Correcting Position Error in Precipitation Data Using Image Morphing

Remote Sensing

Camille Le Coz¹  Marie-claire ten Veldhuis¹  Nick van de Giesen¹  Martin Verlaan²  Arnold Heemink² 

[1] Faculty of Civil Engineering and Geosciences, Water Resources, Delft University of Technology, 2628 CN Delft, The Netherlands;Faculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, 2628 CN Delft, The Netherlands;

关键词: precipitation estimation;    satellite-based precipitation;    gauge data;    imerg;    tahmo;    morphing;    field displacement;   

DOI  :  10.3390/rs11212557

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown