用雨量计和雷达资料模拟降雨强度的时空中尺度模式
Spatial--temporal mesoscale modeling of rainfall intensity using gage and radar data
摘要：
需要以非常高的空间和时间分辨率水平的网格化降雨强度估计值作为天气预报模式的主要输入，以获得准确的降水预报，并验证降水预报模式的性能。这些网格化的降雨场也是预测山洪暴发的水文模型的主要驱动力，它们对于与暴雨相关的灾害预测至关重要。降雨量信息可以从雨量器获得，这些雨量器提供相对准确的点参考位置实际降雨量值的估计，但它们不能很好地表征降雨场的空间和时间结构。多普勒雷达数据提供了更好的空间和时间覆盖范围，但多普勒雷达测量的是有效雷达反射率（$Ze$）而不是降雨率（$R$）。因此，从雷达数据的降水估计遭受各种不确定性，由于其测量原理和从$Ze$到$R$的转换。我们引入了一个框架，结合雷达反射率和测量数据，通过编写一个潜在的不可观测的时空过程与真实的降雨值方面的降雨信息的不同来源。我们使用空间逻辑斯谛回归的潜在的真实降雨过程的数据来源的降雨概率建模。我们的特点不同来源的偏差和误差的测量仪和雷达数据，我们估计真实的降雨强度与后验预测分布，条件的观测数据。
英文摘要：
Gridded estimated rainfall intensity values at very high spatial and temporal resolution levels are needed as main inputs for weather prediction models to obtain accurate precipitation forecasts, and to verify the performance of precipitation forecast models. These gridded rainfall fields are also the main driver for hydrological models that forecast flash floods, and they are essential for disaster prediction associated with heavy rain. Rainfall information can be obtained from rain gages that provide relatively accurate estimates of the actual rainfall values at point-referenced locations, but they do not characterize well enough the spatial and temporal structure of the rainfall fields. Doppler radar data offer better spatial and temporal coverage, but Doppler radar measures effective radar reflectivity ($Ze$) rather than rainfall rate ($R$). Thus, rainfall estimates from radar data suffer from various uncertainties due to their measuring principle and the conversion from $Ze$ to $R$. We introduce a framework to combine radar reflectivity and gage data, by writing the different sources of rainfall information in terms of an underlying unobservable spatial temporal process with the true rainfall values. We use spatial logistic regression to model the probability of rain for both sources of data in terms of the latent true rainfall process. We characterize the different sources of bias and error in the gage and radar data and we estimate the true rainfall intensity with its posterior predictive distribution, conditioning on the observed data.
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