Verification of Solid Precipitation Forecasts from Numerical Weather Prediction Models in Norway

被引:18
作者
Koltzow, Morten [1 ]
Casati, Barbara [2 ]
Haiden, Thomas [3 ]
Valkonen, Teresa [1 ]
机构
[1] Norwegian Meteorol Inst, Oslo, Norway
[2] Environm & Climate Change Canada, Toronto, ON, Canada
[3] ECMWF, Reading, Berks, England
基金
欧盟地平线“2020”;
关键词
Forecast verification/skill; Forecasting; Numerical weather prediction/forecasting; Model errors; Model evaluation/performance;
D O I
10.1175/WAF-D-20-0060.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Assessing the quality of precipitation forecasts requires observations, but all precipitation observations have associated uncertainties making it difficult to quantify the true forecast quality. One of the largest uncertainties is due to the wind-induced undercatch of solid precipitation gauge measurements. This study discusses how this impacts the verification of precipitation forecasts for Norway for one global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and one high-resolution, limited-area model [Applications of Research to Operations at Mesoscale (MEPS)]. First, the forecasts are compared with high-quality reference measurements (less undercatch) and with more simple measurement equipment commonly available (substantial undercatch) at the Haukeliseter observation site. Then the verification is extended to include all Norwegian observation sites: 1) stratified by wind speed, since calm (windy) conditions experience less (more) undercatch; and 2) by applying transfer functions, which convert measured precipitation to what would have been measured with high-quality equipment with less undercatch, before the forecast-observation comparison is performed. Results show that the wind-induced undercatch of solid precipitation has a substantial impact on verification results. Furthermore, applying transfer functions to adjust for wind-induced undercatch of solid precipitation gives a more realistic picture of true forecast capabilities. In particular, estimates of systematic forecast biases are improved, and to a lesser degree, verification scores like correlation, RMSE, ETS, and stable equitable error in probability space (SEEPS). However, uncertainties associated with applying transfer functions are substantial and need to be taken into account in the verification process. Precipitation forecast verification for liquid and solid precipitation should be done separately whenever possible.
引用
收藏
页码:2279 / 2292
页数:14
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