四川地区EC细网格模式2m温度偏差订正研究
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摘要
本文选取2017年1～12月ECMWF(European Centre for Medium-Range Weather Forecasting)细网格模式168h预报时效的2m温度场和对应时段四川地区157个国家站的观测资料,对比分析了模式温度预报的系统性偏差特征,采用15日周期的滑动双权重平均法对2m温度预报产品进行偏差订正,并与四川省气象台现有的主、客观预报产品进行对比,结果表明:(1)EC模式对低温的预报准确率远高于高温预报准确率;订正后高、低温预报准确率均有显著提高,其中低温平均提高了20.5%,高温提高了31.2%,平均绝对误差分别减小约1.1℃和2.9℃。(2)EC模式高温预报的逐月差异明显比低温预报逐月差异大,订正后差异明显减小,且各月的高、低温预报准确率均有显著提升,订正后各月高、低温的平均绝对误差均在2℃之内。(3)EC模式对于低温和高温的预报在全省均大致呈现负的系统性误差,且高温预报的系统性误差明显比低温预报的系统性误差大,订正后2m温度预报的系统性误差均明显降低,全省大部分地区维持在±1℃之间。(4)与四川省气象台现有的主、客观预报产品对比显示,对于高低温预报均是EC订正后准确率最高、平均绝对误差最小,订正效果较为理想。
Based on the 2 m temperature 168 h forecast of ECMWF during the period from January to December 2017 and the observation data of the same period, the systematic deviation characteristics of 2 m temperature prediction are analyzed. Then the bi-weight moving mean method with a 15 day sliding period is conducted to the model to acquire a correction field, which is compared with the existing subjective and objective forecast products in Sichuan Meteorological Observatory. The results show that:(1) The prediction accuracy of lowest temperature is much higher than that of highest temperature for EC model, and both of them have been obviously improved after the correction. On average the lowest temperature accuracy has increased by 20.5% while the highest temperature accuracy has increased by 31.2%.The average absolute errors have decreased about 1.1℃ and 2.9℃ respectively.(2) The monthly difference of accuracy for the highest temperature,which has been markedly reduced after correction, is significantly greater than that for the lowest temperature. The accuracy for both the highest and lowest temperature have been significantly increased, while the mean absolute error for all months are within 2℃.(3) The forecasts of the lowest temperature and highest temperature both present negative systematic bias in the whole province, and the systematic bias of the highest temperature is obviously larger than that of the lowest temperature. The systematic biases have been significantly decreased after the correction, maintaining between ±1 ℃ in most parts of the province.(4) The comparison with the existing subjective and objective forecast products in Sichuan Meteorological Observatory shows that the highest and lowest temperature correction field has the highest accuracy and the minimum average absolute error.
Based on the 2 m temperature 168 h forecast of ECMWF during the period from January to December 2017 and the observation data of the same period, the systematic deviation characteristics of 2 m temperature prediction are analyzed. Then the bi-weight moving mean method with a 15 day sliding period is conducted to the model to acquire a correction field, which is compared with the existing subjective and objective forecast products in Sichuan Meteorological Observatory. The results show that:(1) The prediction accuracy of lowest temperature is much higher than that of highest temperature for EC model, and both of them have been obviously improved after the correction. On average the lowest temperature accuracy has increased by 20.5% while the highest temperature accuracy has increased by 31.2%.The average absolute errors have decreased about 1.1℃ and 2.9℃ respectively.(2) The monthly difference of accuracy for the highest temperature,which has been markedly reduced after correction, is significantly greater than that for the lowest temperature. The accuracy for both the highest and lowest temperature have been significantly increased, while the mean absolute error for all months are within 2℃.(3) The forecasts of the lowest temperature and highest temperature both present negative systematic bias in the whole province, and the systematic bias of the highest temperature is obviously larger than that of the lowest temperature. The systematic biases have been significantly decreased after the correction, maintaining between ±1 ℃ in most parts of the province.(4) The comparison with the existing subjective and objective forecast products in Sichuan Meteorological Observatory shows that the highest and lowest temperature correction field has the highest accuracy and the minimum average absolute error.
引文
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[15] 王婧,徐枝芳,范广洲,等.GRAPES-RAFS系统2m温度偏差订正方法研究[J].气象,2015,41(6):719-726
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[3] 冯慧敏,智协飞,崔慧慧,等.基于多模式集成技术的地面气温精细化预报[J].气象与环境科学,2016,39(4):73-79
[4] 王丹,高红燕,张宏芳,等.一种逐时气温预报方法[J].干旱气象,2015,33(1):89-97
[5] 朱涯,杨鹏武,段长春,等.普洱市宜居气候适宜性分析[J].气象与环境科学,2018,41(2):37-42
[6] 佟华,姚明明,王雨,等.T213L31全球中期数值天气预报系统2m温度预报误差源分析[J].气象,2006,32(2):52-57
[7] 王佳津,王春学,曹萍萍,等.GRAPES全球模式预报检验评估——2016年夏季四川地面要素[J].高原山地气象研究,2017,37(1):34-40
[8] 康岚,冯汉中,屠妮妮,等.Grapes模式预报西南地区夏季2m 温度的检验评估[J].高原山地气象研究,2009,29(2):26-32
[9] 闵晶晶.BJ-RUC系统模式地面气象要素预报效果评估[J].应用气象学报,2014,25(3):265-273
[10] 熊世为,郁凌华,胡姗姗,等.基于ECMWF细网格产品的一种优化BP-MOS气温预报方法[J].干旱气象,2017,35(4):668-673
[11] 邱学兴,王东勇,陈宝峰.T639模式预报系统误差统计和订正方法研究[J].气象,2012,38(5):526-532
[12] 吴钲,方德贤,赵磊,等.重庆地区模式地面温度预报误差分析订正研究[A]//2016年中国气象学会年会论文集[C].北京:气象出版社,2016,431-438
[13] 李莉,李应林,田华,等.T213全球集合预报系统性误差订正研究[J].气象,2011,37(1):31-38
[14] 佟华,郭品文,朱跃建,等.基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术[J].气象,2014,40(1):66-75
[15] 王婧,徐枝芳,范广洲,等.GRAPES-RAFS系统2m温度偏差订正方法研究[J].气象,2015,41(6):719-726