Correcting Position Error in Precipitation Data Using Image Morphing
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 s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/21/2557 |
| _version_ | 1798031765004091392 |
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| author | Camille Le Coz Arnold Heemink Martin Verlaan Marie-claire ten Veldhuis Nick van de Giesen |
| author_facet | Camille Le Coz Arnold Heemink Martin Verlaan Marie-claire ten Veldhuis Nick van de Giesen |
| author_sort | Camille Le Coz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:01:39Z |
| format | Article |
| id | doaj.art-c8c1e2eda82946f4b9f4d6e3fef373c1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T20:01:39Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c8c1e2eda82946f4b9f4d6e3fef373c12022-12-22T04:05:34ZengMDPI AGRemote Sensing2072-42922019-10-011121255710.3390/rs11212557rs11212557Correcting Position Error in Precipitation Data Using Image MorphingCamille Le Coz0Arnold Heemink1Martin Verlaan2Marie-claire ten Veldhuis3Nick van de Giesen4Faculty of Civil Engineering and Geosciences, Water Resources, Delft University of Technology, 2628 CN Delft, The NetherlandsFaculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, 2628 CN Delft, The NetherlandsFaculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, 2628 CN Delft, The NetherlandsFaculty of Civil Engineering and Geosciences, Water Resources, Delft University of Technology, 2628 CN Delft, The NetherlandsFaculty of Civil Engineering and Geosciences, Water Resources, Delft University of Technology, 2628 CN Delft, The NetherlandsRainfall 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.https://www.mdpi.com/2072-4292/11/21/2557precipitation estimationsatellite-based precipitationgauge dataimergtahmomorphingfield displacement |
| spellingShingle | Camille Le Coz Arnold Heemink Martin Verlaan Marie-claire ten Veldhuis Nick van de Giesen Correcting Position Error in Precipitation Data Using Image Morphing Remote Sensing precipitation estimation satellite-based precipitation gauge data imerg tahmo morphing field displacement |
| title | Correcting Position Error in Precipitation Data Using Image Morphing |
| title_full | Correcting Position Error in Precipitation Data Using Image Morphing |
| title_fullStr | Correcting Position Error in Precipitation Data Using Image Morphing |
| title_full_unstemmed | Correcting Position Error in Precipitation Data Using Image Morphing |
| title_short | Correcting Position Error in Precipitation Data Using Image Morphing |
| title_sort | correcting position error in precipitation data using image morphing |
| topic | precipitation estimation satellite-based precipitation gauge data imerg tahmo morphing field displacement |
| url | https://www.mdpi.com/2072-4292/11/21/2557 |
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