Bias‐corrected short‐range ensemble forecasts of near surface variables

A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration program on improved high temperature forecasting during the summer of 2003 is evaluated. Results from this short-range ensemble system indicate that using the past complete 12 days of forecasts to bias correct today's forecast yields ensemble mean forecasts of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speed that are competitive with or better than those available from any of the model output statistics presently generated operationally in the United States. However, the bias-corrected ensemble system provides more than just the ensemble mean forecast. Probabilities produced by this system are skilful and reliable, and have been found to be valuable when evaluated in a cost-loss model. The ensemble appears to provide better guidance for more unlikely events, such as very warm temperatures, that likely have the greatest economic significance. Industries that are sensitive to the weather, such as power companies, transportation and agriculture, may benefit from the probability information provided. Thus, it is possible to develop post-processing for short-range ensemble forecasting systems that is competitive with or better than traditional post-processing techniques, thereby allowing the rapid production of useful and accurate guidance forecasts of many near surface variables. Copyright © 2005 Royal Meteorological Society

[1]  David J. Stensrud,et al.  Gridpoint Predictions of High Temperature from a Mesoscale Model , 1996 .

[2]  H. Pan,et al.  Interaction between soil hydrology and boundary-layer development , 1987 .

[3]  David J. Stensrud,et al.  Short-Range Ensemble Predictions of 2-m Temperature and Dewpoint Temperature over New England , 2003 .

[4]  M. Homleid,et al.  Diurnal Corrections of Short-Term Surface Temperature Forecasts Using the Kalman Filter , 1995 .

[5]  Stanley G. Benjamin,et al.  Performance of Different Soil Model Configurations in Simulating Ground Surface Temperature and Surface Fluxes , 1997 .

[6]  Marcel Vallée,et al.  The Canadian Updateable Model Output Statistics (UMOS) System: Design and Development Tests , 2002 .

[7]  Stanley G. Benjamin,et al.  Parameterization of cold-season processes in the MAPS land-surface scheme , 2000 .

[8]  Robert L. Vislocky,et al.  Improved Model Output Statistics Forecasts through Model Consensus , 1995 .

[9]  E. Grimit,et al.  Initial Results of a Mesoscale Short-Range Ensemble Forecasting System over the Pacific Northwest , 2002 .

[10]  W. J. Steenburgh,et al.  An Evaluation of Mesoscale-Model-Based Model Output Statistics (MOS) during the 2002 Olympic and Paralympic Winter Games , 2004 .

[11]  T. Black.  The new NMC mesoscale Eta Model: description and forecast examples , 1994 .

[12]  A. H. Murphy,et al.  Probabilistic temperature forecasts: The case for an operational program , 1979 .

[13]  Richard T. McNider,et al.  An Optimal Model Output Calibration Algorithm Suitable for Objective Temperature Forecasting , 1999 .

[14]  Stanley G. Benjamin,et al.  An Isentropic Mesoα-Scale Analysis System and Its Sensitivity to Aircraft and Surface Observations , 1989 .

[15]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[16]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[17]  Thomas M. Hamill,et al.  Verification of Eta–RSM Short-Range Ensemble Forecasts , 1997 .

[18]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

[19]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[20]  R. Stull.  An Introduction to Boundary Layer Meteorology , 1988 .

[21]  Geoff DiMego,et al.  21.3 The NOAA/NWS/NCEP Short Range Ensemble Forecast (SREF) system: Evaluation of an initial condition vs multiple model physics ensemble approach , 2004 .

[22]  T. Hamill,et al.  Evaluation of Eta-RSM Ensemble Probabilistic Precipitation Forecasts , 1998 .

[23]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[24]  David J. Stensrud,et al.  Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems , 2000 .

[25]  Valery J. Dagostaro,et al.  New NGM-Based MOS Guidance for Maximum/Minimum Temperature, Probability of Precipitation, Cloud Amount, and Surface Wind , 1990 .

[26]  Chermelle Engel,et al.  Operational Consensus Forecasts , 2005 .

[27]  J. Swets.  The Relative Operating Characteristic in Psychology , 1973, Science.

[28]  D. Richardson.  Skill and relative economic value of the ECMWF ensemble prediction system , 2000 .

[29]  M. Kanamitsu,et al.  The NMC Nested Regional Spectral Model , 1994 .