A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting
A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting
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DOI：
10.1016/s0022-1694(00)00360-7
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发表时间：
2000-12
影响因子：
6.4
通讯作者：
M. Grecu;W. Krajewski
中科院分区：
文献类型：
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作者：
M. Grecu;W. Krajewski
我们提出了基于雷达的定量降水预报技术的广泛评估结果。利用俄克拉荷马州塔尔萨的WSR-88D雷达的大量雷达观测数据，我们评估了几种技术，包括持久性、平流和基于神经网络的方案。我们的研究范围仅限于极短的交货时间预测，最多为3小时。我们考虑了从4×4 km2到32×32 km2的几种空间分辨率。该方案的性能评估使用几个流行的标准，包括相关系数，乘法偏差和检测概率。讨论了平均风暴强度和降雨强度积分对可预报极限的影响。研究得出的重要结论是：(1)平流是影响有用预报的最重要的物理过程；(2)更大、更强的风暴更容易预测；(3)时空整合显著扩展了可预测性极限。
We present the results of an extensive evaluation of radar-based quantitative precipitation forecasting techniques. Using a large data set of radar observations from the Tulsa, Oklahoma, WSR-88D radar we evaluate several techniques, including persistence, advection, and neural-network-based schemes. The scope of our study is limited to very-short-term forecast lead-times of up to three hours. We consider several spatial resolutions ranging from 4×4 km2to 32×32 km2. Performance of the schemes is evaluated using several popular criteria that include correlation coefficient, multiplicative bias, and probability of detection. We discuss the effects of average storm intensity and rainfall intensity integration on the predictability limits. The most significant conclusions from the study are: (1) advection is the most important physical process that impacts useful predictions; (2) larger and more intense storms are easier to forecast; and (3) both spatial and temporal integration significantly extends the predictability limits.