| MOS温度预报中最优训练期方案 |
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| 引用本文： | 吴启树,韩美,郭弘,苏同华.MOS温度预报中最优训练期方案[J].应用气象学报,2016,27(4):426-434. |
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| 作者姓名： | 吴启树 韩美 郭弘 苏同华 |
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| 作者单位： | 福建省气象台，福州 350001 |
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| 基金项目： | 资助项目： 公益性行业（气象）科研专项（GYHY201406014）,中国气象局预报员专项（CMAYBY2016 034） |
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| 摘 要： | 以欧洲中期天气预报中心 (ECMWF) 模式细网格地面气温为预报因子，设计多种训练期方案进行2014—2015年福建省气象站每日两次1~7 d的日最高气温和日最低气温MOS (model output statistics) 预报，并进行检验和改进。准对称混合滑动训练期方法为取预报日之前和前1年预报日之后相同日数的样本混合而成，分1年期或多年期。结果表明:准对称混合滑动训练期方案优于滑动训练期方案和传统季节固定期分类方案，且2年期优于1年期。以不同周期确定最佳训练期日数的方案应用对比显示，以年为评估周期优于以月为评估周期以及更短时间周期。在2015年日最高气温和日最低气温MOS预报中，基于上年度评估所得最佳训练期日数，2年期准对称混合滑动训练期方案较ECMWF模式细网格产品质量有较大提高，优于预报员预报，有较好的应用参考价值。
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| 关 键 词： | MOS预报 日最高气温和日最低气温 训练期 准对称方法 |
| 收稿时间： | 2016-04-11 |
The Optimal Training Period Scheme of MOS Temperature Forecast |
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| Wu Qishu,Han Mei,Guo Hong and Su Tonghua.The Optimal Training Period Scheme of MOS Temperature Forecast[J].Quarterly Journal of Applied Meteorology,2016,27(4):426-434. |
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| Authors: | Wu Qishu Han Mei Guo Hong and Su Tonghua |
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| Affiliation: | Fujian Provincial Meteorological Observatory, Fuzhou 350001 |
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| Abstract: | Based on air temperature from ECMWF data, three groups of training period schemes that include original schemes, improved schemes and application schemes are designed to test and compare results of daily maximum and minimum temperature of national stations in Fujian Province twice per day from 2014 to 2015. For the quasi symmetrical mixed running training period (QSRTP) method, several days of current year before forecasting day and equal numbers of days in last year after are adopted separately to compose an initial 1 year or multi year dataset. When using multiple year data, lengths of the optimal training period every year are different or the same in each scheme. From three original schemes, it is found that the methods of QSRTP are much better than other running training period methods and traditional fixed period classification, and the number of optimal training day is more stable. The QSRTP with 2 year data shows better performance than that with 1 year data for comparison of improvement schemes, and different training period lengths lead to better performance in 2 year evaluation schemes. Considering model version updating and the continuity of weather, lengths of the optimal training period before the first year are slightly longer than that in the second year. Similar to the traditional method, the optimal training period of original and improved schemes are obtained after verification rather than before forecast. Three application schemes with different terms of evaluation are designed to test the stability, the usability and seasonal patterns of the optimal training period. Based on 1 year evaluation, total samples of the training period are stable with the best forecasting score. In terms of the monthly evaluation, the best period has no significant patterns with a relatively low score. For 10 day evaluation, the best period varies greatly, but when the forecasting time becomes shorter, the forecast quality becomes better. When the weather changes suddenly, the optimal training days will be quite different. Hindcasting experiment of 2015 suggests that the MOS forecast for daily maximum and minimum temperature using the optimal training period of last year has a much higher score than the original ECMWF products, it is better than the subjective forecast, and the forecast absolute deviation is significantly reduced. With the accumulation of data in the future, the forecast quality would be improved greatly, indicating that the method of multi year QSRTP has an important application prospect on the daily operation. |
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| Keywords: | MOS forecast daily maximum and minimum temperature training period quasi symmetrical method |
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