MOS based forecast of 6-hourly area precipitation |
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Authors: | Z Sokol |
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Institution: | (1) Institute of Atmospheric Physics, Acad. Sci. Czech Republic, Boční II/1401, 141 31 Prague 4, Czech Republic |
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Abstract: | A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation
forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships
are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct
the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited
Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river
basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours.
The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool
(October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from
four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data.
The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification
data samples.
The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation
amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are
set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation
amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification.
For the cool season, the decrease is somewhat smaller (7% to 15%). |
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Keywords: | precipitation forecast regression statistical postprocessing MOS |
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