| 事件概率回归估计与降水等级预报 |
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| 引用本文： | 赵声蓉,赵翠光,邵明轩.事件概率回归估计与降水等级预报[J].应用气象学报,2009,20(5):521-529. |
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| 作者姓名： | 赵声蓉 赵翠光 邵明轩 |
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| 作者单位： | 国家气象中心, 北京 100081 |
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| 摘 要： | 该文对比分析概率回归降水等级预报和回归降水等级预报的差异, 2007年秋季至2008年夏季全国平均检验结果表明:概率回归降水等级预报效果好于回归降水等级预报, 尤其是小雨预报, TS评分明显高于回归降水等级预报, 同预报偏差过大情况也有很大改善。进一步分析表明:回归降水等级预报方法在建立小雨预报方程的样本中, 少数较大降水量的样本方差占总方差的百分比过大, 导致预报方程中反映的预报量与预报因子的关系以少数大降水量样本为主, 是造成小雨预报空报过大的原因。与模式降水预报的对比分析表明:概率回归降水等级预报效果好于模式直接降水预报, 模式降水空报较大情况得到改善。
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| 关 键 词： | 事件概率回归估计 降水等级预报 TS评分 空报率 漏报率 预报偏差 |
| 收稿时间： | 2009-01-05 |
Regression Estimate of Event Possibility and Precipitation Categorical Forecast |
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| Shao Mingxuan,Zhao Cuiguang and Shao Mingxuan.Regression Estimate of Event Possibility and Precipitation Categorical Forecast[J].Quarterly Journal of Applied Meteorology,2009,20(5):521-529. |
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| Authors: | Shao Mingxuan Zhao Cuiguang and Shao Mingxuan |
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| Institution: | National Meteorological Center, Beijing 100081 |
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| Abstract: | Objective precipitation forecast is a difficult problem in NWP products interpretation. Because of its characteristics, objective precipitation forecast is a categorical forecast rather than precipitation amount forecast. The differences between two kinds of categorical precipitation forecast are analyzed. One categorical forecast is based on probability regression. Its method is processing original precipitation to 0 and 1 corresponding categories, and then developing forecast equations of different categories to calculate the cri-terions. In real forecasting, the categorical precipitation will be determined through the criterion and the probability forecast of that category. The other forecast is based on regression, the method of which is preprocessing original samples with value smaller than the threshold to category of 0, and then developing forecast equations and criterions.The experimental result from autumn of 2007 to summer of 2008 indicates that probability regression precipitation categorical forecast is better than regression precipitation categorical forecast. Especially when forecasting light rain, the TS score averaged over China using probability regression method is higher than that of regression precipitation categorical forecast, the false alarm ratio is obviously smaller, and also the forecast bias is closer to 1. Through the analysis of predictors and variance contribution of single sample, the cause of these differences becomes obvious. In regression categorical forecast, the variance contribution of a few heavy rain samples is too large. It results in the relation of predictors and precipitation mainly reflected those minority heavy rain samples. That is why the false alarm ratio of regression categorical forecast is too high. It can be shown in comparing analysis that the probability regression categorical precipitation forecast is better than direct model precipitation forecast and the situation that false alarm ratio is too high is improved. |
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| Keywords: | probability regression estimate categorical precipitation forecast TS score false alarm ratio missed event ratio forecast bias |
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