| 尺度分解技术在定量降水临近预报检验中的应用 |
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| 引用本文： | 孔荣,王建捷,梁丰,赵文芳.尺度分解技术在定量降水临近预报检验中的应用[J].应用气象学报,2010,21(5):535-544. |
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| 作者姓名： | 孔荣 王建捷 梁丰 赵文芳 |
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| 作者单位： | 1.中国气象局北京城市气象研究所, 北京 100089 |
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| 基金项目： | 北京市科技计划"奥运绿色通道"项目"北京奥运会国际天气预报示范计划支持技术研究"课题，2007年公益类行业专项"京津冀城市群强对流天气短时临近预报关键技术研究"项目(GYHY，中央级公益性科研院所基本科研业务费专项"强度-尺度等新技术在精细预报检验中的应用"课题 |
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| 摘 要： | 采用2004年Casati提出的强度-尺度检验技术,选取2008年汛期代表不同类型降水(对流云降水、层状云降水、混合云降水)的4个降水过程,从尺度分解角度入手,对"世界气象组织天气研究计划——北京奥运会预报示范项目"(WWRP B08FDP)项目中4个I临近预报参加系统(BJANC,GRAPES-SWIFT,STEPS,CARDS)的1h定量降水预报进行时空尺度分解检验,研究降水预报技巧与降水时空尺度和强度之间的关系。结果表明:尽管目前国际先进的临近预报系统的水平分辨率已高达1～2km,但其有技巧的临近预报能力主要集中于空间尺度大于32km、时间尺度大于1h的降水系统,而对小于这些尺度的降水系统预报能力仍非常有限;在不同时空尺度的临近预报降水误差中,60%以上的误差来自于空间尺度小于8km的降水,85%以上的误差来自于时间尺度小于1h的降水,传统的外推技术不能满足这些较小时空尺度降水预报的需求,要发展有效的预报方法来提高较小时空尺度降水的预报能力。将基于外推的临近预报和基于稠密观测资料、快速更新的数值预报的潜势预报相结合可能是一条有效的解决途径。
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| 关 键 词： | 空间降水检验 尺度分解 小波变换 |
| 收稿时间： | 2009-07-13 |
Applying Scale Decomposition Method to Verification of Quantitative Precipitation Nowcasts |
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| Kong Rong,Wang Jianjie,Liang Feng and Zhao Wenfang.Applying Scale Decomposition Method to Verification of Quantitative Precipitation Nowcasts[J].Quarterly Journal of Applied Meteorology,2010,21(5):535-544. |
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| Authors: | Kong Rong Wang Jianjie Liang Feng and Zhao Wenfang |
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| Affiliation: | 1.Institute of Urban Meteorology, CMA, Beijing 1000892.Beijing Meteorological, Bureau, Beijing 100089 |
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| Abstract: | Verification of quantitative precipitation forecast has always been a challenge due to the high discontinuity of rainfall in spatial and temporal scales. Conventional methods (TS and MSE score, etc.) cannot meet the need for verification of high resolution (1—2 km) forecasts. In recent years, several new spatial verification approaches which can give more information about the complex spatial structure of forecast field have been developed, such as the intensity-scale verification method, the feather based (or object-oriented) verification method, neighborhood (or fuzzy) verification method and field deformation approaches etc. The attempt to use the intensity scale technique is introduced by Casati et al. to assess radar based 1 hour quantitative precipitation forecasts for 4 different nowcasting systems (BJANC, GRAPES SWIFT, STEPS, and CARDS) which attended the World Weather Research Program for the Beijing 2008 Olympic Games (WWRP B08FDP). The intensity-scale verification approach accounts for the spatial structure of the forecast field and allows the skill score to be diagnosed as a function of the spatial scale of the forecast error and intensity of the precipitation events. Different precipitation types (convective, stratiform and mixed type) during B08FDP demo period are selected to get representative results, it shows that these nowcasting systems exhibit forecasting skills only when the precipitation system is above 32 km spatial scale and last longer than 1 hour despite the using of most advanced systems in the world with the high resolution of 1 to 2 km. For spatial scale lower than that, the forecasting ability is very limited, which indicates that product performance characterized at different spatial scale should be considered in the applying of nowcasting products. When analyzing the forecasting errors with different spatial and temporal scales, it's found that more than 60% and more than 85% of the forecasting error come from spatial scales smaller than 8 km and time scales smaller than 1 hour respectively. Improvements in smaller scales precipitation forecasts are important. Most nowcasting systems explore the linear extrapolation technique to make 0 to 2 hours nowcasts, yet the valid extrapolation time is very limited, especially for smaller scale (less than 1 hour) convective systems, which is mainly caused by the nonlinear development of the convections. Therefore, more information about the circulation which has close relation to the movement and development of the storm should be considered. So far, the most popular way is to blend the radar based extrapolation with the dense observations and numerical model based potential forecast. |
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| Keywords: | spatial verification of precipitation scale decomposition wavelet transform |
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