摘要
基于数值天气预报误差在时间上的相依性,采用BP神经网络方法建立预测数值模式非系统性预报误差的模型,并利用2003-2007年T213模式分析场和24 h高度预报场资料验证了该模型的预测能力,结果表明:所建立的3层BP神经网络模型对未来24 h的非系统性预报误差有较好的预估能力,对大多数样本而言所估测的非系统性预报误差的分布特征和其真值较为一致。BP神经网络模型估测的非系统性预报误差可以在系统性预报误差订正的基础上进一步对预报做出修正,其订正效果好于仅进行系统性预报误差订正的效果。
Based on the temporal dependence of forecast errors derived from numerical weather prediction,the back-propagation(BP) neural network is used to establish the prediction model for predicting non-systematic forecast error.The effectiveness of this model is tested with the analysis and 24-hours forecast data produced by T213 model from 2003 to 2007.The results show that the established BP neural network model has a good ability on predicting non-systematic error in the next 24 hours.For most of 332 test samples,the spatial distribution of the predicted non-systematic errors is consistent with the truth.The non-systematic error estimated by BP neural network model can further correct forecasts on the basis of the systematic error correction,and its correction effectiveness is better than that of the systematic error correction only.For 332 test samples,the effective rate of systematic error correction on forecasts is 61%,but the effective rate of nonsystematic error further correction can increase to 82%.
出处
《高原气象》
CSCD
北大核心
2015年第6期1751-1757,共7页
Plateau Meteorology
基金
公益性行业(气象)科研专项(GYHY201206009)
国家重点基础研究发展计划(973)项目(2013CB430102)
国家自然科学基金项目(41275102
40875063)
兰州大学中央高校基本科研业务费专项(lzujbky-2013-k16)
新世纪优秀人才支持计划项目(NCET-11-0213)
关键词
BP神经网络
数值天气预报
预报误差
非系统误差
误差订正
BP neural network
Numerical weather prediction
Forecast error
Non-systematic error
Error correction
