太陽輻射預報滾動訂正方法研究
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國家高技術研究發展計劃項目(2011AA05A302)、國家自然科學基金面上項目(編號:41275114)資助


Rolling Correction Methods for Global Radiation Forecasting
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    摘要:

    利用2013年1月至2014年12月北京南郊觀象臺逐時觀測總輻射以及BJRUC模式系統預報的該站未來24 h逐時總輻射、云量、水汽混合比、云水、云冰含量等14個氣象要素數據,運用多種線性訂正方案對總輻射預報值進行訂正,重點分析了不同方法、不同季節、不同樣本數的訂正效果差異。結果表明:①不同季節輻射訂正的影響因素不盡相同,需采用不同的組合訂正因子,其中總輻射、2 m比濕、2 m相對濕度、低云量、中云量、高云量、總云量、云水雨冰雪霰混合比、水汽混合比可作為推薦因子;②最優樣本數選取時需考慮季節差異;③逐時滾動訂正方案的訂正效果較好,明顯優于非滾動方案。訂正后總輻射誤差顯著減小,而且79%的時刻有改進,明顯減小了預報偏大的系統誤差;④冬春季訂正效果優于夏秋季,這與云的宏觀和微觀物理量預報效果的季節差異有關。本文研究結果可為太陽能資源評估、總輻射和光伏電站發電量預報提供有效的改進方法。

    Abstract:

    Based on the hourly global radiation data collected at Beijing Weather Observatory and 24hour forecasting data of 14 conventional meteorological elements using the BJRUC model, three linear correction methods are used to correct the forecasted global radiation data. Seasonal differences of corrected effects using different correction schemes and sample numbers are discussed in detail. The best rolling correction schemes are investigated. Results show: (1) Influencing factors for correcting global radiation are different in 4 seasons, and primary factors such as global radiation, specific humidity and relative humidity at 2 m, low and middle cloud cover, water vapor mixing ratios, total water mixing ratios (cloud, rain, ice, snow, and graupel) are recommended to for correction of global radiation. (2) Seasonal differences of the best sample number for different correction schemes should be considered. (3) The corrected effects using the hourly rolling scheme are better than those by using other models. By means of the hourly rolling scheme, the average absolute error, mean relative error, and relative root mean are obviously reduced, and 79% of total data are improved after correction. Furthermore, absolute errors of the corrected global radiation present the typical normal distribution pattern. The scatter distribution of corrected and observed values is more concentrated, and larger positive errors are improved after correction. (4) The correction effects in spring and winter are better than those in autumn and summer. It may be caused by seasonal differences of forecasting errors of macroscopic and microscopic cloud physical parameters.

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蘇兆達,程興宏,楊興東,王在文,刁志剛,范水勇,黃鶴.太陽輻射預報滾動訂正方法研究[J].氣象科技,2016,44(2):259~268

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  • 收稿日期:2015-04-13
  • 定稿日期:2015-08-04
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  • 在線發布日期: 2016-04-20
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