Abstract
Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in the rainfall occurrence or the intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. We investigated the use of warping to correct such errors. The goal was to gauge-adjust satellite-based estimates with respect to the position and the timing of the rain event, instead of its intensity. Warping is a field-deformation method that transforms an image into another one. We compared two methods, spatial warping focusing on the position errors and time warping for the timing errors. They were evaluated on two case studies: a synthetic rainfall event represented by an ellipse and a rain event in southern Ghana during the monsoon season. In both cases, the two warping methods reduced significantly the respective targeted (position or timing) errors. In the southern Ghana case, the average position error was decreased by about 45 km by the spatial warping and the average timing error was decreased from more than 1 h to 0.2 h by the time warping. Both warping methods also improved the continuous statistics on the intensity: the correlation went from 0.18 to at least 0.62 after warping in the southern Ghana case. The spatial warping seems more interesting because of its positive impact on both position and timing errors.
Highlights
An increasing number of satellite-based rainfall estimates, with ever finer resolution, are becoming available
The use of warping to correct position and timing errors in rainfall estimates was tested on two case studies
The first one was a synthetic case where the rainfall events were represented by ellipses
Summary
An increasing number of satellite-based rainfall estimates, with ever finer resolution, are becoming available. They are valuable in Africa where the gauge network is not dense enough to represent the high variability of the rainfall during the monsoon season. Gauge data are often used for bias correction, but can be used for calibration or be merged with other estimates. These methods mostly focus on the intensity of the rainfall. We wanted to investigate the possibility to gauge-adjust a rainfall estimate with respect to the position and timing of the event instead of its intensity
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.