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本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集，利用BP(Back propagation)神经网络后报风场，将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段，改善WRF模式对强降水的预报效果。以2018年5月22日青岛地区强降水为例，利用青岛地区7个气象站的观测数据和雷达回波图检验优化方法对强降水的后报效果。结果表明，松弛逼近BP后报风场后，降水强度有了明显改善，相比于不松弛逼近任何数据的WRF模式，松弛逼近BP后报风场的WRF模式24 h降水量误差减少了8.62 mm,但后报降水量仍弱于实际降水量。
Abstract：The GFS(Global Forecast System) analysis data and forecast data are used as the training set and test set of BP(Back Propagation) neural network to hindcast wind filed. In order to optimize the forecasting ability of WRF(Weather Research and Forecasting) model on heavy precipitation, the wind field of BP hindcast are nudged into the hindcast stage of WRF model. Taking the heavy precipitation in Qingdao on 22 May, 2018 as an example, the forecasting effects of the optimization method on heavy precipitation are evaluated by observation data from meteorological stations in Qingdao. The results show that the precipitation intensity of WRF model is significantly improved after nudging the wind filed of BP hindcast. Compared with the WRF model without nudging, the 24-hr precipitation of the optimization method increased by 8.62 mm. However, the 24-hr precipitation hindcast by the optimization method is still weaker than the actual precipitation.
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①η layers levels:= 1.000,0.993,0.983,0.970,0.954,0.934,0.909,0.880,0.845,0.807,0.765,0.719,0.672,0.622,0.571,0.520,0.468,0.420,0.376,0.335,0.298,0.263,0.231,0.202,0.175,0.150,0.127,0.106,0.088,0.070,0.055,0.040,0.026,0.013,0.000.
基本信息:
DOI：10.16441/j.cnki.hdxb.20220018
中图分类号:P457.6
引用信息:
[1]李晓东,海尚飞,于诗赟,等.基于BP神经网络预报风场改善WRF对强降水的预报效果——以青岛为例[J],2023,53(04):111-124.DOI:10.16441/j.cnki.hdxb.20220018.
基金信息:
中央高校基本科研业务费专项(201861003)资助~~