scholarly journals Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

2019 ◽  
Vol 147 (11) ◽  
pp. 4241-4259 ◽  
Author(s):  
Paul J. Roebber ◽  
John Crockett
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Evolutionary Programming ◽  
The United States ◽  
Training Data ◽  
New Approach ◽  
Numerical Weather ◽  
Probabilistic Forecasts

Abstract An evolutionary programming postprocessor, using coevolution in a predator–prey ecosystem model, is developed and applied both to 72-h, 2-m temperature forecasts for the conterminous United States and southern Canada and to 60-min nowcasts of convection occurrence for the United States east of 94°W. The new approach improves deterministic and probabilistic forecasts of surface temperature relative to bias-corrected numerical weather prediction forecasts and to an earlier version of evolutionary programming forecasts for these same data. The new method also improves deterministic performance for an artificial neural network trained and evaluated for these same data. Additionally, the new approach substantially improves these forecasts’ reliability, as evidenced by reductions in the occurrence of excessive outliers in the rank histogram. The coevolutionary postprocessor also improves deterministic nowcasts of convection occurrence when compared to those produced by the National Weather Service’s AutoNowCaster system and to those obtained using multiple logistic regression. Notably, the degree of improvement relative to traditional methods appears to be problem dependent, while the training and implementation of such a system requires additional effort. However, the coevolutionary system is shown to be robust to imbalances between the frequency of positive and null events in the training data, unlike many postprocessing methods; to be implementable and effective in an adaptive mode, removing the need for retraining as inputs (such as numerical weather prediction model data) change; and to provide a useful, alternative perspective on the likelihood of event occurrence when used in combination with other methods.

The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system

2021 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Ghassan J. Alaka ◽  
Henry R. Winterbottom
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Sequential Data ◽  
Prediction System ◽  
Numerical Weather ◽  
Hwrf Model ◽  
Sequential Data Assimilation

AbstractLimited-area numerical weather prediction models currently run operationally in the United States follow a “partially-cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data pre-processing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

scholarly journals The National Earth System Prediction Capability: Coordinating the Giant

2017 ◽  
Vol 98 (2) ◽  
pp. 239-252 ◽  
Author(s):  
Jessie C. Carman ◽  
Daniel P. Eleuterio ◽  
Timothy C. Gallaudet ◽  
Gerald L. Geernaert ◽  
Patrick A. Harr ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Department Of Energy ◽  
Earth System ◽  
Numerical Weather ◽  
Interagency Coordination ◽  
Research Technology ◽  
Prediction Capability ◽  
The U.S

Abstract The United States has had three operational numerical weather prediction centers since the Joint Numerical Weather Prediction Unit was closed in 1958. This led to separate paths for U.S. numerical weather prediction, research, technology, and operations, resulting in multiple community calls for better coordination. Since 2006, the three operational organizations—the U.S. Air Force, the U.S. Navy, and the National Weather Service—and, more recently, the Department of Energy, the National Aeronautics and Space Administration, the National Science Foundation, and the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research, have been working to increase coordination. This increasingly successful effort has resulted in the establishment of a National Earth System Prediction Capability (National ESPC) office with responsibility to further interagency coordination and collaboration. It has also resulted in sharing of data through an operational global ensemble, common software standards, and model components among the agencies. This article discusses the drivers, the progress, and the future of interagency collaboration.

50th Anniversary of Operational Numerical Weather Prediction

2007 ◽  
Vol 88 (5) ◽  
pp. 639-650 ◽  
Author(s):  
Kristine Harper ◽  
Louis W. Uccellini ◽  
Eugenia Kalnay ◽  
Kenneth Carey ◽  
Lauren Morone
Keyword(s):  
Air Force ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
50Th Anniversary ◽  
Well Being ◽  
The United States ◽  
Numerical Weather ◽  
American Meteorological Society ◽  
College Park ◽  
The U.S

The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a “Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,” on 14–17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

scholarly journals Air Quality Predictions with an Analog Ensemble

2018 ◽  
Author(s):  
Luca Delle Monache ◽  
Stefano Alessandrini ◽  
Irina Djalalova ◽  
James Wilczak ◽  
Jason C. Knievel
Keyword(s):  
Air Quality ◽  
Environmental Protection ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Model Based ◽  
Computational Expense ◽  
Probabilistic Forecasts ◽  
A Current

Abstract. The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. AnEn estimates the probability of future observations of a predictand based on a current deterministic numerical weather prediction and an archive of prior analog predictions paired with prior observations. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. The authors apply AnEn to observations from the Environmental Protection Agency's (EPA's) AIRNow network and to forecasts from the Community Multiscale Air Quality (CMAQ). Compared to raw forecasts from CMAQ, deterministic forecasts of O3 and PM2.5 based on AnEn's mean have lower errors, both systemic and random, and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

2015 ◽  
Vol 54 (5) ◽  
pp. 1039-1059 ◽  
Author(s):  
John R. Mecikalski ◽  
John K. Williams ◽  
Christopher P. Jewett ◽  
David Ahijevych ◽  
Anita LeRoy ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Satellite Observations ◽  
False Alarms ◽  
Learning Approaches ◽  
Convective Initiation ◽  
Numerical Weather ◽  
Probabilistic Forecasts ◽  
Independent Case

AbstractThe Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data

2010 ◽  
Vol 49 (8) ◽  
pp. 1742-1755 ◽  
Author(s):  
Rod Frehlich ◽  
Robert Sharman ◽  
Francois Vandenberghe ◽  
Wei Yu ◽  
Yubao Liu ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Structure Function ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Spatial Filter ◽  
Structure Functions ◽  
Model Output ◽  
Numerical Weather ◽  
Nwp Model

Abstract Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn2 are compared with nighttime profiles of Cn2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

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