基于欧洲中期天气预报中心(ECMWF)、中国气象局(CMA)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、英国气象局(UKMO)五个模式集成的交互式全球大集合预报系统(THORPEX Interactive Grand Global Ensemble,简称TIGGE)资料集的确定性预报、集合预报以及地面降水观测数据,采用多模式集成平均(EMN)、消除偏差集成平均(BREM)、滑动训练期超级集合方法(R_SUP)对2018年华南汛期(4-9月)粤港澳大湾区的降水预报开展了评估检验.总体而言,多模式集成预报方法在大湾区前汛期降水预报的均方根误差平均比后汛期高2 mm;多模式集成预报方法的预报能力在前汛期随着预报时效的延长而呈持续下降趋势,后汛期则表现为短期(24～72 h)下降、中期(72～168 h)持续平稳的变化特点.与预先的假设差异主要表现在:对前、后汛期的降水预报综合表现最好的均是数学原理相对简单的EMN,而BREM和R_SUP的空间平均评分指标则稍差,但其在降水落区预报中仍有较好的预报技巧.
Evaluation of Multi-model Integrated Forecast Method for Precipitation in Rainy Seasons of Guangdong-Hong Kong-Macao Greater Bay Area
This study evaluated the precipitation forecasts in the Guangdong-Hong Kong-Macao Greater Bay Area during the 2018 South China rainy seasons(April to September)using data from the THORPEX Interactive Grand Global Ensemble dataset.The dataset comprised deterministic forecasts,ensemble forecasts,and ground precipitation observations from models of five organizations:the European Centre for Medium-Range Weather Forecasts,the China Meteorological Administration,the Japan Meteorological Agency,the National Centers for Environmental Prediction of the United States,and the UK Meteorological Office.Three multi-model integrated forecast methods,namely the multi-model ensemble average(EMN),the bias-removed ensemble average(BREM),and the sliding training period superensemble method(R_SUP),were employed for the evaluation.The results showed that,in general,the root mean square error of precipitation forecasts in the first rainy season of the Guangdong-Hong Kong-Macao Greater Bay Area was higher than that in the second rainy season,with an average difference of 2 mm.The forecasting ability of the multi-mode integrated forecasting method showed a continuous and stable decline trend in the first rainy season as the forecast lead time increased.In contrast,in the second rainy season,it showed a stable decline in the short term(24～72 hours)and remained stable in the medium term(72～168 hours).EMN,which has a relatively simple mathematical principle,showed the best comprehensive performance in the precipitation forecast of the two rainy seasons.BREM and R_SUP achieved slightly lower spatial average scores,but they still demonstrated good forecasting skills in predicting precipitation areas.
Guangdong-Hong Kong-Macao Greater Bay Areamulti-model integrated forecastevaluation
国家科技期刊平台
NSTL
万方数据
