Hydrometeorological Accuracy Enhancement via Postprocessing of Numerical Weather Forecasts in Complex Terrain

Abstract Statistical postprocessing techniques such as model output statistics are used by national weather centers to improve the skill of numerical forecasts. However, many of these techniques require an extensive database to develop, maintain, and update the postprocessed forecasts. This paper explores alternative postprocessing techniques for temperature and precipitation based on weighted-average and recursive formulations of forecast–observation paired data that do not require extensive database management, yet provide distinct error reduction over direct model output. For maximum and minimum daily temperatures, seven different postprocessing methods were tested based on direct model output error for forecast days 1–8. The methods were tested on a 1-yr series of daily temperature values averaged over 19 stations in complex terrain in southwestern British Columbia, Canada. For daily quantitative precipitation forecasts, three different postprocessing methods were tested over a 6-month wet season peri...

[1]  Paul Zarchan,et al.  Fundamentals of Kalman Filtering: A Practical Approach , 2001 .

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

[3]  Clifford F. Mass,et al.  Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting , 2005 .

[4]  E. Ebert.  Ability of a Poor Man's Ensemble to Predict the Probability and Distribution of Precipitation , 2001 .

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

[6]  Chris Snyder,et al.  Ensemble Forecasting in the Short to Medium Range: Report from a Workshop , 2000 .

[7]  L. Wilson,et al.  The Canadian Updateable Model Output Statistics (UMOS) System: Validation against Perfect Prog , 2003 .

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

[9]  Qi Mao,et al.  Quantitative Precipitation Forecasting for the Tennessee and Cumberland River Watersheds Using the NCEP Regional Spectral Model , 2000 .

[10]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction , 2006 .

[11]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[12]  Ronald B. Smith,et al.  Evaluation of an Orographic Precipitation Model , 2005 .

[13]  Henryk Modzelewski,et al.  Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction , 2003 .

[14]  Ramesh Vellore,et al.  Quantitative Precipitation Forecasting of Wintertime Storms in the Sierra Nevada: Sensitivity to the Microphysical Parameterization and Horizontal Resolution , 2005 .

[15]  X. Bian,et al.  Evaluation of real-time high-resolution MM5 predictions over the Great Lakes region , 2005 .

[16]  Operational regional prediction , 1997 .

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

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

[19]  Thomas M. Hamill,et al.  Hypothesis Tests for Evaluating Numerical Precipitation Forecasts , 1999 .

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

[21]  X. Bian,et al.  Evaluation of Real-Time High-Resolution MM 5 Predictions over the Great Lakes Region , 2004 .

[22]  A. Georgakakos,et al.  Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management , 2001 .

[23]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 1. A new ensemble design , 2006 .

[24]  Kenneth J. Westrick,et al.  An Evaluation of a High-Resolution Hydrometeorological Modeling System for Prediction of a Cool-Season Flood Event in a Coastal Mountainous Watershed , 2001 .

[25]  Box Ge,et al.  Time series analysis: forecasting and control rev. ed. , 1976 .

[26]  Richard W. Katz,et al.  On the Use of Autoregressive-Moving Average Processes to Model Meteorological Time Series , 1981 .

[27]  B. M. Lewis,et al.  OBJECTIVE PREDICTION OF FIVE-DAY MEAN TEMPERATURES DURING WINTER , 1959 .

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

[29]  B. Jou,et al.  MM5 Ensemble Mean Precipitation Forecasts in the Taiwan Area for Three Early Summer Convective (Mei-Yu) Seasons , 2004 .

[30]  M. Clark,et al.  Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow , 2004 .

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

[32]  S. M. Bozic.  Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation , 1980 .

[33]  A. Staniforth,et al.  The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part II: Results , 1998 .

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

[35]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[36]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[37]  David J. Stensrud,et al.  Bias‐corrected short‐range ensemble forecasts of near surface variables , 2005 .

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

[39]  A. Staniforth,et al.  The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation , 1998 .