ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 46 Issue 10 > 2020,46(10):1272-1285. DOI:10.7519/j.issn.1000-0526.2020.10.002 Prev Next
Evaluation on the 2016-2018 Fine Gridded Precipitation and Temperature Forecasting
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Abstract:
To comprehensively evaluate the fine gridded forecasts, based on site observation and gridpoint analysis data, the verification of precipitation and temperature forecasts of the 2016-2018 national gridded guidance forecast, provincial and municipal revised feedback gridded forecasts were carried out, and compared with the GRAPES and ECMWF model outputs. The results show that the fine gridded forecast operation can realize the chain of increasing value step by step from numerical model system, national guidance forecast to provincial and municipal revised feedback product. The national guidance precipitation, daily high and low temperature forecasts have a significant improvement on the basis of the model forecast products. Compared to ECMWF model, light rain ETS score of the precipitation is increased by 9%-37%, the maximum increase of rain storm ETS score reaches 41% and the maximum decrease of daily high temperature RMSE reaches 16%. The daily high/low temperature of the provincial and municipal revised feedback products can further have the reduced error on the basis of the national guidance forecast, and the optimal forecast results can be obtained. However, the national guidance products need to be further improved, with emphasis on controlling the false alarm rate of the heavy rain forecast, improving the significant advance deviation of the afternoon precipitation peak, improving the ability to characterize spatially refined precipitation, and improving the forecast statistical model for the lowest temperature. Besides, as the provincial and municipal revised feedback products have failed to correct the national guidance forecast well in the aspects of precipitation and hour temperature forecast, they need to be further strengthened. The fine gridded forecast products can replace the urban weather forecast, which is beneficial to the intensive and efficient forecasting process.
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