论文

基于WRF模式的太阳辐射预报初步试验研究

  • 何晓凤 ,
  • 周荣卫 ,
  • 申彦波 ,
  • 石磊
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  • 中国气象局公共气象服务中心, 北京 100081;2. 中国神华能源股份有限公司国华电力分公司, 北京 100025

收稿日期: 2012-12-31

  网络出版日期: 2015-04-28

基金资助

公益性行业(气象)科研专项(GYHY201306048)

Preliminary Study on Solar Radiation Forecasting with WRF Model

  • HE Xiaofeng ,
  • ZHOU Rongwei ,
  • SHEN Yanbo ,
  • SHI Lei
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  • Public Weather Service Center, China Meteorological Administration, Beijing 100081, China;2. China Shenhua Energy Company Limited Guohua Power Branch, Beijing 100025, China

Received date: 2012-12-31

  Online published: 2015-04-28

摘要

采用中尺度气象模式WRF(Weather Research Forecast)对北京地区的太阳辐射进行了4个典型月的逐时预报试验, 用南郊观象台的辐射观测数据对预报结果进行了对比分析和初步订正试验。结果表明: 在现有模式条件下, 5 km分辨率的短波辐射预报结果和1 km分辨率预报结果无明显差别; WRF模式对太阳辐射的预报性能在晴天较好, 多云天次之, 在满云或阴雨天最差; 通过误差分解发现, 位相偏差、系统偏差及振幅偏差在各月对均方根误差的贡献有明显差异; 针对模式预报结果的系统偏差和振幅偏差.经过简单的线性订正可以较明显地改进模式预报结果; 双偏订正(DBC)法比线性回归(LR)法对预报误差的改进效果略明显; 仅通过简单的线性订正, 位相差很难消除, 需要针对位相差研究新的订正方法。

本文引用格式

何晓凤 , 周荣卫 , 申彦波 , 石磊 . 基于WRF模式的太阳辐射预报初步试验研究[J]. 高原气象, 2015 , 34(2) : 463 -469 . DOI: 10.7522/j.issn.1000-0534.2013.00167

Abstract

Weather Research Forecast (WRF) model was used in experimental forecast of hourly solar radiation in four typical months in Beijing area. The solar radiation observational data of Nanjiao weather observatory were used to evaluate and correct the forecasting result. Conclusions were as follows: the forecasting result of solar radiation with 5 km horizontal resolution was very similar to that with 1 km horizontal resolution in condition of the existing model. The forecast effect of solar radiation in sunny days was the best with WRF model, the secondary was in cloudy days, and the worst was in fully cloudy days or rainy days. Through analysis of error decomposition, the contribution of phase bias, system bias and amplitude bias was different obviously in each month. The ordinary linear correction method was useful to remove most of system bias and amplitude bias obviously, and in general, the correcting effect of double bias correction method was slightly better than that by linear regression method. The phase bias was difficult to eliminate only by ordinary linear correction method, so a new correction method should be adopted.

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