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Open AccessArticle

Correcting Position Error in Precipitation Data Using Image Morphing

1
Faculty of Civil Engineering and Geosciences, Water Resources, Delft University of Technology, 2628 CN Delft, The Netherlands
2
Faculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, 2628 CN Delft, The Netherlands
3
Deltares, 2600 MH Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2557; https://doi.org/10.3390/rs11212557
Received: 24 September 2019 / Revised: 21 October 2019 / Accepted: 28 October 2019 / Published: 31 October 2019
(This article belongs to the Special Issue Remote Sensing of Precipitation: Part II)
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. View Full-Text
Keywords: precipitation estimation; satellite-based precipitation; gauge data; IMERG; TAHMO; morphing; field displacement precipitation estimation; satellite-based precipitation; gauge data; IMERG; TAHMO; morphing; field displacement
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MDPI and ACS Style

Le Coz, C.; Heemink, A.; Verlaan, M.; ten Veldhuis, M.-C.; van de Giesen, N. Correcting Position Error in Precipitation Data Using Image Morphing. Remote Sens. 2019, 11, 2557.

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  • Externally hosted supplementary file 1
    Link: https://github.com/clecoz/precipitation-morphing.git
    Description: Python scripts for the automatic registration and morphing. The example script permits to reproduce the synthetic case presented in the manuscript.
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