DERF2.0模式11-40天逐日环流预报效果评估
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摘要
利用1983-2013年DERF2.0模式预报资料和ECMWFERA-Interim逐日再分析资料,评估了DERF2.0模式未来11-40d环流预报效果,结果表明:200hPa高度场预报平均效果最好,预报与实况的距平相关系数(ACC)通过显著性检验达到17d,500hPa高度场为13d,200hPa纬向风场仅11d,200hPa经向风场和850hPa纬向风场预报效果最差,预报与实况的ACC均未通过显著性检验。热带地区200hPa和500hPa高度场预报效果最好,11-40d预报与实况的ACC均通过了显著性检验。
Using DERF2.0 model forecast data and ECMWF ERA-Interim daily reanalysis data from 1983 to 2013, the circulation forecast of DERF2.0 model for 11-40 days is evaluated. The results show that the average forecasting of 200 hPa height field is the best, the anomaly correlation coefficient(ACC) between the forecast and the realities has passed the significance test for 17 days, which of 500 hPa height field and the 200 hPa zonal wind field is 13 days and 11 days, respectively. The forecast of 200 hPa meridional wind field and 850 hPa zonal wind field have maximum deviations, and both the forecasting and actual ACC have not passed the significance test. The 200 hPa and 500 hPa height fields in tropical areas have the most accurate predictions, and the ACC of forecast and realities have passed the significant test.
Using DERF2.0 model forecast data and ECMWF ERA-Interim daily reanalysis data from 1983 to 2013, the circulation forecast of DERF2.0 model for 11-40 days is evaluated. The results show that the average forecasting of 200 hPa height field is the best, the anomaly correlation coefficient(ACC) between the forecast and the realities has passed the significance test for 17 days, which of 500 hPa height field and the 200 hPa zonal wind field is 13 days and 11 days, respectively. The forecast of 200 hPa meridional wind field and 850 hPa zonal wind field have maximum deviations, and both the forecasting and actual ACC have not passed the significance test. The 200 hPa and 500 hPa height fields in tropical areas have the most accurate predictions, and the ACC of forecast and realities have passed the significant test.
引文
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[5]覃卫坚,李耀先,廖雪萍.广西热带气旋的暴雨统计分析及数值模拟[J].气象研究与应用,2013,34(1):1-6.
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[8]覃卫坚,李耀先,陈思蓉.同期MJO对广西汛期强降水过程的影响[J].气象研究与应用,2017,38(4):18-21.
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[10]覃卫坚,李耀先,陈思蓉,等.粒子群-神经网络在华南夏季降水短期气候预测中应用研究[J].气象研究与应用,2015,36(2):1-7.
[11]覃卫坚,陆虹,黄志,等.粒子群-神经网络在广西寒露风日数预报中的应用[J].气象与环境学报,2015,31(6):158-162.
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