针对模式风场的格点预报订正方案对比
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摘要
为了将格点观测融合产品用于模式预报产品的滚动订正中,获得精准的预报效果,使用国家气象信息中心HRCLDAS(High Resolution China Meteorological Administration Land Data Assimilation System)业务系统产生的高频次格点风场融合产品作为实况资料,采用两种风场模型和8种格点误差订正方案,对模式风预报产品进行订正预报试验,试验选择欧洲中期天气预报中心10m风预报产品的2017年1月1日—2月28日以及2017年6月1日—7月31日两个时间段,进行了预报模拟试验,对8种格点误差订正方案的订正结果进行检验,同时将订正场插值到站点,使用国家级2400个地面气象站风场资料进行站点检验,结果表明:无论从格点还是站点检验的平均绝对偏差、准确率、绝对偏差分布频率结果看,采用基于模式和实况因子的全格点滑动建模订正方案具有最佳的订正效果。
The meshing forecast products is an important direction for the future development of China Meteorological Administration. With the development of the grid forecast business and approaching of Beijing Olympic Winter Games in 2022, the forecast of the wind is very important. In order to promptly correct forecast results using grid observation fusion products, grid forecasting products with higher resolution and accuracy are obtained, and the high-frequency grid wind fusion products generated by the HRCLDAS(High Resolution China Meteorological Administration Land Data Assimilation System) system of National Meteorological Information Center as observations are studied. Eight different error correction methods and two different wind field models are used to correct European Centre for Medium-Range Weather Forecasts(ECMWF) 10 m wind forecast field. The test sample time is selected from 1 January 2017 to 28 February 2017, and from 1 June 2017 to 31 July 2017, and two forecast simulations are conducted. In each trial,24 h corrected forecast test is carried out for two start times at 1400 BT and 2000 BT, and eight different correction methods are used to correct the prediction of ECMWF 10 m wind forecast field. Grid forecast verification is performed on grid results. At the same time, grid prediction results from 8 corrected methods is interpolated to 2400 national surface meteorological stations and station forecast verification is performed on grid results. From the grid verification result and site verification result of two trials, using the latest observations as a predictor, wind forecast effects of 3 — 6 h is significantly improved. For the correction of the wind direction, the correction effects are slightly improved. Results show that the two-factor model with dynamic coefficient has the best correction effects on the average absolute error, accuracy and absolute error distribution frequency of both grid and site test. Sliding modeling allows the correction model to follow the trend of ECMWF 10 m wind forecast system error. After the optimal method is corrected,the wind speed error in most parts of South China, East China and North China is below 1 m · s~(-1), especially the large error is significantly reduced, and the wind direction error is also reduced. However, there are still some mean absolute error of 1 — 3 m · s~(-1) in the Qinghai-Tibet Plateau, central Xinjiang and Inner Mongolia. Due to local effects of the wind, the correction field of the interpolation to the site still has a certain gap with the actual situation of the site. If prediction result fusion technology is carried out, it is expected that there will be better grid prediction results.
The meshing forecast products is an important direction for the future development of China Meteorological Administration. With the development of the grid forecast business and approaching of Beijing Olympic Winter Games in 2022, the forecast of the wind is very important. In order to promptly correct forecast results using grid observation fusion products, grid forecasting products with higher resolution and accuracy are obtained, and the high-frequency grid wind fusion products generated by the HRCLDAS(High Resolution China Meteorological Administration Land Data Assimilation System) system of National Meteorological Information Center as observations are studied. Eight different error correction methods and two different wind field models are used to correct European Centre for Medium-Range Weather Forecasts(ECMWF) 10 m wind forecast field. The test sample time is selected from 1 January 2017 to 28 February 2017, and from 1 June 2017 to 31 July 2017, and two forecast simulations are conducted. In each trial,24 h corrected forecast test is carried out for two start times at 1400 BT and 2000 BT, and eight different correction methods are used to correct the prediction of ECMWF 10 m wind forecast field. Grid forecast verification is performed on grid results. At the same time, grid prediction results from 8 corrected methods is interpolated to 2400 national surface meteorological stations and station forecast verification is performed on grid results. From the grid verification result and site verification result of two trials, using the latest observations as a predictor, wind forecast effects of 3 — 6 h is significantly improved. For the correction of the wind direction, the correction effects are slightly improved. Results show that the two-factor model with dynamic coefficient has the best correction effects on the average absolute error, accuracy and absolute error distribution frequency of both grid and site test. Sliding modeling allows the correction model to follow the trend of ECMWF 10 m wind forecast system error. After the optimal method is corrected,the wind speed error in most parts of South China, East China and North China is below 1 m · s~(-1), especially the large error is significantly reduced, and the wind direction error is also reduced. However, there are still some mean absolute error of 1 — 3 m · s~(-1) in the Qinghai-Tibet Plateau, central Xinjiang and Inner Mongolia. Due to local effects of the wind, the correction field of the interpolation to the site still has a certain gap with the actual situation of the site. If prediction result fusion technology is carried out, it is expected that there will be better grid prediction results.
引文
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[6]陈豫英,刘还珠,陈楠,等.基于聚类天气分型的KNN方法在风预报中的应用.应用气象学报,2008,19(5):564-572.
[7]曾晓青,赵声蓉,段云霞.基于MOS方法的风向预测方案对比研究.气象与环境学报,2013,29(6):140-144.
[8]王彬滨,吴息,余江,等.谐波分析方法在沿海风速数值预报订正的应用.热带气象学报,2016,32(5):752-758.
[9]石岚,徐丽娜,郝玉珠.基于风速高相关分区的风电场风速预报订正.应用气象学报,2016,27(4):506-512.
[10]姚日升,涂小萍,蒋璐璐,等.浙江近海冬季大风风速推算和ASCAT风速订正方法探讨.气象,2016,42(5):621-627.
[11]胡海川,黄彬,魏晓琳.我国近海洋面10m风速集合预报客观订正方.气象,2017,43(7):856-862.
[12]韩帅,师春香,姜志伟,等.CMA高分辨率陆面数据同化系统(IIRCLDAS-V1. 0)研发及进展.气象科技进展,2018,8(1):102-108.
[13]韩帅,师春香,姜立鹏,等.CLDAS土壤湿度模拟结果及评估.应用气象学报,2017,28(3):369-378.
[14]张涛,苗春生,王新.LAPS与STMAS地面气温融合效果对比试验.高原气象,2014,33(3):743-752.
[15] Klein W II,Lewis B M,Enger I. Objective prediction of fiveday mean temperatures during winter. Journal of Atmospheric Sciences,1959,16(6):672-682.
[16] Glahn II R, Lowry D A. The use of model output statistics(MOS)in objective weather forecasting. J Appl Meteor1972,11(8):1203-1211.
[17]吴启树,韩美,郭弘,等.MOS温度预报中最优训练期方案.应用气象学报,2016,27(4):426-434.
[18]刘还珠,赵声蓉,陆志善,等.国家气象中心气象要素的客观预报——MOS系统.应用气象学报,2004,15(2):181-191.
[19]车钦,赵声蓉,范广洲.华北地区极端温度MOS预报的季节划分.应用气象学报,2011,22(4):429-436.
[2]张萌,于海鹏,黄建平,等.GRAPES_GFS 2. 0模式系统误差评估.应用气象学报,2018,29(5):571-583.
[3]闵晶晶.BJ-RUC系统模式地面气象要素预报效果评估.应用气象学报,2014,25(3):265-273.
[4]黄丽萍,陈德辉,邓莲堂,等.GRAPES_Meso V4. 0主要技术改进和预报效果检验.应用气象学报,2017,28(1):25-37.
[5]许杨,陈正洪,杨宏青,等.风电场风电功率短期预报方法比较.应用气象学报,2013,24(5):625-630.
[6]陈豫英,刘还珠,陈楠,等.基于聚类天气分型的KNN方法在风预报中的应用.应用气象学报,2008,19(5):564-572.
[7]曾晓青,赵声蓉,段云霞.基于MOS方法的风向预测方案对比研究.气象与环境学报,2013,29(6):140-144.
[8]王彬滨,吴息,余江,等.谐波分析方法在沿海风速数值预报订正的应用.热带气象学报,2016,32(5):752-758.
[9]石岚,徐丽娜,郝玉珠.基于风速高相关分区的风电场风速预报订正.应用气象学报,2016,27(4):506-512.
[10]姚日升,涂小萍,蒋璐璐,等.浙江近海冬季大风风速推算和ASCAT风速订正方法探讨.气象,2016,42(5):621-627.
[11]胡海川,黄彬,魏晓琳.我国近海洋面10m风速集合预报客观订正方.气象,2017,43(7):856-862.
[12]韩帅,师春香,姜志伟,等.CMA高分辨率陆面数据同化系统(IIRCLDAS-V1. 0)研发及进展.气象科技进展,2018,8(1):102-108.
[13]韩帅,师春香,姜立鹏,等.CLDAS土壤湿度模拟结果及评估.应用气象学报,2017,28(3):369-378.
[14]张涛,苗春生,王新.LAPS与STMAS地面气温融合效果对比试验.高原气象,2014,33(3):743-752.
[15] Klein W II,Lewis B M,Enger I. Objective prediction of fiveday mean temperatures during winter. Journal of Atmospheric Sciences,1959,16(6):672-682.
[16] Glahn II R, Lowry D A. The use of model output statistics(MOS)in objective weather forecasting. J Appl Meteor1972,11(8):1203-1211.
[17]吴启树,韩美,郭弘,等.MOS温度预报中最优训练期方案.应用气象学报,2016,27(4):426-434.
[18]刘还珠,赵声蓉,陆志善,等.国家气象中心气象要素的客观预报——MOS系统.应用气象学报,2004,15(2):181-191.
[19]车钦,赵声蓉,范广洲.华北地区极端温度MOS预报的季节划分.应用气象学报,2011,22(4):429-436.
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