Evaluation and Error Correction of the ECMWF Subseasonal Precipitation Forecast over Eastern China during Summer

Huanran He,Kejian Gong,Suxiang Yao,Anning Huang

doi:10.1155/2020/1920841

Abstract

Subseasonal-to-seasonal (S2S) prediction is a highly regarded skill around the world. To improve the S2S forecast skill, an S2S prediction project and an extensive database have been established. In this study, the European Center for Medium-Range Weather Forecasts (ECMWF) model hindcast, which participates in the S2S prediction project, is systematically assessed by focusing on the hindcast quality for the summer accumulated ten-day precipitation at lead times of 0–30 days during 1995–2014 in eastern China. Additionally, the hindcast error is corrected by utilizing the preceding sea surface temperature (SST). The metrics employed to measure the ECMWF hindcast performance indicate that the ECMWF model performance drops as the lead time increases and exhibits strong interannual differences among the five subregions of eastern China. In addition, the precipitation forecast skill of the ECMWF hindcast is best at approximately 15 days in some areas of Southeast China; after correcting the forecast error, the forecast skill is increased to 30 days. At lead times of 0–30 days, regardless of whether the forecast error is corrected, the root mean square errors are lowest in Northeast China. After correcting the forecast error, the performance of the ECMWF hindcast shows better improvement in depicting the quantity and temporal and spatial variation of precipitation at lead times of 0–30 days in eastern China. The false alarm ratio (FAR), probability of detection (POD), and equitable threat score (ETS) reveal that the ECMWF model has a preferable performance at forecasting accumulated ten-day precipitation rates of approximately 20∼50 mm and indicates an improved hindcast quality after the forecast error correction. In short, adopting the preceding SST to correct the summer subseasonal precipitation of the ECMWF hindcast is preferable.

Highlights

Traditional weather forecasting is limited to 2 weeks and is mainly influenced by atmospheric initial conditions [1,2,3]
The European Center for Medium-Range Weather Forecasts (ECMWF) model hindcast, which participates in the S2S prediction project, is assessed. e focus is placed on the performance of the model in forecasting summer subseasonal precipitation at lead times of 0–30 days over the period of 1995–2014 in eastern China, and the hindcast error is corrected to improve the capability of the ECMWF hindcast
E performance of the ECMWF hindcast is relatively superior among the Japan Meteorological Agency (JMA), China Meteorological Administration (CMA), and ECMWF S2S models. e ECMWF hindcast shows a higher temporal correlation at lead times of 0–5 days, and its precipitation forecasting quality decreases gradually with an increasing lead time. e agreement between the ECMWF hindcast and observed summer accumulated ten-day precipitation with regard to the temporal variation of precipitation reveals that the performance of the ECMWF hindcast is regionally powerful. e highest temporal correlation is always reflected in Southeast China (Reg1), and the useful forecast skill of the hindcast is approximately 15 days in some areas of this subregion

Summary

Introduction

Traditional weather forecasting is limited to 2 weeks and is mainly influenced by atmospheric initial conditions [1,2,3]. Seasonal climate predictability is substantially affected by the underlying boundary forcing, such as sea surface temperature (SST) and land surface anomalies [4,5,6]. Subseasonal forecasting, which fills a gap between traditional mediumterm weather and seasonal climate forecasting, is significantly influenced by both atmospheric initial conditions and boundary forcing [7, 8]. Compared to weather and seasonal climate forecasting, subseasonal forecasting is considered a “predictability desert” [9] and is at a relatively early stage of development [8]. Under the background of global warming, extreme weather events, especially droughts and floods in summer, are relatively frequent and continuous over China. Some studies have shown the potential sources of subseasonal predictability, such as the state of El Nino-Southern Oscillation (ENSO) [10], the MaddenJulian Oscillation (MJO) [11,12,13], initial soil moisture conditions [14, 15], snow cover [16] and sea-ice conditions [17], stratosphere-troposphere interactions [18, 19], and tropicalextratropical teleconnections [20, 21].

Methods

Results

Conclusion