Abstract
Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our case a rainfall field, into another one, by applying a spatial transformation. A benefit of this approach is that it can take both the position and the intensity of a rain event into account. Its potential is investigated with two case studies. In the first case, the rain events are synthetic, represented by elliptic shapes, while the second case uses real data from a rainfall event occurring during the monsoon season in southern Ghana. In the second case, the satellite-based estimate IMERG-Late (Integrated Multi-Satellite Retrievals for GPM ) is adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. The results show that the position errors can be corrected, while preserving the higher spatial variability of the satellite-based estimate.
Highlights
Precipitation is an important variable in weather and climate research and many other applications
We will look at the performance of the automatic registration and morphing procedure
We have investigated the use of a morphing approach for the gauge-adjustment of satellite-based rainfall estimates with respect to position error
Summary
Precipitation is an important variable in weather and climate research and many other applications. Estimating precipitation accurately is difficult because of its high spatial and temporal variability. This is especially true for sub-Saharan Africa, where most of the rainfall is produced during the monsoon season by convective rainstorms, which are very localized [1,2]. The gauge networks in Africa are not dense enough to derive high resolution precipitation estimates. Satellites do not measure precipitation directly but have the advantage of covering large areas. This is especially interesting for Africa where gauge networks are sparse and there are almost no radar observations available
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