Abstract
Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.
Highlights
Flood is a hazard with potentially serious consequences of loss of life and economic costs which occur more frequently with the impact of climate change and human activities [1,2]
According to the above research, Scheme 3 has the best designed canonical events to reduce the deviation of ensemble forecast
The processed results were evaluated by R, root mean square error (RMSE), Nash efficiency coefficient (NSE), Rank histogram (RH), and Continuous ranked probability skill score (CRPSS) after many calculations, in which the former three indices were used to evaluate the ensemble mean and the latter indices were used to evaluate the ensemble distribution
Summary
Flood is a hazard with potentially serious consequences of loss of life and economic costs which occur more frequently with the impact of climate change and human activities [1,2]. Aiming to accelerate the improvements in the accuracy of one-day to two-week high-impact weather forecasts, the World Meteorological Organization launched a 10-year plan (2005–2015) of THORPEX and established the TIGGE database, which consists of ensemble forecast data from 10 global NWP centers, starting from October 2006 [13]. Rank histograms [17], Bayesian processor method [18], quantile mapping method [19], and ensemble pre-processor [20,21] have all greatly promoted the development and application of NWP This kind of methods have a certain good performance when dealing with most meteorological elements, but not with precipitation, due to precipitation may not happen at some time, which is difficult to simulate the statistical fitting distribution.
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