Stochastic modelling of rainfall from satellite data
Stochastic modelling of rainfall from satellite data
复制标题
根据卫星数据对降雨量进行随机建模
DOI：
10.1016/j.jhydrol.2007.08.014
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发表时间：
2007
影响因子：
6.4
通讯作者：
D. Grimes
中科院分区：
文献类型：
--
作者：
C. Teo;D. Grimes
卫星降雨量监测因其覆盖全球而广泛用于气候学研究，但对业务目的也非常重要，特别是在非洲等缺乏地面降雨量数据的地区。卫星降雨量估计作为水文和农业模型的投入具有巨大的潜在效益，因为其具有真实的可用性、低成本和全空间覆盖。需要解决的一个问题是这些估计的不确定性。这一点在评估非线性模型（径流量或作物产量）输出的可能误差时特别重要，因为非线性模型利用在一个地区汇总的降雨量估计数作为输入。正确评估降雨量的不确定性是重要的，因为它必须考虑到这些因素。本文介绍了一种估计卫星降雨量值的不确定性的方法。该方法首先涉及随机校准，它完整地描述了给定卫星值的降雨发生概率和降雨量的pdf，其次是基于随机校准生成降雨场集合，但每个集合成员内具有正确的空间相关结构。这是通过使用地质统计顺序模拟来实现的。以这种方式生成的集合可以用于估计更大空间尺度上的不确定性。以西非冈比亚的日降雨量监测为例，说明了该方法的可行性。
Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method.