Both models (FANN/WFANN) overestimated PM2.5 forecasted values in the last quarter of time series. A potential improvement of the forecasted values could be the integration of a smoothing algorithm to adjust the PM2.5 model outputs.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...

Methods of Technological Forecasting,

DTIC Science & Technology

1977-05-01

Trend Extrapolation Progress Curve Analogy Trend Correlation Substitution Analysis or Substitution Growth Curves Envelope Curve Advances in the State of...the Art Technological Mapping Contextual Mapping Matrix Input-Output Analysis Mathematical Models Simulation Models Dynamic Modelling. CHAPTER IV...Generation Interaction between Needs and Possibilities Map of the Technological Future — (‘ross- Impact Matri x Discovery Matrix Morphological Analysis

Use of High-resolution WRF Simulations to Forecast Lightning Threat

NASA Technical Reports Server (NTRS)

McCaul, William E.; LaCasse, K.; Goodman, S. J.

2006-01-01

Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of recent forecast models such as WRF, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Six-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data yield the most realistic simulations. An array of subjective and objective statistical metrics are employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

A study comparison of two system model performance in estimated lifted index over Indonesia.

NASA Astrophysics Data System (ADS)

lestari, Juliana tri; Wandala, Agie

2018-05-01

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

Post-processing of global model output to forecast point rainfall

NASA Astrophysics Data System (ADS)

Hewson, Tim; Pillosu, Fatima

2016-04-01

ECMWF (the European Centre for Medium range Weather Forecasts) has recently embarked upon a new project to post-process gridbox rainfall forecasts from its ensemble prediction system, to provide probabilistic forecasts of point rainfall. The new post-processing strategy relies on understanding how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. We use a number of simple global model parameters, such as the convective rainfall fraction, to anticipate the sub-grid variability, and then post-process each ensemble forecast into a pdf (probability density function) for a point-rainfall total. The final forecast will comprise the sum of the different pdfs from all ensemble members. The post-processing is essentially a re-calibration exercise, which needs only rainfall totals from standard global reporting stations (and forecasts) to train it. High density observations are not needed. This presentation will describe results from the initial 'proof of concept' study, which has been remarkably successful. Reference will also be made to other useful outcomes of the work, such as gaining insights into systematic model biases in different synoptic settings. The special case of orographic rainfall will also be discussed. Work ongoing this year will also be described. This involves further investigations of which model parameters can provide predictive skill, and will then move on to development of an operational system for predicting point rainfall across the globe. The main practical benefit of this system will be a greatly improved capacity to predict extreme point rainfall, and thereby provide early warnings, for the whole world, of flash flood potential for lead times that extend beyond day 5. This will be incorporated into the suite of products output by GLOFAS (the GLObal Flood Awareness System) which is hosted at ECMWF. As such this work offers a very cost-effective approach to satisfying user needs right around the world. This field has hitherto relied on using very expensive high-resolution ensembles; by their very nature these can only run over small regions, and only for lead times up to about 2 days.

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.

Adaptive correction of ensemble forecasts

NASA Astrophysics Data System (ADS)

Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane

2017-04-01

Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO-LEPS ensemble forecasts. Deterministic verification scores (e.g., mean absolute error, bias) and probabilistic scores (e.g., CRPS) are used to evaluate the post-processing techniques. We conclude that the new adaptive method outperforms the simpler running bias-correction. The proposed adaptive method often outperforms the MBM method in removing bias. The MBM method has the advantage of correcting the ensemble spread, although it needs more training data.

Limitations of JEDI Models | Jobs and Economic Development Impact Models |

Science.gov Websites

precise forecast. The Jobs and Economic Development Impact (JEDI) models are input-output based models for assessing economic impacts and jobs, including JEDI (see Chapter 5, pp. 136-142). The most not reflect many other economic impacts that could affect real-world impacts on jobs from the project

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

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

2015-06-23

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

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Comparing Aircraft Observations of Snowfall to Forecasts Using Single or Two Moment Bulk Water Microphysics Schemes

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.

2010-01-01

High resolution weather forecast models with explicit prediction of hydrometeor type, size distribution, and fall speed may be useful in the development of precipitation retrievals, by providing representative characteristics of frozen hydrometeors. Several single or double-moment microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, allowing for the prediction of up to three ice species. Each scheme incorporates different assumptions regarding the characteristics of their ice classes, particularly in terms of size distribution, density, and fall speed. In addition to the prediction of hydrometeor content, these schemes must accurately represent the vertical profile of water vapor to account for possible attenuation, along with the size distribution, density, and shape characteristics of ice crystals that are relevant to microwave scattering. An evaluation of a particular scheme requires the availability of field campaign measurements. The Canadian CloudSat/CALIPSO Validation Project (C3VP) obtained measurements of ice crystal shapes, size distributions, fall speeds, and precipitation during several intensive observation periods. In this study, C3VP observations obtained during the 22 January 2007 synoptic-scale snowfall event are compared against WRF model output, based upon forecasts using four single-moment and two double-moment schemes available as of version 3.1. Schemes are compared against aircraft observations by examining differences in size distribution, density, and content. In addition to direct measurements from aircraft probes, simulated precipitation can also be converted to equivalent, remotely sensed characteristics through the use of the NASA Goddard Satellite Data Simulator Unit. Outputs from high resolution forecasts are compared against radar and satellite observations emphasizing differences in assumed crystal shape and size distribution characteristics.

Visualizing Coastal Erosion, Overwash and Coastal Flooding in New England

NASA Astrophysics Data System (ADS)

Young Morse, R.; Shyka, T.

2017-12-01

Powerful East Coast storms and their associated storm tides and large, battering waves can lead to severe coastal change through erosion and re-deposition of beach sediment. The United States Geological Survey (USGS) has modeled such potential for geological response using a storm-impact scale that compares predicted elevations of hurricane-induced water levels and associated wave action to known elevations of coastal topography. The resulting storm surge and wave run-up hindcasts calculate dynamic surf zone collisions with dune structures using discrete regime categories of; "collision" (dune erosion), "overwash" and "inundation". The National Weather Service (NWS) recently began prototyping this empirical technique under the auspices of the North Atlantic Regional Team (NART). Real-time erosion and inundation forecasts were expanded to include both tropical and extra-tropical cyclones along vulnerable beaches (hotspots) on the New England coast. Preliminary results showed successful predictions of impact during hurricane Sandy and several intense Nor'easters. The forecasts were verified using observational datasets, including "ground truth" reports from Emergency Managers and storm-based, dune profile measurements organized through a Maine Sea Grant partnership. In an effort to produce real-time visualizations of this forecast output, the Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS) and the Gulf of Maine Research Institute (GMRI) partnered with NART to create graphical products of wave run-up levels for each New England "hotspot". The resulting prototype system updates the forecasts twice daily and allows users the ability to adjust atmospheric and sea state input into the calculations to account for model errors and forecast uncertainty. This talk will provide an overview of the empirical wave run-up calculations, the system used to produce forecast output and a demonstration of the new web based tool.

Development of a flood early warning system and communication with end-users: the Vipava/Vipacco case study in the KULTURisk FP7 project

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Caronna, Paolo; Ranzi, Roberto

2014-05-01

Within the framework of risk communication, the goal of an early warning system is to support the interaction between technicians and authorities (and subsequently population) as a prevention measure. The methodology proposed in the KULTURisk FP7 project aimed to build a closer collaboration between these actors, in the perspective of promoting pro-active actions to mitigate the effects of flood hazards. The transnational (Slovenia/ Italy) Soča/Isonzo case study focused on this concept of cooperation between stakeholders and hydrological forecasters. The DIMOSHONG_VIP hydrological model was calibrated for the Vipava/Vipacco River (650 km2), a tributary of the Soča/Isonzo River, on the basis of flood events occurred between 1998 and 2012. The European Centre for Medium-Range Weather Forecasts (ECMWF) provided the past meteorological forecasts, both deterministic (1 forecast) and probabilistic (51 ensemble members). The resolution of the ECMWF grid is currently about 15 km (Deterministic-DET) and 30 km (Ensemble Prediction System-EPS). A verification was conducted to validate the flood-forecast outputs of the DIMOSHONG_VIP+ECMWF early warning system. Basic descriptive statistics, like event probability, probability of a forecast occurrence and frequency bias were determined. Some performance measures were calculated, such as hit rate (probability of detection) and false alarm rate (probability of false detection). Relative Opening Characteristic (ROC) curves were generated both for deterministic and probabilistic forecasts. These analysis showed a good performance of the early warning system, in respect of the small size of the sample. A particular attention was spent to the design of flood-forecasting output charts, involving and inquiring stakeholders (Alto Adriatico River Basin Authority), hydrology specialists in the field, and common people. Graph types for both forecasted precipitation and discharge were set. Three different risk thresholds were identified ("attention", "pre-alarm" or "alert", "alarm"), with an "icon-style" representation, suitable for communication to civil protection stakeholders or the public. Aiming at showing probabilistic representations in a "user-friendly" way, we opted for the visualization of the single deterministic forecasted hydrograph together with the 5%, 25%, 50%, 75% and 95% percentiles bands of the Hydrological Ensemble Prediction System (HEPS). HEPS is generally used for 3-5 days hydrological forecasts, while the error due to incorrect initial data is comparable to the error due to the lower resolution with respect to the deterministic forecast. In the short term forecasting (12-48 hours) the HEPS-members show obviously a similar tendency; in this case, considering its higher resolution, the deterministic forecast is expected to be more effective. The plot of different forecasts in the same chart allows the use of model outputs from 4/5 days to few hours before a potential flood event. This framework was built to help a stakeholder, like a mayor, a civil protection authority, etc, in the flood control and management operations, and was designed to be included in a wider decision support system.

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

PubMed

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-06-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Roberts, J. B.

2014-12-01

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, Jason B.

2014-01-01

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.

Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

NASA Astrophysics Data System (ADS)

DeMott, C. A.; Klingaman, N. P.

2017-12-01

Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

Transportation Sector Model of the National Energy Modeling System. Volume 1

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. Themore » current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.« less

Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

NASA Astrophysics Data System (ADS)

Fries, K. J.; Kerkez, B.

2017-12-01

We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

A new solar power output prediction based on hybrid forecast engine and decomposition model.

PubMed

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Causes and Model Skill of the Persistent Intense Rainfall and Flooding in Paraguay during the Austral Summer 2015-2016

NASA Astrophysics Data System (ADS)

Doss-Gollin, J.; Munoz, A. G.; Pastén, M.

2017-12-01

During the austral summer 2015-16 severe flooding displaced over 150,000 people on the Paraguay River system in Paraguay, Argentina, and Southern Brazil. This flooding was out of phase with the typical seasonal cycle of the Paraguay River, and was driven by repeated intense rainfall events in the Lower Paraguay River basin. Using a weather typing approach within a diagnostic framework, we show that enhanced moisture inflow from the low-level jet and local convergence associated with baroclinic systems favored the development of mesoscale convective activity and enhanced precipitation. The observed circulation patterns were made more likely by the cross-timescale interactions of multiple climate mechanisms including the strong, mature El Niño event and an active Madden-Julien Oscillation in phases four and five. We also perform a comparison of the rainfall predictability using seasonal forecasts from the Latin American Observatory of Climate Events (OLE2) and sub-seasonal forecasts produced by the ECMWF. We find that the model output precipitation field exhibited limited skill at lead times beyond the synoptic timescale, but that a Model Output Statistics (MOS) approach, in which the leading principal components of the observed rainfall field are regressed on the leading principal components of model-simulated rainfall fields, substantially improves spatial representation of rainfall forecasts. Possible implications for flood preparedness are briefly discussed.

Electron Flux Models for Different Energies at Geostationary Orbit

NASA Technical Reports Server (NTRS)

Boynton, R. J.; Balikhin, M. A.; Sibeck, D. G.; Walker, S. N.; Billings, S. A.; Ganushkina, N.

2016-01-01

Forecast models were derived for energetic electrons at all energy ranges sampled by the third-generation Geostationary Operational Environmental Satellites (GOES). These models were based on Multi-Input Single-Output Nonlinear Autoregressive Moving Average with Exogenous inputs methodologies. The model inputs include the solar wind velocity, density and pressure, the fraction of time that the interplanetary magnetic field (IMF) was southward, the IMF contribution of a solar wind-magnetosphere coupling function proposed by Boynton et al. (2011b), and the Dst index. As such, this study has deduced five new 1 h resolution models for the low-energy electrons measured by GOES (30-50 keV, 50-100 keV, 100-200 keV, 200-350 keV, and 350-600 keV) and extended the existing >800 keV and >2 MeV Geostationary Earth Orbit electron fluxes models to forecast at a 1 h resolution. All of these models were shown to provide accurate forecasts, with prediction efficiencies ranging between 66.9% and 82.3%.

Forecasting hotspots using predictive visual analytics approach

DOEpatents

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Numerical Model Metrics Tools in Support of Navy Operations

NASA Astrophysics Data System (ADS)

Dykes, J. D.; Fanguy, P.

2017-12-01

Increasing demands of accurate ocean forecasts that are relevant to the Navy mission decision makers demand tools that quickly provide relevant numerical model metrics to the forecasters. Increasing modelling capabilities with ever-higher resolution domains including coupled and ensemble systems as well as the increasing volume of observations and other data sources to which to compare the model output requires more tools for the forecaster to enable doing more with less. These data can be appropriately handled in a geographic information system (GIS) fused together to provide useful information and analyses, and ultimately a better understanding how the pertinent model performs based on ground truth.. Oceanographic measurements like surface elevation, profiles of temperature and salinity, and wave height can all be incorporated into a set of layers correlated to geographic information such as bathymetry and topography. In addition, an automated system that runs concurrently with the models on high performance machines matches routinely available observations to modelled values to form a database of matchups with which statistics can be calculated and displayed, to facilitate validation of forecast state and derived variables. ArcMAP, developed by Environmental Systems Research Institute, is a GIS application used by the Naval Research Laboratory (NRL) and naval operational meteorological and oceanographic centers to analyse the environment in support of a range of Navy missions. For example, acoustic propagation in the ocean is described with a three-dimensional analysis of sound speed that depends on profiles of temperature, pressure and salinity predicted by the Navy Coastal Ocean Model. The data and model output must include geo-referencing information suitable for accurately placing the data within the ArcMAP framework. NRL has developed tools that facilitate merging these geophysical data and their analyses, including intercomparisons between model predictions as well as comparison to validation data. This methodology produces new insights and facilitates identification of potential problems in ocean prediction.

Modeling and Analysis of Geoelectric Fields: Extended Solar Shield

NASA Astrophysics Data System (ADS)

Ngwira, C. M.; Pulkkinen, A. A.

2016-12-01

In the NASA Applied Sciences Program Solar Shield project, an unprecedented first-principles-based system to forecast geomagnetically induced current (GIC) in high-voltage power transmission systems was developed. Rapid progress in the field of numerical physics-based space environment modeling has led to major developments over the past few years. In this study modeling and analysis of induced geoelectric fields is discussed. Specifically, we focus on the successful incorporation of 3-D EM transfer functions in the modeling of E-fields, and on the analysis of near real-time simulation outputs used in the Solar Shield forecast system. The extended Solar Shield is a collaborative project between DHS, NASA, NOAA, CUA and EPRI.

Super short term forecasting of photovoltaic power generation output in micro grid

NASA Astrophysics Data System (ADS)

Gong, Cheng; Ma, Longfei; Chi, Zhongjun; Zhang, Baoqun; Jiao, Ran; Yang, Bing; Chen, Jianshu; Zeng, Shuang

2017-01-01

The prediction model combining data mining and support vector machine (SVM) was built. Which provide information of photovoltaic (PV) power generation output for economic operation and optimal control of micro gird, and which reduce influence of power system from PV fluctuation. Because of the characteristic which output of PV rely on radiation intensity, ambient temperature, cloudiness, etc., so data mining was brought in. This technology can deal with large amounts of historical data and eliminate superfluous data, by using fuzzy classifier of daily type and grey related degree. The model of SVM was built, which can dock with information from data mining. Based on measured data from a small PV station, the prediction model was tested. The numerical example shows that the prediction model is fast and accurate.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

Restrictive Factors and Output Forecast of Green Development of Agricultural Industry Based on Gray System

NASA Astrophysics Data System (ADS)

Sun, Fengru

2018-01-01

This paper analyzes the characteristics of agricultural products from the perspective of agricultural production, farmers’ income, adjustment of agricultural structure and environmental improvement, and analyzes the characteristics of agricultural products in LanZhou area. Through data mining and empirical analysis, the regional agriculture (1) forecasting model of gray system with dynamic data processing, combined with the output data of lily in 2004-2003, the yield prediction is predicted and the fitting state is good and the error is small. Finally, combined with the relevant characteristics of the local characteristics of the agricultural industry to make reference, by changing the characteristics of agricultural production as the center of the mindset, and agricultural industrialization and organic combination, take the characteristics of efficient industrialization of agricultural products.

Verification of different forecasts of Hungarian Meteorological Service

NASA Astrophysics Data System (ADS)

Feher, B.

2009-09-01

In this paper I show the results of the forecasts made by the Hungarian Meteorological Service. I focus on the general short- and medium-range forecasts, which contains cloudiness, precipitation, wind speed and temperature for six regions of Hungary. I would like to show the results of some special forecasts as well, such as precipitation predictions which are made for the catchment area of Danube and Tisza rivers, and daily mean temperature predictions used by Hungarian energy companies. The product received by the user is made by the general forecaster, but these predictions are based on the ALADIN and ECMWF outputs. Because of these, the product of the forecaster and the models were also verified. Method like this is able to show us, which weather elements are more difficult to forecast or which regions have higher errors. During the verification procedure the basic errors (mean error, mean absolute error) are calculated. Precipitation amount is classified into five categories, and scores like POD, TS, PC,…etc. were defined by contingency table determined by these categories. The procedure runs fully automatically, all the things forecasters have to do is to print the daily result each morning. Beside the daily result, verification is also made for longer periods like week, month or year. Analyzing the results of longer periods we can say that the best predictions are made for the first few days, and precipitation forecasts are less good for mountainous areas, even, the scores of the forecasters sometimes are higher than the errors of the models. Since forecaster receive results next day, it can helps him/her to reduce mistakes and learn the weakness of the models. This paper contains the verification scores, their trends, the method by which these scores are calculated, and some case studies on worse forecasts.

Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.

PubMed

Valizadeh, Nariman; El-Shafie, Ahmed; Mirzaei, Majid; Galavi, Hadi; Mukhlisin, Muhammad; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

PubMed Central

Mirzaei, Majid; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting. PMID:24790567

Application of Recent Advances in Forward Modeling of Emissions from Boreal and Temperate Wildfires to Real-time Forecasting of Aerosol and Trace Gas Concentrations

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Reid, J. S.; Kasischke, E. S.; Allen, D. J.

2005-12-01

The magnitude of trace gas and aerosol emissions from wildfires is a scientific problem with important implications for atmospheric composition, and is also integral to understanding carbon cycling in terrestrial ecosystems. Recent ecological research on modeling wildfire emissions has integrated theoretical advances derived from ecological fieldwork with improved spatial and temporal databases to produce "post facto" estimates of emissions with high spatial and temporal resolution. These advances have been shown to improve agreement with atmospheric observations at coarse scales, but can in principle be applied to applications, such as forecasting, at finer scales. However, several of the approaches employed in these forward models are incompatible with the requirements of real-time forecasting, requiring modification of data inputs and calculation methods. Because of the differences in data inputs used for real-time and "post-facto" emissions modeling, the key uncertainties in the forward problem are not necessarily the same for these two applications. However, adaptation of these advances in forward modeling to forecasting applications has the potential to improve air quality forecasts, and also to provide a large body of experimental data which can be used to constrain crucial uncertainties in current conceptual models of wildfire emissions. This talk describes a forward modeling method developed at the University of Maryland and its application to the Fire Locating and Modeling of Burning Emissions (FLAMBE) system at the Naval Research Laboratory. Methods for applying the outputs of the NRL aerosol forecasting system to the inverse problem of constraining emissions will also be discussed. The system described can use the feedback supplied by atmospheric observations to improve the emissions source description in the forecasting model, and can also be used for hypothesis testing regarding fire behavior and data inputs.

Sensitivity of WRF precipitation field to assimilation sources in northeastern Spain

NASA Astrophysics Data System (ADS)

Lorenzana, Jesús; Merino, Andrés; García-Ortega, Eduardo; Fernández-González, Sergio; Gascón, Estíbaliz; Hermida, Lucía; Sánchez, José Luis; López, Laura; Marcos, José Luis

2015-04-01

Numerical weather prediction (NWP) of precipitation is a challenge. Models predict precipitation after solving many physical processes. In particular, mesoscale NWP models have different parameterizations, such as microphysics, cumulus or radiation schemes. These facilitate, according to required spatial and temporal resolutions, precipitation fields with increasing reliability. Nevertheless, large uncertainties are inherent to precipitation forecasting. Consequently, assimilation methods are very important. The Atmospheric Physics Group at the University of León in Spain and the Castile and León Supercomputing Center carry out daily weather prediction based on the Weather Research and Forecasting (WRF) model, covering the entire Iberian Peninsula. Forecasts of severe precipitation affecting the Ebro Valley, in the southern Pyrenees range of northeastern Spain, are crucial in the decision-making process for managing reservoirs or initializing runoff models. These actions can avert floods and ensure uninterrupted economic activity in the area. We investigated a set of cases corresponding to intense or severe precipitation patterns, using a rain gauge network. Simulations were performed with a dual objective, i.e., to analyze forecast improvement using a specific assimilation method, and to study the sensitivity of model outputs to different types of assimilation data. A WRF forecast model initialized by an NCEP SST analysis was used as the control run. The assimilation was based on the Meteorological Assimilation Data Ingest System (MADIS) developed by NOAA. The MADIS data used were METAR, maritime, ACARS, radiosonde, and satellite products. The results show forecast improvement using the suggested assimilation method, and differences in the accuracy of forecast precipitation patterns varied with the assimilation data source.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Personalized glucose forecasting for type 2 diabetes using data assimilation

PubMed Central

Albers, David J.; Gluckman, Bruce; Ginsberg, Henry; Hripcsak, George; Mamykina, Lena

2017-01-01

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges. PMID:28448498

Using Satellite-Based Earth Science Data in a Public Health Decision-Support System to Track and Forecast Pollen Events

NASA Astrophysics Data System (ADS)

Hudspeth, W. B.; Budge, A.

2013-12-01

There is widespread recognition within the public health community that ongoing changes in climate are expected to increasingly pose threats to human health. Environmentally induced health risks to populations with respiratory illnesses are a growing concern globally. Of particular concern are dust and smoke events carrying PM2.5 and PM10 particle sizes, ozone, and pollen. There is considerable interest in documenting the precise linkages between changing patterns in the climate and how these shifts impact the prevalence of respiratory illnesses. The establishment of these linkages can drive the development of early warning and forecasting systems to alert health care professionals of impending air-quality events. As a component of a larger NASA-funded project on Integration of Airborne Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems, the Earth Data Analysis Center (EDAC) at the University of New Mexico, is developing web-based visualization and analysis services for forecasting pollen concentration data. This decision-support system, New Mexico's Environmental Public Health Tracking System (NMEPHTS), funded by the Centers for Disease Control (CDC) Environmental Public Health Tracking Network (EPHTN), aims to improve health awareness and services by linking health effects data with levels and frequency of environmental exposure. The forecast of atmospheric events with high pollen concentrations has employed a modified version of the DREAM (Dust Regional Atmospheric Model, a verified model for atmospheric dust transport modeling. In this application, PREAM (Pollen Regional Atmospheric Model) models pollen emission using a MODIS-derived phenology of Juniperus spp. communities. Model outputs are verified and validated with ground-based records of pollen release timing and quantities. Outputs of the PREAM model are post-processed and archived in EDAC's Geographic Storage, Transformation, and Retrieval Engine (GStore) database. The GStore geospatial services platform provides general purpose web services based upon the REST service model, and is capable of data discovery, access, and publication functions, metadata delivery functions, data transformation, and auto-generated OGC services for those data products that can support those services. These services are in turn ingested by New Mexico's EPHTN where end users in the public health community can then assess environmental-pubic health data associations. Advances in web mapping and related technologies open new doors for data providers and users that can deliver data and information in near-real time. In the public health community these technologies are being used to enhance disease and syndromic surveillance systems, visualize environmentally-related events such as pollen and dust events, and to provide focused mapping and analysis capabilities on the desktop. Here we present the current results of the project, and will focus on the challenges encountered in providing reliable and accurate forecast of pollen concentrations, as well as the experience of integrating output results and services into end user applications that can provide timely and meaningful alerts and forecasts.

Automated flare forecasting using a statistical learning technique

NASA Astrophysics Data System (ADS)

Yuan, Yuan; Shih, Frank Y.; Jing, Ju; Wang, Hai-Min

2010-08-01

We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24-hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.

A multi-source data assimilation framework for flood forecasting: Accounting for runoff routing lags

NASA Astrophysics Data System (ADS)

Meng, S.; Xie, X.

2015-12-01

In the flood forecasting practice, model performance is usually degraded due to various sources of uncertainties, including the uncertainties from input data, model parameters, model structures and output observations. Data assimilation is a useful methodology to reduce uncertainties in flood forecasting. For the short-term flood forecasting, an accurate estimation of initial soil moisture condition will improve the forecasting performance. Considering the time delay of runoff routing is another important effect for the forecasting performance. Moreover, the observation data of hydrological variables (including ground observations and satellite observations) are becoming easily available. The reliability of the short-term flood forecasting could be improved by assimilating multi-source data. The objective of this study is to develop a multi-source data assimilation framework for real-time flood forecasting. In this data assimilation framework, the first step is assimilating the up-layer soil moisture observations to update model state and generated runoff based on the ensemble Kalman filter (EnKF) method, and the second step is assimilating discharge observations to update model state and runoff within a fixed time window based on the ensemble Kalman smoother (EnKS) method. This smoothing technique is adopted to account for the runoff routing lag. Using such assimilation framework of the soil moisture and discharge observations is expected to improve the flood forecasting. In order to distinguish the effectiveness of this dual-step assimilation framework, we designed a dual-EnKF algorithm in which the observed soil moisture and discharge are assimilated separately without accounting for the runoff routing lag. The results show that the multi-source data assimilation framework can effectively improve flood forecasting, especially when the runoff routing has a distinct time lag. Thus, this new data assimilation framework holds a great potential in operational flood forecasting by merging observations from ground measurement and remote sensing retrivals.

Forecasting of wet snow avalanche activity: Proof of concept and operational implementation

NASA Astrophysics Data System (ADS)

Gobiet, Andreas; Jöbstl, Lisa; Rieder, Hannes; Bellaire, Sascha; Mitterer, Christoph

2017-04-01

State-of-the-art tools for the operational assessment of avalanche danger include field observations, recordings from automatic weather stations, meteorological analyses and forecasts, and recently also indices derived from snowpack models. In particular, an index for identifying the onset of wet-snow avalanche cycles (LWCindex), has been demonstrated to be useful. However, its value for operational avalanche forecasting is currently limited, since detailed, physically based snowpack models are usually driven by meteorological data from automatic weather stations only and have therefore no prognostic ability. Since avalanche risk management heavily relies on timely information and early warnings, many avalanche services in Europe nowadays start issuing forecasts for the following days, instead of the traditional assessment of the current avalanche danger. In this context, the prognostic operation of detailed snowpack models has recently been objective of extensive research. In this study a new, observationally constrained setup for forecasting the onset of wet-snow avalanche cycles with the detailed snow cover model SNOWPACK is presented and evaluated. Based on data from weather stations and different numerical weather prediction models, we demonstrate that forecasts of the LWCindex as indicator for wet-snow avalanche cycles can be useful for operational warning services, but is so far not reliable enough to be used as single warning tool without considering other factors. Therefore, further development currently focuses on the improvement of the forecasts by applying ensemble techniques and suitable post processing approaches to the output of numerical weather prediction models. In parallel, the prognostic meteo-snow model chain is operationally used by two regional avalanche warning services in Austria since winter 2016/2017 for the first time. Experiences from the first operational season and first results from current model developments will be reported.

Mesoscale data assimilation for a local severe rainfall event with the NHM-LETKF system

NASA Astrophysics Data System (ADS)

Kunii, M.

2013-12-01

This study aims to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches. Here, the local ensemble transform Kalman filter (LETKF) is implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM). The newly developed NHM-LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance. One-way nested analysis in which a finer-resolution LETKF is conducted by using the outputs of an outer model also becomes feasible. These new contents should enhance the potential of the LETKF for convective scale events. The NHM-LETKF is applied to a local severe rainfall event in Japan in 2012. Comparison of the root mean square errors between the model first guess and analysis reveals that the system assimilates observations appropriately. Analysis ensemble spreads indicate a significant increase around the time torrential rainfall occurred, which would imply an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully capture intense rainfalls, suggesting that the system can work effectively for local severe weather. Investigation of probabilistic forecasts by ensemble forecasting indicates that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme is also tested. The experiment results demonstrate that assimilation with a finer-resolution model provides an advantage in the quantitative precipitation forecasting of local severe weather conditions.

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

Forecasting the Effects of Higher Education Appropriations on Local Economies. AIR 1986 Annual Forum Paper.

ERIC Educational Resources Information Center

Prewitt, Sidney A.; And Others

An economic model of the effects of colleges on their communities was developed. The Texas Input-Output Model was modified into a higher education budgetary model. Included were the positive benefits of tax savings and estimates of the net effect on various communities in which state-supported colleges and universities are located. The output…

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

PubMed Central

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-01-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

High-Resolution WRF Forecasts of Lightning Threat

NASA Technical Reports Server (NTRS)

Goodman, S. J.; McCaul, E. W., Jr.; LaCasse, K.

2007-01-01

Tropical Rainfall Measuring Mission (TRMM)lightning and precipitation observations have confirmed the existence of a robust relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of the Weather Research and Forecast (WRF) model, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Initial experiments using 6-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. The WRF has been initialized on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data. An array of subjective and objective statistical metrics is employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

2017-04-01

Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive uncertainty properly. Additionally for the key locations at the international waterways Rhine, Elbe and Danube three competing forecast approaches are currently tested in a pre-operational set-up in order to generate monthly to seasonal (up to 3 months) forecasts: (1) the well-known Ensemble Streamflow Prediction approach (ensemble based on historical meteorology), (2) coupling hydrological models with post-processed outputs from ECMWF's general circulation model (System 4), and (3) a purely statistical approach based on the stable relationship (teleconnection) of global or regional oceanic, climate and hydrological data with river flows. The current results, still pre-operational, reveal the existence of a valuable predictability of water levels and streamflow also at monthly up to seasonal time-scales along the larger rivers used as waterways in Germany. Last but not least insight into the technical set-up of the aforementioned forecasting systems operated at the Federal Institute of Hydrology, which are based on a Delft-FEWS application, will be given focussing on the step-wise extension of the former system by integrating new components in order to meet the growing needs of the customers and to improve and extend the forecast portfolio for waterway users.

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

NASA Astrophysics Data System (ADS)

Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara

2015-09-01

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

NASA Technical Reports Server (NTRS)

Zavordsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

2014-01-01

Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use cases.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

PubMed

Viboud, Cécile; Sun, Kaiyuan; Gaffey, Robert; Ajelli, Marco; Fumanelli, Laura; Merler, Stefano; Zhang, Qian; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro

2018-03-01

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens. Published by Elsevier B.V.

A Method for Forecasting the Commercial Air Traffic Schedule in the Future

NASA Technical Reports Server (NTRS)

Long, Dou; Lee, David; Gaier, Eric; Johnson, Jesse; Kostiuk, Peter

1999-01-01

This report presents an integrated set of models that forecasts air carriers' future operations when delays due to limited terminal-area capacity are considered. This report models the industry as a whole, avoiding unnecessary details of competition among the carriers. To develop the schedule outputs, we first present a model to forecast the unconstrained flight schedules in the future, based on the assumption of rational behavior of the carriers. Then we develop a method to modify the unconstrained schedules, accounting for effects of congestion due to limited NAS capacities. Our underlying assumption is that carriers will modify their operations to keep mean delays within certain limits. We estimate values for those limits from changes in planned block times reflected in the OAG. Our method for modifying schedules takes many means of reducing the delays into considerations, albeit some of them indirectly. The direct actions include depeaking, operating in off-hours, and reducing hub airports'operations. Indirect actions include using secondary airports, using larger aircraft, and selecting new hub airports, which, we assume, have already been modeled in the FAA's TAF. Users of our suite of models can substitute an alternative forecast for the TAF.

Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

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

Brown, C. W.; Hood, Raleigh R.; Long, Wen

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat modelsmore » of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.« less

[Application of artificial neural networks in forecasting the number of circulatory system diseases death toll].

PubMed

Zhang, Ying; Shao, Yi; Shang, Kezheng; Wang, Shigong; Wang, Jinyan

2014-09-01

Set up the model of forecasting the number of circulatorys death toll based on back-propagation (BP) artificial neural networks discuss the relationship between the circulatory system diseases death toll meteorological factors and ambient air pollution. The data of tem deaths, meteorological factors, and ambient air pollution within the m 2004 to 2009 in Nanjing were collected. On the basis of analyzing the ficient between CSDDT meteorological factors and ambient air pollution, leutral network model of CSDDT was built for 2004 - 2008 based on factors and ambient air pollution within the same time, and the data of 2009 est the predictive power of the model. There was a closely system diseases relationship between meteorological factors, ambient air pollution and the circulatory system diseases death toll. The ANN model structure was 17 -16 -1, 17 input notes, 16 hidden notes and 1 output note. The training precision was 0. 005 and the final error was 0. 004 999 42 after 487 training steps. The results of forecast show that predict accuracy over 78. 62%. This method is easy to be finished with smaller error, and higher ability on circulatory system death toll on independent prediction, which can provide a new method for forecasting medical-meteorological forecast and have the value of further research.

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

NASA Astrophysics Data System (ADS)

Vogel, Peter; Knippertz, Peter; Fink, Andreas H.; Schlueter, Andreas; Gneiting, Tilmann

2018-04-01

Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable, and underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007-2014, but overall have little added value compared to climatology. We suspect that the parametrization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

Why Do Model Tropical Cyclones Grow Progressively in Size and Decay in Intensity after Reaching Maturity

DTIC Science & Technology

2015-08-17

the distribution of azimuthally-averaged diabatic heating rate derived from the MM5 output. The coefficients of this equation are deter- mined by the...contributions to the intensification of Hurricane Opal as diagnosed from a GFDL model forecast. Mon. Wea. Rev., 130, 1866–1881. Montgomery, M. T., M. E

Short-Term Energy Outlook Model Documentation: Petroleum Products Supply Module

EIA Publications

2013-01-01

The Petroleum Products Supply Module of the Short-Term Energy Outlook (STEO) model provides forecasts of petroleum refinery inputs (crude oil, unfinished oils, pentanes plus, liquefied petroleum gas, motor gasoline blending components, and aviation gasoline blending components) and refinery outputs (motor gasoline, jet fuel, distillate fuel, residual fuel, liquefied petroleum gas, and other petroleum products).

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The impact of convection in the West African monsoon region on global weather forecasts - explicit vs. parameterised convection simulations using the ICON model

NASA Astrophysics Data System (ADS)

Pante, Gregor; Knippertz, Peter

2017-04-01

The West African monsoon is the driving element of weather and climate during summer in the Sahel region. It interacts with mesoscale convective systems (MCSs) and the African easterly jet and African easterly waves. Poor representation of convection in numerical models, particularly its organisation on the mesoscale, can result in unrealistic forecasts of the monsoon dynamics. Arguably, the parameterisation of convection is one of the main deficiencies in models over this region. Overall, this has negative impacts on forecasts over West Africa itself but may also affect remote regions, as waves originating from convective heating are badly represented. Here we investigate those remote forecast impacts based on daily initialised 10-day forecasts for July 2016 using the ICON model. One set of simulations employs the default setup of the global model with a horizontal grid spacing of 13 km. It is compared with simulations using the 2-way nesting capability of ICON. A second model domain over West Africa (the nest) with 6.5 km grid spacing is sufficient to explicitly resolve MCSs in this region. In the 2-way nested simulations, the prognostic variables of the global model are influenced by the results of the nest through relaxation. The nest with explicit convection is able to reproduce single MCSs much more realistically compared to the stand-alone global simulation with parameterised convection. Explicit convection leads to cooler temperatures in the lower troposphere (below 500 hPa) over the northern Sahel due to stronger evaporational cooling. Overall, the feedback of dynamic variables from the nest to the global model shows clear positive effects when evaluating the output of the global domain of the 2-way nesting simulation and the output of the stand-alone global model with ERA-Interim re-analyses. Averaged over the 2-way nested region, bias and root mean squared error (RMSE) of temperature, geopotential, wind and relative humidity are significantly reduced in the lower troposphere. Outside Africa over the Atlantic or in Europe the effect of the 2-way nesting becomes visible after some days of simulation. The changes in error measures are not as clear as in the nesting region itself but still improvements for some variables at different altitudes are evident, most likely due to a better representation of African easterly waves and Rossby waves. This work shows the importance of the West African region for global weather forecasts and the potential of convective permitting modelling in this region to improve the forecasts even far away from Africa in the future.

The probability forecast evaluation of hazard and storm wind over the territories of Russia and Europe

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2012-04-01

The results of the probability forecast methods of summer storm and hazard wind over territories of Russia and Europe are submitted at this paper. These methods use the hydrodynamic-statistical model of these phenomena. The statistical model was developed for the recognition of the situation involving these phenomena. For this perhaps the samples of the values of atmospheric parameters (n=40) for the presence and for the absence of these phenomena of storm and hazard wind were accumulated. The compressing of the predictors space without the information losses was obtained by special algorithm (k=7<19m/s, the values of 65%24m/s, the values of 75%29m/s or the area of the tornado and strong squalls. The evaluation of this probability forecast was provided by criterion of Brayer. The estimation was successful and was equal for the European part of Russia B=0,37. The application of the probability forecast of storm and hazard winds allows to mitigate the economic losses when the errors of the first and second kinds of storm wind categorical forecast are not so small. A lot of examples of the storm wind probability forecast are submitted at this report.

Features Based Assessments of Warm Season Convective Precipitation Forecasts From the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Bytheway, Janice L.

Forecast models have seen vast improvements in recent years, via increased spatial and temporal resolution, rapid updating, assimilation of more observational data, and continued development and improvement of the representation of the atmosphere. One such model is the High Resolution Rapid Refresh (HRRR) model, a 3 km, hourly-updated, convection-allowing model that has been in development since 2010 and running operationally over the contiguous US since 2014. In 2013, the HRRR became the only US model to assimilate radar reflectivity via diabatic assimilation, a process in which the observed reflectivity is used to induce a latent heating perturbation in the model initial state in order to produce precipitation in those areas where it is indicated by the radar. In order to support the continued development and improvement of the HRRR model with regard to forecasts of convective precipitation, the concept of an assessment is introduced. The assessment process aims to connect model output with observations by first validating model performance then attempting to connect that performance to model assumptions, parameterizations and processes to identify areas for improvement. Observations from remote sensing platforms such as radar and satellite can provide valuable information about three-dimensional storm structure and microphysical properties for use in the assessment, including estimates of surface rainfall, hydrometeor types and size distributions, and column moisture content. A features-based methodology is used to identify warm season convective precipitating objects in the 2013, 2014, and 2015 versions of HRRR precipitation forecasts, Stage IV multisensor precipitation products, and Global Precipitation Measurement (GPM) core satellite observations. Quantitative precipitation forecasts (QPFs) are evaluated for biases in hourly rainfall intensity, total rainfall, and areal coverage in both the US Central Plains (29-49N, 85-105W) and US Mountain West (29-49N, 105-125W). Features identified in the model and Stage IV were tracked through time in order to evaluate forecasts through several hours of the forecast period. The 2013 version of the model was found to produce significantly stronger convective storms than observed, with a slight southerly displacement from the observed storms during the peak hours of convective activity (17-00 UTC). This version of the model also displayed a strong relationship between atmospheric water vapor content and cloud thickness over the central plains. In the 2014 and 2015 versions of the model, storms in the western US were found to be smaller and weaker than the observed, and satellite products (brightness temperatures and reflectivities) simulated using model output indicated that many of the forecast storms contained too much ice above the freezing level. Model upgrades intended to decrease the biases seen in early versions include changes to the reflectivity assimilation, the addition of sub-grid scale cloud parameterizations, changes to the representation of surface processes and the addition of aerosol processes to the microphysics. The effects of these changes are evident in each successive version of the model, with reduced biases in intensity, elimination of the southerly bias, and improved representation of the onset of convection.

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

NASA Astrophysics Data System (ADS)

Pattanayak, Sujata; Mohanty, U. C.

2018-06-01

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

Effects of temperature on flood forecasting: analysis of an operative case study in Alpine basins

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Salandin, A.; Rabuffetti, D.; Montani, A.; Borgonovo, E.; Mancini, M.

2013-04-01

In recent years the interest in the forecast and prevention of natural hazards related to hydro-meteorological events has increased the challenge for numerical weather modelling, in particular for limited area models, to improve the quantitative precipitation forecasts (QPF) for hydrological purposes. After the encouraging results obtained in the MAP D-PHASE Project, we decided to devote further analyses to show recent improvements in the operational use of hydro-meteorological chains, and above all to better investigate the key role played by temperature during snowy precipitation. In this study we present a reanalysis simulation of one meteorological event, which occurred in November 2008 in the Piedmont Region. The attention is focused on the key role of air temperature, which is a crucial feature in determining the partitioning of precipitation in solid and liquid phase, influencing the quantitative discharge forecast (QDF) into the Alpine region. This is linked to the basin ipsographic curve and therefore by the total contributing area related to the snow line of the event. In order to assess hydrological predictions affected by meteorological forcing, a sensitivity analysis of the model output was carried out to evaluate different simulation scenarios, considering the forecast effects which can radically modify the discharge forecast. Results show how in real-time systems hydrological forecasters have to consider also the temperature uncertainty in forecasts in order to better understand the snow dynamics and its effect on runoff during a meteorological warning with a crucial snow line over the basin. The hydrological ensemble forecasts are based on the 16 members of the meteorological ensemble system COSMO-LEPS (developed by ARPA-SIMC) based on the non-hydrostatic model COSMO, while the hydrological model used to generate the runoff simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano.

Predicting Near-surface Winds with WindNinja for Wind Energy Applications

NASA Astrophysics Data System (ADS)

Wagenbrenner, N. S.; Forthofer, J.; Shannon, K.; Butler, B.

2016-12-01

WindNinja is a high-resolution diagnostic wind model widely used by operational wildland fire managers to predict how near-surface winds may influence fire behavior. Many of the features which have made WindNinja successful for wildland fire are also important for wind energy applications. Some of these features include flexible runtime options which allow the user to initialize the model with coarser scale weather model forecasts, sparse weather station observations, or a simple domain-average wind for what-if scenarios; built-in data fetchers for required model inputs, including gridded terrain and vegetation data and operational weather model forecasts; relatively fast runtimes on simple hardware; an extremely user-friendly interface; and a number of output format options, including KMZ files for viewing in Google Earth and GeoPDFs which can be viewed in a GIS. The recent addition of a conservation of mass and momentum solver based on OpenFOAM libraries further increases the utility of WindNinja to modelers in the wind energy sector interested not just in mean wind predictions, but also in turbulence metrics. Here we provide an evaluation of WindNinja forecasts based on (1) operational weather model forecasts and (2) weather station observations provided by the MesoWest API. We also compare the high-resolution WindNinja forecasts to the coarser operational weather model forecasts. For this work we will use the High Resolution Rapid Refresh (HRRR) model and the North American Mesoscale (NAM) model. Forecasts will be evaluated with data collected in the Birch Creek valley of eastern Idaho, USA between June-October 2013. Near-surface wind, turbulence data, and vertical wind and temperature profiles were collected at very high spatial resolution during this field campaign specifically for use in evaluating high-resolution wind models like WindNinja. This work demonstrates the ability of WindNinja to generate very high-resolution wind forecasts for wind energy applications and evaluates the forecasts produced by two different initialization methods with data collected in a broad valley surrounded by complex terrain.

Trends in the predictive performance of raw ensemble weather forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

2015-04-01

Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near-surface wind speed, suggests that improvements to the atmospheric model have an effect quite different from what calibration by statistical post-processing is doing. That is, they are increasing potential skill. Thus this study indicates that (a) further model development is important even if one is just interested in point forecasts, and (b) statistical post-processing is important because it will keep adding skill in the foreseeable future.

Hazard mitigation with cloud model based rainfall and convective data

NASA Astrophysics Data System (ADS)

Gernowo, R.; Adi, K.; Yulianto, T.; Seniyatis, S.; Yatunnisa, A. A.

2018-05-01

Heavy rain in Semarang 15 January 2013 causes flood. It is related to dynamic of weather’s parameter, especially with convection process, clouds and rainfall data. In this case, weather condition analysis uses Weather Research and Forecasting (WRF) model used to analyze. Some weather’s parameters show significant result. Their fluctuations prove there is a strong convection that produces convective cloud (Cumulonimbus). Nesting and 2 domains on WRF model show good output to represent weather’s condition commonly. The results of this study different between output cloud cover rate of observation result and output of model around 6-12 hours is because spinning-up of processing. Satellite Images of MTSAT (Multifunctional Transport Satellite) are used as a verification data to prove the result of WRF. White color of satellite image is Coldest Dark Grey (CDG) that indicates there is cloud’s top. This image consolidates that the output of WRF is good enough to analyze Semarang’s condition when the case happened.

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.

An application of ensemble/multi model approach for wind power production forecast.

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

An Overview of the National Weather Service National Water Model

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

NASA Astrophysics Data System (ADS)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

Enhancement of Local Climate Analysis Tool

NASA Astrophysics Data System (ADS)

Horsfall, F. M.; Timofeyeva, M. M.; Dutton, J.

2012-12-01

The National Oceanographic and Atmospheric Administration (NOAA) National Weather Service (NWS) will enhance its Local Climate Analysis Tool (LCAT) to incorporate specific capabilities to meet the needs of various users including energy, health, and other communities. LCAT is an online interactive tool that provides quick and easy access to climate data and allows users to conduct analyses at the local level such as time series analysis, trend analysis, compositing, correlation and regression techniques, with others to be incorporated as needed. LCAT uses principles of Artificial Intelligence in connecting human and computer perceptions on application of data and scientific techniques in multiprocessing simultaneous users' tasks. Future development includes expanding the type of data currently imported by LCAT (historical data at stations and climate divisions) to gridded reanalysis and General Circulation Model (GCM) data, which are available on global grids and thus will allow for climate studies to be conducted at international locations. We will describe ongoing activities to incorporate NOAA Climate Forecast System (CFS) reanalysis data (CFSR), NOAA model output data, including output from the National Multi Model Ensemble Prediction System (NMME) and longer term projection models, and plans to integrate LCAT into the Earth System Grid Federation (ESGF) and its protocols for accessing model output and observational data to ensure there is no redundancy in development of tools that facilitate scientific advancements and use of climate model information in applications. Validation and inter-comparison of forecast models will be included as part of the enhancement to LCAT. To ensure sustained development, we will investigate options for open sourcing LCAT development, in particular, through the University Corporation for Atmospheric Research (UCAR).

Routine High-Resolution Forecasts/Analyses for the Pacific Disaster Center: User Manual

NASA Technical Reports Server (NTRS)

Roads, John; Han, J.; Chen, S.; Burgan, R.; Fujioka, F.; Stevens, D.; Funayama, D.; Chambers, C.; Bingaman, B.; McCord, C.;

The Use of the Regional Navy Coastal Ocean Model (RNCOM) by the US Navy in Operational Oceanography

NASA Astrophysics Data System (ADS)

Rayburn, J. T.

2016-02-01

The operational RNCOM is a 1/30° resolution nested model run daily by the Naval Oceanographic Office (NAVOCEANO), Stennis Space Center, Mississippi. Operational RNCOM areas are used in combination with the Global HYbrid Coordinate Ocean Model (HYCOM) to provide full global model coverage with enhanced resolution for temperature, salinity, currents in key areas. This talk will discuss two aspects of RNCOM. First, it will focus on how the model is configured. As a nested model, issues to consider include the source of boundary condition, boundary placement, and observational inputs. Secondly, this talk will focus on the strengths and weaknesses RNCOM demonstrates in accurately characterizing ocean condition with respect to HYCOM and how this regional model's output is used by NAVOCEANO Ocean Forecasters to develop operational forecasts.

Retrieving quantifiable social media data from human sensor networks for disaster modeling and crisis mapping

NASA Astrophysics Data System (ADS)

Aulov, Oleg

This dissertation presents a novel approach that utilizes quantifiable social media data as a human aware, near real-time observing system, coupled with geophysical predictive models for improved response to disasters and extreme events. It shows that social media data has the potential to significantly improve disaster management beyond informing the public, and emphasizes the importance of different roles that social media can play in management, monitoring, modeling and mitigation of natural and human-caused extreme disasters. In the proposed approach Social Media users are viewed as "human sensors" that are "deployed" in the field, and their posts are considered to be "sensor observations", thus different social media outlets all together form a Human Sensor Network. We utilized the "human sensor" observations, as boundary value forcings, to show improved geophysical model forecasts of extreme disaster events when combined with other scientific data such as satellite observations and sensor measurements. Several recent extreme disasters are presented as use case scenarios. In the case of the Deepwater Horizon oil spill disaster of 2010 that devastated the Gulf of Mexico, the research demonstrates how social media data from Flickr can be used as a boundary forcing condition of GNOME oil spill plume forecast model, and results in an order of magnitude forecast improvement. In the case of Hurricane Sandy NY/NJ landfall impact of 2012, we demonstrate how the model forecasts, when combined with social media data in a single framework, can be used for near real-time forecast validation, damage assessment and disaster management. Owing to inherent uncertainties in the weather forecasts, the NOAA operational surge model only forecasts the worst-case scenario for flooding from any given hurricane. Geolocated and time-stamped Instagram photos and tweets allow near real-time assessment of the surge levels at different locations, which can validate model forecasts, give timely views of the actual levels of surge, as well as provide an upper bound beyond which the surge did not spread. Additionally, we developed AsonMaps---a crisis-mapping tool that combines dynamic model forecast outputs with social media observations and physical measurements to define the regions of event impacts.

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Validating the southern variant forest vegetation simulator height predictions on southeastern hardwoods in Kentucky and Tennessee

Treesearch

Bernard R. Parresol; Steven C. Stedman

2004-01-01

The accuracy of forest growth and yield forecasts affects the quality of forest management decisions (Rauscher et al. 2000). Users of growth and yield models want assurance that model outputs are reasonable and mimic local/regional forest structure and composition and accurately reflect the influences of stand dynamics such as competition and disturbance. As such,...

Estimates of emergency operating capacity in U.S. manufacturing industries: 1994--2005

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

Belzer, D.B.

1997-02-01

To develop integrated policies for mobilization preparedness, planners require estimates and projections of available productive capacity during national emergency conditions. This report develops projections of national emergency operating capacity (EOC) for 458 US manufacturing industries at the 4-digit Standard Industrial Classification (SIC) level. These measures are intended for use in planning models that are designed to predict the demands for detailed industry sectors that would occur under conditions such as a military mobilization or a major national disaster. This report is part of an ongoing series of studies prepared by the Pacific Northwest National Laboratory to support mobilization planning studiesmore » of the Federal Emergency Planning Agency/US Department of Defense (FEMA/DOD). Earlier sets of EOC estimates were developed in 1985 and 1991. This study presents estimates of EOC through 2005. As in the 1991 study, projections of capacity were based upon extrapolations of equipment capital stocks. The methodology uses time series regression models based on industry data to obtain a response function of industry capital stock to levels of industrial output. The distributed lag coefficients of these response function are then used with projected outputs to extrapolate the 1994 level of EOC. Projections of industrial outputs were taken from the intermediate-term forecast of the US economy prepared by INFORUM (Interindustry Forecasting Model, University of Maryland) in the spring of 1996.« less

How Hydroclimate Influences the Effectiveness of Particle Filter Data Assimilation of Streamflow in Initializing Short- to Medium-range Streamflow Forecasts

NASA Astrophysics Data System (ADS)

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

2017-12-01

Short- to medium-range (1- to 7-day) streamflow forecasts are important for flood control operations and in issuing potentially life-save flood warnings. In the U.S., the National Weather Service River Forecast Centers (RFCs) issue such forecasts in real time, depending heavily on a manual data assimilation (DA) approach. Forecasters adjust model inputs, states, parameters and outputs based on experience and consideration of a range of supporting real-time information. Achieving high-quality forecasts from new automated, centralized forecast systems will depend critically on the adequacy of automated DA approaches to make analogous corrections to the forecasting system. Such approaches would further enable systematic evaluation of real-time flood forecasting methods and strategies. Toward this goal, we have implemented a real-time Sequential Importance Resampling particle filter (SIR-PF) approach to assimilate observed streamflow into simulated initial hydrologic conditions (states) for initializing ensemble flood forecasts. Assimilating streamflow alone in SIR-PF improves simulated streamflow and soil moisture during the model spin up period prior to a forecast, with consequent benefits for forecasts. Nevertheless, it only consistently limits error in simulated snow water equivalent during the snowmelt season and in basins where precipitation falls primarily as snow. We examine how the simulated initial conditions with and without SIR-PF propagate into 1- to 7-day ensemble streamflow forecasts. Forecasts are evaluated in terms of reliability and skill over a 10-year period from 2005-2015. The focus of this analysis is on how interactions between hydroclimate and SIR-PF performance impact forecast skill. To this end, we examine forecasts for 5 hydroclimatically diverse basins in the western U.S. Some of these basins receive most of their precipitation as snow, others as rain. Some freeze throughout the mid-winter while others experience significant mid-winter melt events. We describe the methodology and present seasonal and inter-basin variations in DA-enhanced forecast skill.

A comparison of model short-range forecasts and the ARM Microbase data

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

Hnilo, J J

2006-09-22

For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the 'Microbase' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averagedmore » to 3hourly data for direct comparison to our model output.« less

Preliminary results and assessment of the MAR outputs over High Mountain Asia

NASA Astrophysics Data System (ADS)

Linares, M.; Tedesco, M.; Margulis, S. A.; Cortés, G.; Fettweis, X.

2017-12-01

Lack of ground measurements has made the use of regional climate models (RCMs) over the High Mountain Asia (HMA) pivotal for understanding the impact of climate change on the hydrological cycle and on the cryosphere. Here, we show an analysis of the assessment of the outputs of Modèle Atmosphérique Régionale (MAR) model RCM over the HMA region as part of the NASA-funded project `Understanding and forecasting changes in High Mountain Asia snow hydrology via a novel Bayesian reanalysis and modeling approach'. The first step was to evaluate the impact of the different forcings on MAR outputs. To this aim, we performed simulations for the 2007 - 2008 and 2014 - 2015 years forcing MAR at its boundaries either with reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) or from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The comparison between the outputs obtained with the two forcings indicates that the impact on MAR simulations depends on specific parameters. For example, in case of surface pressure the maximum percentage error is 0.09 % while the 2-m air temperature has a maximum percentage error of 103.7%. Next, we compared the MAR outputs with reanalysis data fields over the region of interest. In particular, we evaluated the following parameters: surface pressure, snow depth, total cloud cover, two meter temperature, horizontal wind speed, vertical wind speed, wind speed, surface new solar radiation, skin temperature, surface sensible heat flux, and surface latent heat flux. Lastly, we report results concerning the assessment of MAR surface albedo and surface temperature over the region through MODIS remote sensing products. Next steps are to determine whether RCMs and reanalysis datasets are effective at capturing snow and snowmelt runoff processes in the HMA region through a comparison with in situ datasets. This will help determine what refinements are necessary to improve RCM outputs.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Assimilation of NUCAPS Retrieved Profiles in GSI for Unique Forecasting Applications

NASA Technical Reports Server (NTRS)

Berndt, Emily Beth; Zavodsky, Bradley; Srikishen, Jayanthi; Blankenship, Clay

2015-01-01

Hyperspectral IR profiles can be assimilated in GSI as a separate observation other than radiosondes with only changes to tables in the fix directory. Assimilation of profiles does produce changes to analysis fields and evidenced by: Innovations larger than +/-2.0 K are present and represent where individual profiles impact the final temperature analysis.The updated temperature analysis is colder behind the cold front and warmer in the warm sector. The updated moisture analysis is modified more in the low levels and tends to be drier than the original model background Analysis of model output shows: Differences relative to 13-km RAP analyses are smaller when profiles are assimilated with NUCAPS errors. CAPE is under-forecasted when assimilating NUCAPS profiles, which could be problematic for severe weather forecasting Refining the assimilation technique to incorporate an error covariance matrix and creating a separate GSI module to assimilate satellite profiles may improve results.

A comparison of observed and numerically predicted eddy kinetic energy budgets for a developing extratropical cyclone

NASA Technical Reports Server (NTRS)

Dare, P. M.; Smith, P. J.

1983-01-01

The eddy kinetic energy budget is calculated for a 48-hour forecast of an intense occluding winter cyclone associated with a strong well-developed jet stream. The model output consists of the initialized (1200 GMT January 9, 1975) and the 12, 24, 36, and 48 hour forecast fields from the Drexel/NCAR Limited Area Mesoscale Prediction System (LAMPS) model. The LAMPS forecast compares well with observations for the first 24 hours, but then overdevelops the low-level cyclone while inadequately developing the upper-air wave and jet. Eddy kinetic energy was found to be concentrated in the upper-troposphere with maxima flanking the primary trough. The increases in kinetic energy were found to be due to an excess of the primary source term of kinetic energy content, which is the horizontal flux of eddy kinetic energy over the primary sinks, and the generation and dissipation of eddy kinetic energy.

Model documentation Natural Gas Transmission and Distribution Model of the National Energy Modeling System. Volume 1

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

NONE

1996-02-26

The Natural Gas Transmission and Distribution Model (NGTDM) of the National Energy Modeling System is developed and maintained by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting. This report documents the archived version of the NGTDM that was used to produce the natural gas forecasts presented in the Annual Energy Outlook 1996, (DOE/EIA-0383(96)). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic approach, and provides detail on the methodology employed. Previously this report represented Volume I of amore » two-volume set. Volume II reported on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.« less

Worldwide transportation/energy demand, 1975-2000. Revised Variflex model projections

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

Ayres, R.U.; Ayres, L.W.

1980-03-01

The salient features of the transportation-energy relationships that characterize the world of 1975 are reviewed, and worldwide (34 countries) long-range transportation demand by mode to the year 2000 is reviewed. A worldwide model is used to estimate future energy demand for transportation. Projections made by the forecasting model indicate that in the year 2000, every region will be more dependent on petroleum for the transportation sector than it was in 1975. This report is intended to highlight certain trends and to suggest areas for further investigation. Forecast methodology and model output are described in detail in the appendices. The reportmore » is one of a series addressing transportation energy consumption; it supplants and replaces an earlier version published in October 1978 (ORNL/Sub-78/13536/1).« less

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

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

Pennock, K.

AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs.more » The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.« less

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

NASA Astrophysics Data System (ADS)

Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

2018-02-01

Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

Wet snow hazard for power lines: a forecast and alert system applied in Italy

NASA Astrophysics Data System (ADS)

Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

2011-09-01

Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

Assessing the Operational Robustness of the Homer Model for Marine Corps Use in Expeditionary Environments

DTIC Science & Technology

2014-06-01

systems. It can model systems including both conventional, diesel powered generators and renewable power sources such as photovoltaic arrays and wind...conducted an experiment where he assessed the capabilities of the HOMER model in forecasting the power output of a solar panel at NPS [32]. In his ex...energy efficiency in expeditionary operations, the HOMER micropower optimization model provides potential to serve as a powerful tool for improving

Sun-to-Earth simulations of geo-effective Coronal Mass Ejections with EUHFORIA: a heliospheric-magnetospheric model chain approach

NASA Astrophysics Data System (ADS)

Scolini, C.; Verbeke, C.; Gopalswamy, N.; Wijsen, N.; Poedts, S.; Mierla, M.; Rodriguez, L.; Pomoell, J.; Cramer, W. D.; Raeder, J.

2017-12-01

Coronal Mass Ejections (CMEs) and their interplanetary counterparts are considered to be the major space weather drivers. An accurate modelling of their onset and propagation up to 1 AU represents a key issue for more reliable space weather forecasts, and predictions about their actual geo-effectiveness can only be performed by coupling global heliospheric models to 3D models describing the terrestrial environment, e.g. magnetospheric and ionospheric codes in the first place. In this work we perform a Sun-to-Earth comprehensive analysis of the July 12, 2012 CME with the aim of testing the space weather predictive capabilities of the newly developed EUHFORIA heliospheric model integrated with the Gibson-Low (GL) flux rope model. In order to achieve this goal, we make use of a model chain approach by using EUHFORIA outputs at Earth as input parameters for the OpenGGCM magnetospheric model. We first reconstruct the CME kinematic parameters by means of single- and multi- spacecraft reconstruction methods based on coronagraphic and heliospheric CME observations. The magnetic field-related parameters of the flux rope are estimated based on imaging observations of the photospheric and low coronal source regions of the eruption. We then simulate the event with EUHFORIA, testing the effect of the different CME kinematic input parameters on simulation results at L1. We compare simulation outputs with in-situ measurements of the Interplanetary CME and we use them as input for the OpenGGCM model, so to investigate the magnetospheric response to solar perturbations. From simulation outputs we extract some global geomagnetic activity indexes and compare them with actual data records and with results obtained by the use of empirical relations. Finally, we discuss the forecasting capabilities of such kind of approach and its future improvements.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Forecasting runout of rock and debris avalanches

USGS Publications Warehouse

Iverson, Richard M.; Evans, S.G.; Mugnozza, G.S.; Strom, A.; Hermanns, R.L.

2006-01-01

Physically based mathematical models and statistically based empirical equations each may provide useful means of forecasting runout of rock and debris avalanches. This paper compares the foundations, strengths, and limitations of a physically based model and a statistically based forecasting method, both of which were developed to predict runout across three-dimensional topography. The chief advantage of the physically based model results from its ties to physical conservation laws and well-tested axioms of soil and rock mechanics, such as the Coulomb friction rule and effective-stress principle. The output of this model provides detailed information about the dynamics of avalanche runout, at the expense of high demands for accurate input data, numerical computation, and experimental testing. In comparison, the statistical method requires relatively modest computation and no input data except identification of prospective avalanche source areas and a range of postulated avalanche volumes. Like the physically based model, the statistical method yields maps of predicted runout, but it provides no information on runout dynamics. Although the two methods differ significantly in their structure and objectives, insights gained from one method can aid refinement of the other.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

Technological change, depletion and environmental policy in the offshore oil and gas industry

NASA Astrophysics Data System (ADS)

Managi, Shunsuke

Technological change is central to maintaining standards of living in modern economies with finite resources and increasingly stringent environmental goals. Successful environmental policies can contribute to efficiency by encouraging, rather than inhibiting, technological innovation. However, little research to date has focused on the design and implementation of environmental regulations that encourage technological progress, or in insuring productivity improvements in the face of depletion of natural resources and increasing stringency of environmental regulations. This study models and measures productivity change, with an application to offshore oil and gas production in the Gulf of Mexico using Data Envelopment Analysis. This is an important application because energy resources are central to sustaining our economy. The net effects of technological progress and depletion on productivity of offshore oil and gas production are measured using a unique field-level set of data of production from all wells in the Gulf of Mexico over the time period from 1946--1998. Results are consistent with the hypothesis that technological progress has mitigated depletion effects over the study period, but the pattern differs from the conventional wisdom for nonrenewable resource industries. The Porter Hypothesis was recast, and revised version was tested. The Porter Hypothesis states that well designed environmental regulations can potentially contribute to productive efficiency in the long run by encouraging innovation. The Porter Hypothesis was recast to include market and nonmarket outputs. Our results support the recast version of Porter hypothesis, which examine productivity of joint production of market and environmental outputs. But we find no evidence for the standard formulation of the Porter hypothesis, that increased stringency of environmental regulation lead to increased productivity of market outputs and therefore increased industry profits. The model is used to forecast market and environmental outputs under alternative policy scenarios. Reliable baseline forecast and response to different policy actions of production and pollution are critical to the formation of sound energy and environmental policy. Forecast of production and pollution until year 2050 are generated from the model. Detailed policy scenarios provide quantitative assessments of potential benefits that indicate the significance of potential benefits of technological change and well-designed environmental policy.

The impact of the characteristics of volcanic ash on forecasting.

NASA Astrophysics Data System (ADS)

Beckett, Frances; Hort, Matthew; Millington, Sarah; Stevenson, John; Witham, Claire

2013-04-01

The eruption of Eyjafjallajökull during April - May 2010 and Grímsvötn in May 2011, Iceland, caused the widespread dispersion of volcanic ash across the NE Atlantic, and ultimately into UK and European airspace. This resulted in thousands of flights to and from affected countries across Europe to be cancelled. The Met Office, UK, is the home of the London VAAC, a Volcanic Ash Advisory Centre, and as such is responsible for providing reports and forecasts for the movement of volcanic ash clouds covering the UK, Iceland and the north-eastern part of the North Atlantic ocean. To forecast the dispersion of volcanic ash requires that the sedimentation of ash particles through the atmosphere is effectively modelled. The settling velocity of an ash particle is a function of its size, shape and density, plus the density and viscosity of the air through which it is falling. We consider the importance of characterising the physical properties of ash when modelling the long range dispersion of ash particles through the atmosphere. Using the Reynolds number dependent scheme employed by NAME, the Lagrangian particle model used operationally by the Met Office, we calculate the settling velocity and thus the maximum travel distance of an ash particle through an idealised atmosphere as a function of its size, shape and density. The results are compared to measured particle sizes from deposits across Europe following the eruption of Eyjafjallajökull in 2010. Further, the particle size distribution (PSD) of ash in a volcanic cloud with time is modelled using NAME: the particle density distribution and particle shape factor are varied and the modelled PSD compared to the PSD measured in the ash cloud during the eruption of Eyjafjallajökull in 2010 by the FAAM research aircraft. The influence of the weather on PSD is also considered by comparing model output using an idealised atmosphere to output using NWP driven meteorological fields. We discuss the sensitivity of forecasts of the dispersion of volcanic ash to the representation of particle characteristics in NAME, the importance of representing the weather in ash fall models, and the implications of these results for the operational forecasting of volcanic ash dispersion at the London VAAC.

SMOS Soil Moisture Data Assimilation in the NASA Land Information System: Impact on LSM Initialization and NWP Forecasts

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Case, Jonathan L.; Zavodsky, Bradley

2015-01-01

Land surface models are important components of numerical weather prediction (NWP) models, partitioning incoming energy into latent and sensitive heat fluxes that affect boundary layer growth and destabilization. During warm-season months, diurnal heating and convective initiation depend strongly on evapotranspiration and available boundary layer moisture, which are substantially affected by soil moisture content. Therefore, to properly simulate warm-season processes in NWP models, an accurate initialization of the land surface state is important for accurately depicting the exchange of heat and moisture between the surface and boundary layer. In this study, soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) satellite radiometer are assimilated into the Noah Land Surface Model via an Ensemble Kalman Filter embedded within the NASA Land Information System (LIS) software framework. The output from LIS-Noah is subsequently used to initialize runs of the Weather Research and Forecasting (WRF) NWP model. The impact of assimilating SMOS retrievals is assessed by initializing the WRF model with LIS-Noah output obtained with and without SMOS data assimilation. The southeastern United States is used as the domain for a preliminary case study. During the summer months, there is extensive irrigation in the lower Mississippi Valley for rice and other crops. The irrigation is not represented in the meteorological forcing used to drive the LIS-Noah integration, but the irrigated areas show up clearly in the SMOS soil moisture retrievals, resulting in a case with a large difference in initial soil moisture conditions. The impact of SMOS data assimilation on both Noah soil moisture fields and on short-term (0-48 hour) WRF weather forecasts will be presented.

On-line applications of numerical models in the Black Sea GIS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Khaliulin, A.; Zodiatis, G.; Nikolaidis, A.; Nikolaidis, M.; Stylianou, Stavros

2017-09-01

The Black Sea Geographical Information System (GIS) is developed based on cutting edge information technologies, and provides automated data processing and visualization on-line. Mapserver is used as a mapping service; the data are stored in MySQL DBMS; PHP and Python modules are utilized for data access, processing, and exchange. New numerical models can be incorporated in the GIS environment as individual software modules, compiled for a server-based operational system, providing interaction with the GIS. A common interface allows setting the input parameters; then the model performs the calculation of the output data in specifically predefined files and format. The calculation results are then passed to the GIS for visualization. Initially, a test scenario of integration of a numerical model into the GIS was performed, using software, developed to describe a two-dimensional tsunami propagation in variable basin depth, based on a linear long surface wave model which is legitimate for more than 5 m depth. Furthermore, the well established oil spill and trajectory 3-D model MEDSLIK (http://www.oceanography.ucy.ac.cy/medslik/) was integrated into the GIS with more advanced GIS functionality and capabilities. MEDSLIK is able to forecast and hind cast the trajectories of oil pollution and floating objects, by using meteo-ocean data and the state of oil spill. The MEDSLIK module interface allows a user to enter all the necessary oil spill parameters, i.e. date and time, rate of spill or spill volume, forecasting time, coordinates, oil spill type, currents, wind, and waves, as well as the specification of the output parameters. The entered data are passed on to MEDSLIK; then the oil pollution characteristics are calculated for pre-defined time steps. The results of the forecast or hind cast are then visualized upon a map.

Forecasting of Storm Surge Floods Using ADCIRC and Optimized DEMs

NASA Technical Reports Server (NTRS)

Valenti, Elizabeth; Fitzpatrick, Patrick

2005-01-01

Increasing the accuracy of storm surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite element based codes. It affords a capability for simulating tidal circulation and storm surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate.

Forecasting the portuguese stock market time series by using artificial neural networks

NASA Astrophysics Data System (ADS)

Isfan, Monica; Menezes, Rui; Mendes, Diana A.

2010-04-01

In this paper, we show that neural networks can be used to uncover the non-linearity that exists in the financial data. First, we follow a traditional approach by analysing the deterministic/stochastic characteristics of the Portuguese stock market data and some typical features are studied, like the Hurst exponents, among others. We also simulate a BDS test to investigate nonlinearities and the results are as expected: the financial time series do not exhibit linear dependence. Secondly, we trained four types of neural networks for the stock markets and used the models to make forecasts. The artificial neural networks were obtained using a three-layer feed-forward topology and the back-propagation learning algorithm. The quite large number of parameters that must be selected to develop a neural network forecasting model involves some trial and as a consequence the error is not small enough. In order to improve this we use a nonlinear optimization algorithm to minimize the error. Finally, the output of the 4 models is quite similar, leading to a qualitative forecast that we compare with the results of the application of k-nearest-neighbor for the same time series.

New technique for ensemble dressing combining Multimodel SuperEnsemble and precipitation PDF

NASA Astrophysics Data System (ADS)

Cane, D.; Milelli, M.

2009-09-01

The Multimodel SuperEnsemble technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the ensemble dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.

Regional Development Impacts Multi-Regional - Multi-Industry Model (MRMI) Users Manual,

DTIC Science & Technology

1982-09-01

indicators, described in Chapter 2, are estimated as well. Finally, MRMI is flexible, as it can incorporate alternative macroeconomic , national inter...national and regional economic contexts and data sources for estimating macroeconomic and direct impacts data. Considerations for ensuring consistency...Chapter 4 is devoted to model execution and the interpretation of its output. As MRMI forecasts are based upon macroeconomic , national inter-industry

Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change [includes Supporting Information

Treesearch

Seth J. Wenger; Daniel J. Isaak; Charlie Luce; Helen M. Neville; Kurt D. Fausch; Jason B. Dunham; Daniel C. Dauwalter; Michael K. Young; Marketa M. Elsner; Bruce E. Rieman; Alan F. Hamlet; Jack E. Williams

2011-01-01

Broad-scale studies of climate change effects on freshwater species have focused mainly on temperature, ignoring critical drivers such as flow regime and biotic interactions. We use downscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows and increased temperatures on four interacting species of trout...

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

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

Zhang, Xuesong; Liang, Faming; Yu, Beibei

2011-11-09

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associatedmore » with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.« less

A probabilistic approach to the drag-based model

NASA Astrophysics Data System (ADS)

Napoletano, Gianluca; Forte, Roberta; Moro, Dario Del; Pietropaolo, Ermanno; Giovannelli, Luca; Berrilli, Francesco

2018-02-01

The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.

Predicting the Magnetic Properties of ICMEs: A Pragmatic View

NASA Astrophysics Data System (ADS)

Riley, P.; Linker, J.; Ben-Nun, M.; Torok, T.; Ulrich, R. K.; Russell, C. T.; Lai, H.; de Koning, C. A.; Pizzo, V. J.; Liu, Y.; Hoeksema, J. T.

2017-12-01

The southward component of the interplanetary magnetic field plays a crucial role in being able to successfully predict space weather phenomena. Yet, thus far, it has proven extremely difficult to forecast with any degree of accuracy. In this presentation, we describe an empirically-based modeling framework for estimating Bz values during the passage of interplanetary coronal mass ejections (ICMEs). The model includes: (1) an empirically-based estimate of the magnetic properties of the flux rope in the low corona (including helicity and field strength); (2) an empirically-based estimate of the dynamic properties of the flux rope in the high corona (including direction, speed, and mass); and (3) a physics-based estimate of the evolution of the flux rope during its passage to 1 AU driven by the output from (1) and (2). We compare model output with observations for a selection of events to estimate the accuracy of this approach. Importantly, we pay specific attention to the uncertainties introduced by the components within the framework, separating intrinsic limitations from those that can be improved upon, either by better observations or more sophisticated modeling. Our analysis suggests that current observations/modeling are insufficient for this empirically-based framework to provide reliable and actionable prediction of the magnetic properties of ICMEs. We suggest several paths that may lead to better forecasts.

Pathways to designing and running an operational flood forecasting system: an adventure game!

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma

2017-04-01

In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Forecasting Global Horizontal Irradiance Using the LETKF and a Combination of Advected Satellite Images and Sparse Ground Sensors

NASA Astrophysics Data System (ADS)

Harty, T. M.; Lorenzo, A.; Holmgren, W.; Morzfeld, M.

2017-12-01

The irradiance incident on a solar panel is the main factor in determining the power output of that panel. For this reason, accurate global horizontal irradiance (GHI) estimates and forecasts are critical when determining the optimal location for a solar power plant, forecasting utility scale solar power production, or forecasting distributed, behind the meter rooftop solar power production. Satellite images provide a basis for producing the GHI estimates needed to undertake these objectives. The focus of this work is to combine satellite derived GHI estimates with ground sensor measurements and an advection model. The idea is to use accurate but sparsely distributed ground sensors to improve satellite derived GHI estimates which can cover large areas (the size of a city or a region of the United States). We use a Bayesian framework to perform the data assimilation, which enables us to produce irradiance forecasts and associated uncertainties which incorporate both satellite and ground sensor data. Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). The advection model, driven by wind forecasts from a numerical weather model, simulates cloud motion between measurements. We use the Local Ensemble Transform Kalman Filter (LETKF) to perform the data assimilation. We present preliminary results towards making such a system useful in an operational context. We explain how localization and inflation in the LETKF, perturbations of wind-fields, and random perturbations of the advection model, affect the accuracy of our estimates and forecasts. We present experiments showing the accuracy of our forecasted GHI over forecast-horizons of 15 mins to 1 hr. The limitations of our approach and future improvements are also discussed.

Forecast of geomagnetic storms using CME parameters and the WSA-ENLIL model

NASA Astrophysics Data System (ADS)

Moon, Y.; Lee, J.; Jang, S.; Na, H.; Lee, J.

2013-12-01

Intense geomagnetic storms are caused by coronal mass ejections (CMEs) from the Sun and their forecast is quite important in protecting space- and ground-based technological systems. The onset and strength of geomagnetic storms depend on the kinematic and magnetic properties of CMEs. Current forecast techniques mostly use solar wind in-situ measurements that provide only a short lead time. On the other hand, techniques using CME observations near the Sun have the potential to provide 1-3 days of lead time before the storm occurs. Therefore, one of the challenging issues is to forecast interplanetary magnetic field (IMF) southward components and hence geomagnetic storm strength with a lead-time on the order of 1-3 days. We are going to answer the following three questions: (1) when does a CME arrive at the Earth? (2) what is the probability that a CME can induce a geomagnetic storm? and (3) how strong is the storm? To address the first question, we forecast the arrival time and other physical parameters of CMEs at the Earth using the WSA-ENLIL model with three CME cone types. The second question is answered by examining the geoeffective and non-geoeffective CMEs depending on CME observations (speed, source location, earthward direction, magnetic field orientation, and cone-model output). The third question is addressed by examining the relationship between CME parameters and geomagnetic indices (or IMF southward component). The forecast method will be developed with a three-stage approach, which will make a prediction within four hours after the solar coronagraph data become available. We expect that this study will enable us to forecast the onset and strength of a geomagnetic storm a few days in advance using only CME parameters and the physics-based models.

Simulating seasonal tropical cyclone intensities at landfall along the South China coast

NASA Astrophysics Data System (ADS)

Lok, Charlie C. F.; Chan, Johnny C. L.

2018-04-01

A numerical method is developed using a regional climate model (RegCM3) and the Weather Forecast and Research (WRF) model to predict seasonal tropical cyclone (TC) intensities at landfall for the South China region. In designing the model system, three sensitivity tests have been performed to identify the optimal choice of the RegCM3 model domain, WRF horizontal resolution and WRF physics packages. Driven from the National Centers for Environmental Prediction Climate Forecast System Reanalysis dataset, the model system can produce a reasonable distribution of TC intensities at landfall on a seasonal scale. Analyses of the model output suggest that the strength and extent of the subtropical ridge in the East China Sea are crucial to simulating TC landfalls in the Guangdong and Hainan provinces. This study demonstrates the potential for predicting TC intensities at landfall on a seasonal basis as well as projecting future climate changes using numerical models.

Season-ahead streamflow forecast informed tax strategies for semi-arid water rights markets

NASA Astrophysics Data System (ADS)

Delorit, J. D.; Block, P. J.

2016-12-01

In many semi-arid regions multisectoral demands stress available water supplies. The Elqui River valley of north central Chile, which draws on limited capacity reservoirs supplied largely by annually variable snowmelt, is one of these cases. This variability forces water managers to develop demand-based allocation strategies which have typically resulted in water right volume reductions, applied equally per right. Compounding this issue is often deferred or delayed infrastructure investments, which has been linked Chile's Coasian approach to water markets, under which rights holders do not pay direct procurement costs, non-use fees, nor taxes. Here we build upon our previous research using forecasts of likely water rights reductions, informed by season-ahead prediction models of October-January (austral growing season) streamflow, to construct annual, forecast-sensitive, per right tax. We believe this tax, to be borne by right holders, will improve the beneficial use of water resources by stimulating water rights trading and improving system efficiency by generating funds for infrastructure investment, thereby reducing free-ridership and conflict between rights holders. Research outputs will include sectoral per right tax assessments, tax revenue generation, Elqui River valley economic output, and water rights trading activity.

Advances in a distributed approach for ocean model data interoperability

USGS Publications Warehouse

Signell, Richard P.; Snowden, Derrick P.

2014-01-01

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

A domain analysis approach to clear-air turbulence forecasting using high-density in-situ measurements

NASA Astrophysics Data System (ADS)

Abernethy, Jennifer A.

Pilots' ability to avoid clear-air turbulence (CAT) during flight affects the safety of the millions of people who fly commercial airlines and other aircraft, and turbulence costs millions in injuries and aircraft maintenance every year. Forecasting CAT is not straightforward, however; microscale features like the turbulence eddies that affect aircraft (100m) are below the current resolution of operational numerical weather prediction (NWP) models, and the only evidence of CAT episodes, until recently, has been sparse, subjective reports from pilots known as PIREPs. To forecast CAT, researchers use a simple weighted sum of top-performing turbulence indicators derived from NWP model outputs---termed diagnostics---based on their agreement with current PIREPs. However, a new, quantitative source of observation data---high-density measurements made by sensor equipment and software on aircraft, called in-situ measurements---is now available. The main goal of this thesis is to develop new data analysis and processing techniques to apply to the model and new observation data, in order to improve CAT forecasting accuracy. This thesis shows that using in-situ data improves forecasting accuracy and that automated machine learning algorithms such as support vector machines (SVM), logistic regression, and random forests, can match current performance while eliminating almost all hand-tuning. Feature subset selection is paired with the new algorithms to choose diagnostics that predict well as a group rather than individually. Specializing forecasts and choice of diagnostics by geographic region further improves accuracy because of the geographic variation in turbulence sources. This work uses random forests to find climatologically-relevant regions based on these variations and implements a forecasting system testbed which brings these techniques together to rapidly prototype new, regionalized versions of operational CAT forecasting systems.

Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

NASA Astrophysics Data System (ADS)

Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

2017-04-01

River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized to enhance this flexible approach to set up the model. As distinct from both stochastic models, the ANN and its related conjunction methods Wavelet-ANN (WANN) models are effective to handle non-linear systems and have been developed with antecedent flows as inputs to forecast up to 12-months lead-time for the Iffezheim reservoir. In the ANN and WANN models, the Feed Forward Back Propagation method (FFBP) is applied. The sigmoid activity and linear functions were used with several different neurons for the hidden layers and for the output layer, respectively. To compare the accuracy of the different models and identify the most suitable model for reliable forecasting, four quantitative standard statistical performance evaluation measures, the root mean square error (RMSE), the mean bias error (MAE) and the determination correlation coefficient (DC), are employed. The results reveal that the ARIMA (2, 1, 2) performs better than Fiering, ANN and WANN models. Further, the WANN model is found to be slightly better than the ANN model for forecasting monthly inflow of the Iffezheim reservoir. As a result, by using the ARIMA model, the predicted and observed values agree reasonably well.

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

The Evolving Urban Community and Military Installations: A Dynamic Spatial Decision Support System for Sustainable Military Communities

DTIC Science & Technology

2007-01-01

focus on identifying growth by income and housing costs. These, and other models are focused on the city itself and deal with growth over the course...2. This model employs a set of econometric models to project future population, household, and employment. The landscape is gridded into one... model in LEAM (LEAMecon) forecasts changes in output, employment and income over time based on changes in the market, technology, productivity and

Experiences from coordinated national-level landslide and flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

2015-04-01

While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

Study of Regional Downscaled Climate and Air Quality in the United States

NASA Astrophysics Data System (ADS)

Gao, Y.; Fu, J. S.; Drake, J.; Lamarque, J.; Lam, Y.; Huang, K.

2011-12-01

Due to the increasing anthropogenic greenhouse gas emissions, the global and regional climate patterns have significantly changed. Climate change has exerted strong impact on ecosystem, air quality and human life. The global model Community Earth System Model (CESM v1.0) was used to predict future climate and chemistry under projected emission scenarios. Two new emission scenarios, Representative Community Pathways (RCP) 4.5 and RCP 8.5, were used in this study for climate and chemistry simulations. The projected global mean temperature will increase 1.2 and 1.7 degree Celcius for the RCP 4.5 and RCP 8.5 scenarios in 2050s, respectively. In order to take advantage of local detailed topography, land use data and conduct local climate impact on air quality, we downscaled CESM outputs to 4 km by 4 km Eastern US domain using Weather Research and Forecasting (WRF) Model and Community Multi-scale Air Quality modeling system (CMAQ). The evaluations between regional model outputs and global model outputs, regional model outputs and observational data were conducted to verify the downscaled methodology. Future climate change and air quality impact were also examined on a 4 km by 4 km high resolution scale.

A Comparison of Model Short-Range Forecasts and the ARM Microbase Data Fourth Quarter ARM Science Metric

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

Hnilo, J.

2006-09-19

For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the “Microbase” value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averagedmore » to 3hourly data for direct comparison to our model output.« less

Impact of Local Sensors

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) wind tower network and the CCAFS (XMR) daily rawinsonde observations (RAOB) to issue and verify wind advisories and warnings for operations. These observations are also used by the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) in Houston, Texas and the NWS Melbourne, Florida (NWS MLB) to initialize their locally-run mesoscale models. In addition, SMG uses these observations to support shuttle landings at the Shuttle Landing Facility (SLF). Due to impending budget cuts, some or all of the KSC/CCAFS wind towers on the east-central Florida mainland and the XMR RAOBs may be eliminated. The locations of the mainland towers and XMR RAOB site are shown in Figure I. The loss of these data may impact the forecast capability of the 45 WS, SMG and NWS MLB. The AMU was tasked to conduct an objective independent modeling study to help determine how important these observations are to the accuracy of the model output used by the forecasters. To accomplish this, the Applied Meteorology Unit (AMU) performed a sensitivity study using the Weather Research and Forecasting (WRF) model initialized with and without KSC/CCAFS wind tower and XMR RAOB data.

Forecasting the Human Pathogen Vibrio Parahaemolyticus in Shellfish Tissue within Long Island Sound

NASA Astrophysics Data System (ADS)

Whitney, M. M.; DeRosia-Banick, K.

2016-02-01

Vibrio parahaemolyticus (Vp) is a marine bacterium that occurs naturally in brackish and saltwater environments and may be found in higher concentrations in the warmest months. Vp is a growing threat to producing safe seafood. Consumption of shellfish with high Vp levels can result in gastrointestinal human illnesses. Management response to Vp-related illness outbreaks includes closure of shellfish growing areas. Water quality observations, Vp measurements, and model forecasts are key components to effective management of shellfish growing areas. There is a clear need for observations within the growing area themselves. These areas are offshore of coastal stations and typically inshore of the observing system moorings. New field observations in Long Island Sound (LIS) shellfish growing areas are described and their agreement with high-resolution satellite sea surface temperature data is discussed. A new dataset of Vp concentrations in shellfish tissue is used to determine the LIS-specific Vp vs. temperature relationship following methods in the FDA pre-harvest Vp risk model. This information is combined with output from a high-resolution hydrodynamic model of LIS to make daily forecasts of Vp levels. The influence of river inflows, the role of heat waves, and predictions for future warmer climates are discussed. The key elements of this observational-modeling approach to pathogen forecasting are extendable to other coastal systems.

Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

NASA Astrophysics Data System (ADS)

Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

2017-04-01

Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums shows a mean improvement of more than 40% in CRPS when compared to bilinearly interpolated uncalibrated ensemble forecasts. The validation on randomly selected grid points, representing the true height distribution over Austria, still indicates a mean improvement of 35%. The applied statistical model is currently set up for 6-hourly and daily accumulation periods, but will be extended to a temporal resolution of 1-3 hours within a new probabilistic nowcasting system operated by ZAMG.

Agent Based Model of Livestock Movements

NASA Astrophysics Data System (ADS)

Miron, D. J.; Emelyanova, I. V.; Donald, G. E.; Garner, G. M.

The modelling of livestock movements within Australia is of national importance for the purposes of the management and control of exotic disease spread, infrastructure development and the economic forecasting of livestock markets. In this paper an agent based model for the forecasting of livestock movements is presented. This models livestock movements from farm to farm through a saleyard. The decision of farmers to sell or buy cattle is often complex and involves many factors such as climate forecast, commodity prices, the type of farm enterprise, the number of animals available and associated off-shore effects. In this model the farm agent's intelligence is implemented using a fuzzy decision tree that utilises two of these factors. These two factors are the livestock price fetched at the last sale and the number of stock on the farm. On each iteration of the model farms choose either to buy, sell or abstain from the market thus creating an artificial supply and demand. The buyers and sellers then congregate at the saleyard where livestock are auctioned using a second price sealed bid. The price time series output by the model exhibits properties similar to those found in real livestock markets.

Multivariate Statistical Postprocessing of Ensemble Forcasts of Precipitation and Temperature over four River Basins in California

NASA Astrophysics Data System (ADS)

Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett; He, Minxue; Henkel, Arthur

2017-04-01

Hydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological inputs to hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but are often biased and underdispersive, and therefore require statistical postprocessing. In hydrological applications it is crucial that spatial and temporal (i.e. between different forecast lead times) dependencies as well as dependence between the two weather variables is adequately represented by the recalibrated forecasts. We present a study with temperature and precipitation forecasts over four river basins over California that are postprocessed with a variant of the nonhomogeneous Gaussian regression method (Gneiting et al., 2005) and the censored, shifted gamma distribution approach (Scheuerer and Hamill, 2015) respectively. For modelling spatial, temporal and inter-variable dependence we propose a variant of the Schaake Shuffle (Clark et al., 2005) that uses spatio-temporal trajectories of observed temperture and precipitation as a dependence template, and chooses the historic dates in such a way that the divergence between the marginal distributions of these trajectories and the univariate forecast distributions is minimized. For the four river basins considered in our study, this new multivariate modelling technique consistently improves upon the Schaake Shuffle and yields reliable spatio-temporal forecast trajectories of temperature and precipitation that can be used to force hydrological forecast systems. References: Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5, pp.243-262. Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS. Monthly Weather Review, 133, pp.1098-1118. Scheuerer, M., Hamill, T.M., 2015. Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143, pp.4578-4596. Scheuerer, M., Hamill, T.M., Whitin, B., He, M., and Henkel, A., 2016: A method for preferential selection of dates in the Schaake shuffle approach to constructing spatio-temporal forecast fields of temperature and precipitation. Water Resources Research, submitted.

Modeling and predicting intertidal variations of the salinity field in the Bay/Delta

USGS Publications Warehouse

Knowles, Noah; Uncles, Reginald J.

1995-01-01

One approach to simulating daily to monthly variability in the bay is the development of intertidal model using tidally-averaged equations and a time step on the order of the day.  An intertidal numerical model of the bay's physics, capable of portraying seasonal and inter-annual variability, would have several uses.  Observations are limited in time and space, so simulation could help fill the gaps.  Also, the ability to simulate multi-year episodes (eg, an extended drought) could provide insight into the response of the ecosystem to such events.  Finally, such a model could be used in a forecast mode wherein predicted delta flow is used as model input, and predicted salinity distribution is output with estimates days and months in advance.  This note briefly introduces such a tidally-averaged model (Uncles and Peterson, in press) and a corresponding predictive scheme for baywide forecasting.

Evaluation of an operational water cycle prediction system for the Laurentian Great Lakes and St. Lawrence River

NASA Astrophysics Data System (ADS)

Fortin, Vincent; Durnford, Dorothy; Smith, Gregory; Dyck, Sarah; Martinez, Yosvany; Mackay, Murray; Winter, Barbara

2017-04-01

Environment and Climate Change Canada (ECCC) is implementing new numerical guidance products based on fully coupled numerical models to better inform the public as well as specialized users on the current and future state of various components of the water cycle, including stream flow and water levels. Outputs from this new system, named the Water Cycle Prediction System (WCPS), have been available for the Great Lakes and St. Lawrence River watershed since June 2016. WCPS links together ECCC's weather forecasting model, GEM, the 2-D ice model C-ICE, the 3-D lake and ocean model NEMO, and a 2-D hydrological model, WATROUTE. Information concerning the water cycle is passed between the models at intervals varying from a few minutes to one hour. It currently produces two forecasts per day for the next three days of the complete water cycle in the Great Lakes region, the largest freshwater lake system in the world. Products include spatially-varying precipitation, evaporation, river discharge, water level anomalies, surface water temperatures, ice coverage, and surface currents. These new products are of interest to water resources and management authority, flood forecasters, hydroelectricity producers, navigation, environmental disaster managers, search and rescue teams, agriculture, and the general public. This presentation focuses on the evaluation of various elements forecasted by the system, and weighs the advantages and disadvantages of running the system fully coupled.

A spectral method for spatial downscaling | Science Inventory ...

EPA Pesticide Factsheets

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this paper, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July, 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing ch

Global Ocean Forecast System (GOFS) Version 2.6. User’s Manual

DTIC Science & Technology

2010-03-31

odimens.D, which takes the rivers.dat flow levels, inputs an SST and sea surface salinity (SSS) climatology from GDEM , and outputs the orivs_1.D...Center for Medium-range Weather Forecast GB GigaByte GDEM Global Digital Elevation Map GOFS Global Ocean Forecast System HPCMP High Performance

High resolution statistical downscaling of the EUROSIP seasonal prediction. Application for southeastern Romania

NASA Astrophysics Data System (ADS)

Busuioc, Aristita; Dumitrescu, Alexandru; Dumitrache, Rodica; Iriza, Amalia

2017-04-01

Seasonal climate forecasts in Europe are currently issued at the European Centre for Medium-Range Weather Forecasts (ECMWF) in the form of multi-model ensemble predictions available within the "EUROSIP" system. Different statistical techniques to calibrate, downscale and combine the EUROSIP direct model output are used to optimize the quality of the final probabilistic forecasts. In this study, a statistical downscaling model (SDM) based on canonical correlation analysis (CCA) is used to downscale the EUROSIP seasonal forecast at a spatial resolution of 1km x 1km over the Movila farm placed in southeastern Romania. This application is achieved in the framework of the H2020 MOSES project (http://www.moses-project.eu). The combination between monthly standardized values of three climate variables (maximum/minimum temperatures-Tmax/Tmin, total precipitation-Prec) is used as predictand while combinations of various large-scale predictors are tested in terms of their availability as outputs in the seasonal EUROSIP probabilistic forecasting (sea level pressure, temperature at 850 hPa and geopotential height at 500 hPa). The predictors are taken from the ECMWF system considering 15 members of the ensemble, for which the hindcasts since 1991 until present are available. The model was calibrated over the period 1991-2014 and predictions for summers 2015 and 2016 were achieved. The calibration was made for the ensemble average as well as for each ensemble member. The model was developed for each lead time: one month anticipation for June, two months anticipation for July and three months anticipation for August. The main conclusions from these preliminary results are: best predictions (in terms of the anomaly sign) for Tmax (July-2 months anticipation, August-3 months anticipation) for both years (2015, 2016); for Tmin - good predictions only for August (3 months anticipation ) for both years; for precipitation, good predictions for July (2 months anticipation) in 2015 and August (3 months anticipation) in 2016; failed prediction for June (1-month anticipation) for all parameters. To see if the results obtained for 2015 and 2016 summers are in agreement with the general ECMWF model performance in forecast of the three predictors used in the CCA SDM calibration, the mean bias and root mean square errors (RMSE) calculated over the entire period in each grid point, for each ensemble member and ensemble average were computed. The obtained results are confirmed, showing highest ECMWF performance in forecasting of the three predictors for 3 months anticipation (August) and lowest performance for one month anticipation (June). The added value of the CCA SDM in forecasting local Tmax/Tmin and total precipitation was compared to the ECMWF performance using nearest grid point method. Comparisons were performed for the 1991-2014 period, taking into account the forecast made in May for July. An important improvement was found for the CCA SDM predictions in terms of the RMSE value (computed against observations) for Tmax/Tmin and less for precipitation. The tests are in progress for the other summer months (June, July).

Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

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

Florita, Anthony R; Elgindy, Tarek; Hodge, Brian S

A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmosphericmore » measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.« less

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Tracking Expected Improvements of Decadal Prediction in Climate Services

NASA Astrophysics Data System (ADS)

Suckling, E.; Thompson, E.; Smith, L. A.

2013-12-01

Physics-based simulation models are ultimately expected to provide the best available (decision-relevant) probabilistic climate predictions, as they can capture the dynamics of the Earth System across a range of situations, situations for which observations for the construction of empirical models are scant if not nonexistent. This fact in itself provides neither evidence that predictions from today's Earth Systems Models will outperform today's empirical models, nor a guide to the space and time scales on which today's model predictions are adequate for a given purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales. The skill of these forecasts is contrasted with that of state-of-the-art climate models, and the challenges faced by each approach are discussed. The focus is on providing decision-relevant probability forecasts for decision support. An empirical model, known as Dynamic Climatology is shown to be competitive with CMIP5 climate models on decadal scale probability forecasts. Contrasting the skill of simulation models not only with each other but also with empirical models can reveal the space and time scales on which a generation of simulation models exploits their physical basis effectively. It can also quantify their ability to add information in the formation of operational forecasts. Difficulties (i) of information contamination (ii) of the interpretation of probabilistic skill and (iii) of artificial skill complicate each modelling approach, and are discussed. "Physics free" empirical models provide fixed, quantitative benchmarks for the evaluation of ever more complex climate models, that is not available from (inter)comparisons restricted to only complex models. At present, empirical models can also provide a background term for blending in the formation of probability forecasts from ensembles of simulation models. In weather forecasting this role is filled by the climatological distribution, and can significantly enhance the value of longer lead-time weather forecasts to those who use them. It is suggested that the direct comparison of simulation models with empirical models become a regular component of large model forecast intercomparison and evaluation. This would clarify the extent to which a given generation of state-of-the-art simulation models provide information beyond that available from simpler empirical models. It would also clarify current limitations in using simulation forecasting for decision support. No model-based probability forecast is complete without a quantitative estimate if its own irrelevance; this estimate is likely to increase as a function of lead time. A lack of decision-relevant quantitative skill would not bring the science-based foundation of anthropogenic warming into doubt. Similar levels of skill with empirical models does suggest a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to clearly state such weaknesses of a given generation of simulation models, while clearly stating their strength and their foundation, risks the credibility of science in support of policy in the long term.

Application of global weather and climate model output to the design and operation of wind-energy systems

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

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Sensitivity of sea-level forecasting to the horizontal resolution and sea surface forcing for different configurations of an oceanographic model of the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bressan, Lidia; Valentini, Andrea; Paccagnella, Tiziana; Montani, Andrea; Marsigli, Chiara; Stefania Tesini, Maria

2017-04-01

At the Hydro-meteo-climate service of the Regional environmental agency of Emilia-Romagna, Italy (Arpae-SIMC), the oceanographic numerical model AdriaROMS is used in the operational forecasting suite to compute sea level, temperature, salinity and 3-D current fields of the Adriatic Sea (northern Mediterranean Sea). In order to evaluate the performance of the sea-level forecast and to study different configurations of the ROMS model, two marine storms occurred on the Emilia Romagna coast during the winter 2015-2016 are investigated. The main focus of this study is to analyse the sensitivity of the model to the horizontal resolution and to the meteorological forcing. To this end, the model is run with two different configurations and with two horizontal grids at 1 and 2 km resolution. To study the influence of the meteorological forcing, the two storms have been reproduced by running ROMS in ensemble mode, forced by the 16-members of the meteorological ensemble COSMO-LEPS system. Possible optimizations of the model set-up are deduced by the comparison of the different run outputs.

Do quantitative decadal forecasts from GCMs provide decision relevant skill?

NASA Astrophysics Data System (ADS)

Suckling, E. B.; Smith, L. A.

2012-04-01

It is widely held that only physics-based simulation models can capture the dynamics required to provide decision-relevant probabilistic climate predictions. This fact in itself provides no evidence that predictions from today's GCMs are fit for purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales, where it is argued that these 'physics free' forecasts provide a quantitative 'zero skill' target for the evaluation of forecasts based on more complicated models. It is demonstrated that these zero skill models are competitive with GCMs on decadal scales for probability forecasts evaluated over the last 50 years. Complications of statistical interpretation due to the 'hindcast' nature of this experiment, and the likely relevance of arguments that the lack of hindcast skill is irrelevant as the signal will soon 'come out of the noise' are discussed. A lack of decision relevant quantiative skill does not bring the science-based insights of anthropogenic warming into doubt, but it does call for a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to do so may risk the credibility of science in support of policy in the long term. The performance amongst a collection of simulation models is evaluated, having transformed ensembles of point forecasts into probability distributions through the kernel dressing procedure [1], according to a selection of proper skill scores [2] and contrasted with purely data-based empirical models. Data-based models are unlikely to yield realistic forecasts for future climate change if the Earth system moves away from the conditions observed in the past, upon which the models are constructed; in this sense the empirical model defines zero skill. When should a decision relevant simulation model be expected to significantly outperform such empirical models? Probability forecasts up to ten years ahead (decadal forecasts) are considered, both on global and regional spatial scales for surface air temperature. Such decadal forecasts are not only important in terms of providing information on the impacts of near-term climate change, but also from the perspective of climate model validation, as hindcast experiments and a sufficient database of historical observations allow standard forecast verification methods to be used. Simulation models from the ENSEMBLES hindcast experiment [3] are evaluated and contrasted with static forecasts of the observed climatology, persistence forecasts and against simple statistical models, called dynamic climatology (DC). It is argued that DC is a more apropriate benchmark in the case of a non-stationary climate. It is found that the ENSEMBLES models do not demonstrate a significant increase in skill relative to the empirical models even at global scales over any lead time up to a decade ahead. It is suggested that the contsruction and co-evaluation with the data-based models become a regular component of the reporting of large simulation model forecasts. The methodology presented may easily be adapted to other forecasting experiments and is expected to influence the design of future experiments. The inclusion of comparisons with dynamic climatology and other data-based approaches provide important information to both scientists and decision makers on which aspects of state-of-the-art simulation forecasts are likely to be fit for purpose. [1] J. Bröcker and L. A. Smith. From ensemble forecasts to predictive distributions, Tellus A, 60(4), 663-678 (2007). [2] J. Bröcker and L. A. Smith. Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, 22, 382-388 (2006). [3] F. J. Doblas-Reyes, A. Weisheimer, T. N. Palmer, J. M. Murphy and D. Smith. Forecast quality asessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts, ECMWF Technical Memorandum, 621 (2010).

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

NASA Technical Reports Server (NTRS)

Chatterji, Gano B.; Gyarfas, Brett; Chan, William N.; Meyn, Larry A.

2006-01-01

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Study on SOC wavelet analysis for LiFePO4 battery

NASA Astrophysics Data System (ADS)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

Improving the prediction accuracy of SOC can reduce the complexity of the conservative and control strategy of the strategy such as the scheduling, optimization and planning of LiFePO4 battery system. Based on the analysis of the relationship between the SOC historical data and the external stress factors, the SOC Estimation-Correction Prediction Model based on wavelet analysis is established. Using wavelet neural network prediction model is of high precision to achieve forecast link, external stress measured data is used to update parameters estimation in the model, implement correction link, makes the forecast model can adapt to the LiFePO4 battery under rated condition of charge and discharge the operating point of the variable operation area. The test results show that the method can obtain higher precision prediction model when the input and output of LiFePO4 battery are changed frequently.

The Aegean Sea marine security decision support system

NASA Astrophysics Data System (ADS)

Perivoliotis, L.; Krokos, G.; Nittis, K.; Korres, G.

2011-05-01

As part of the integrated ECOOP (European Coastal Sea Operational observing and Forecasting System) project, HCMR upgraded the already existing standalone Oil Spill Forecasting System for the Aegean Sea, initially developed for the Greek Operational Oceanography System (POSEIDON), into an active element of the European Decision Support System (EuroDeSS). The system is accessible through a user friendly web interface where the case scenarios can be fed into the oil spill drift model component, while the synthetic output contains detailed information about the distribution of oil spill particles and the oil spill budget and it is provided both in text based ECOOP common output format and as a series of sequential graphics. The main development steps that were necessary for this transition were the modification of the forcing input data module in order to allow the import of other system products which are usually provided in standard formats such as NetCDF and the transformation of the model's calculation routines to allow use of current, density and diffusivities data in z instead of sigma coordinates. During the implementation of the Aegean DeSS, the system was used in operational mode in order support the Greek marine authorities in handling a real accident that took place in North Aegean area. Furthermore, the introduction of common input and output files by all the partners of EuroDeSS extended the system's interoperability thus facilitating data exchanges and comparison experiments.

The Aegean sea marine security decision support system

NASA Astrophysics Data System (ADS)

Perivoliotis, L.; Krokos, G.; Nittis, K.; Korres, G.

2011-10-01

As part of the integrated ECOOP (European Coastal Sea Operational observing and Forecasting System) project, HCMR upgraded the already existing standalone Oil Spill Forecasting System for the Aegean Sea, initially developed for the Greek Operational Oceanography System (POSEIDON), into an active element of the European Decision Support System (EuroDeSS). The system is accessible through a user friendly web interface where the case scenarios can be fed into the oil spill drift model component, while the synthetic output contains detailed information about the distribution of oil spill particles and the oil spill budget and it is provided both in text based ECOOP common output format and as a series of sequential graphics. The main development steps that were necessary for this transition were the modification of the forcing input data module in order to allow the import of other system products which are usually provided in standard formats such as NetCDF and the transformation of the model's calculation routines to allow use of current, density and diffusivities data in z instead of sigma coordinates. During the implementation of the Aegean DeSS, the system was used in operational mode in order to support the Greek marine authorities in handling a real accident that took place in North Aegean area. Furthermore, the introduction of common input and output files by all the partners of EuroDeSS extended the system's interoperability thus facilitating data exchanges and comparison experiments.

A Real-Time Offshore Weather Risk Advisory System

NASA Astrophysics Data System (ADS)

Jolivet, Samuel; Zemskyy, Pavlo; Mynampati, Kalyan; Babovic, Vladan

2015-04-01

Offshore oil and gas operations in South East Asia periodically face extended downtime due to unpredictable weather conditions, including squalls that are accompanied by strong winds, thunder, and heavy rains. This downtime results in financial losses. Hence, a real time weather risk advisory system is developed to provide the offshore Oil and Gas (O&G) industry specific weather warnings in support of safety and environment security. This system provides safe operating windows based on sensitivity of offshore operations to sea state. Information products for safety and security include area of squall occurrence for the next 24 hours, time before squall strike, and heavy sea state warning for the next 3, 6, 12 & 24 hours. These are predicted using radar now-cast, high resolution Numerical Weather Prediction (NWP) and Data Assimilation (DA). Radar based now-casting leverages the radar data to produce short term (up to 3 hours) predictions of severe weather events including squalls/thunderstorms. A sea state approximation is provided through developing a translational model based on these predictions to risk rank the sensitivity of operations. A high resolution Weather Research and Forecasting (WRF, an open source NWP model) is developed for offshore Brunei, Malaysia and the Philippines. This high resolution model is optimized and validated against the adaptation of temperate to tropical met-ocean parameterization. This locally specific parameters are calibrated against federated data to achieve a 24 hour forecast of high resolution Convective Available Potential Energy (CAPE). CAPE is being used as a proxy for the risk of squall occurrence. Spectral decomposition is used to blend the outputs of the now-cast and the forecast in order to assimilate near real time weather observations as an implementation of the integration of data sources. This system uses the now-cast for the first 3 hours and then the forecast prediction horizons of 3, 6, 12 & 24 hours. The output is a 24 hour window of high resolution/accuracy forecasts leveraging available data-model integration and CAPE prediction. The systems includes dissemination of WRF outputs over the World Wide Web. Components of the system (including WRF computational engine and results dissemination modules) are deployed in to computational cloud. This approach tends to increase system robustness and sustainability. The creation of such a system to share information between the public and private sectors and across territorial boundaries is an important step towards the next generation of governance for climate risk and extreme weather offshore. The system benefits offshore operators by reducing downtime related to accidents and incidents; eliminate unnecessary hiring costs related to waiting on weather; and improve the efficiency and planning of transport and logistics by providing a rolling weather risk advisory.

User’s guide and reference to Ash3d: a three-dimensional model for Eulerian atmospheric tephra transport and deposition

USGS Publications Warehouse

Mastin, Larry G.; Randall, Michael J.; Schwaiger, Hans F.; Denlinger, Roger P.

2013-01-01

Ash3d is a three-dimensional Eulerian atmospheric model for tephra transport, dispersal, and deposition, written by the authors to study and forecast hazards of volcanic ash clouds and tephra fall. In this report, we explain how to set up simulations using both a web interface and an ASCII input file, and how to view and interpret model output. We also summarize the architecture of the model and some of its properties.

Global Turbulence Decision Support for Aviation

NASA Astrophysics Data System (ADS)

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

2009-09-01

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

WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

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

A Study of Subseasonal Predictability of the Atmospheric Circulation Low-frequency Modes based on SL-AV forecasts

NASA Astrophysics Data System (ADS)

Kruglova, Ekaterina; Kulikova, Irina; Khan, Valentina; Tischenko, Vladimir

2017-04-01

The subseasonal predictability of low-frequency modes and the atmospheric circulation regimes is investigated based on the using of outputs from global Semi-Lagrangian (SL-AV) model of the Hydrometcentre of Russia and Institute of Numerical Mathematics of Russian Academy of Science. Teleconnection indices (AO, WA, EA, NAO, EU, WP, PNA) are used as the quantitative characteristics of low-frequency variability to identify zonal and meridional flow regimes with focus on control distribution of high impact weather patterns in the Northern Eurasia. The predictability of weekly and monthly averaged indices is estimated by the methods of diagnostic verification of forecast and reanalysis data covering the hindcast period, and also with the use of the recommended WMO quantitative criteria. Characteristics of the low frequency variability have been discussed. Particularly, it is revealed that the meridional flow regimes are reproduced by SL-AV for summer season better comparing to winter period. It is shown that the model's deterministic forecast (ensemble mean) skill at week 1 (days 1-7) is noticeably better than that of climatic forecasts. The decrease of skill scores at week 2 (days 8-14) and week 3( days 15-21) is explained by deficiencies in the modeling system and inaccurate initial conditions. It was noticed the slightly improvement of the skill of model at week 4 (days 22-28), when the condition of atmosphere is more determined by the flow of energy from the outside. The reliability of forecasts of monthly (days 1-30) averaged indices is comparable to that at week 1 (days 1-7). Numerical experiments demonstrated that the forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the using of probabilistic approach based on ensemble forecasts. It is shown that the quality of forecasts of the regimes of circulation like blocking is higher, than that of zonal flow.

NAM - Eta to NMM conversion 20060613

Science.gov Websites

North American Mesoscale (NAM) time slot. Currently the the Eta forecast model is used for the NAM, but Eta and its output will be available at the same time. There will be some minor addition of products MB per file (time step). - The following fields will be added: - omega @ 10, 20, & 30 mb - height

Contrail Cirrus Forecasts for the ML-CIRRUS Experiment and Some Comparison Results

NASA Astrophysics Data System (ADS)

Schumann, Ulrich; Graf, Kaspar; Bugliaro, Luca; Dörnbrack, Andreas; Giez, Andreas; Jurkat, Tina; Kaufmann, Stefan; Krämer, Martina; Minikin, Andreas; Schäfler, Andreas; Voigt, Christiane; Wirth, Martin; Zahn, Andreas; Ziereis, Helmut

2015-04-01

Model simulations with the contrail cirrus prediction model CoCiP driven by numerical weather prediction (NWP) data provided from the European Centre for Medium Range Forecasts (ECMWF) and global aircraft waypoint data show a mean computed cover (for optical depth larger than 0.1) of 0.23% globally, and 5.4% over mid Europe (Schumann and Graf, JGR, 2013). The computed mean longwave radiative forcing (RF) reaches 3 W m-2 over mid Europe (10°W-20°E and 40°N-55°N), and 0.13 W m-2 globally. The global net RF is about 40-60% smaller because of compensating shortwave cooling induced by contrails during daytime. The results depend on several model details such as the number of ice particles forming from aircraft soot emissions, the contrail plume dispersion, ice particle sedimentation etc., all influencing contrail life time and their optical properties. The quantitative results depend also strongly on ambient relative humidity, vertical motion and on ice water content of other cirrus predicted by the NWP model. In order to test and possibly improve this and other contrail models, high-quality observations are needed to which multi-parameter model output can be compared. The Mid-Latitude Cirrus Experiment ML-CIRRUS was performed (see C. Voigt et al., this conference) with a suite of in-situ and Lidar instruments for airborne measurements on the research aircraft HALO. Before and during the mission, CoCiP was run daily to provide 3-days forecasts of contrail cover using operational ECMWF forecasts and historical traffic data. CoCiP forecast output was made available in an internet tool twice a day for experiment planning. The one-day and two-day contrail forecasts often showed only small differences. Still, most recent forecasts and detailed satellite observations results were transmitted via satellite link to the crew for onboard campaign optimization. After the campaign, a data base of realistic air traffic data has been setup from various sources, and CoCiP was rerun with improved ECMWF-NWP data (at one-hour time resolution). The model results are included in the HALO mission data bank, and the results are available for comparison to in-situ data. The data are useful for identifying aircraft and other sources for measured air properties. The joint analysis of observations and model result has basically just started. Preliminary results from comparisons with lidar-measured extinction profiles, in-situ measured humidity, nitrogen oxides, and aerosol and ice particle concentrations, and with meteorological observations (wind, temperature etc.) illustrate the expected gain in insight. The contrail forecasts have been checked by comparison to available data including satellite data and HALO observations. During the campaign, it became obvious that predicted contrail cirrus cover compared qualitatively mostly well with what was found when HALO reached predicted cirrus regions. From the analysis of the measured data, some examples of significant correlation between model results and observations have been found. However, the quantitative agreement is not uniform. As expected, nature is far more variable than a model can predict. The observed optical properties of cirrus and contrails vary far more in time and space than predicted. Local values were often far higher or lower than mean values. A one-to-one correlation between local observations and model results is not to be expected. This inhomogeneity may have consequences for the climate impact of aviation induced cloud changes.

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

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

High Resolution Modeling in Mountainous Terrain for Water Resource Management: AN Extreme Precipitation Event Case Study

NASA Astrophysics Data System (ADS)

Masarik, M. T.; Watson, K. A.; Flores, A. N.; Anderson, K.; Tangen, S.

2016-12-01

The water resources infrastructure of the Western US is designed to deliver reliable water supply to users and provide recreational opportunities for the public, as well as afford flood control for communities by buffering variability in precipitation and snow storage. Thus water resource management is a balancing act of meeting multiple objectives while trying to anticipate and mitigate natural variability of water supply. Currently, the forecast guidance available to personnel managing resources in mountainous terrain is lacking in two ways: the spatial resolution is too coarse, and there is a gap in the intermediate time range (10-30 days). To address this need we examine the effectiveness of using the Weather Research and Forecasting (WRF) model, a state of the art, regional, numerical weather prediction model, as a means to generate high-resolution weather guidance in the intermediate time range. This presentation will focus on a reanalysis and hindcasting case study of the extreme precipitation and flooding event in the Payette River Basin of Idaho during the period of June 2nd-4th, 2010. For the reanalysis exercise we use NCEP's Climate Forecast System Reanalysis (CFSR) and the North American Regional Reanalysis (NARR) data sets as input boundary conditions to WRF. The model configuration includes a horizontal spatial resolution of 3km in the outer nest, and 1 km in the inner nest, with output temporal resolution of 3 hrs and 1 hr, respectively. The hindcast simulations, which are currently underway, will make use of the NCEP Climate Forecast System Reforecast (CFSRR) data. The current state of these runs will be discussed. Preparations for the second of two components in this project, weekly WRF forecasts during the intense portion of the water year, will be briefly described. These forecasts will use the NCEP Climate Forecast System version 2 (CFSv2) operational forecast data as boundary conditions to provide forecast guidance geared towards water resource managers out to a lead time of 30 days. We are particularly interested in the degree to which there is forecast skill in basinwide precipitation occurrence, departure from climatology, timing, and amount in the intermediate time range.

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions.

PubMed

Brooks, Logan C; Farrow, David C; Hyun, Sangwon; Tibshirani, Ryan J; Rosenfeld, Roni

2018-06-15

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

A comparison of ensemble post-processing approaches that preserve correlation structures

NASA Astrophysics Data System (ADS)

Schefzik, Roman; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

Despite the fact that ensemble forecasts address the major sources of uncertainty, they exhibit biases and dispersion errors and therefore are known to improve by calibration or statistical post-processing. For instance the ensemble model output statistics (EMOS) method, also known as non-homogeneous regression approach (Gneiting et al., 2005) is known to strongly improve forecast skill. EMOS is based on fitting and adjusting a parametric probability density function (PDF). However, EMOS and other common post-processing approaches apply to a single weather quantity at a single location for a single look-ahead time. They are therefore unable of taking into account spatial, inter-variable and temporal dependence structures. Recently many research efforts have been invested in designing post-processing methods that resolve this drawback but also in verification methods that enable the detection of dependence structures. New verification methods are applied on two classes of post-processing methods, both generating physically coherent ensembles. A first class uses the ensemble copula coupling (ECC) that starts from EMOS but adjusts the rank structure (Schefzik et al., 2013). The second class is a member-by-member post-processing (MBM) approach that maps each raw ensemble member to a corrected one (Van Schaeybroeck and Vannitsem, 2015). We compare variants of the EMOS-ECC and MBM classes and highlight a specific theoretical connection between them. All post-processing variants are applied in the context of the ensemble system of the European Centre of Weather Forecasts (ECMWF) and compared using multivariate verification tools including the energy score, the variogram score (Scheuerer and Hamill, 2015) and the band depth rank histogram (Thorarinsdottir et al., 2015). Gneiting, Raftery, Westveld, and Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., {133}, 1098-1118. Scheuerer and Hamill, 2015. Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities. Mon. Wea. Rev. {143},1321-1334. Schefzik, Thorarinsdottir, Gneiting. Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Science {28},616-640, 2013. Thorarinsdottir, M. Scheuerer, and C. Heinz, 2015. Assessing the calibration of high-dimensional ensemble forecasts using rank histograms, arXiv:1310.0236. Van Schaeybroeck and Vannitsem, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.

Improved Dust Forecast Products for Southwest Asia Forecasters through Dust Source Database Advancements

NASA Astrophysics Data System (ADS)

Brooks, G. R.

2011-12-01

Dust storm forecasting is a critical part of military theater operations in Afghanistan and Iraq as well as other strategic areas of the globe. The Air Force Weather Agency (AFWA) has been using the Dust Transport Application (DTA) as a forecasting tool since 2001. Initially developed by The Johns Hopkins University Applied Physics Laboratory (JHUAPL), output products include dust concentration and reduction of visibility due to dust. The performance of the products depends on several factors including the underlying dust source database, treatment of soil moisture, parameterization of dust processes, and validity of the input atmospheric model data. Over many years of analysis, seasonal dust forecast biases of the DTA have been observed and documented. As these products are unique and indispensible for U.S. and NATO forces, amendments were required to provide the best forecasts possible. One of the quickest ways to scientifically address the dust concentration biases noted over time was to analyze the weaknesses in, and adjust the dust source database. Dust source database strengths and weaknesses, the satellite analysis and adjustment process, and tests which confirmed the resulting improvements in the final dust concentration and visibility products will be shown.

A robust method to forecast volcanic ash clouds

USGS Publications Warehouse

Denlinger, Roger P.; Pavolonis, Mike; Sieglaff, Justin

2012-01-01

Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an efficient means to assess all of the hazards associated with these ash clouds.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

NASA Astrophysics Data System (ADS)

Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

2014-05-01

The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

Delivery of Forecasted Atmospheric Ozone and Dust for the New Mexico Environmental Public Health Tracking System - An Open Source Geospatial Solution

NASA Astrophysics Data System (ADS)

Hudspeth, W. B.; Sanchez-Silva, R.; Cavner, J. A.

2010-12-01

New Mexico's Environmental Public Health Tracking System (EPHTS), funded by the Centers for Disease Control (CDC) Environmental Public Health Tracking Network (EPHTN), aims to improve health awareness and services by linking health effects data with levels and frequency of environmental exposure. As a public health decision-support system, EPHTS systems include: state-of-the-art statistical analysis tools; geospatial visualization tools; data discovery, extraction, and delivery tools; and environmental/public health linkage information. As part of its mandate, EPHTS issues public health advisories and forecasts of environmental conditions that have consequences for human health. Through a NASA-funded partnership between the University of New Mexico and the University of Arizona, NASA Earth Science results are fused into two existing models (the Dust Regional Atmospheric Model (DREAM) and the Community Multiscale Air Quality (CMAQ) model) in order to improve forecasts of atmospheric dust, ozone, and aerosols. The results and products derived from the outputs of these models are made available to an Open Source mapping component of the New Mexico EPHTS. In particular, these products are integrated into a Django content management system using GeoDjango, GeoAlchemy, and other OGC-compliant geospatial libraries written in the Python and C++ programming languages. Capabilities of the resultant mapping system include indicator-based thematic mapping, data delivery, and analytical capabilities. DREAM and CMAQ outputs can be inspected, via REST calls, through temporal and spatial subsetting of the atmospheric concentration data across analytical units employed by the public health community. This paper describes details of the architecture and integration of NASA Earth Science into the EPHTS decision-support system.

Exploring Alternate Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, Scott R.; Jedlovec, Gary J.

2009-01-01

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Real-Time Very High-Resolution Regional 4D Assimilation in Supporting CRYSTAL-FACE Experiment

NASA Technical Reports Server (NTRS)

Wang, Donghai; Minnis, Patrick

2004-01-01

To better understand tropical cirrus cloud physical properties and formation processes with a view toward the successful modeling of the Earth's climate, the CRYSTAL-FACE (Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment) field experiment took place over southern Florida from 1 July to 29 July 2002. During the entire field campaign, a very high-resolution numerical weather prediction (NWP) and assimilation system was performed in support of the mission with supercomputing resources provided by NASA Center for Computational Sciences (NCCS). By using NOAA NCEP Eta forecast for boundary conditions and as a first guess for initial conditions assimilated with all available observations, two nested 15/3 km grids are employed over the CRYSTAL-FACE experiment area. The 15-km grid covers the southeast US domain, and is run two times daily for a 36-hour forecast starting at 0000 UTC and 1200 UTC. The nested 3-km grid covering only southern Florida is used for 9-hour and 18-hour forecasts starting at 1500 and 0600 UTC, respectively. The forecasting system provided more accurate and higher spatial and temporal resolution forecasts of 4-D atmospheric fields over the experiment area than available from standard weather forecast models. These forecasts were essential for flight planning during both the afternoon prior to a flight day and the morning of a flight day. The forecasts were used to help decide takeoff times and the most optimal flight areas for accomplishing the mission objectives. See more detailed products on the web site http://asd-www.larc.nasa.gov/mode/crystal. The model/assimilation output gridded data are archived on the NASA Center for Computational Sciences (NCCS) UniTree system in the HDF format at 30-min intervals for real-time forecasts or 5-min intervals for the post-mission case studies. Particularly, the data set includes the 3-D cloud fields (cloud liquid water, rain water, cloud ice, snow and graupe/hail).

Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination

NASA Astrophysics Data System (ADS)

Hadi, Sinan Jasim; Tombul, Mustafa

2018-06-01

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow.

Using neural networks for prediction of air pollution index in industrial city

NASA Astrophysics Data System (ADS)

Rahman, P. A.; Panchenko, A. A.; Safarov, A. M.

2017-10-01

This scientific paper is dedicated to the use of artificial neural networks for the ecological prediction of state of the atmospheric air of an industrial city for capability of the operative environmental decisions. In the paper, there is also the described development of two types of prediction models for determining of the air pollution index on the basis of neural networks: a temporal (short-term forecast of the pollutants content in the air for the nearest days) and a spatial (forecast of atmospheric pollution index in any point of city). The stages of development of the neural network models are briefly overviewed and description of their parameters is also given. The assessment of the adequacy of the prediction models, based on the calculation of the correlation coefficient between the output and reference data, is also provided. Moreover, due to the complexity of perception of the «neural network code» of the offered models by the ordinary users, the software implementations allowing practical usage of neural network models are also offered. It is established that the obtained neural network models provide sufficient reliable forecast, which means that they are an effective tool for analyzing and predicting the behavior of dynamics of the air pollution in an industrial city. Thus, this scientific work successfully develops the urgent matter of forecasting of the atmospheric air pollution index in industrial cities based on the use of neural network models.

Forecasting snowmelt flooding over Britain using the Grid-to-Grid model: a review and assessment of methods

NASA Astrophysics Data System (ADS)

Dey, Seonaid R. A.; Moore, Robert J.; Cole, Steven J.; Wells, Steven C.

2017-04-01

In many regions of high annual snowfall, snowmelt modelling can prove to be a vital component of operational flood forecasting and warning systems. Although Britain as a whole does not experience prolonged periods of lying snow, with the exception of the Scottish Highlands, the inclusion of snowmelt modelling can still have a significant impact on the skill of flood forecasts. Countrywide operational flood forecasts over Britain are produced using the national Grid-to-Grid (G2G) distributed hydrological model. For Scotland, snowmelt is included in these forecasts through a G2G snow hydrology module involving temperature-based snowfall/rainfall partitioning and functions for temperature-excess snowmelt, snowpack storage and drainage. Over England and Wales, the contribution of snowmelt is included by pre-processing the precipitation prior to input into G2G. This removes snowfall diagnosed from weather model outputs and adds snowmelt from an energy budget land surface scheme to form an effective liquid water gridded input to G2G. To review the operational options for including snowmelt modelling in G2G over Britain, a project was commissioned by the Environment Agency through the Flood Forecasting Centre (FFC) for England and Wales and in partnership with the Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW). Results obtained from this snowmelt review project will be reported on here. The operational methods used by the FFC and SEPA are compared on past snowmelt floods, alongside new alternative methods of treating snowmelt. Both case study and longer-term analyses are considered, covering periods selected from the winters 2009-2010, 2012-2013, 2013-2014 and 2014-2015. Over Scotland, both of the snowmelt methods used operationally by FFC and SEPA provided a clear improvement to the river flow simulations. Over England and Wales, fewer and less significant snowfall events occurred, leading to less distinction in the results between the methods. It is noted that, for all methods considered, large uncertainties remain in flood forecasts influenced by snowmelt. Understanding and quantifying these uncertainties should lead to more informed flood forecasts and associated guidance information.

SWIFT2: Software for continuous ensemble short-term streamflow forecasting for use in research and operations

NASA Astrophysics Data System (ADS)

Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.

2016-04-01

Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The accessibility via interactive modelling languages is particularly amenable to using SWIFT2 in exploratory research, with a dynamic and versatile experimental modelling workflow. This does not come at the expense of the stability and reliability required for use in operations, where only mature and stable components are used.

Towards Developing a Regional Drought Information System for Lower Mekong

NASA Astrophysics Data System (ADS)

Dutta, R.; Jayasinghe, S.; Basnayake, S. B.; Apirumanekul, C.; Pudashine, J.; Granger, S. L.; Andreadis, K.; Das, N. N.

2016-12-01

With the climate and weather patterns changing over the years, the Lower Mekong Basin have been experiencing frequent and prolonged droughts resulting in severe damage to the agricultural sector affecting food security and livelihoods of the farming community. However, the Regional Drought Information System (RDIS) for Lower Mekong countries would help prepare vulnerable communities from frequent and severe droughts through monitoring, assessing and forecasting of drought conditions and allowing decision makers to take effective decisions in terms of providing early warning, incentives to farmers, and adjustments to cropping calendars and so on. The RDIS is an integrated system that is being designed for drought monitoring, analysis and forecasting based on the need to meet the growing demand of an effective monitoring system for drought by the lower Mekong countries. The RDIS is being built on four major components that includes earth observation component, meteorological data component, database storage and Regional Hydrologic Extreme Assessment System (RHEAS) framework while the outputs from the system will be made open access to the public through a web-based user interface. The system will run on the RHEAS framework that allows both nowcasting and forecasting using hydrological and crop simulation models such as the Variable Infiltration Capacity (VIC) model and the Decision Support System for Agro-Technology Transfer (DSSAT) model respectively. The RHEAS allows for a tightly constrained observation based drought and crop yield information system that can provide customized outputs on drought that includes root zone soil moisture, Standard Precipitation Index (SPI), Standard Runoff Index (SRI), Palmer Drought Severity Index (PDSI) and Crop Yield and can integrate remote sensing products, along with evapotranspiration and soil moisture data. The anticipated outcomes from the RDIS is to improve the operational, technological and institutional capabilities of lower Mekong countries to prepare for and respond towards drought situations and providing policy makers with current and forecast drought indices for decision making on adjusting cropping calendars as well as planning short and long term mitigation measures.

Application of WRF - SWAT OpenMI 2.0 based models integration for real time hydrological modelling and forecasting

NASA Astrophysics Data System (ADS)

Bugaets, Andrey; Gonchukov, Leonid

2014-05-01

Intake of deterministic distributed hydrological models into operational water management requires intensive collection and inputting of spatial distributed climatic information in a timely manner that is both time consuming and laborious. The lead time of the data pre-processing stage could be essentially reduced by coupling of hydrological and numerical weather prediction models. This is especially important for the regions such as the South of the Russian Far East where its geographical position combined with a monsoon climate affected by typhoons and extreme heavy rains caused rapid rising of the mountain rivers water level and led to the flash flooding and enormous damage. The objective of this study is development of end-to-end workflow that executes, in a loosely coupled mode, an integrated modeling system comprised of Weather Research and Forecast (WRF) atmospheric model and Soil and Water Assessment Tool (SWAT 2012) hydrological model using OpenMI 2.0 and web-service technologies. Migration SWAT into OpenMI compliant involves reorganization of the model into a separate initialization, performing timestep and finalization functions that can be accessed from outside. To save SWAT normal behavior, the source code was separated from OpenMI-specific implementation into the static library. Modified code was assembled into dynamic library and wrapped into C# class implemented the OpenMI ILinkableComponent interface. Development of WRF OpenMI-compliant component based on the idea of the wrapping web-service clients into a linkable component and seamlessly access to output netCDF files without actual models connection. The weather state variables (precipitation, wind, solar radiation, air temperature and relative humidity) are processed by automatic input selection algorithm to single out the most relevant values used by SWAT model to yield climatic data at the subbasin scale. Spatial interpolation between the WRF regular grid and SWAT subbasins centroid (which are coinciding as virtual weather stations) realized as OpenMI AdaptedOutput. In order to make sure that SWAT-WRF integration technically sounds and preevaluate the impact of the climatic data resolution on the model parameters a number of test calculations were performed with different time-spatial aggregation of WRF output. Numerical experiments were carried out for the period of 2012-2013 on the Komarovka river watershed (former Primorskaya water-balance station) located in the small mountains landscapes in the western part of the Khankaiskaya plain. The watershed outlet is equipped with the automatic water level and rain gauging stations of Primorie Hydrometeorological Agency (Prigidromet http://primgidromet.ru) observation network. Spatial structure of SWAT simulation realized by ArcSWAT 2012 with 10m DEM resolution and 1:50000 soils and landuse cover. Sensitivity analysis and calibration are performed with SWAT CUP. WRF-SWAT composition is assembled in the GUI OpenMI. For the test basin in most cases the simulation results show that the predicted and measured water levels demonstrate acceptable agreement. Enforcing SWAT with WRF output avoids some semi-empirical model approximation, replaces a native weather generator for WRF forecast interval and improved upon the operational streamflow forecast. It is anticipated that leveraging direct use of the WRF variables (not only substituted standard SWAT input) will have good potential to make SWAT more physically sound.

Magnetohydrodynamic modelling of solar disturbances in the interplanetary medium

NASA Astrophysics Data System (ADS)

Dryer, M.

1985-12-01

A scientifically constructed series of interplanetary magnetohydrodynamic models is made that comprise the foundations for a composite solar terrestrial environment model. These models, unique in the field of solar wind physics, include both 2-1/2D as well as 3D time dependent codes that will lead to future operational status. We have also developed a geomagnetic storm forecasting strategy, referred to as the Solar Terrestrial Environment Model (STEM/2000), whereby these models would be appended in modular fashion to solar, magnetosphere, ionosphere, thermosphere, and neutral atmosphere models. We stress that these models, while still not appropriate at this date for operational use, outline a strategy or blueprint for the future. This strategy, if implemented in its essential features, offers a high probability for technology transfer from theory to operational testing within, approximately, a decade. It would ensure that real time observations would be used to drive physically based models that outputs of which would be used by space environment forecasters.

Assessment of the forecast skill of spring onset in the NMME experiment

NASA Astrophysics Data System (ADS)

Carrillo, C. M.; Ault, T.

2017-12-01

This study assesses the predictability of spring onset using an index of its interannual variability. We use the North American Multi-Model Ensemble (NMME) experiment to assess this predictability. The input dataset to compute spring onset index, SI-x, were treated with a daily joint bias correction (JBC) approach, and the SI-x outputs were post-processed using three ensemble model output statistic (EMOS) approaches—logistic regression, Gaussian Ensemble Dressing, and non-homogeneous Gaussian regression. These EMOS approaches quantify the effect of training period length and ensemble size on forecast skill. The highest range of predictability for the timing spring onset is from 10 to 60 days, and it is located along a narrow band between 35° to 45°N in the US. Using rank probability scores based on quantiles (q), a forecast threshold (q) of 0.5 provides a range of predictability that falls into two categories 10-40 and 40-60 days, which seems to represent the effect of the intra-seasonal scale. Using higher thresholds (q=0.6 and 0.7) predictability shows lower range with values around 10-30 days. The post-processing work using JBC improves the predictability skill by 13% from uncorrected results. Using EMOS, a significant positive change in the skill score is noted in regions where the skill with JBC shows evidence of improvement. The consensus of these techniques shows that regions of better predictability can be expanded.

Forecast of long term coal supply and mining conditions: Model documentation and results

NASA Technical Reports Server (NTRS)

1980-01-01

A coal industry model was developed to support the Jet Propulsion Laboratory in its investigation of advanced underground coal extraction systems. The model documentation includes the programming for the coal mining cost models and an accompanying users' manual, and a guide to reading model output. The methodology used in assembling the transportation, demand, and coal reserve components of the model are also described. Results presented for 1986 and 2000, include projections of coal production patterns and marginal prices, differentiated by coal sulfur content.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

Analysis of Summertime Convective Initiation in Central Alabama Using the Land Information System

NASA Technical Reports Server (NTRS)

James, Robert S.; Case, Jonathan L.; Molthan, Andrew L.; Jedlovec, Gary J.

2011-01-01

During the summer months in the southeastern United States, convective initiation presents a frequent challenge to operational forecasters. Thunderstorm development has traditionally been referred to as random due to their disorganized, sporadic appearance and lack of atmospheric forcing. Horizontal variations in land surface characteristics such as soil moisture, soil type, land and vegetation cover could possibly be a focus mechanism for afternoon convection during the summer months. The NASA Land Information System (LIS) provides a stand-alone land surface modeling framework that incorporates these varying soil and vegetation properties, antecedent precipitation, and atmospheric forcing to represent the soil state at high resolution. The use of LIS as a diagnostic tool may help forecasters to identify boundaries in land surface characteristics that could correlate to favored regions of convection initiation. The NASA Shortterm Prediction Research and Transition (SPoRT) team has been collaborating with the National Weather Service Office in Birmingham, AL to help incorporate LIS products into their operational forecasting methods. This paper highlights selected convective case dates from summer 2009 when synoptic forcing was weak, and identifies any boundaries in land surface characteristics that may have contributed to convective initiation. The LIS output depicts the effects of increased sensible heat flux from urban areas on the development of convection, as well as convection along gradients in land surface characteristics and surface sensible and latent heat fluxes. These features may promote mesoscale circulations and/or feedback processes that can either enhance or inhibit convection. With this output previously unavailable to operational forecasters, LIS provides a new tool to forecasters in order to help eliminate the randomness of summertime convective initiation.

Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; McPhee, James; Vargas, Ximena

2012-09-01

Data scarcity has traditionally precluded the application of advanced hydrologic techniques in developing countries. In this paper, we evaluate the performance of a flood forecasting scheme in a sparsely monitored catchment based on distributed hydrologic modeling, discharge assimilation, and numerical weather predictions with explicit validation uncertainty analysis. For the hydrologic component of our framework, we apply TopNet to the Cautin River basin, located in southern Chile, using a fully distributed a priori parameterization based on both literature-suggested values and data gathered during field campaigns. Results obtained from this step indicate that the incremental effort spent in measuring directly a set of model parameters was insufficient to represent adequately the most relevant hydrologic processes related to spatiotemporal runoff patterns. Subsequent uncertainty validation performed over a six month ensemble simulation shows that streamflow uncertainty is better represented during flood events, due to both the increase of state perturbation introduced by rainfall and the flood-oriented calibration strategy adopted here. Results from different assimilation configurations suggest that the upper part of the basin is the major source of uncertainty in hydrologic process representation and hint at the usefulness of interpreting assimilation results in terms of model input and parameterization inadequacy. Furthermore, in this case study the violation of Markovian state properties by the Ensemble Kalman filter did affect the numerical results, showing that an explicit treatment of the time delay between the generation of surface runoff and the arrival at the basin outlet is required in the assimilation scheme. Peak flow forecasting results demonstrate that there is a major problem with the Weather Research and Forecasting model outputs, which systematically overestimate precipitation over the catchment. A final analysis performed for a large flooding event that occurred in July 2006 shows that, in the absence of bias introduced by an incorrect model calibration, the updating of both model states and meteorological forecasts contributes to a better representation of streamflow uncertainty and to better hydrologic forecasts.

An operational coupled wave-current forecasting system for the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

2012-04-01

Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave heights. Part of the activity has been funded by the EU FP VII program (project "MICORE", contract n. 202798) and by the Regione Veneto regional law 15/2007 (Progetto "MARINA").

Atmospheric model development in support of SEASAT. Volume 2: Analysis models

NASA Technical Reports Server (NTRS)

Langland, R. A.

1977-01-01

As part of the SEASAT program of NASA, two sets of analysis programs were developed for the Jet Propulsion Laboratory. One set of programs produce 63 x 63 horizontal mesh analyses on a polar stereographic grid. The other set produces 187 x 187 third mesh analyses. The parameters analyzed include sea surface temperature, sea level pressure and twelve levels of upper air temperature, height and wind analyses. The analysis output is used to initialize the primitive equation forecast models.

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system simulations will help to develop a seamless integration with the boundary systems in the service-gap area with new insights into our scientific understanding of such complex systems. A visualization system is being developed to allow stake holders and the community to have access to the flood forecasting for their region with sufficient lead time.

Sales forecasting newspaper with ARIMA: A case study

NASA Astrophysics Data System (ADS)

Permatasari, Carina Intan; Sutopo, Wahyudi; Hisjam, Muh.

2018-02-01

People are beginning to switch to using digital media for their daily activities, including changes in newspaper reading patterns to electronic news. In uncertainty trend, the customers of printed newspaper also have switched to electronic news. It has some negative effects on the printed newspaper demand, where there is often an inaccuracy of supply with demand which means that many newspapers are returned. The aim of this paper is to predict printed newspaper demand as accurately as possible to minimize the number of returns, to keep off the missed sales and to restrain the oversupply. The autoregressive integrated moving average (ARIMA) models were adopted to predict the right number of newspapers for a real case study of a newspaper company in Surakarta. The model parameters were found using maximum likelihood method. Then, the software Eviews 9 were utilized to forecasting any particular variables in the newspaper industry. This paper finally presents the appropriate of modeling and sales forecasting newspaper based on the output of the ARIMA models. In particular, it can be recommended to use ARIMA (1, 1, 0) model in predicting the number of newspapers. ARIMA (1, 1, 0) model was chosen from three different models that it provides the smallest value of the mean absolute percentage error (MAPE).

How seasonal forecast could help a decision maker: an example of climate service for water resource management

NASA Astrophysics Data System (ADS)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

Specification of ISS Plasma Environment Variability

NASA Technical Reports Server (NTRS)

Minow, Joseph I.; Neergaard, Linda F.; Bui, Them H.; Mikatarian, Ronald R.; Barsamian, H.; Koontz, Steven L.

2004-01-01

Quantifying spacecraft charging risks and associated hazards for the International Space Station (ISS) requires a plasma environment specification for the natural variability of ionospheric temperature (Te) and density (Ne). Empirical ionospheric specification and forecast models such as the International Reference Ionosphere (IRI) model typically only provide long term (seasonal) mean Te and Ne values for the low Earth orbit environment. This paper describes a statistical analysis of historical ionospheric low Earth orbit plasma measurements from the AE-C, AE-D, and DE-2 satellites used to derive a model of deviations of observed data values from IRI-2001 estimates of Ne, Te parameters for each data point to provide a statistical basis for modeling the deviations of the plasma environment from the IRI model output. Application of the deviation model with the IRI-2001 output yields a method for estimating extreme environments for the ISS spacecraft charging analysis.

Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation

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

Baker, Kyri; Hug, Gabriela; Li, Xin

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast ofmore » the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.« less

Application of a deep-learning method to the forecast of daily solar flare occurrence using Convolution Neural Network

NASA Astrophysics Data System (ADS)

Shin, Seulki; Moon, Yong-Jae; Chu, Hyoungseok

2017-08-01

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT 195Å, and 304Å from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the overfitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). Our model can immediately be applied to automatic forecasting service when image data are available.

Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

NASA Astrophysics Data System (ADS)

Restrepo, P.; Schaake, J.

2009-04-01

Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result from forest fires or levee breaches, and slow, as the result of watershed reforestation, reforestation or urban development; 5) an expanded suite of products, including soil moisture and temperature forecasts, and water quality constituents; and 6) a comprehensive verification system to assess the effectiveness of the other 5 goals. To this end, the research plan places an emphasis on research of models with parameters that can be derived from physical watershed characteristics. Purely physically based models may be unattainable or impractical, and, therefore, models resulting from a combination of physically and conceptually approached processes may be required With respect to the hydrometeorological forcings the research plan emphasizes the development of improved precipitation estimation techniques through the synthesis of radar, rain gauge, satellite, and numerical weather prediction model output, particularly in those areas where ground-based sensors are inadequate to detect spatial variability in precipitation. Better estimation and forecasting of precipitation are most likely to be achieved by statistical merging of remote-sensor observations and forecasts from high-resolution numerical prediction models. Enhancements to the satellite-based precipitation products will include use of TRMM precipitation data in preparation for information to be supplied by the Global Precipitation Mission satellites not yet deployed. Because of a growing need for services in water resources, including low-flow forecasts for water supply customers, we will be directing research into coupled surface-groundwater models that will eventually replace the groundwater component of the existing models, and will be part of the new generation of models. Finally, the research plan covers the directions of research for probabilistic forecasting using ensembles, data assimilation and the verification and validation of both deterministic and probabilistic forecasts.

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Application of Convolution Neural Network to the forecasts of flare classification and occurrence using SOHO MDI data

NASA Astrophysics Data System (ADS)

Park, Eunsu; Moon, Yong-Jae

2017-08-01

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.86 for flare classification and 0.84 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

Puget Sound Operational Forecast System - A Real-time Predictive Tool for Marine Resource Management and Emergency Responses

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

Yang, Zhaoqing; Khangaonkar, Tarang; Chase, Jared M.

2009-12-01

To support marine ecological resource management and emergency response and to enhance scientific understanding of physical and biogeochemical processes in Puget Sound, a real-time Puget Sound Operational Forecast System (PS-OFS) was developed by the Coastal Ocean Dynamics & Ecosystem Modeling group (CODEM) of Pacific Northwest National Laboratory (PNNL). PS-OFS employs the state-of-the-art three-dimensional coastal ocean model and closely follows the standards and procedures established by National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). PS-OFS consists of four key components supporting the Puget Sound Circulation and Transport Model (PS-CTM): data acquisition, model execution and product archive, model skill assessment,more » and model results dissemination. This paper provides an overview of PS-OFS and its ability to provide vital real-time oceanographic information to the Puget Sound community. PS-OFS supports pacific northwest region’s growing need for a predictive tool to assist water quality management, fish stock recovery efforts, maritime emergency response, nearshore land-use planning, and the challenge of climate change and sea level rise impacts. The structure of PS-OFS and examples of the system inputs and outputs, forecast results are presented in details.« less

Near real time wind energy forecasting incorporating wind tunnel modeling

NASA Astrophysics Data System (ADS)

Lubitz, William David

A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power production of a wind farm in the Altamont Pass, California, USA from 12 to 48 hours in advance. It is of modular construction and designed to also allow diagnostic forecasting using archived RSF data, thereby allowing different methods of completing each forecasting step to be tested and compared using the same input data. Wind-tunnel investigations of the effect of wind direction and hill geometry on wind speed-up above a hill were conducted. Field data from an Altamont Pass, California site was used to evaluate several speed-up prediction algorithms, both with and without wind direction adjustment. These algorithms were found to be of limited usefulness for the complex terrain case evaluated. Wind-tunnel and numerical simulation-based methods were developed for determining a wind farm power curve (the relation between meteorological conditions at a point in the wind farm and the power production of the wind farm). Both methods, as well as two methods based on fits to historical data, ultimately showed similar levels of accuracy: mean absolute errors predicting power production of 5 to 7 percent of the wind farm power capacity. The downscaling of RSF forecast data to the wind farm was found to be complicated by the presence of complex terrain. Poor results using the geostrophic drag law and regression methods motivated the development of a database search method that is capable of forecasting not only wind speeds but also power production with accuracy better than persistence.

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China.

PubMed

Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

2013-06-01

Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 - 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 - 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term. Copyright © 2013 Elsevier Ltd. All rights reserved.

Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

NASA Technical Reports Server (NTRS)

Valenti, Elizabeth; Fitzpatrick, Patrick

2006-01-01

Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an eight-node Linux computer cluster. Each node contained dual 2-GHz processors, 2GB of memory, and a 40GB hard drive. The digital elevation model (DEM) for this region was specified using a combination of Navy data (over water), NOAA data (for the coastline), and optimized Interferometric Synthetic Aperture Radar data (over land). This high-resolution topographical data of the Mississippi coastal region provided the ADCIRC model with improved input with which to calculate improved storm-surge forecasts.

Qualitative Validation of the IMM Model for ISS and STS Programs

NASA Technical Reports Server (NTRS)

Kerstman, E.; Walton, M.; Reyes, D.; Boley, L.; Saile, L.; Young, M.; Arellano, J.; Garcia, Y.; Myers, J. G.

2016-01-01

To validate and further improve the Integrated Medical Model (IMM), medical event data were obtained from 32 ISS and 122 STS person-missions. Using the crew characteristics from these observed missions, IMM v4.0 was used to forecast medical events and medical resource utilization. The IMM medical condition incidence values were compared to the actual observed medical event incidence values, and the IMM forecasted medical resource utilization was compared to actual observed medical resource utilization. Qualitative comparisons of these parameters were conducted for both the ISS and STS programs. The results of these analyses will provide validation of IMM v4.0 and reveal areas of the model requiring adjustments to improve the overall accuracy of IMM outputs. This validation effort should result in enhanced credibility of the IMM and improved confidence in the use of IMM as a decision support tool for human space flight.

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.

Integrated Mode Choice, Small Aircraft Demand, and Airport Operations Model User's Guide

NASA Technical Reports Server (NTRS)

Yackovetsky, Robert E. (Technical Monitor); Dollyhigh, Samuel M.

2004-01-01

A mode choice model that generates on-demand air travel forecasts at a set of GA airports based on changes in economic characteristics, vehicle performance characteristics such as speed and cost, and demographic trends has been integrated with a model to generate itinerate aircraft operations by airplane category at a set of 3227 airports. Numerous intermediate outputs can be generated, such as the number of additional trips diverted from automobiles and schedule air by the improved performance and cost of on-demand air vehicles. The total number of transported passenger miles that are diverted is also available. From these results the number of new aircraft to service the increased demand can be calculated. Output from the models discussed is in the format to generate the origin and destination traffic flow between the 3227 airports based on solutions to a gravity model.

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

PubMed

Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

PubMed Central

Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality. PMID:28932237

MEDSLIK oil spill model recent developments

NASA Astrophysics Data System (ADS)

Lardner, Robin; Zodiatis, George

2016-04-01

MEDSLIK oil spill model recent developments Robin Lardner and George Zodiatis Oceanography Center, University of Cyprus, 1678 Nicosia, Cyprus MEDSLIK is a well established 3D oil spill model that predicts the transport, fate and weathering of oil spills and is used by several response agencies and institutions around the Mediterranean, the Black seas and worldwide. MEDSLIK has been used operationally for real oil spill accidents and for preparedness in contingency planning within the framework of pilot projects with REMPEC-Regional Marine Pollution Emergency Response Centre for the Mediterranean Sea and EMSA-European Maritime Safety Agency. MEDSLIK has been implemented in many EU funded projects regarding oil spill predictions using the operational ocean forecasts, as for example the ECOOP, NEREIDs, RAOP-Med, EMODNET MedSea Check Point. Within the frame of MEDESS4MS project, MEDSLIK is at the heart of the MEDESS4MS multi model oil spill prediction system. The MEDSLIK oil spill model contains among other, the following features: a built-in database with 240 different oil types characteristics, assimilation of oil slick observations from in-situ or aerial, to correct the predictions, virtual deployment of oil booms and/or oil skimmers/dispersants, continuous or instantaneous oil spills from moving or drifting ships whose slicks merge can be modelled together, multiple oil spill predictions from different locations, backward simulations for tracking the source of oil spill pollution, integration with AIS data upon the availability of AIS data, sub-surface oil spills at any given water depth, coupling with SAR satellite data. The MEDSLIK can be used for operational intervention for any user-selected region in the world if the appropriate coastline, bathymetry and meteo-ocean forecast files are provided. MEDSLIK oil spill model has been extensively validated in the Mediterranean Sea, both in real oil spill incidents (i.e. during the Lebanese oil pollution crisis in summer 2006, the biggest oil pollution event in the Eastern Mediterranean so far) and through inter-comparison using drifters. The quality of the MEDSLIK oil spill model predictions depends on the quality of the meteo-ocean forecasting data that will be used. The guidelines set by the MEDESS4MS project to harmonize the meteo-ocean, oil spill and trajectory models input/output formats are implemented in MEDSLIK to suit the operational oil spill predictions. The output results of the trajectory predictions may available in the MEDESS4MS output standards (XML)and in ASCII, while the images in BMP or PNG, TIF,GIF, JPG (image), in KML (Google Earth).

Tsunami Forecasting in the Atlantic Basin

NASA Astrophysics Data System (ADS)

Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

2012-12-01

The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre-computation - starting with those sources that carry the highest risk. Model computation zones are confined to regions at risk to save computation time. For example, Atlantic sources have been shown to not propagate into the Gulf of Mexico. Therefore, fine grid computations are not performed in the Gulf for Atlantic sources. Outputs from the Atlantic model include forecast marigrams at selected sites, maximum amplitudes, drawdowns, and currents for all coastal points. The maximum amplitude maps will be supplemented with contoured energy flux maps which show more clearly the effects of bathymetric features on tsunami wave propagation. During an event, forecast marigrams will be compared to observations to adjust the model results. The modified forecasts will then be used to set alert levels between coastal breakpoints, and provided to emergency management.

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

NASA Astrophysics Data System (ADS)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

Flood Warning and Forecasting System in Slovakia

NASA Astrophysics Data System (ADS)

Leskova, Danica

2016-04-01

In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

Benefits of Sharing Information: Supermodel Ensemble and Applications in South America

NASA Astrophysics Data System (ADS)

Dias, P. L.

2006-05-01

A model intercomparison program involving a large number of academic and operational institutions has been implemented in South America since 2003, motivated by the SALLJEX Intercomparison Program in 2003 (a research program focused on the identification of the role of the Andes low level jet moisture transport from the Amazon to the Plata basin) and the WMO/THORPEX (www.wmo.int/thorpex) goals to improve predictability through the proper combination of numerical weather forecasts. This program also explores the potential predictability associated with the combination of a large number of possible scenarios in the time scale of a few days to up to 15 days. Five academic institutions and five operational forecasting centers in several countries in South America, 1 academic institution in the USA, and the main global forecasting centers (NCEP, UKMO, ECMWF) agreed to provide numerical products based on operational and experimental models. The metric for model validation is concentrated on the fit of the forecast to surface observations. Meteorological data from airports, synoptic stations operated by national weather services, automatic data platforms maintained by different institutions, the PIRATA buoys etc are all collected through LDM/NCAR or direct transmission. Approximately 40 models outputs are available on a daily basis, twice a day. A simple procedure based on data assimilation principles was quite successful in combining the available forecasts in order to produce temperature, dew point, wind, pressure and precipitation forecasts at station points in S. America. The procedure is based on removing each model bias at the observational point and a weighted average based on the mean square error of the forecasts. The base period for estimating the bias and mean square error is of the order of 15 to 30 days. Products of the intercomparison model program and the optimal statistical combination of the available forecasts are public and available in real time (www.master.iag.usp.br/). Monitoring of the use of the products reveal a growing trend in the last year (reaching about 10.000 accesses per day in recent months). The intercomparison program provides a rich data set for educational products (real time use in Synoptic Meteorology and Numerical Weather Forecasting lectures), operational weather forecasts in national or regional weather centers and for research purposes. During the first phase of the program it was difficult to convince potential participants to share the information in the public homepage. However, as the system evolved, more and more institutions became associated with the program. The general opinion of the participants is that the system provides an unified metric for evaluation, a forum for discussion of the physical origin of the model forecast differences and therefore improvement of the quality of the numerical guidance.

Forecasting: Exercises to Enhance Learning from Business Simulations

ERIC Educational Resources Information Center

Clark, Timothy S.; Kent, Brian M.

2013-01-01

Forecasting the outputs of dynamic systems develops a richer understanding of relevant inputs and their interrelationships than merely observing them ex post. Academic business simulations foster students' development of this critical competency, but learning outcomes can be significantly augmented with relatively simple, complementary exercises…

Determining relevant parameters for a statistical tropical cyclone genesis tool based upon global model output

NASA Astrophysics Data System (ADS)

Halperin, D.; Hart, R. E.; Fuelberg, H. E.; Cossuth, J.

2013-12-01

Predicting tropical cyclone (TC) genesis has been a vexing problem for forecasters. While the literature describes environmental conditions which are necessary for TC genesis, predicting if and when a specific disturbance will organize and become a TC remains a challenge. As recently as 5-10 years ago, global models possessed little if any skill in forecasting TC genesis. However, due to increased resolution and more advanced model parameterizations, we have reached the point where global models can provide useful TC genesis guidance to operational forecasters. A recent study evaluated five global models' ability to predict TC genesis out to four days over the North Atlantic basin (Halperin et al. 2013). The results indicate that the models are indeed able to capture the genesis time and location correctly a fair percentage of the time. The study also uncovered model biases. For example, probability of detection and false alarm rate varies spatially within the basin. Also, as expected, the models' performance decreases with increasing lead time. In order to explain these and other biases, it is useful to analyze the model-indicated genesis events further to determine whether or not there are systematic differences between successful forecasts (hits), false alarms, and miss events. This study will examine composites of a number of physically-relevant environmental parameters (e.g., magnitude of vertical wind shear, aerially averaged mid-level relative humidity) and disturbance-based parameters (e.g., 925 hPa maximum wind speed, vertical alignment of relative vorticity) among each TC genesis event classification (i.e., hit, false alarm, miss). We will use standard statistical tests (e.g., Student's t test, Mann-Whitney-U Test) to calculate whether or not any differences are statistically significant. We also plan to discuss how these composite results apply to a few illustrative case studies. The results may help determine which aspects of the forecast are (in)correct and whether the incorrect aspects can be bias-corrected. This, in turn, may allow us to further enhance probabilistic forecasts of TC genesis.

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

NASA Astrophysics Data System (ADS)

Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

2017-07-01

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

Operational 0-3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

NASA Astrophysics Data System (ADS)

Sokol, Z.; Kitzmiller, D.; Pešice, P.; Guan, S.

2009-05-01

The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.

National Water Model assessment for water management needs over the Western United States.

NASA Astrophysics Data System (ADS)

Viterbo, F.; Thorstensen, A.; Cifelli, R.; Hughes, M.; Johnson, L.; Gochis, D.; Wood, A.; Nowak, K.; Dahm, K.

2017-12-01

The NOAA National Water Model (NWM) became operational in August 2016, providing the first ever, real-time distributed high-resolution forecasts for the continental United States. Since the model predictions occur at the CONUS scale, there is a need to evaluate the NWM in different regions to assess the wide variety and heterogeneity of hydrological processes that are included (e.g., snow melting, ice freezing, flash flooding events). In particular, to address water management needs in the western U.S., a collaborative project between the Bureau of Reclamation, NOAA, and NCAR is ongoing to assess the NWM performance for reservoir inflow forecasting needs and water management operations. In this work, the NWM is evaluated using different forecast ranges (short to medium) and retrospective historical runs forced by North American Land Data Assimilation System (NLDAS) analysis to assess the NWM skills over key headwaters watersheds in the western U.S. that are of interest to the Bureau of Reclamation. The streamflow results are analyzed and compared with the available observations at the gauge sites, evaluating different NWM operational versions together with the already existing local River Forecast Center forecasts. The NWM uncertainty is also considered, evaluating the propagation of the precipitation forcing uncertainties in the resulting hydrograph. In addition, the possible advantages of high-resolution distributed output variables (such as soil moisture, evapotranspiration fluxes) are investigated, to determine the utility of such information for water managers in terms of watershed characteristics in areas that traditionally have not had any forecast information. The results highlight the NWM's ability to provide high-resolution forecast information in space and time. As anticipated, the performance is best in regions that are dominated by natural flows and where the model has benefited from efforts toward parameter calibration. In highly regulated basins, the water management operations result in NWM overestimation of the peak flows and too fast recession curves. As a future project goal, some reforecasts will be run on target locations, ingesting water management information into the NWM and comparing the new results with the actual operational forecast.

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters. Part 1; Equivalent Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, Galina; DaSilva, Arlindo M.; Norris, Peter M.; Platnick, Steven E.

2013-01-01

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Exploring Alternative Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, Scott R.

2009-01-01

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Kuznetsova, M.; Maddox, M.; Pulkkinen, A.; Hesse, M.; Rastaetter, L.; Macneice, P.; Taktakishvili, A.; Berrios, D.; Chulaki, A.; Zheng, Y.;

Mesoscale Numerical Simulations of the IAS Circulation

NASA Astrophysics Data System (ADS)

Mooers, C. N.; Ko, D.

2008-05-01

Real-time nowcasts and forecasts of the IAS circulation have been made for several years with mesoscale resolution using the Navy Coastal Ocean Model (NCOM) implemented for the IAS. It is commonly called IASNFS and is driven by the lower resolution Global NCOM on the open boundaries, synoptic atmospheric forcing obtained from the Navy Global Atmospheric Prediction System (NOGAPS), and assimilated satellite-derived sea surface height anomalies and sea surface temperature. Here, examples of the model output are demonstrated; e.g., Gulf of Mexico Loop Current eddy shedding events and the meandering Caribbean Current jet and associated eddies. Overall, IASNFS is ready for further analysis, application to a variety of studies, and downscaling to even higher resolution shelf models. Its output fields are available online through NOAA's National Coastal Data Development Center (NCDDC), located at the Stennis Space Center.

Gaussian process regression for forecasting battery state of health

NASA Astrophysics Data System (ADS)

Richardson, Robert R.; Osborne, Michael A.; Howey, David A.

2017-07-01

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

Considering inventory distributions in a stochastic periodic inventory routing system

NASA Astrophysics Data System (ADS)

Yadollahi, Ehsan; Aghezzaf, El-Houssaine

2017-07-01

Dealing with the stochasticity of parameters is one of the critical issues in business and industry nowadays. Supply chain planners have difficulties in forecasting stochastic parameters of a distribution system. Demand rates of customers during their lead time are one of these parameters. In addition, holding a huge level of inventory at the retailers is costly and inefficient. To cover the uncertainty of forecasting demand rates, researchers have proposed the usage of safety stock to avoid stock-out. However, finding the precise level of safety stock depends on forecasting the statistical distribution of demand rates and their variations in different settings among the planning horizon. In this paper the demand rate distributions and its parameters are taken into account for each time period in a stochastic periodic IRP. An analysis of the achieved statistical distribution of the inventory and safety stock level is provided to measure the effects of input parameters on the output indicators. Different values for coefficient of variation are applied to the customers' demand rate in the optimization model. The outcome of the deterministic equivalent model of SPIRP is simulated in form of an illustrative case.

CARICOF - The Caribbean Regional Climate Outlook Forum

NASA Astrophysics Data System (ADS)

Van Meerbeeck, Cedric

2013-04-01

Regional Climate Outlook Forums (RCOFs) are viewed as a critical building block in the Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO). The GFCS seeks to extend RCOFs to all vulnerable regions of the world such as the Caribbean, of which the entire population is exposed to water- and heat-related natural hazards. An RCOF is initially intended to identify gaps in information and technical capability; facilitate research cooperation and data exchange within and between regions, and improve coordination within the climate forecasting community. A focus is given on variations in climate conditions on a seasonal timescale. In this view, the relevance of a Caribbean RCOF (CARICOF) is the following: while the seasonality of the climate in the Caribbean has been well documented, major gaps in knowledge exist in terms of the drivers in the shifts of amplitude and phase of seasons (as evidenced from the worst region-wide drought period in recent history during 2009-2010). To address those gaps, CARICOF has brought together National Weather Services (NWSs) from 18 territories under the coordination of the Caribbean Institute for Meteorology and Hydrology (CIMH), to produce region-wide, consensus, seasonal climate outlooks since March 2012. These outlooks include tercile rainfall forecasts, sea and air surface temperature forecasts as well as the likely evolution of the drivers of seasonal climate variability in the region, being amongst others the El Niño Southern Oscillation or tropical Atlantic and Caribbean Sea temperatures. Forecasts for both the national-scale forecasts made by the NWSs and CIMH's regional-scale forecast amalgamate output from several forecasting tools. These currently include: (1) statistical models such as Canonical Correlation Analysis run with the Climate Predictability Tool, providing tercile rainfall forecasts at weather station scale; (2) a global outlooks published by the WMO appointed Global Producing Centres (GPCs). Indications are that the current seasonal forecasting system used by CARICOF has produced reliable outlooks than previously available. Nevertheless, through its forum platform, areas for further development are continuously being defined, which are then implemented through efficient information exchanges between and hands-on training of forecasters. Finally, the disaster research and emergency management communities have shown that effective early warnings of impending hazards need to be complemented by information on the risks actually posed by the hazards and pathways for action. CARICOF is to address this issue by designing the outputs of the seasonal climate outlooks such that they can then effectively feed into an early warning information system of seasonal climate variability related hazards to its constituent countries' and territories major socio-economic sectors.

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

NASA Astrophysics Data System (ADS)

Schmitz, G. H.; Cullmann, J.

2008-10-01

SummaryThe Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and - optionally, if backwater effects have a significant impact on the flow regime - a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) - portraying the rainfall-runoff process - and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF - essentially consisting of the coupled "hydrologic" PoNN and "hydrodynamic" MLFN - to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km 2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.

Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

NASA Astrophysics Data System (ADS)

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

2017-04-01

In an automated forecast system, hydrologic data assimilation (DA) performs the valuable function of correcting raw simulated watershed model states to better represent external observations, including measurements of streamflow, snow, soil moisture, and the like. Yet the incorporation of automated DA into operational forecasting systems has been a long-standing challenge due to the complexities of the hydrologic system, which include numerous lags between state and output variations. To help demonstrate that such methods can succeed in operational automated implementations, we present results from the real-time application of an ensemble particle filter (PF) for short-range (7 day lead) ensemble flow forecasts in western US river basins. We use the System for Hydromet Applications, Research and Prediction (SHARP), developed by the National Center for Atmospheric Research (NCAR) in collaboration with the University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. SHARP is a fully automated platform for short-term to seasonal hydrologic forecasting applications, incorporating uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions through ensemble methods. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 temperature and precipitation time series through conceptual and physically-oriented models. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. The PF selects and/or weights and resamples the IHCs that are most consistent with external streamflow observations, and uses the particles to initialize a streamflow forecast ensemble driven by ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS). We apply this method in real-time for several basins in the western US that are important for water resources management, and perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts skill. This presentation describes findings, including a comparison of sequential and non-sequential particle weighting methods.

Forecasting approaches to the Mekong River

NASA Astrophysics Data System (ADS)

Plate, E. J.

2009-04-01

Hydrologists distinguish between flood forecasts, which are concerned with events of the immediate future, and flood predictions, which are concerned with events that are possible, but whose date of occurrence is not determined. Although in principle both involve the determination of runoff from rainfall, the analytical approaches differ because of different objectives. The differences between the two approaches will be discussed, starting with an analysis of the forecasting process. The Mekong River in south-east Asia is used as an example. Prediction is defined as forecast for a hypothetical event, such as the 100-year flood, which is usually sufficiently specified by its magnitude and its probability of occurrence. It forms the basis for designing flood protection structures and risk management activities. The method for determining these quantities is hydrological modeling combined with extreme value statistics, today usually applied both to rainfall events and to observed river discharges. A rainfall-runoff model converts extreme rainfall events into extreme discharges, which at certain gage points along a river are calibrated against observed discharges. The quality of the model output is assessed against the mean value by means of the Nash-Sutcliffe quality criterion. The result of this procedure is a design hydrograph (or a family of design hydrographs) which are used as inputs into a hydraulic model, which converts the hydrograph into design water levels according to the hydraulic situation of the location. The accuracy of making a prediction in this sense is not particularly high: hydrologists know that the 100-year flood is a statistical quantity which can be estimated only within comparatively wide error bounds, and the hydraulics of a river site, in particular under conditions of heavy sediment loads has many uncertainties. Safety margins, such as additional freeboards are arranged to compensate for the uncertainty of the prediction. Forecasts, on the other hand, have as objective to obtain an accurate hydrograph of the near future. The method by means of which this is done is not as important as the accuracy of the forecast. A mathematical rainfall-runoff model is not necessarily a good forecast model. It has to be very carefully designed, and in many cases statistical models are found to give better results than mathematical models. Forecasters have the advantage of knowing the course of the hydrographs up to the point in time where forecasts have to be made. Therefore, models can be calibrated on line against the hydrograph of the immediate past. To assess the quality of a forecast, the quality criterion should not be based on the mean value, as does the Nash-Sutcliffe criterion, but should be based on the best forecast given the information up to the forecast time. Without any additional information, the best forecast when only the present day value is known is to assume a no-change scenario, i.e. to assume that the present value does not change in the immediate future. For the Mekong there exists a forecasting system which is based on a rainfall-runoff model operated by the Mekong River Commission. This model is found not to be adequate for forecasting for periods longer than one or two days ahead. Improvements are sought through two approaches: a strictly deterministic rainfall-runoff model, and a strictly statistical model based on regression with upstream stations. The two approaches are com-pared, and suggestions are made how to best combine the advantages of both approaches. This requires that due consideration is given to critical hydraulic conditions of the river at and in between the gauging stations. Critical situations occur in two ways: when the river overtops, in which case the rainfall-runoff model is incomplete unless overflow losses are considered, and at the confluence with tributaries. Of particular importance is the role of the large Tonle Sap Lake, which dampens the hydrograph downstream of Phnom Penh. The effect of these components of river hydraulics on forecasting accuracy will be assessed.

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

NASA Astrophysics Data System (ADS)

Manzanas, R.; Gutiérrez, J. M.

2018-05-01

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981-2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile-quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.

Ensemble Data Assimilation of Wind and Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2016-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Wind speed and irradiation forecast from NWP system are however subject to several sources of error. The quality of the wind power prediction is mainly penalized by forecast error of the NWP model in the planetary boundary layer (PBL), which is characterized by high spatial and temporal fluctuations of the wind speed. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, the absorption of condensed water or aerosol optical depth are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, NWP model data can be corrected by post-processing techniques such as model output statistics and calibration using historical observational data. Additionally, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence the model error is reduced in the forecast. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. The numerous wind farm and PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation and wind speed through their power measurements. The accuracy of the NWP data may thus be enhanced by extending the observations in the assimilation by this new source of information. Wind power data can serve as indirect measurements of wind speed at hub height. The impact on the NWP model is potentially interesting since conventional observation network lacks measurements in this part of the PBL. Photovoltaic power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Additionally, since the latter kind of data is not limited to the vertical column above or below the detector. It may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the DA method (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first assimilation results are presented and discussed.

Monitoring and forecasting the 2009-2010 severe drought in Southwest China

NASA Astrophysics Data System (ADS)

Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

2015-12-01

From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

Evaluation of a regional assimilation system coupled with the WRF-chem model

NASA Astrophysics Data System (ADS)

Liu, Yan-an; Gao, Wei; Huang, Hung-lung; Strabala, Kathleen; Liu, Chaoshun; Shi, Runhe

2013-09-01

Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.

Set-up and validation of a Delft-FEWS based coastal hazard forecasting system

NASA Astrophysics Data System (ADS)

Valchev, Nikolay; Eftimova, Petya; Andreeva, Nataliya

2017-04-01

European coasts are increasingly threatened by hazards related to low-probability and high-impact hydro-meteorological events. Uncertainties in hazard prediction and capabilities to cope with their impact lie in both future storm pattern and increasing coastal development. Therefore, adaptation to future conditions requires a re-evaluation of coastal disaster risk reduction (DRR) strategies and introduction of a more efficient mix of prevention, mitigation and preparedness measures. The latter presumes that development of tools, which can manage the complex process of merging data and models and generate products on the current and expected hydro-and morpho-dynamic states of the coasts, such as forecasting system of flooding and erosion hazards at vulnerable coastal locations (hotspots), is of vital importance. Output of such system can be of an utmost value for coastal stakeholders and the entire coastal community. In response to these challenges, Delft-FEWS provides a state-of-the-art framework for implementation of such system with vast capabilities to trigger the early warning process. In addition, this framework is highly customizable to the specific requirements of any individual coastal hotspot. Since its release many Delft-FEWS based forecasting system related to inland flooding have been developed. However, limited number of coastal applications was implemented. In this paper, a set-up of Delft-FEWS based forecasting system for Varna Bay (Bulgaria) and a coastal hotspot, which includes a sandy beach and port infrastructure, is presented. It is implemented in the frame of RISC-KIT project (Resilience-Increasing Strategies for Coasts - toolKIT). The system output generated in hindcast mode is validated with available observations of surge levels, wave and morphodynamic parameters for a sequence of three short-duration and relatively weak storm events occurred during February 4-12, 2015. Generally, the models' performance is considered as very good and results obtained - quite promising for reliable prediction of both boundary conditions and coastal hazard and gives a good basis for estimation of onshore impact.

Evaluation of multiple hydraulic models in generating design/near-real time flood inundation extents under various geophysical settings

NASA Astrophysics Data System (ADS)

Liu, Z.; Rajib, M. A.; Jafarzadegan, K.; Merwade, V.

2015-12-01

Application of land surface/hydrologic models within an operational flood forecasting system can provide probable time of occurrence and magnitude of streamflow at specific locations along a stream. Creating time-varying spatial extent of flood inundation and depth requires the use of a hydraulic or hydrodynamic model. Models differ in representing river geometry and surface roughness which can lead to different output depending on the particular model being used. The result from a single hydraulic model provides just one possible realization of the flood extent without capturing the uncertainty associated with the input or the model parameters. The objective of this study is to compare multiple hydraulic models toward generating ensemble flood inundation extents. Specifically, relative performances of four hydraulic models, including AutoRoute, HEC-RAS, HEC-RAS 2D, and LISFLOOD are evaluated under different geophysical conditions in several locations across the United States. By using streamflow output from the same hydrologic model (SWAT in this case), hydraulic simulations are conducted for three configurations: (i) hindcasting mode by using past observed weather data at daily time scale in which models are being calibrated against USGS streamflow observations, (ii) validation mode using near real-time weather data at sub-daily time scale, and (iii) design mode with extreme streamflow data having specific return periods. Model generated inundation maps for observed flood events both from hindcasting and validation modes are compared with remotely sensed images, whereas the design mode outcomes are compared with corresponding FEMA generated flood hazard maps. The comparisons presented here will give insights on probable model-specific nature of biases and their relative advantages/disadvantages as components of an operational flood forecasting system.

Operational skill assessment of the IBI-MFC Ocean Forecasting System within the frame of the CMEMS.

NASA Astrophysics Data System (ADS)

Lorente Jimenez, Pablo; Garcia-Sotillo, Marcos; Amo-Balandron, Arancha; Aznar Lecocq, Roland; Perez Gomez, Begoña; Levier, Bruno; Alvarez-Fanjul, Enrique

2016-04-01

Since operational ocean forecasting systems (OOFSs) are increasingly used as tools to support high-stakes decision-making for coastal management, a rigorous skill assessment of model performance becomes essential. In this context, the IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). A comprehensive web validation tool named NARVAL (North Atlantic Regional VALidation) has been developed to routinely monitor IBI performance and to evaluate model's veracity and prognostic capabilities. Three-dimensional comparisons are carried out on a different time basis ('online mode' - daily verifications - and 'delayed mode' - for longer time periods -) using a broad variety of in-situ (buoys, tide-gauges, ARGO-floats, drifters and gliders) and remote-sensing (satellite and HF radars) observational sources as reference fields to validate against the NEMO model solution. Product quality indicators and meaningful skill metrics are automatically computed not only averaged over the entire IBI domain but also over specific sub-regions of particular interest from a user perspective (i.e. coastal or shelf areas) in order to determine IBI spatial and temporal uncertainty levels. A complementary aspect of NARVAL web tool is the intercomparison of different CMEMS forecast model solutions in overlapping areas. Noticeable efforts are in progress in order to quantitatively assess the quality and consistency of nested system outputs by setting up specific intercomparison exercises on different temporal and spatial scales, encompassing global configurations (CMEMS Global system), regional applications (NWS and MED ones) and local high-resolution coastal models (i.e. the PdE SAMPA system in the Gibraltar Strait). NARVAL constitutes a powerful approach to increase our knowledge on the IBI-MFC forecast system and aids us to inform CMEMS end users about the provided ocean forecasting products' confidence level by routinely delivering QUality Information Documents (QUIDs). It allows the detection of strengths and weaknesses in the modeling of several key physical processes and the understanding of potential sources of discrepancies in IBI predictions. Once the numerical model shortcomings are identified, potential improvements can be achieved thanks to reliable upgrades, making evolve IBI OOFS towards more refined and advanced versions.

Coupling Recruitment Forecasts with Economics in the Gulf of Maine's American Lobster Fishery

NASA Astrophysics Data System (ADS)

Wahle, R.; Oppenheim, N.; Brady, D. C.; Dayton, A.; Sun, C. H. J.

2016-02-01

Accurate predictions of fishery recruitment and landings represent an important goal of fisheries science and management, but linking environmental drivers of fish population dynamics to financial markets remains a challenge. A fundamental step in that process is understanding the environmental drivers of fishery recruitment. American lobster (Homarus americanus) populations of the northwest Atlantic have been undergoing a dramatic surge, mostly driven by increases the Gulf of Maine. Settler-recruit models that track cohorts after larvae settle to the sea bed are proving useful in predicting subsequent fishery recruitment some 5-7 years later. Here we describe new recruitment forecasting models for the lobster fishery at 11 management areas from Southern New England to Atlantic Canada. We use an annual survey of juvenile year-class strength and environmental indicators to parameterize growth and mortality terms in the model. As a consequence of a recent widespread multi-year downturn in larval settlement, our models suggest that the peak in lobster abundance in the Gulf of Maine will be passed in the near future. We also present initial steps in the coupling of forecast data with economic models for the fishery. We anticipate that these models will give stakeholders and policy makers time to consider their management choices for this most valuable of the region's fisheries. Our vision is to couple our forecast model outputs to an economic model that captures the dynamics of market forces in the New England and Canadian Maritime lobster fisheries. It will then be possible to estimate the financial status of the fishery several years in advance. This early warning system could mitigate the adverse effects of a fluctuating fishery on the coastal communities that are perilously dependent upon it.

A comparison of water vapor quantities from model short-range forecasts and ARM observations

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

Hnilo, J J

2006-03-17

Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGPmore » and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.« less

A Comparison of Water Vapor Quantities from Model Short-Range Forecasts and ARM Observations (in English; Croatian)

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

Hnilo, J.

2006-03-17

Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the “Mergedsounding” value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGPmore » and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.« less

Goals of Quality in Doctoral Studies and Forecasted Outcomes

ERIC Educational Resources Information Center

Zelvys, Rimantas

2007-01-01

This article discusses the quality assurance policy of doctoral studies implemented in Lithuania and its probable outcomes are forecasted. In scientific literature the quality of education is commonly defined as a holistic phenomenon composed of the quality of initial conditions, quality of process and quality of outputs. The accomplished document…

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

NASA Astrophysics Data System (ADS)

Gaetani, Francesco; Baptiste Filippi, Jean; Simeoni, Albert; D'Andrea, Mirko

2010-05-01

Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough data-set of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach will be provided with reference to Sardinia and Corsica Islands, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of about 3000 wildfires burnt in Sardinia and Corsica in 2007 and 2008 will be used to evaluate the capability of HI coupled with the outputs of the Fire Weather model to forecast the actual risk in time and in space.

Application of a wind-wave-current coupled model in the Catalan coast (NW Mediterranean sea), for wind energy purposes

NASA Astrophysics Data System (ADS)

María Palomares, Ana; Navarro, Jorge; Grifoll, Manel; Pallares, Elena; Espino, Manuel

2016-04-01

This work shows the main results of the HAREAMAR project (including HAREMAR, ENE2012-38772-C02-01 and DARDO, ENE2012-38772-C02-02 projects), concerning the local Wind, Wave and Current simulation at St. Jordi Bay (NW Mediterranean Sea). Offshore Wind Energy has become one of the main topics within the research in Wind Energy research. Although there are quite a few models with a high level of reliability for wind simulation and prediction in onshore places, the wind prediction needs further investigations for adaptation to the Offshore emplacements, taking into account the interaction atmosphere-ocean. The main problem in these ocean areas is the lack of wind data, which neither allows for characterizing the energy potential and wind behaviour in a particular place, nor validating the forecasting models. The main objective of this work is to reduce the local prediction errors, in order to make the meteo-oceanographic hindcast and forecast more reliable. The COAWST model (Coupled-Ocean-Atmosphere-Wave Sediment Transport Model; Warner et al., 2010) system has been implemented in the region considering a set of downscaling nested meshes to obtain high-resolution outputs in the region. The adaptation to this particular area, combining the different wind, wave and ocean model domains has been far from simple, because the grid domains for the three models differ significantly. This work shows the main results of the COAWST model implementation to this particular area, including both monthly and other set of tests in different atmospheric situations, especially chosen for their particular interest. The time period considered for the validation is the whole year 2012. A comparative study between the WRF, SWAN and ROMS model outputs (without coupling), the COWAST model outputs, and a buoy measurements moored in the region was performed for this year. References Warner, J.C., Armstrong, B., He, R., and Zambon, J.B., 2010, Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system: Ocean Modeling, 35 (3), 230-244.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.

Use of «MLCM3» software for flash flood forecasting

NASA Astrophysics Data System (ADS)

Sokolova, Daria; Kuzmin, Vadim

2017-04-01

Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community.In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on "traditional" physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfortunately, most of river basins in Russia are poorly gauged or ungauged; besides, lack of hydrogeological data is quite typical, especially, in remote regions of Siberia. However, the developing economy and population safety make us to issue warnings based on reliable forecasts. For this purpose, a new hydrological model, MLCM3 (Multi-Layer Conceptual Model, 3rd generation) has been developed in the Russian State Hydrometeorological University. MLCM3 is a "rainfall-runoff"model with flexible structure and high level of"conceptualization".Model forcing includes precipitation and evaporation data basically coming from NWP model output. Water comes to the outlet through several layers; their number as well as two parameters (thickness and infiltration rate) for each of them, surface flow velocity (when the top layer is full of water) are optimized. The main advantage of the MLCM3, in comparison to the Sacramento Soil Moisture Accounting Model (SAC-SMA), Australian Water Balance Model (AWBM), Soil Moisture Accounting and Routing (SMAR) model and similar models, is that its automatic calibration is very fast and efficient with less volume of information. For instance, in comparison to SAC-SMA, which is calibrated using either Shuffled Complex Evolution algorithm (SCE-UA), or Stepwise Line Search (SLS), automatically calibrated MLCM3 gives better or comparable results without using any "a priori" data or essential processor resources. This advantage allows using the MLCM3 for very fast streamflow prediction in many basins. When assimilated NWP model output data used to force the model, the forecasts accuracy is quite acceptable and enough for automatic warning. Also please note that, in comparison to the 2nd generation of the model, a very useful new option has been added. Now it is possible to set upvariable infiltration rate of the top layer; this option is quite promising in terms of spring floods modeling. (At the moment it is necessary to perform more numerical experiments with snow melting; obtained results will be reported later). Recently new software for MLCM3 was developed. It contains quite usual and understandable options. Formation of the model "input" can be done in manual and automatic mode. Manual or automatic calibration of the model can be performed using either purposely developed for this model optimization algorithm, or Nelder-Mead's one, or SLS. For the model calibration, the multi-scale objective function (MSOF) proposed by Koren is used. It has shown its very high efficiency when model forcing data have high level of uncertainty. Other types of objective functions also can be used, such as mean square error and Nash-Sutcliff criterion. The model showed good results in more than 50 tested basins.

Venus Global Reference Atmospheric Model

NASA Technical Reports Server (NTRS)

Justh, Hilary L.

2017-01-01

Venus Global Reference Atmospheric Model (Venus-GRAM) is an engineering-level atmospheric model developed by MSFC that is widely used for diverse mission applications including: Systems design; Performance analysis; Operations planning for aerobraking, Entry, Descent and Landing, and aerocapture; Is not a forecast model; Outputs include density, temperature, pressure, wind components, and chemical composition; Provides dispersions of thermodynamic parameters, winds, and density; Optional trajectory and auxiliary profile input files Has been used in multiple studies and proposals including NASA Engineering and Safety Center (NESC) Autonomous Aerobraking and various Discovery proposals; Released in 2005; Available at: https://software.nasa.gov/software/MFS-32314-1.

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

NASA Astrophysics Data System (ADS)

Richter, Friedrich; Drusch, Matthias; Kaleschke, Lars; Maaß, Nina; Tian-Kunze, Xiangshan; Mecklenburg, Susanne

2018-03-01

Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.

A warm-season comparison of WRF coupled to the CLM4.0, Noah-MP, and Bucket hydrology land surface schemes over the central USA

NASA Astrophysics Data System (ADS)

Van Den Broeke, Matthew S.; Kalin, Andrew; Alavez, Jose Abraham Torres; Oglesby, Robert; Hu, Qi

2017-11-01

In climate modeling studies, there is a need to choose a suitable land surface model (LSM) while adhering to available resources. In this study, the viability of three LSM options (Community Land Model version 4.0 [CLM4.0], Noah-MP, and the five-layer thermal diffusion [Bucket] scheme) in the Weather Research and Forecasting model version 3.6 (WRF3.6) was examined for the warm season in a domain centered on the central USA. Model output was compared to Parameter-elevation Relationships on Independent Slopes Model (PRISM) data, a gridded observational dataset including mean monthly temperature and total monthly precipitation. Model output temperature, precipitation, latent heat (LH) flux, sensible heat (SH) flux, and soil water content (SWC) were compared to observations from sites in the Central and Southern Great Plains region. An overall warm bias was found in CLM4.0 and Noah-MP, with a cool bias of larger magnitude in the Bucket model. These three LSMs produced similar patterns of wet and dry biases. Model output of SWC and LH/SH fluxes were compared to observations, and did not show a consistent bias. Both sophisticated LSMs appear to be viable options for simulating the effects of land use change in the central USA.

Interoperability challenges in river discharge modelling: A cross domain application scenario

NASA Astrophysics Data System (ADS)

Santoro, Mattia; Andres, Volker; Jirka, Simon; Koike, Toshio; Looser, Ulrich; Nativi, Stefano; Pappenberger, Florian; Schlummer, Manuela; Strauch, Adrian; Utech, Michael; Zsoter, Ervin

2018-06-01

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question. This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS). The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a "River Discharge" application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

The effects of carbon tax on the Oregon economy and state greenhouse gas emissions

NASA Astrophysics Data System (ADS)

Rice, A. L.; Butenhoff, C. L.; Renfro, J.; Liu, J.

2014-12-01

Of the numerous mechanisms to mitigate greenhouse gas emissions on statewide, regional or national scales in the United States, a tax on carbon is perhaps one of the simplest. By taxing emissions directly, the costs of carbon emissions are incorporated into decision-making processes of market actors including consumers, energy suppliers and policy makers. A carbon tax also internalizes the social costs of climate impacts. In structuring carbon tax revenues to reduce corporate and personal income taxes, the negative incentives created by distortionary income taxes can be reduced or offset entirely. In 2008, the first carbon tax in North America across economic sectors was implemented in British Columbia through such a revenue-neutral program. In this work, we investigate the economic and environmental effects of a carbon tax in the state of Oregon with the goal of informing the state legislature, stakeholders and the public. The study investigates 70 different economic sectors in the Oregon economy and six geographical regions of the state. The economic model is built upon the Carbon Tax Analysis Model (C-TAM) to provide price changes in fuel with data from: the Energy Information Agency National Energy Modeling System (EIA-NEMS) Pacific Region Module which provides Oregon-specific energy forecasts; and fuel price increases imposed at different carbon fees based on fuel-specific carbon content and current and projected regional-specific electricity fuel mixes. CTAM output is incorporated into the Regional Economic Model (REMI) which is used to dynamically forecast economic impacts by region and industry sector including: economic output, employment, wages, fiscal effects and equity. Based on changes in economic output and fuel demand, we further project changes in greenhouse gas emissions resulting from economic activity and calculate revenue generated through a carbon fee. Here, we present results of this modeling effort under different scenarios of carbon fee and avenues for revenue repatriation.

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

NASA Technical Reports Server (NTRS)

Gill, Roger; Schnase, John L.

2012-01-01

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Creating Weather System Ensembles Through Synergistic Process Modeling and Machine Learning

NASA Astrophysics Data System (ADS)

Chen, B.; Posselt, D. J.; Nguyen, H.; Wu, L.; Su, H.; Braverman, A. J.

2017-12-01

Earth's weather and climate are sensitive to a variety of control factors (e.g., initial state, forcing functions, etc). Characterizing the response of the atmosphere to a change in initial conditions or model forcing is critical for weather forecasting (ensemble prediction) and climate change assessment. Input - response relationships can be quantified by generating an ensemble of multiple (100s to 1000s) realistic realizations of weather and climate states. Atmospheric numerical models generate simulated data through discretized numerical approximation of the partial differential equations (PDEs) governing the underlying physics. However, the computational expense of running high resolution atmospheric state models makes generation of more than a few simulations infeasible. Here, we discuss an experiment wherein we approximate the numerical PDE solver within the Weather Research and Forecasting (WRF) Model using neural networks trained on a subset of model run outputs. Once trained, these neural nets can produce large number of realization of weather states from a small number of deterministic simulations with speeds that are orders of magnitude faster than the underlying PDE solver. Our neural network architecture is inspired by the governing partial differential equations. These equations are location-invariant, and consist of first and second derivations. As such, we use a 3x3 lon-lat grid of atmospheric profiles as the predictor in the neural net to provide the network the information necessary to compute the first and second moments. Results indicate that the neural network algorithm can approximate the PDE outputs with high degree of accuracy (less than 1% error), and that this error increases as a function of the prediction time lag.

Toward improving hurricane forecasts using the JPL Tropical Cyclone Information System (TCIS): A framework to address the issues of Big Data

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Zavodsky, Bradley T.

2015-01-01

Mesoscale weather conditions can have an adverse effect on space launch, landing, ground processing, and weather advisories, watches, and warnings at the Eastern Range (ER) in Florida and Wallops Flight Facility (WFF) in Virginia. During summer, land-sea interactions across Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger personnel and resources. Many other weak locally-driven low-level boundaries and their interactions with the sea breeze front and each other can also initiate deep convection in the KSC/CCAFS area. These convective processes often last 60 minutes or less and pose a significant challenge to the local forecasters. Surface winds during the transition seasons (spring and fall) pose the most difficulties for the forecasters at WFF. They also encounter problems forecasting convective activity and temperature during those seasons. Therefore, accurate mesoscale model forecasts are needed to better forecast a variety of unique weather phenomena. Global and national scale models cannot properly resolve important local-scale weather features at each location due to their horizontal resolutions being much too coarse. Therefore, a properly tuned local data assimilation (DA) and forecast model at a high resolution is needed to provide improved capability. To accomplish this, a number of sensitivity tests were performed using the Weather Research and Forecasting (WRF) model in order to determine the best DA/model configuration for operational use at each of the space launch ranges to best predict winds, precipitation, and temperature. A set of Perl scripts to run the Gridpoint Statistical Interpolation (GSI)/WRF in real-time were provided by NASA's Short-term Prediction Research and Transition Center (SPoRT). The GSI can analyze many types of observational data including satellite, radar, and conventional data. The GSI/WRF scripts use a cycled GSI system similar to the operational North American Mesoscale (NAM) model. The scripts run a 12-hour pre-cycle in which data are assimilated from 12 hours prior up to the model initialization time. A number of different model configurations were tested for both the ER and WFF by varying the horizontal resolution on which the data assimilation was done. Three different grid configurations were run for the ER and two configurations were run for WFF for archive cases from 27 Aug 2013 through 10 Nov 2013. To quantify model performance, standard model output will be compared to the Meteorological Assimilation Data Ingest System (MADIS) data. The MADIS observation data will be compared to the WRF forecasts using the Model Evaluation Tools (MET) verification package. In addition, the National Centers for Environmental Prediction's Stage IV precipitation data will be used to validate the WRF precipitation forecasts. The author will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for each space launch range.

The Impact of Microphysics and Planetary Boundary Layer Physics on Model Simulation of U.S. Deep South Summer Convection

NASA Technical Reports Server (NTRS)

McCaul, Eugene W., Jr.; Case, Jonathan L.; Zavodsky, Bradley T.; Srikishen, Jayanthi; Medlin, Jeffrey M.; Wood, Lance

2014-01-01

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics pararneterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRn Center to select NOAAlNWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boWldary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage oflightuing activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

The Impacts of Microphysics and Planetary Boundary Layer Physics on Model Simulations of U. S. Deep South Summer Convection

NASA Technical Reports Server (NTRS)

McCaul, E. W., Jr.; Case, J. L.; Zavodsky, B. T.; Srikishen, J.; Medlin, J. M.; Wood, L.

2014-01-01

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics parameterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRT Center to select NOAA/NWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boundary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage of lightning activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

Production data from five major geothermal fields in Nevada analysed using a physiostatistical algorithm developed for oil and gas: temperature decline forecasts and type curves

NASA Astrophysics Data System (ADS)

Kuzma, H. A.; Golubkova, A.; Eklund, C.

2015-12-01

Nevada has the second largest output of geothermal energy in the United States (after California) with 14 major power plants producing over 425 megawatts of electricity meeting 7% of the state's total energy needs. A number of wells, particularly older ones, have shown significant temperature and pressure declines over their lifetimes, adversely affecting economic returns. Production declines are almost universal in the oil and gas (O&G) industry. BetaZi (BZ) is a proprietary algorithm which uses a physiostatistical model to forecast production from the past history of O&G wells and to generate "type curves" which are used to estimate the production of undrilled wells. Although BZ was designed and calibrated for O&G, it is a general purpose diffusion equation solver, capable of modeling complex fluid dynamics in multi-phase systems. In this pilot study, it is applied directly to the temperature data from five Nevada geothermal fields. With the data appropriately normalized, BZ is shown to accurately predict temperature declines. The figure shows several examples of BZ forecasts using historic data from Steamboat Hills field near Reno. BZ forecasts were made using temperature on a normalized scale (blue) with two years of data held out for blind testing (yellow). The forecast is returned in terms of percentiles of probability (red) with the median forecast marked (solid green). Actual production is expected to fall within the majority of the red bounds 80% of the time. Blind tests such as these are used to verify that the probabilistic forecast can be trusted. BZ is also used to compute and accurate type temperature profile for wells that have yet to be drilled. These forecasts can be combined with estimated costs to evaluate the economics and risks of a project or potential capital investment. It is remarkable that an algorithm developed for oil and gas can accurately predict temperature in geothermal wells without significant recasting.

Avoiding the ensemble decorrelation problem using member-by-member post-processing

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2014-05-01

Forecast calibration or post-processing has become a standard tool in atmospheric and climatological science due to the presence of systematic initial condition and model errors. For ensemble forecasts the most competitive methods derive from the assumption of a fixed ensemble distribution. However, when independently applying such 'statistical' methods at different locations, lead times or for multiple variables the correlation structure for individual ensemble members is destroyed. Instead of reastablishing the correlation structure as in Schefzik et al. (2013) we instead propose a calibration method that avoids such problem by correcting each ensemble member individually. Moreover, we analyse the fundamental mechanisms by which the probabilistic ensemble skill can be enhanced. In terms of continuous ranked probability score, our member-by-member approach amounts to skill gain that extends for lead times far beyond the error doubling time and which is as good as the one of the most competitive statistical approach, non-homogeneous Gaussian regression (Gneiting et al. 2005). Besides the conservation of correlation structure, additional benefits arise including the fact that higher-order ensemble moments like kurtosis and skewness are inherited from the uncorrected forecasts. Our detailed analysis is performed in the context of the Kuramoto-Sivashinsky equation and different simple models but the results extent succesfully to the ensemble forecast of the European Centre for Medium-Range Weather Forecasts (Van Schaeybroeck and Vannitsem, 2013, 2014) . References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Schefzik, R., T.L. Thorarinsdottir, and T. Gneiting, 2013: Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling. To appear in Statistical Science 28. [3] Van Schaeybroeck, B., and S. Vannitsem, 2013: Reliable probabilities through statistical post-processing of ensemble forecasts. Proceedings of the European Conference on Complex Systems 2012, Springer proceedings on complexity, XVI, p. 347-352. [4] Van Schaeybroeck, B., and S. Vannitsem, 2014: Ensemble post-processing using member-by-member approaches: theoretical aspects, under review.

Projecting county pulpwood production with historical production and macro-economic variables

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...

New watershed-based climate forecast products for hydrologists and water managers

NASA Astrophysics Data System (ADS)

Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

2017-12-01

Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-­Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time-space resolutions for some periods (e.g., weeks 3-4) is unusably low, but for other periods, and for multi-month leads with NMME, precipitation and particularly temperature forecasts exhibit useful skill. Website: http://hydro.rap.ucar.edu/s2s/

Sensitivity analysis and uncertainty estimation in ash concentration simulations and tephra deposit daily forecasted at Mt. Etna, in Italy

NASA Astrophysics Data System (ADS)

Prestifilippo, Michele; Scollo, Simona; Tarantola, Stefano

2015-04-01

The uncertainty in volcanic ash forecasts may depend on our knowledge of the model input parameters and our capability to represent the dynamic of an incoming eruption. Forecasts help governments to reduce risks associated with volcanic eruptions and for this reason different kinds of analysis that help to understand the effect that each input parameter has on model outputs are necessary. We present an iterative approach based on the sequential combination of sensitivity analysis, parameter estimation procedure and Monte Carlo-based uncertainty analysis, applied to the lagrangian volcanic ash dispersal model PUFF. We modify the main input parameters as the total mass, the total grain-size distribution, the plume thickness, the shape of the eruption column, the sedimentation models and the diffusion coefficient, perform thousands of simulations and analyze the results. The study is carried out on two different Etna scenarios: the sub-plinian eruption of 22 July 1998 that formed an eruption column rising 12 km above sea level and lasted some minutes and the lava fountain eruption having features similar to the 2011-2013 events that produced eruption column high up to several kilometers above sea level and lasted some hours. Sensitivity analyses and uncertainty estimation results help us to address the measurements that volcanologists should perform during volcanic crisis to reduce the model uncertainty.

Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

NASA Astrophysics Data System (ADS)

Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

2017-12-01

Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River, while ocean tide can also propagate into this region. By considering the influence of Tonle Sap Lake and the Mekong River through multi-variable regression analysis, the forecasting results from Prek Kdam to Chau Doc/Tan Chau reach RMSE from about 0.3 - 0.65 m and correlation coefficient about 0.93- 0.97 with 5-day lead time.

Forecasting and Hindcasting Waves With the SWAN Model in the Southern California Bight

DTIC Science & Technology

2007-01-01

2006, there are active relevant websites run by the Coastal Data Information Program ( CDIP ). * Corresponding author. Fax: +1 228 688 4759. An...word "blocking" here implies that an island is completely output locations are locations of CDIP instruments (all buoys, blocking wave energy from some...direction. Blocking is not the except for 073). Some CDIP locations are referred to by three- only problem associated with geographic resolution, of

A Robust Design Approach to Cost Estimation: Solar Energy for Marine Corps Expeditionary Operations

DTIC Science & Technology

2014-04-30

areas as photovoltaic arrays for power harvesting, light emitting diodes (LED) for decreased energy consumption, and improved battery and smart power ...conversion system that allows Marines to power systems with solar energy. Each GREENS is comprised of eight photovoltaic array panels, four high-energy...Brandon Newell conducted an experiment where he assessed the capabilities of the HOMER model in forecasting the power output of a solar panel at the

An operational mesoscale ensemble data assimilation and prediction system: E-RTFDDA

NASA Astrophysics Data System (ADS)

Liu, Y.; Hopson, T.; Roux, G.; Hacker, J.; Xu, M.; Warner, T.; Swerdlin, S.

2009-04-01

Mesoscale (2-2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, and physics processes that are parameterized in NWP models play a great role. Complex interactions of synoptic circulations, regional and local terrain, land-surface heterogeneity, and associated physical properties, and the physical processes of radiative transfer, cloud and precipitation and boundary layer mixing, are crucial in shaping regional weather and climate. Mesoscale ensemble analysis and prediction should sample the uncertainties of mesoscale modeling systems in representing these factors. An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system has been developed at NCAR. E-RTFDDA contains diverse ensemble perturbation approaches that consider uncertainties in all major system components to produce multi-scale continuously-cycling probabilistic data assimilation and forecasting. A 30-member E-RTFDDA system with three nested domains with grid sizes of 30, 10 and 3.33 km has been running on a Department of Defense high-performance computing platform since September 2007. It has been applied at two very different US geographical locations; one in the western inter-mountain area and the other in the northeastern states, producing 6 hour analyses and 48 hour forecasts, with 4 forecast cycles a day. The operational model outputs are analyzed to a) assess overall ensemble performance and properties, b) study terrain effect on mesoscale predictability, c) quantify the contribution of different ensemble perturbation approaches to the overall forecast skill, and d) assess the additional contributed skill from an ensemble calibration process based on a quantile-regression algorithm. The system and the results will be reported at the meeting.

A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

NASA Astrophysics Data System (ADS)

Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

2017-06-01

The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

Comparative Performance Evaluation of Rainfall-runoff Models, Six of Black-box Type and One of Conceptual Type, From The Galway Flow Forecasting System (gffs) Package, Applied On Two Irish Catchments

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Shamseldin, A. Y.

The "Galway Real-Time River Flow Forecasting System" (GFFS) is a software pack- age developed at the Department of Engineering Hydrology, of the National University of Ireland, Galway, Ireland. It is based on a selection of lumped black-box and con- ceptual rainfall-runoff models, all developed in Galway, consisting primarily of both the non-parametric (NP) and parametric (P) forms of two black-box-type rainfall- runoff models, namely, the Simple Linear Model (SLM-NP and SLM-P) and the seasonally-based Linear Perturbation Model (LPM-NP and LPM-P), together with the non-parametric wetness-index-based Linearly Varying Gain Factor Model (LVGFM), the black-box Artificial Neural Network (ANN) Model, and the conceptual Soil Mois- ture Accounting and Routing (SMAR) Model. Comprised of the above suite of mod- els, the system enables the user to calibrate each model individually, initially without updating, and it is capable also of producing combined (i.e. consensus) forecasts us- ing the Simple Average Method (SAM), the Weighted Average Method (WAM), or the Artificial Neural Network Method (NNM). The updating of each model output is achieved using one of four different techniques, namely, simple Auto-Regressive (AR) updating, Linear Transfer Function (LTF) updating, Artificial Neural Network updating (NNU), and updating by the Non-linear Auto-Regressive Exogenous-input method (NARXM). The models exhibit a considerable range of variation in degree of complexity of structure, with corresponding degrees of complication in objective func- tion evaluation. Operating in continuous river-flow simulation and updating modes, these models and techniques have been applied to two Irish catchments, namely, the Fergus and the Brosna. A number of performance evaluation criteria have been used to comparatively assess the model discharge forecast efficiency.

TRAVEL FORECASTER

NASA Technical Reports Server (NTRS)

Mauldin, L. E.

1994-01-01

Business travel planning within an organization is often a time-consuming task. Travel Forecaster is a menu-driven, easy-to-use program which plans, forecasts cost, and tracks actual vs. planned cost for business-related travel of a division or branch of an organization and compiles this information into a database to aid the travel planner. The program's ability to handle multiple trip entries makes it a valuable time-saving device. Travel Forecaster takes full advantage of relational data base properties so that information that remains constant, such as per diem rates and airline fares (which are unique for each city), needs entering only once. A typical entry would include selection with the mouse of the traveler's name and destination city from pop-up lists, and typed entries for number of travel days and purpose of the trip. Multiple persons can be selected from the pop-up lists and multiple trips are accommodated by entering the number of days by each appropriate month on the entry form. An estimated travel cost is not required of the user as it is calculated by a Fourth Dimension formula. With this information, the program can produce output of trips by month with subtotal and total cost for either organization or sub-entity of an organization; or produce outputs of trips by month with subtotal and total cost for international-only travel. It will also provide monthly and cumulative formats of planned vs. actual outputs in data or graph form. Travel Forecaster users can do custom queries to search and sort information in the database, and it can create custom reports with the user-friendly report generator. Travel Forecaster 1.1 is a database program for use with Fourth Dimension Runtime 2.1.1. It requires a Macintosh Plus running System 6.0.3 or later, 2Mb of RAM and a hard disk. The standard distribution medium for this package is one 3.5 inch 800K Macintosh format diskette. Travel Forecaster was developed in 1991. Macintosh is a registered trademark of Apple Computer, Inc. Fourth Dimension is a registered trademark of Acius, Inc.

A methodology for long range prediction of air transportation

NASA Technical Reports Server (NTRS)

Ayati, M. B.; English, J. M.

1980-01-01

The paper describes the methodology for long-time projection of aircraft fuel requirements. A new concept of social and economic factors for future aviation industry which provides an estimate of predicted fuel usage is presented; it includes air traffic forecasts and lead times for producing new engines and aircraft types. An air transportation model is then developed in terms of an abstracted set of variables which represent the entire aircraft industry on a macroscale. This model was evaluated by testing the required output variables from a model based on historical data over the past decades.

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged precipitation and lagged mean daily flow as candidate inputs. Model performance metric show that the CNPSA method had higher performance (with an efficiency of 0.76). Model output was used to assess the risk of extreme peak flows for a given day using an inverse possibility-to-probability transformation.

Control vocabulary software designed for CMIP6

NASA Astrophysics Data System (ADS)

Nadeau, D.; Taylor, K. E.; Williams, D. N.; Ames, S.

2016-12-01

The Coupled Model Intercomparison Project Phase 6 (CMIP6) coordinates a number of intercomparison activities and includes many more experiments than its predecessor, CMIP5. In order to organize and facilitate use of the complex collection of expected CMIP6 model output, a standard set of descriptive information has been defined, which must be stored along with the data. This standard information enables automated machine interpretation of the contents of all model output files. The standard metadata is stored in compliance with the Climate and Forecast (CF) standard, which ensures that it can be interpreted and visualized by many standard software packages. Additional attributes (not standardized by CF) are required by CMIP6 to enhance identification of models and experiments, and to provide additional information critical for interpreting the model results. To ensure that CMIP6 data complies with the standards, a python program called "PrePARE" (Pre-Publication Attribute Reviewer for the ESGF) has been developed to check the model output prior to its publication and release for analysis. If, for example, a required attribute is missing or incorrect (e.g., not included in the reference CMIP6 controlled vocabularies), then PrePare will prevent publication. In some circumstances, missing attributes can be created or incorrect attributes can be replaced automatically by PrePARE, and the program will warn users about the changes that have been made. PrePARE provides a final check on model output assuring adherence to a baseline conformity across the output from all CMIP6 models which will facilitate analysis by climate scientists. PrePARE is flexible and can be easily modified for use by similar projects that have a well-defined set of metadata and controlled vocabularies.

Mapping urban environmental noise: a land use regression method.

PubMed

Xie, Dan; Liu, Yi; Chen, Jining

2011-09-01

Forecasting and preventing urban noise pollution are major challenges in urban environmental management. Most existing efforts, including experiment-based models, statistical models, and noise mapping, however, have limited capacity to explain the association between urban growth and corresponding noise change. Therefore, these conventional methods can hardly forecast urban noise at a given outlook of development layout. This paper, for the first time, introduces a land use regression method, which has been applied for simulating urban air quality for a decade, to construct an urban noise model (LUNOS) in Dalian Municipality, Northwest China. The LUNOS model describes noise as a dependent variable of surrounding various land areas via a regressive function. The results suggest that a linear model performs better in fitting monitoring data, and there is no significant difference of the LUNOS's outputs when applied to different spatial scales. As the LUNOS facilitates a better understanding of the association between land use and urban environmental noise in comparison to conventional methods, it can be regarded as a promising tool for noise prediction for planning purposes and aid smart decision-making.

Handling Input and Output for COAMPS

NASA Technical Reports Server (NTRS)

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

2007-01-01

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

A probabilistic neural network based approach for predicting the output power of wind turbines

NASA Astrophysics Data System (ADS)

Tabatabaei, Sajad

2017-03-01

Finding the authentic predicting tools of eliminating the uncertainty of wind speed forecasts is highly required while wind power sources are strongly penetrating. Recently, traditional predicting models of generating point forecasts have no longer been trustee. Thus, the present paper aims at utilising the concept of prediction intervals (PIs) to assess the uncertainty of wind power generation in power systems. Besides, this paper uses a newly introduced non-parametric approach called lower upper bound estimation (LUBE) to build the PIs since the forecasting errors are unable to be modelled properly by applying distribution probability functions. In the present proposed LUBE method, a PI combination-based fuzzy framework is used to overcome the performance instability of neutral networks (NNs) used in LUBE. In comparison to other methods, this formulation more suitably has satisfied the PI coverage and PI normalised average width (PINAW). Since this non-linear problem has a high complexity, a new heuristic-based optimisation algorithm comprising a novel modification is introduced to solve the aforesaid problems. Based on data sets taken from a wind farm in Australia, the feasibility and satisfying performance of the suggested method have been investigated.

Exploration of OMI Products for Air Quality Applications Through Comparisons with Models and Observations

NASA Technical Reports Server (NTRS)

Pickering, K. E.; Ziemke, J.; Bucsela, E.; Gleason, J.; Marufu, L.; Dickerson, R.; Mathur, R.; Davidson, P.; Duncan, B.; Bhartia, P. K.

2006-01-01

The Ozone Monitoring Instrument (OMI) on board NASA s Aura satellite was launched in July 2004, and is now providing daily global observations of total column ozone, NO2, and SO2, as well as aerosol information. Algorithms have also been developed to produce daily tropospheric ozone and NO2 products. The tropospheric ozone product reported here is a tropospheric residual computed through use of Aura Microwave Limb Sounder (MLS) ozone profile data to quantify stratospheric ozone. We are investigating the applicability of OMI products for use in air quality modeling, forecasting, and analysis. These investigations include comparison of the OMI tropospheric O3 and NO2 products with global and regional models and with lower tropospheric aircraft observations. Large-scale transport of pollution seen in the OM1 tropospheric O3 data is compared with output from NASA's Global Modeling Initiative global chemistry and transport model. On the regional scale we compare the OMI tropospheric O3 and NO2 with fields from the National Oceanic and Atmospheric Administration and Environmental Protection Agency (NOAA/EPA) operational Eta/CMAQ air quality forecasting model over the eastern United States. This 12-km horizontal resolution model output is roughly of equivalent resolution to the OMI pixel data. Correlation analysis between lower tropospheric aircraft O3 profile data taken by the University of Maryland over the Mid-Atlantic States and OMI tropospheric column mean volume mixing ratio for O3 will be presented. These aircraft data are representative of the lowest 3 kilometers of the atmosphere, the region in which much of the locally-generated and regionally-transported ozone exists.

A Sensor Driven Probabilistic Method for Enabling Hyper Resolution Flood Simulations

NASA Astrophysics Data System (ADS)

Fries, K. J.; Salas, F.; Kerkez, B.

2016-12-01

A reduction in the cost of sensors and wireless communications is now enabling researchers and local governments to make flow, stage and rain measurements at locations that are not covered by existing USGS or state networks. We ask the question: how should these new sources of densified, street-level sensor measurements be used to make improved forecasts using the National Water Model (NWM)? Assimilating these data "into" the NWM can be challenging due to computational complexity, as well as heterogeneity of sensor and other input data. Instead, we introduce a machine learning and statistical framework that layers these data "on top" of the NWM outputs to improve high-resolution hydrologic and hydraulic forecasting. By generalizing our approach into a post-processing framework, a rapidly repeatable blueprint is generated for for decision makers who want to improve local forecasts by coupling sensor data with the NWM. We present preliminary results based on case studies in highly instrumented watersheds in the US. Through the use of statistical learning tools and hydrologic routing schemes, we demonstrate the ability of our approach to improve forecasts while simultaneously characterizing bias and uncertainty in the NWM.

Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

NASA Technical Reports Server (NTRS)

Wobus, Cameron; Reynolds, Lara; Jones, Russell; Horton, Radley; Smith, Joel; Fries, J. Stephen; Tryby, Michael; Spero, Tanya; Nolte, Chris

2015-01-01

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

NASA Astrophysics Data System (ADS)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

Cb-LIKE - Thunderstorm forecasts up to six hours with fuzzy logic

NASA Astrophysics Data System (ADS)

Köhler, Martin; Tafferner, Arnold

2016-04-01

Thunderstorms with their accompanying effects like heavy rain, hail, or downdrafts cause delays and flight cancellations and therefore high additional cost for airlines and airport operators. A reliable thunderstorm forecast up to several hours could provide more time for decision makers in air traffic for an appropriate reaction on possible storm cells and initiation of adequate counteractions. To provide the required forecasts Cb-LIKE (Cumulonimbus-LIKElihood) has been developed at the DLR (Deutsches Zentrum für Luft- und Raumfahrt) Institute of Atmospheric Physics. The new algorithm is an automated system which designates areas with possible thunderstorm development by using model data of the COSMO-DE weather model, which is driven by the German Meteorological Service (DWD). A newly developed "Best-Member- Selection" method allows the automatic selection of that particular model run of a time-lagged COSMO- DE model ensemble, which matches best the current thunderstorm situation. Thereby the application of the best available data basis for the calculation of the thunderstorm forecasts by Cb-LIKE is ensured. Altogether there are four different modes for the selection of the best member. Four atmospheric parameters (CAPE, vertical wind velocity, radar reflectivity and cloud top temperature) of the model output are used within the algorithm. A newly developed fuzzy logic system enables the subsequent combination of the model parameters and the calculation of a thunderstorm indicator within a value range of 12 up to 88 for each grid point of the model domain for the following six hours in one hour intervals. The higher the indicator value the more the model parameters imply the development of thunderstorms. The quality of the Cb-LIKE thunderstorm forecasts was evaluated by a substantial verification using a neighborhood verification approach and multi-event contingency tables. The verification was performed for the whole summer period of 2012. On the basis of a deterministic object comparison with heavy precipitation cells observed by the radar-based thunderstorm tracking algorithm Rad-TRAM, several verification scores like BIAS, POD, FAR and CSI were calculated to identify possible advantages of the new algorithm. The presentation illustrates in detail the concept of the Cb-LIKE algorithm with regard to the fuzzy logic system and the Best-Member-Selection. Additionally some case studies and the most important results of the verification will be shown. The implementation of the forecasts into the DLR WxFUSION system, an user oriented forecasting system for air traffic, will also be included.

Benchmarking Ensemble Streamflow Prediction Skill in the UK

NASA Astrophysics Data System (ADS)

Harrigan, Shaun; Smith, Katie; Parry, Simon; Tanguy, Maliko; Prudhomme, Christel

2017-04-01

Skilful hydrological forecasts at weekly to seasonal lead times would be extremely beneficial for decision-making in operational water management, especially during drought conditions. Hydro-meteorological ensemble forecasting systems are an attractive approach as they use two sources of streamflow predictability: (i) initial hydrologic conditions (IHCs), where soil moisture, groundwater and snow storage states can provide an estimate of future streamflow situations, and (ii) atmospheric predictability, where skilful forecasts of weather and climate variables can be used to force hydrological models. In the UK, prediction of rainfall at long lead times and for summer months in particular is notoriously difficult given the large degree of natural climate variability in ocean influenced mid-latitude regions, but recent research has uncovered exciting prospects for improved rainfall skill at seasonal lead times due to improved prediction of the North Atlantic Oscillation. However, before we fully understand what this improved atmospheric predictability might mean in terms of improved hydrological forecasts, we must first evaluate how much skill can be gained from IHCs alone. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions. The aim of this study is therefore to benchmark when (lead time/forecast initialisation month) and where (spatial pattern/catchment characteristics) ESP is skilful across a diverse set of catchments in the UK. Forecast skill was evaluated seamlessly from lead times of 1-day to 12-months and forecasts were initialised at the first of each month over the 1965-2015 hindcast period. This ESP output also provides a robust benchmark against which to assess how much improvement in skill can be achieved when meteorological forecasts are incorporated (next steps). To provide a 'tough to beat' benchmark, several variants of ESP with increasing complexity were produced, including better model representation of hydrological processes and sub-sampling of historic climate sequences (e.g. NAO+/NAO- years). This work is part of the Improving Predictions of Drought for User Decision Making (IMPETUS) project and provides insight to where advancements in atmospheric predictability is most needed in the UK in the context of water management.

The potential of air-sea interactions for improving summertime North Atlantic seasonal forecasts

NASA Astrophysics Data System (ADS)

Ossó, Albert; Shaffrey, Len; Dong, Buwen; Sutton, Rowan

2017-04-01

Delivering skillful summertime seasonal forecasts of the Northern Hemisphere (NH) mid-latitude climate is a key unresolved issue for the climate science community. Current climate models have some skill in forecasting the wintertime NH mid-latitude circulation but very limited skill during summertime. To explore the potential predictability of the summertime climate we analyze lagged correlation patterns between the SSTs and summer atmospheric circulation in the North Atlantic both in observations and climate model outputs. We find observational evidence in the ERA-Interim (1979-2015) reanalysis and the HadSLP2 and HadISST data of an SST pattern forced by late winter atmospheric circulation persisting from winter to early summer that excites an anticyclonic summer SLP anomaly west of the British Isles. We show that the atmospheric response is driven through the action of turbulent heat fluxes and changes on the background baroclinicity. The lagged atmospheric response to the SSTs could be exploited for summertime predictability over Western Europe. We find a statistical significant correlation of over 0.6 between April-May North Atlantic SSTs and the June-August North Atlantic SLP anomaly. The previous findings are further explored using 120 years of coupled ocean-atmosphere HadGEM3-GC2 model simulation. The climate model qualitatively reproduces the observed spatial relationship between the late winter and spring SSTs and summertime circulation, although the correlations are substantially weaker than observed.

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

PubMed Central

Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C.; Rinaldo, Andrea

2018-01-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated. PMID:29768401

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

PubMed

Pasetto, Damiano; Finger, Flavio; Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C; Azman, Andrew S; Luquero, Francisco J; Bertuzzo, Enrico; Rinaldo, Andrea

2018-05-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

Forecasting Geomagnetic Activity Using Kalman Filters

NASA Astrophysics Data System (ADS)

Veeramani, T.; Sharma, A.

2006-05-01

The coupling of energy from the solar wind to the magnetosphere leads to the geomagnetic activity in the form of storms and substorms and are characterized by indices such as AL, Dst and Kp. The geomagnetic activity has been predicted near-real time using local linear filter models of the system dynamics wherein the time series of the input solar wind and the output magnetospheric response were used to reconstruct the phase space of the system by a time-delay embedding technique. Recently, the radiation belt dynamics have been studied using a adaptive linear state space model [Rigler et al. 2004]. This was achieved by assuming a linear autoregressive equation for the underlying process and an adaptive identification of the model parameters using a Kalman filter approach. We use such a model for predicting the geomagnetic activity. In the case of substorms, the Bargatze et al [1985] data set yields persistence like behaviour when a time resolution of 2.5 minutes was used to test the model for the prediction of the AL index. Unlike the local linear filters, which are driven by the solar wind input without feedback from the observations, the Kalman filter makes use of the observations as and when available to optimally update the model parameters. The update procedure requires the prediction intervals to be long enough so that the forecasts can be used in practice. The time resolution of the data suitable for such forecasting is studied by taking averages over different durations.

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Forecasting Maintenance Shortcomings of a Planned Equipment Density Listing in Support of Expeditionary Missions

DTIC Science & Technology

2017-06-01

importantly, it examines the methodology used to build the class IX block embarked on ship prior to deployment. The class IX block is defined as a repository...compared to historical data to evaluate model and simulation outputs. This thesis provides recommendations on improving the methodology implemented in...improving the level of organic support available to deployed units. More importantly, it examines the methodology used to build the class IX block

Large eddy simulations and reduced models of the Unsteady Atmospheric Boundary Layer

NASA Astrophysics Data System (ADS)

Momen, M.; Bou-Zeid, E.

2013-12-01

Most studies of the dynamics of Atmospheric Boundary Layers (ABLs) have focused on steady geostrophic conditions, such as the classic Ekman boundary layer problem. However, real-world ABLs are driven by a time-dependent geostrophic forcing that changes at sub-diurnal scales. Hence, to advance our understanding of the dynamics of atmospheric flows, and to improve their modeling, the unsteady cases have to be analyzed and understood. This is particularly relevant to new applications related to wind energy (e.g. short-term forecast of wind power changes) and pollutant dispersion (forecasting of rapid changes in wind velocity and direction after an accidental spill), as well as to classic weather prediction and hydrometeorological applications. The present study aims to investigate the ABL behavior under variable forcing and to derive a simple model to predict the ABL response under these forcing fluctuations. Simplifications of the governing Navier-Stokes equations, with the Coriolis force, are tested using LES and then applied to derive a physical model of the unsteady ABL. LES is then exploited again to validate the analogy and the output of the simpler model. Results from the analytical model, as well as LES outputs, open the way for inertial oscillations to play an important role in the dynamics. Several simulations with different variable forcing patterns are then conducted to investigate some of the characteristics of the unsteady ABL such as resonant frequency, ABL response time, equilibrium states, etc. The variability of wind velocity profiles and hodographs, turbulent kinetic energy, and vertical profiles of the total stress and potential temperature are also examined. Wind Hodograph of the Unsteady ABL at Different Heights - This figure shows fluctuations in the mean u and v components of the velocity as time passes due to variable geostrophic forcing

Fog Simulations Based on Multi-Model System: A Feasibility Study

NASA Astrophysics Data System (ADS)

Shi, Chune; Wang, Lei; Zhang, Hao; Zhang, Su; Deng, Xueliang; Li, Yaosun; Qiu, Mingyan

2012-05-01

Accurate forecasts of fog and visibility are very important to air and high way traffic, and are still a big challenge. A 1D fog model (PAFOG) is coupled to MM5 by obtaining the initial and boundary conditions (IC/BC) and some other necessary input parameters from MM5. Thus, PAFOG can be run for any area of interest. On the other hand, MM5 itself can be used to simulate fog events over a large domain. This paper presents evaluations of the fog predictability of these two systems for December of 2006 and December of 2007, with nine regional fog events observed in a field experiment, as well as over a large domain in eastern China. Among the simulations of the nine fog events by the two systems, two cases were investigated in detail. Daily results of ground level meteorology were validated against the routine observations at the CMA observational network. Daily fog occurrences for the two study periods was validated in Nanjing. General performance of the two models for the nine fog cases are presented by comparing with routine and field observational data. The results of MM5 and PAFOG for two typical fog cases are verified in detail against field observations. The verifications demonstrated that all methods tended to overestimate fog occurrence, especially for near-fog cases. In terms of TS/ETS, the LWC-only threshold with MM5 showed the best performance, while PAFOG showed the worst. MM5 performed better for advection-radiation fog than for radiation fog, and PAFOG could be an alternative tool for forecasting radiation fogs. PAFOG did show advantages over MM5 on the fog dissipation time. The performance of PAFOG highly depended on the quality of MM5 output. The sensitive runs of PAFOG with different IC/BC showed the capability of using MM5 output to run the 1D model and the high sensitivity of PAFOG on cloud cover. Future works should intensify the study of how to improve the quality of input data (e.g. cloud cover, advection, large scale subsidence) for the 1D model, particularly how to eliminate near-fog case in fog forecasting.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

MULTIREGION: a simulation-forecasting model of BEA economic area population and employment. [Bureau of Economic Analysis

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

Olsen, R.J.; Westley, G.W.; Herzog, H.W. Jr.

This report documents the development of MULTIREGION, a computer model of regional and interregional socio-economic development. The MULTIREGION model interprets the economy of each BEA economic area as a labor market, measures all activity in terms of people as members of the population (labor supply) or as employees (labor demand), and simultaneously simulates or forecasts the demands and supplies of labor in all BEA economic areas at five-year intervals. In general the outputs of MULTIREGION are intended to resemble those of the Water Resource Council's OBERS projections and to be put to similar planning and analysis purposes. This report hasmore » been written at two levels to serve the needs of multiple audiences. The body of the report serves as a fairly nontechnical overview of the entire MULTIREGION project; a series of technical appendixes provide detailed descriptions of the background empirical studies of births, deaths, migration, labor force participation, natural resource employment, manufacturing employment location, and local service employment used to construct the model.« less

Improving Estimates of Regional Infrasound Propagation by Incorporating Three-Dimensional Weather Modeling

NASA Astrophysics Data System (ADS)

McKenna, M. H.; Alter, R. E.; Swearingen, M. E.; Wilson, D. K.

2017-12-01

Many larger sources, such as volcanic eruptions and nuclear detonations, produce infrasound (acoustic waves with a frequency lower than humans can hear, namely 0.1-20 Hz) that can propagate over global scales. But many smaller infrastructure sources, such as bridges, dams, and buildings, also produce infrasound, though with a lower amplitude that tends to propagate only over regional scales (up to 150 km). In order to accurately calculate regional-scale infrasound propagation, we have incorporated high-resolution, three-dimensional forecasts from the Weather Research and Forecasting (WRF) meteorological model into a signal propagation modeling system called Environmental Awareness for Sensor and Emitter Employment (EASEE), developed at the US Army Engineer Research and Development Center. To quantify the improvement of infrasound propagation predictions with more realistic weather data, we conducted sensitivity studies with different propagation ranges and horizontal resolutions and compared them to default predictions with no weather model data. We describe the process of incorporating WRF output into EASEE for conducting these acoustic propagation simulations and present the results of the aforementioned sensitivity studies.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

PubMed

Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

2018-08-01

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

A near real time regional JPSS and GOES-R data assimilation system for high impact weather research and applications

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Coupling fast all-season soil strength land surface model with weather research and forecasting model to assess low-level icing in complex terrain

NASA Astrophysics Data System (ADS)

Sines, Taleena R.

Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models' abilities to accurately predict icing in complex terrain. Non- icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases. (Abstract shortened by UMI.).

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Technical Reports Server (NTRS)

Cohen, Charlie; Robertson, Franklin; Molod, Andrea

2014-01-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Cohen, C.

2014-12-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Range-Specific High-Resolution Mesoscale Model Setup: Data Assimilation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2014-01-01

Mesoscale weather conditions can have an adverse effect on space launch, landing, and ground processing at the Eastern Range (ER) in Florida and Wallops Flight Facility (WFF) in Virginia. During summer, land-sea interactions across Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger personnel and resources. Many other weak locally driven low-level boundaries and their interactions with the sea breeze front and each other can also initiate deep convection in the KSC/CCAFS area. Some of these other boundaries include the Indian River breeze front, Banana River breeze front, outflows from previous convection, horizontal convective rolls, convergence lines from other inland bodies of water such as Lake Okeechobee, the trailing convergence line from convergence of sea breeze fronts due to the shape of Cape Canaveral, frictional convergence lines from the islands in the Bahamas, convergence lines from soil moisture differences, convergence lines from cloud shading, and others. All these subtle weak boundary interactions often make forecasting of operationally important weather very difficult at KSC/CCAFS during the convective season (May-Oct). These convective processes often build quickly, last a short time (60 minutes or less), and occur over small distances, all of which also poses a significant challenge to the local forecasters who are responsible for issuing weather advisories, watches, and warnings. Surface winds during the transition seasons of spring and fall pose the most difficulties for the forecasters at WFF. They also encounter problems forecasting convective activity and temperature during those seasons. Therefore, accurate mesoscale model forecasts are needed to aid in their decision making. Both the ER and WFF would benefit greatly from high-resolution mesoscale model output to better forecast a variety of unique weather phenomena. Global and national scale models cannot properly resolve important local-scale weather features at each location due to their horizontal resolutions being much too coarse. Therefore, a properly tuned model at a high resolution is needed to provide improved capability. This task is a multi-year effort in which the Applied Meteorology Unit (AMU) will tune the Weather Research and Forecasting (WRF) model individually for each range. The goal of the first year, the results of which are in this report, was to tune the WRF model based on the best model resolution and run time while using reasonable computing capabilities. To accomplish this, the ER and WFF supported the tasking of the AMU to perform a number of sensitivity tests in order to determine the best model configuration for operational use at each of the ranges to best predict winds, precipitation, and temperature (Watson 2013). This task is a continuation of that work and will provide a recommended local data assimilation (DA) and numerical forecast model design optimized for the ER and WFF to support space launch activities. The model will be optimized for local weather challenges at both ranges.

Weight and cost forecasting for advanced manned space vehicles

NASA Technical Reports Server (NTRS)

Williams, Raymond

1989-01-01

A mass and cost estimating computerized methology for predicting advanced manned space vehicle weights and costs was developed. The user friendly methology designated MERCER (Mass Estimating Relationship/Cost Estimating Relationship) organizes the predictive process according to major vehicle subsystem levels. Design, development, test, evaluation, and flight hardware cost forecasting is treated by the study. This methodology consists of a complete set of mass estimating relationships (MERs) which serve as the control components for the model and cost estimating relationships (CERs) which use MER output as input. To develop this model, numerous MER and CER studies were surveyed and modified where required. Additionally, relationships were regressed from raw data to accommodate the methology. The models and formulations which estimated the cost of historical vehicles to within 20 percent of the actual cost were selected. The result of the research, along with components of the MERCER Program, are reported. On the basis of the analysis, the following conclusions were established: (1) The cost of a spacecraft is best estimated by summing the cost of individual subsystems; (2) No one cost equation can be used for forecasting the cost of all spacecraft; (3) Spacecraft cost is highly correlated with its mass; (4) No study surveyed contained sufficient formulations to autonomously forecast the cost and weight of the entire advanced manned vehicle spacecraft program; (5) No user friendly program was found that linked MERs with CERs to produce spacecraft cost; and (6) The group accumulation weight estimation method (summing the estimated weights of the various subsystems) proved to be a useful method for finding total weight and cost of a spacecraft.

The new Met Office strategy for seasonal forecasts

NASA Astrophysics Data System (ADS)

Hewson, T. D.

2012-04-01

In October 2011 the Met Office began issuing a new-format UK seasonal forecast, called "The 3-month Outlook". Government interest in a UK-relevant product had been heightened by infrastructure issues arising during the severe cold of previous winters. At the same time there was evidence that the Met Office's "GLOSEA4" long range forecasting system exhibited some hindcast skill for the UK, that was comparable to its hindcast skill for the larger (and therefore less useful) 'northern Europe' region. Also, the NAO- and AO- signals prevailing in the previous two winters had been highlighted by the GLOSEA4 model well in advance. This presentation will initially give a brief overview of GLOSEA4, describing key features such as evolving sea-ice, a well-resolved stratosphere, and the perturbation strategy. Skill measures will be shown, along with forecasts for the last 3 winters. The new structure 3-month outlook will then be described and presented. Previously, our seasonal forecasts had been based on a tercile approach. The new format outlook aims to substantially improve upon this by illustrating graphically, and with text, the full range of possible outcomes, and by placing those outcomes in the context of climatology. In one key component the forecast pdfs (probability density functions) are displayed alongside climatological pdfs. To generate the forecast pdf we take the bias-corrected GLOSEA4 output (42 members), and then incorporate, via expert team, all other relevant information. Firstly model forecasts from other centres are examined. Then external 'forcing factors', such as solar, and the state of the land-ocean-ice system, are referenced, assessing how well the models represent their influence, and bringing in statistical relationships where appropriate. The expert team thereby decides upon any changes to the GLOSEA4 data, employing an interactive tool to shift, expand or contract the forecast pdfs accordingly. The full modification process will be illustrated during the presentation. Another key component of the 3-month outlook is the focus it places on potential hazards and impacts. To date specific references have been made to snow and ice disruption, to replenishment expectation for regions suffering water supply shortages, and to windstorm frequency. This aspect will be discussed, showing also some subjective verification. In future we hope to extend the 3-month outlook framework to other parts of the world, notably Africa, a region where the Met Office, with DfID support, is working collaboratively to improve real-time long range forecasts. Brief reference will also be made to such activities.

Modeling olive-crop forecasting in Tunisia

NASA Astrophysics Data System (ADS)

Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

2017-05-01

Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

Analysis of information systems for hydropower operations

NASA Technical Reports Server (NTRS)

Sohn, R. L.; Becker, L.; Estes, J.; Simonett, D.; Yeh, W. W. G.

1976-01-01

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined.

Performance Optimization of Marine Science and Numerical Modeling on HPC Cluster

PubMed Central

Yang, Dongdong; Yang, Hailong; Wang, Luming; Zhou, Yucong; Zhang, Zhiyuan; Wang, Rui; Liu, Yi

2017-01-01

Marine science and numerical modeling (MASNUM) is widely used in forecasting ocean wave movement, through simulating the variation tendency of the ocean wave. Although efforts have been devoted to improve the performance of MASNUM from various aspects by existing work, there is still large space unexplored for further performance improvement. In this paper, we aim at improving the performance of propagation solver and data access during the simulation, in addition to the efficiency of output I/O and load balance. Our optimizations include several effective techniques such as the algorithm redesign, load distribution optimization, parallel I/O and data access optimization. The experimental results demonstrate that our approach achieves higher performance compared to the state-of-the-art work, about 3.5x speedup without degrading the prediction accuracy. In addition, the parameter sensitivity analysis shows our optimizations are effective under various topography resolutions and output frequencies. PMID:28045972

Analysis of information systems for hydropower operations: Executive summary

NASA Technical Reports Server (NTRS)

Sohn, R. L.; Becker, L.; Estes, J.; Simonett, D.; Yeh, W.

1976-01-01

An analysis was performed of the operations of hydropower systems, with emphasis on water resource management, to determine how aerospace derived information system technologies can effectively increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined in detail to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results were used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept was outlined.

A supply model for nurse workforce projection in Malaysia.

PubMed

Abas, Zuraida Abal; Ramli, Mohamad Raziff; Desa, Mohamad Ishak; Saleh, Nordin; Hanafiah, Ainul Nadziha; Aziz, Nuraini; Abidin, Zaheera Zainal; Shibghatullah, Abdul Samad; Rahman, Ahmad Fadzli Nizam Abdul; Musa, Haslinda

2017-08-18

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

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

Madankan, R.; Pouget, S.; Singla, P., E-mail: psingla@buffalo.edu

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This papermore » presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.« less

Growth of Errors and Uncertainties in Medium Range Ensemble Forecasts of U.S. East Coast Cool Season Extratropical Cyclones

NASA Astrophysics Data System (ADS)

Zheng, Minghua

Cool-season extratropical cyclones near the U.S. East Coast often have significant impacts on the safety, health, environment and economy of this most densely populated region. Hence it is of vital importance to forecast these high-impact winter storm events as accurately as possible by numerical weather prediction (NWP), including in the medium-range. Ensemble forecasts are appealing to operational forecasters when forecasting such events because they can provide an envelope of likely solutions to serve user communities. However, it is generally accepted that ensemble outputs are not used efficiently in NWS operations mainly due to the lack of simple and quantitative tools to communicate forecast uncertainties and ensemble verification to assess model errors and biases. Ensemble sensitivity analysis (ESA), which employs a linear correlation and regression between a chosen forecast metric and the forecast state vector, can be used to analyze the forecast uncertainty development for both short- and medium-range forecasts. The application of ESA to a high-impact winter storm in December 2010 demonstrated that the sensitivity signals based on different forecast metrics are robust. In particular, the ESA based on the leading two EOF PCs can separate sensitive regions associated with cyclone amplitude and intensity uncertainties, respectively. The sensitivity signals were verified using the leave-one-out cross validation (LOOCV) method based on a multi-model ensemble from CMC, ECMWF, and NCEP. The climatology of ensemble sensitivities for the leading two EOF PCs based on 3-day and 6-day forecasts of historical cyclone cases was presented. It was found that the EOF1 pattern often represents the intensity variations while the EOF2 pattern represents the track variations along west-southwest and east-northeast direction. For PC1, the upper-level trough associated with the East Coast cyclone and its downstream ridge are important to the forecast uncertainty in cyclone strength. The initial differences in forecasting the ridge along the west coast of North America impact the EOF1 pattern most. For PC2, it was shown that the shift of the tri-polar structure is most significantly related to the cyclone track forecasts. The EOF/fuzzy clustering tool was applied to diagnose the scenarios in operational ensemble forecast of East Coast winter storms. It was shown that the clustering method could efficiently separate the forecast scenarios associated with East Coast storms based on the 90-member multi-model ensemble. A scenario-based ensemble verification method has been proposed and applied it to examine the capability of different EPSs in capturing the analysis scenarios for historical East Coast cyclone cases at lead times of 1-9 days. The results suggest that the NCEP model performs better in short-range forecasts in capturing the analysis scenario although it is under-dispersed. The ECMWF ensemble shows the best performance in the medium range. The CMC model is found to show the smallest percentage of members in the analysis group and a relatively high missing rate, suggesting that it is less reliable regarding capturing the analysis scenario when compared with the other two EPSs. A combination of NCEP and CMC models has been found to reduce the missing rate and improve the error-spread skill in medium- to extended-range forecasts. Based on the orthogonal features of the EOF patterns, the model errors for 1-6-day forecasts have been decomposed for the leading two EOF patterns. The results for error decomposition show that the NCEP model tends to better represent both EOF1 and EOF2 patterns by showing less intensity and displacement errors during 1-3 days. The ECMWF model is found to have the smallest errors in both EOF1 and EOF2 patterns during 4-6 days. We have also found that East Coast cyclones in the ECMWF forecast tend to be towards the southwest of the other two models in representing the EOF2 pattern, which is associated with the southwest-northeast shifting of the cyclone. This result suggests that ECMWF model may have a tendency to show a closer-to-shore solution in forecasting East Coast winter storms. The downstream impacts of Rossby wave packets (RWPs) on the predictability of winter storms are investigated to explore the source of ensemble uncertainties. The composited RWPA anomalies show that there are enhanced RWPs propagating across the Pacific in both large-error and large-spread cases over the verification regions. There are also indications that the errors might propagate with a speed comparable with the group velocity of RWPs. Based on the composite results as well as our observations of the operation daily RWPA, a conceptual model of errors/uncertainty development associated with RWPs has been proposed to serve as a practical tool to understand the evolution of forecast errors and uncertainties associated with the coherent RWPs originating from upstream as far as western Pacific. (Abstract shortened by ProQuest.).

Mountain Glaciers and Ice Caps

USGS Publications Warehouse

Ananichheva, Maria; Arendt, Anthony; Hagen, Jon-Ove; Hock, Regine; Josberger, Edward G.; Moore, R. Dan; Pfeffer, William Tad; Wolken, Gabriel J.

2011-01-01

Projections of future rates of mass loss from mountain glaciers and ice caps in the Arctic focus primarily on projections of changes in the surface mass balance. Current models are not yet capable of making realistic forecasts of changes in losses by calving. Surface mass balance models are forced with downscaled output from climate models driven by forcing scenarios that make assumptions about the future rate of growth of atmospheric greenhouse gas concentrations. Thus, mass loss projections vary considerably, depending on the forcing scenario used and the climate model from which climate projections are derived. A new study in which a surface mass balance model is driven by output from ten general circulation models (GCMs) forced by the IPCC (Intergovernmental Panel on Climate Change) A1B emissions scenario yields estimates of total mass loss of between 51 and 136 mm sea-level equivalent (SLE) (or 13% to 36% of current glacier volume) by 2100. This implies that there will still be substantial glacier mass in the Arctic in 2100 and that Arctic mountain glaciers and ice caps will continue to influence global sea-level change well into the 22nd century.

An application of hybrid downscaling model to forecast summer precipitation at stations in China

NASA Astrophysics Data System (ADS)

Liu, Ying; Fan, Ke

2014-06-01

A pattern prediction hybrid downscaling method was applied to predict summer (June-July-August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

Development of a short-term irradiance prediction system using post-processing tools on WRF-ARW meteorological forecasts in Spain

NASA Astrophysics Data System (ADS)

Rincón, A.; Jorba, O.; Baldasano, J. M.

2010-09-01

The increased contribution of solar energy in power generation sources requires an accurate estimation of surface solar irradiance conditioned by geographical, temporal and meteorological conditions. The knowledge of the variability of these factors is essential to estimate the expected energy production and therefore help stabilizing the electricity grid and increase the reliability of available solar energy. The use of numerical meteorological models in combination with statistical post-processing tools may have the potential to satisfy the requirements for short-term forecasting of solar irradiance for up to several days ahead and its application in solar devices. In this contribution, we present an assessment of a short-term irradiance prediction system based on the WRF-ARW mesoscale meteorological model (Skamarock et al., 2005) and several post-processing tools in order to improve the overall skills of the system in an annual simulation of the year 2004 in Spain. The WRF-ARW model is applied with 4 km x 4 km horizontal resolution and 38 vertical layers over the Iberian Peninsula. The hourly model irradiance is evaluated against more than 90 surface stations. The stations are used to assess the temporal and spatial fluctuations and trends of the system evaluating three different post-processes: Model Output Statistics technique (MOS; Glahn and Lowry, 1972), Recursive statistical method (REC; Boi, 2004) and Kalman Filter Predictor (KFP, Bozic, 1994; Roeger et al., 2003). A first evaluation of the system without post-processing tools shows an overestimation of the surface irradiance, due to the lack of atmospheric absorbers attenuation different than clouds not included in the meteorological model. This produces an annual BIAS of 16 W m-2 h-1, annual RMSE of 106 W m-2 h-1 and annual NMAE of 42%. The largest errors are observed in spring and summer, reaching RMSE of 350 W m-2 h-1. Results using Kalman Filter Predictor show a reduction of 8% of RMSE, 83% of BIAS, and NMAE decreases down to 32%. The REC method shows a reduction of 6% of RMSE, 79% of BIAS, and NMAE decreases down to 28%. When comparing stations at different altitudes, the overestimation is enhanced at coastal stations (less than 200m) up to 900 W m-2 h-1. The results allow us to analyze strengths and drawbacks of the irradiance prediction system and its application in the estimation of energy production from photovoltaic system cells. References Boi, P.: A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements, Meteorol. Appl., 11, 245-251, 2004. Bozic, S.: Digital and Kalman filtering, John Wiley, Hoboken, New Jersey, 2nd edn., 1994. Glahn, H. and Lowry, D.: The use of Model Output Statistics (MOS) in Objective Weather Forecasting, Applied Meteorology, 11, 1203-1211, 1972. Roeger, C., Stull, R., McClung, D., Hacker, J., Deng, X., and Modzelewski, H.: Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction, Weather and forecasting, 18, 1140-1160, 2003. Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D. M., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 2, Tech. Rep. NCAR/TN-468+STR, NCAR Technical note, 2005.

Operational Hydrologic Forecasts in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

2013-12-01

The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of average discharge for water years 1879-2012) or greater were observed at TDA during the following periods: 29 March to 12 April, 5 May to 11 May, and 19 June to 29 June. Precipitation and temperature forecasts during these periods are shown along with changes in the model simulated snowpack. We evaluate the performance of the ensemble mean 10 days in advance of each of these three events, and comment on how the distribution of ensemble members affected forecast confidence in each situation.

On optimal control problem for conservation law modelling one class of highly re-entrant production systems

NASA Astrophysics Data System (ADS)

D'Apice, Ciro; Kogut, Peter I.

2017-07-01

We discuss the optimal control problem stated as the minimization in the L2-sense of the mismatch between the actual out-flux and a demand forecast for a hyperbolic conservation law that models a highly re-entrant production system. The output of the factory is described as a function of the work in progress and the position of the so-called push-pull point (PPP) where we separate the beginning of the factory employing a push policy from the end of the factory, which uses a pull policy.

A new framework to increase the efficiency of large-scale solar power plants.

NASA Astrophysics Data System (ADS)

Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

Maintaining a Local Data Integration System in Support of Weather Forecast Operations

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

2010-01-01

Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-07-01

The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index (SPEI) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006-2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg-Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945-0.9990, 0.0466-0.1117, and 0.0013-0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash-Sutcliffe Coefficient of Efficiency were between 0.932-0.959 and 0.977-0.998, respectively. When checked for the severity (S), duration (D) and peak intensity (I) of drought events determined from the simulated and observed SPEI, differences in drought parameters ranged from - 1.41-0.64%, - 2.17-1.92% and - 3.21-1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly SPEI and its drought-related properties in the region of study.

How Clean is your Local Air? Here's an app for that

NASA Astrophysics Data System (ADS)

Maskey, M.; Yang, E.; Christopher, S. A.; Keiser, K.; Nair, U. S.; Graves, S. J.

2011-12-01

Air quality is a vital element of our environment. Accurate and localized air quality information is critical for characterizing environmental impacts at the local and regional levels. Advances in location-aware handheld devices and air quality modeling have enabled a group of UAHuntsville scientists to develop a mobile app, LocalAQI, that informs users of current conditions and forecasts of up to twenty-four hours, of air quality indices. The air quality index is based on Community Multiscale Air Quality Modeling System (CMAQ). UAHuntsville scientists have used satellite remote sensing products as inputs to CMAQ, resulting in forecast guidance for particulate matter air quality. The CMAQ output is processed to compute a standardized air quality index. Currently, the air quality index is available for the eastern half of the United States. LocalAQI consists of two main views: air quality index view and map view. The air quality index view displays current air quality for the zip code of a location of interest. Air quality index value is translated into a color-coded advisory system. In addition, users are able to cycle through available hourly forecasts for a location. This location-aware app defaults to the current air quality of user's location. The map view displays color-coded air quality information for the eastern US with an ability to animate through the available forecasts. The app is developed using a cross-platform native application development tool, appcelerator; hence LocalAQI is available for iOS and Android-based phones and pads.

Operational Planning of Channel Airlift Missions Using Forecasted Demand

DTIC Science & Technology

2013-03-01

tailored to the specific problem ( Metaheuristics , 2005). As seen in the section Cargo Loading Algorithm , heuristic methods are often iterative...that are equivalent to the forecasted cargo amount. The simulated pallets are then used in a heuristic cargo loading algorithm . The loading... algorithm places cargo onto available aircraft (based on real schedules) given the date and the destination and outputs statistics based on the aircraft ton

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

WRF model performance under flash-flood associated rainfall

NASA Astrophysics Data System (ADS)

Mejia-Estrada, Iskra; Bates, Paul; Ángel Rico-Ramírez, Miguel

2017-04-01

Understanding the natural processes that precede the occurrence of flash floods is crucial to improve the future flood projections in a changing climate. Using numerical weather prediction tools allows to determine one of the triggering conditions for these particularly dangerous events, difficult to forecast due to their short lead-time. However, simulating the spatial and temporal evolution of the rainfall that leads to a rapid rise in river levels requires determining the best model configuration without compromising the computational efficiency. The current research involves the results of the first part of a cascade modeling approach, where the Weather Research and Forecasting (WRF) model is used to simulate the heavy rainfall in the east of the UK in June 2012 when stationary thunderstorms caused 2-hour accumulated values to match those expected in the whole month of June over the city of Newcastle. The optimum model set-up was obtained after extensive testing regarding physics parameterizations, spin-up times, datasets used as initial conditions and model resolution and nesting, hence determining its sensitivity to reproduce localised events of short duration. The outputs were qualitatively and quantitatively assessed using information from the national weather radar network as well as interpolated rainfall values from gauges, respectively. Statistical and skill score values show that the model is able to produce reliable accumulated precipitation values while explicitly solving the atmospheric equations in high resolution domains as long as several hydrometeors are considered with a spin-up time that allows the model to assimilate the initial conditions without going too far back in time from the event of interest. The results from the WRF model will serve as input to run a semi-distributed hydrological model to determine the rainfall-runoff relationship within an uncertainty assessment framework that will allow evaluating the implications of assumptions at the top of the modeling process in the final outputs of the cascade.

Development of a GIS-based integrated framework for coastal seiches monitoring and forecasting: A North Jiangsu shoal case study

NASA Astrophysics Data System (ADS)

Qin, Rufu; Lin, Liangzhao

2017-06-01

Coastal seiches have become an increasingly important issue in coastal science and present many challenges, particularly when attempting to provide warning services. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of a Seiches Monitoring and Forecasting Integration Framework (SMAF-IF). The SMAF-IF is an integrated system with different types of sensors and numerical models and incorporates the Geographic Information System (GIS) and web techniques, which focuses on coastal seiche events detection and early warning in the North Jiangsu shoal, China. The in situ sensors perform automatic and continuous monitoring of the marine environment status and the numerical models provide the meteorological and physical oceanographic parameter estimates. A model outputs processing software was developed in C# language using ArcGIS Engine functions, which provides the capabilities of automatically generating visualization maps and warning information. Leveraging the ArcGIS Flex API and ASP.NET web services, a web based GIS framework was designed to facilitate quasi real-time data access, interactive visualization and analysis, and provision of early warning services for end users. The integrated framework proposed in this study enables decision-makers and the publics to quickly response to emergency coastal seiche events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and forecasting.

Reliable probabilities through statistical post-processing of ensemble predictions

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2013-04-01

We develop post-processing or calibration approaches based on linear regression that make ensemble forecasts more reliable. We enforce climatological reliability in the sense that the total variability of the prediction is equal to the variability of the observations. Second, we impose ensemble reliability such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. In general the attractors of the model and reality are inhomogeneous. Therefore ensemble spread displays a variability not taken into account in standard post-processing methods. We overcome this by weighting the ensemble by a variable error. The approaches are tested in the context of the Lorenz 96 model (Lorenz 1996). The forecasts become more reliable at short lead times as reflected by a flatter rank histogram. Our best method turns out to be superior to well-established methods like EVMOS (Van Schaeybroeck and Vannitsem, 2011) and Nonhomogeneous Gaussian Regression (Gneiting et al., 2005). References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Lorenz, E. N., 1996: Predictability - a problem partly solved. Proceedings, Seminar on Predictability ECMWF. 1, 1-18. [3] Van Schaeybroeck, B., and S. Vannitsem, 2011: Post-processing through linear regression, Nonlin. Processes Geophys., 18, 147.

Serving Real-Time Point Observation Data in netCDF using Climate and Forecasting Discrete Sampling Geometry Conventions

NASA Astrophysics Data System (ADS)

Ward-Garrison, C.; May, R.; Davis, E.; Arms, S. C.

2016-12-01

NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. The Climate and Forecasting (CF) metadata conventions for netCDF foster the ability to work with netCDF files in general and useful ways. These conventions include metadata attributes for physical units, standard names, and spatial coordinate systems. While these conventions have been successful in easing the use of working with netCDF-formatted output from climate and forecast models, their use for point-based observation data has been less so. Unidata has prototyped using the discrete sampling geometry (DSG) CF conventions to serve, using the THREDDS Data Server, the real-time point observation data flowing across the Internet Data Distribution (IDD). These data originate in text format reports for individual stations (e.g. METAR surface data or TEMP upper air data) and are converted and stored in netCDF files in real-time. This work discusses the experiences and challenges of using the current CF DSG conventions for storing such real-time data. We also test how parts of netCDF's extended data model can address these challenges, in order to inform decisions for a future version of CF (CF 2.0) that would take advantage of features of the netCDF enhanced data model.

Multi-pollutant surface objective analyses and mapping of air quality health index over North America.

PubMed

Robichaud, Alain; Ménard, Richard; Zaïtseva, Yulia; Anselmo, David

2016-01-01

Air quality, like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. To help protect exposed populations, many countries have put in place real-time air quality nowcasting and forecasting capabilities. We present in this paper an optimal combination of air quality measurements and model outputs and show that it leads to significant improvements in the spatial representativeness of air quality. The product is referred to as multi-pollutant surface objective analyses (MPSOAs). Moreover, based on MPSOA, a geographical mapping of the Canadian Air Quality Health Index (AQHI) is also presented which provides users (policy makers, public, air quality forecasters, and epidemiologists) with a more accurate picture of the health risk anytime and anywhere in Canada and the USA. Since pollutants can also behave as passive atmospheric tracers, they provide information about transport and dispersion and, hence, reveal synoptic and regional meteorological phenomena. MPSOA could also be used to build air pollution climatology, compute local and national trends in air quality, and detect systematic biases in numerical air quality (AQ) models. Finally, initializing AQ models at regular time intervals with MPSOA can produce more accurate air quality forecasts. It is for these reasons that the Canadian Meteorological Centre (CMC) in collaboration with the Air Quality Research Division (AQRD) of Environment Canada has recently implemented MPSOA in their daily operations.

Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

USGS Publications Warehouse

Raia, S.; Alvioli, M.; Rossi, M.; Baum, R.L.; Godt, J.W.; Guzzetti, F.

2013-01-01

Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Uncertainty in Ecohydrological Modeling in an Arid Region Determined with Bayesian Methods

PubMed Central

Yang, Junjun; He, Zhibin; Du, Jun; Chen, Longfei; Zhu, Xi

2016-01-01

In arid regions, water resources are a key forcing factor in ecosystem circulation, and soil moisture is the critical link that constrains plant and animal life on the soil surface and underground. Simulation of soil moisture in arid ecosystems is inherently difficult due to high variability. We assessed the applicability of the process-oriented CoupModel for forecasting of soil water relations in arid regions. We used vertical soil moisture profiling for model calibration. We determined that model-structural uncertainty constituted the largest error; the model did not capture the extremes of low soil moisture in the desert-oasis ecotone (DOE), particularly below 40 cm soil depth. Our results showed that total uncertainty in soil moisture prediction was improved when input and output data, parameter value array, and structure errors were characterized explicitly. Bayesian analysis was applied with prior information to reduce uncertainty. The need to provide independent descriptions of uncertainty analysis (UA) in the input and output data was demonstrated. Application of soil moisture simulation in arid regions will be useful for dune-stabilization and revegetation efforts in the DOE. PMID:26963523

Short-term solar flare prediction using image-case-based reasoning

NASA Astrophysics Data System (ADS)

Liu, Jin-Fu; Li, Fei; Zhang, Huai-Peng; Yu, Da-Ren

2017-10-01

Solar flares strongly influence space weather and human activities, and their prediction is highly complex. The existing solutions such as data based approaches and model based approaches have a common shortcoming which is the lack of human engagement in the forecasting process. An image-case-based reasoning method is introduced to achieve this goal. The image case library is composed of SOHO/MDI longitudinal magnetograms, the images from which exhibit the maximum horizontal gradient, the length of the neutral line and the number of singular points that are extracted for retrieving similar image cases. Genetic optimization algorithms are employed for optimizing the weight assignment for image features and the number of similar image cases retrieved. Similar image cases and prediction results derived by majority voting for these similar image cases are output and shown to the forecaster in order to integrate his/her experience with the final prediction results. Experimental results demonstrate that the case-based reasoning approach has slightly better performance than other methods, and is more efficient with forecasts improved by humans.

Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool

NASA Astrophysics Data System (ADS)

Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.

2018-06-01

Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.

On the Predictability of Northeast Monsoon Rainfall over South Peninsular India in General Circulation Models

NASA Astrophysics Data System (ADS)

Nair, Archana; Acharya, Nachiketa; Singh, Ankita; Mohanty, U. C.; Panda, T. C.

2013-11-01

In this study the predictability of northeast monsoon (Oct-Nov-Dec) rainfall over peninsular India by eight general circulation model (GCM) outputs was analyzed. These GCM outputs (forecasts for the whole season issued in September) were compared with high-resolution observed gridded rainfall data obtained from the India Meteorological Department for the period 1982-2010. Rainfall, interannual variability (IAV), correlation coefficients, and index of agreement were examined for the outputs of eight GCMs and compared with observation. It was found that the models are able to reproduce rainfall and IAV to different extents. The predictive power of GCMs was also judged by determining the signal-to-noise ratio and the external error variance; it was noted that the predictive power of the models was usually very low. To examine dominant modes of interannual variability, empirical orthogonal function (EOF) analysis was also conducted. EOF analysis of the models revealed they were capable of representing the observed precipitation variability to some extent. The teleconnection between the sea surface temperature (SST) and northeast monsoon rainfall was also investigated and results suggest that during OND the SST over the equatorial Indian Ocean, the Bay of Bengal, the central Pacific Ocean (over Nino3 region), and the north and south Atlantic Ocean enhances northeast monsoon rainfall. This observed phenomenon is only predicted by the CCM3v6 model.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

National Water Model: Providing the Nation with Actionable Water Intelligence

NASA Astrophysics Data System (ADS)

Aggett, G. R.; Bates, B.

2017-12-01

The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.

A Model For Rapid Estimation of Economic Loss

NASA Astrophysics Data System (ADS)

Holliday, J. R.; Rundle, J. B.

2012-12-01

One of the loftier goals in seismic hazard analysis is the creation of an end-to-end earthquake prediction system: a "rupture to rafters" work flow that takes a prediction of fault rupture, propagates it with a ground shaking model, and outputs a damage or loss profile at a given location. So far, the initial prediction of an earthquake rupture (either as a point source or a fault system) has proven to be the most difficult and least solved step in this chain. However, this may soon change. The Collaboratory for the Study of Earthquake Predictability (CSEP) has amassed a suite of earthquake source models for assorted testing regions worldwide. These models are capable of providing rate-based forecasts for earthquake (point) sources over a range of time horizons. Furthermore, these rate forecasts can be easily refined into probabilistic source forecasts. While it's still difficult to fully assess the "goodness" of each of these models, progress is being made: new evaluation procedures are being devised and earthquake statistics continue to accumulate. The scientific community appears to be heading towards a better understanding of rupture predictability. Ground shaking mechanics are better understood, and many different sophisticated models exists. While these models tend to be computationally expensive and often regionally specific, they do a good job at matching empirical data. It is perhaps time to start addressing the third step in the seismic hazard prediction system. We present a model for rapid economic loss estimation using ground motion (PGA or PGV) and socioeconomic measures as its input. We show that the model can be calibrated on a global scale and applied worldwide. We also suggest how the model can be improved and generalized to non-seismic natural disasters such as hurricane and severe wind storms.

Diversity modelling for electrical power system simulation

NASA Astrophysics Data System (ADS)

Sharip, R. M.; Abu Zarim, M. A. U. A.

2013-12-01

This paper considers diversity of generation and demand profiles against the different future energy scenarios and evaluates these on a technical basis. Compared to previous studies, this research applied a forecasting concept based on possible growth rates from publically electrical distribution scenarios concerning the UK. These scenarios were created by different bodies considering aspects such as environment, policy, regulation, economic and technical. In line with these scenarios, forecasting is on a long term timescale (up to every ten years from 2020 until 2050) in order to create a possible output of generation mix and demand profiles to be used as an appropriate boundary condition for the network simulation. The network considered is a segment of rural LV populated with a mixture of different housing types. The profiles for the 'future' energy and demand have been successfully modelled by applying a forecasting method. The network results under these profiles shows for the cases studied that even though the value of the power produced from each Micro-generation is often in line with the demand requirements of an individual dwelling there will be no problems arising from high penetration of Micro-generation and demand side management for each dwellings considered. The results obtained highlight the technical issues/changes for energy delivery and management to rural customers under the future energy scenarios.

Development of a mobile app for flash flood alerting and data cataloging

NASA Astrophysics Data System (ADS)

Gourley, J. J.; Flamig, Z.; Nguyen, M.

2016-12-01

No matter how accurate and specific a forecast of flash flooding is made, there are local nuances with the communities related to the built environment that often dictate the locations and magnitudes of impacts. These are difficult, if not impossible, to identify, classify, and measure using remote sensing methods. This presentation presents a Thriving Earth Exchange project that is developing a mobile app that serves two purposes. First, it will provide detailed forecasts of flash flooding down to the 1-km pixel scale with 10-min updates using the state-of-the-science hydrologic forecasting system called FLASH. The display of model outputs on an app will greatly facilitate their use and can potentially increase first responders' reactions to the specific locations of impending disasters. Then, the first responders will have the capability of reporting the geotagged impacts they are witnessing, including those local "trouble spots". Over time, we will catalog the trouble spots for the community so that they can be flagged in future events. If proven effective, the app will then be advertised in other flood-prone communities and the database will be expanded accordingly. In summary, we are engaging local communities to provide information that can inform and improve future forecasts of flash flood, ultimately reducing their impacts and saving lives.

The Major Stratospheric Sudden Warming of January 2013: Analyses and Forecasts in the GEOS-5 Data Assimilation System

NASA Technical Reports Server (NTRS)

Coy, Lawrence; Pawson, Steven

2014-01-01

We examine the major stratosphere sudden warming (SSW) that occurred on 6 January 2013, using output from the NASA Global Modeling and Assimilation Office (GMAO) GEOS-5 (Goddard Earth Observing System) near-real-time data assimilation system (DAS). Results show that the major SSW of January 2013 falls into the vortex splitting type of SSW, with the initial planetary wave breaking occurring near 10 hPa. The vertical flux of wave activity at the tropopause responsible for the SSW occurred mainly in the Pacific Hemisphere, including the a pulse associated with the preconditioning of the polar vortex by wave 1 identified on 23 December 2012. While most of the vertical wave activity flux was in the Pacific Hemisphere, a rapidly developing tropospheric weather system over the North Atlantic on 28 December is shown to have produced a strong transient upward wave activity flux into the lower stratosphere coinciding with the peak of the SSW event. In addition, the GEOS-5 5-day forecasts accurately predicted the major SSW of January 2013 as well as the upper tropospheric disturbances responsible for the warming. The overall success of the 5-day forecasts provides motivation to produce regular 10-day forecasts with GEOS-5, to better support studies of stratosphere-troposphere interaction.

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Estimation technique of corrective effects for forecasting of reliability of the designed and operated objects of the generating systems

NASA Astrophysics Data System (ADS)

Truhanov, V. N.; Sultanov, M. M.

2017-11-01

In the present article researches of statistical material on the refusals and malfunctions influencing operability of heat power installations have been conducted. In this article the mathematical model of change of output characteristics of the turbine depending on number of the refusals revealed in use has been presented. The mathematical model is based on methods of mathematical statistics, probability theory and methods of matrix calculation. The novelty of this model is that it allows to predict the change of the output characteristic in time, and the operating influences have been presented in an explicit form. As desirable dynamics of change of the output characteristic (function, reliability) the law of distribution of Veybull which is universal is adopted since at various values of parameters it turns into other types of distributions (for example, exponential, normal, etc.) It should be noted that the choice of the desirable law of management allows to determine the necessary management parameters with use of the saved-up change of the output characteristic in general. The output characteristic can be changed both on the speed of change of management parameters, and on acceleration of change of management parameters. In this article the technique of an assessment of the pseudo-return matrix has been stated in detail by the method of the smallest squares and the standard Microsoft Excel functions. Also the technique of finding of the operating effects when finding restrictions both for the output characteristic, and on management parameters has been considered. In the article the order and the sequence of finding of management parameters has been stated. A concrete example of finding of the operating effects in the course of long-term operation of turbines has been shown.

Using Socioeconomic Data to Calibrate Loss Estimates

NASA Astrophysics Data System (ADS)

Holliday, J. R.; Rundle, J. B.

2013-12-01

One of the loftier goals in seismic hazard analysis is the creation of an end-to-end earthquake prediction system: a "rupture to rafters" work flow that takes a prediction of fault rupture, propagates it with a ground shaking model, and outputs a damage or loss profile at a given location. So far, the initial prediction of an earthquake rupture (either as a point source or a fault system) has proven to be the most difficult and least solved step in this chain. However, this may soon change. The Collaboratory for the Study of Earthquake Predictability (CSEP) has amassed a suite of earthquake source models for assorted testing regions worldwide. These models are capable of providing rate-based forecasts for earthquake (point) sources over a range of time horizons. Furthermore, these rate forecasts can be easily refined into probabilistic source forecasts. While it's still difficult to fully assess the "goodness" of each of these models, progress is being made: new evaluation procedures are being devised and earthquake statistics continue to accumulate. The scientific community appears to be heading towards a better understanding of rupture predictability. Ground shaking mechanics are better understood, and many different sophisticated models exists. While these models tend to be computationally expensive and often regionally specific, they do a good job at matching empirical data. It is perhaps time to start addressing the third step in the seismic hazard prediction system. We present a model for rapid economic loss estimation using ground motion (PGA or PGV) and socioeconomic measures as its input. We show that the model can be calibrated on a global scale and applied worldwide. We also suggest how the model can be improved and generalized to non-seismic natural disasters such as hurricane and severe wind storms.

Forecast of drifter trajectories using a Rapid Environmental Assessment based on CTD observations

NASA Astrophysics Data System (ADS)

Sorgente, R.; Tedesco, C.; Pessini, F.; De Dominicis, M.; Gerin, R.; Olita, A.; Fazioli, L.; Di Maio, A.; Ribotti, A.

2016-11-01

A high resolution submesoscale resolving ocean model was implemented in a limited area north of Island of Elba where a maritime exercise, named Serious Game 1 (SG1), took place on May 2014 in the framework of the project MEDESS-4MS (Mediterranean Decision Support System for Marine Safety). During the exercise, CTD data have been collected responding to the necessity of a Rapid Environmental Assessment, i.e. to a rapid evaluation of the marine conditions able to provide sensible information for initialisation of modelling tools, in the scenario of possible maritime accidents. The aim of this paper is to evaluate the impact of such mesoscale-resolving CTD observations on short-term forecasts of the surface currents, within the framework of possible oil-spill related emergencies. For this reason, modelling outputs were compared with Lagrangian observations at sea: the high resolution modelled currents, together with the ones of the coarser sub-regional model WMED, are used to force the MEDSLIK-II oil-spill model to simulate drifter trajectories. Both ocean models have been assessed by comparing the prognostic scalar and vector fields as an independent CTD data set and with real drifter trajectories acquired during SG1. The diagnosed and prognosed circulation reveals that the area was characterised by water masses of Atlantic origin influenced by small mesoscale cyclonic and anti-cyclonic eddies, which govern the spatial and temporal evolution of the drifter trajectories and of the water masses distribution. The assimilation of CTD data into the initial conditions of the high resolution model highly improves the accuracy of the short-term forecast in terms of location and structure of the thermocline and positively influence the ability of the model in reproducing the observed paths of the surface drifters.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

PubMed

Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

2012-03-01

Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit

2012-03-01

Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

Mesoscale Severe Weather Development under Orographic Influences

DTIC Science & Technology

1992-06-30

control procedures will have to operate centrally before data transmission to the field or will have to be enacted in the field by expert meteorologists...can be depicted uniquely and recognizable on the computer screen as " icons ’. (E.g. in the presence of several thunderstorms, each one should be...appropriate Icon at the proper forecast time and coordinate location. From the numerical forecast output (and, If necessary, from climatological or

Flash floods in June and July 2009 in the Czech Republic

NASA Astrophysics Data System (ADS)

Sercl, Petr; Danhelka, Jan; Tyl, Radovan

2010-05-01

Several flash floods occurred in the territory of the Czech Republic during the last decade of June and beginning of July 2009. These events caused vast economic damage and unfortunately there were also 15 fatalities. The complete evaluation of flash floods from the point of view of its meteorological cause, hydrological development and impacts was done under the responsibility of Ministry of Environment of the Czech Republic. Czech Hydrometeorological Institute (CHMI) coordinated this project. The results of the project contain several concrete proposals to reduce the threat of flash floods in the Czech Republic. The proposals were focused on possible future improvements of CHMI forecasting service activities including all other parts of Flood prevention and protection system in the Czech Republic. The synoptic cause of floods was the extraordinary long (12 days is longest in more than 60 years history) presence of eastern cyclonic situation over the Central Europe bringing warm, moist and unstable air masses from Mediterranean and Black Sea area. Very intensive thunderstorms accompanied by torrential rain occurred almost daily. Storm cells were organized in train effect and crossed repeatedly the same places within several hours. The extremity of the flood events was also influenced by soil saturation due to daily occurrence of rainstorms. The peak flows exceeded significantly 100-year of recurrence time in many sites. The observed and mainly unobserved catchments were affected. The detailed fields of rainfall amounts were gained from the adjusted meteorological radar observation. All of the available rainfall measurements at the climatological and rain gage stations were used for the adjustment. Hydraulic and rainfall-runoff models were used to evaluate the hydrological response. It was proved again, that the outputs from currently used meteorological forecasting models are not sufficient for a reliable local forecast of the strong convective storms and their possible consequences - flash floods. Within the frame of the research project SP/1c4/16/07 "Implementation of new techniques for stream flow forecasting tools" (project period 2007-2011, funded by Ministry of Environment) a forecasting system for the estimation of runoff response to torrential rainfall has been developed. CN value automatic update based on antecedent precipitation is used to estimate possible runoff from storm. Ten minutes radar rainfall estimates and COTREC based nowcasting serve as meteorological input. Results of 2009 events hindcast are presented. It proved the underestimation of rainfall by raw radar data and thus the need for real time adjustment of radar estimates based on rain gauge data. The main output from presented forecasting system is an estimation of flash flood risk. Risk estimation is based on exceeding 3 defined thresholds defined as ratios between the estimated peak flow and theoretical 100-year flood on particular basin. The procedures mentioned above were being developed during the period 2008-2009. Intensive testing is expected by CHMI forecasting offices during 2010-2011.

Sub-seasonal Predictability of Heavy Precipitation Events: Implication for Real-time Flood Management in Iran

NASA Astrophysics Data System (ADS)

Najafi, H.; Shahbazi, A.; Zohrabi, N.; Robertson, A. W.; Mofidi, A.; Massah Bavani, A. R.

2016-12-01

Each year, a number of high impact weather events occur worldwide. Since any level of predictability at sub-seasonal to seasonal timescale is highly beneficial to society, international efforts is now on progress to promote reliable Ensemble Prediction Systems for monthly forecasts within the WWRP/WCRP initiative (S2S) project and North American Multi Model Ensemble (NMME). For water resources managers in the face of extreme events, not only can reliable forecasts of high impact weather events prevent catastrophic losses caused by floods but also contribute to benefits gained from hydropower generation and water markets. The aim of this paper is to analyze the predictability of recent severe weather events over Iran. Two recent heavy precipitations are considered as an illustration to examine whether S2S forecasts can be used for developing flood alert systems especially where large cascade of dams are in operation. Both events have caused major damages to cities and infrastructures. The first severe precipitation was is in the early November 2015 when heavy precipitation (more than 50 mm) occurred in 2 days. More recently, up to 300 mm of precipitation is observed within less than a week in April 2016 causing a consequent flash flood. Over some stations, the observed precipitation was even more than the total annual mean precipitation. To analyze the predictive capability, ensemble forecasts from several operational centers including (European Centre for Medium-Range Weather Forecasts (ECMWF) system, Climate Forecast System Version 2 (CFSv2) and Chinese Meteorological Center (CMA) are evaluated. It has been observed that significant changes in precipitation anomalies were likely to be predicted days in advance. The next step will be to conduct thorough analysis based on comparing multi-model outputs over the full hindcast dataset developing real-time high impact weather prediction systems.

Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.