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

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  • 1 University of British Columbia, and BC Hydro Corporation, Vancouver, British Columbia, Canada
  • 2 University of British Columbia, Vancouver, British Columbia, Canada
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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 period.

The different postprocessing methods were compared using several verification metrics, including mean error (for temperature), degree of mass balance (for precipitation), mean absolute error, and threshold error. All of the postprocessing methods improved forecast skill over direct model output. The postprocessing methods for temperature forecasts require a much shorter training period (14 days) than precipitation forecasts (40 days) to accomplish error reduction over direct model output forecasts. The postprocessing methods that weight recent error estimates most heavily perform better in the short term (days 1–4) while methods that weight recent and earlier error estimates more evenly show improving relative performance in the midterm (days 5–8).

For temperature forecasts, Kalman filtering produced slightly better verification scores than the other methods. For precipitation forecasts, a 40-day moving-average weighting function and the best easy systematic estimator method produced the best degree of mass balance results, while a seasonally averaged method produced the lowest mean absolute errors and lowest threshold errors. The methods described in this paper require minimal database management or computer resources to update forecasts, and are especially viable for hydrometeorological applications that require calibrated daily temperature and precipitation forecasts.

Corresponding author address: Doug McCollor, Dept. of Earth and Ocean Sciences, University of British Columbia, 6339 Stores Rd., Vancouver, BC V6T 1Z4, Canada. Email: doug.mccollor@bchydro.bc.ca

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 period.

The different postprocessing methods were compared using several verification metrics, including mean error (for temperature), degree of mass balance (for precipitation), mean absolute error, and threshold error. All of the postprocessing methods improved forecast skill over direct model output. The postprocessing methods for temperature forecasts require a much shorter training period (14 days) than precipitation forecasts (40 days) to accomplish error reduction over direct model output forecasts. The postprocessing methods that weight recent error estimates most heavily perform better in the short term (days 1–4) while methods that weight recent and earlier error estimates more evenly show improving relative performance in the midterm (days 5–8).

For temperature forecasts, Kalman filtering produced slightly better verification scores than the other methods. For precipitation forecasts, a 40-day moving-average weighting function and the best easy systematic estimator method produced the best degree of mass balance results, while a seasonally averaged method produced the lowest mean absolute errors and lowest threshold errors. The methods described in this paper require minimal database management or computer resources to update forecasts, and are especially viable for hydrometeorological applications that require calibrated daily temperature and precipitation forecasts.

Corresponding author address: Doug McCollor, Dept. of Earth and Ocean Sciences, University of British Columbia, 6339 Stores Rd., Vancouver, BC V6T 1Z4, Canada. Email: doug.mccollor@bchydro.bc.ca

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