Abstract
High-resolution inputs of rainfall are important in hydrological sciences, especially for urban hydrology. This is mainly because heavy rainfall-induced events such as flash floods can have a tremendous impact on society given their destructive nature and the short time scales in which they develop. With the development of technologies such as radars, satellites and (commercial) microwave links (CMLs), the spatiotemporal resolutions at which rainfall can be retrieved are becoming higher and higher. For the land surface of The Netherlands, we evaluate here four rainfall products, i.e., link-derived rainfall maps, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG—Global Precipitation Measurement mission), Meteosat Second Generation Cloud Physical Properties (CPP), and Nighttime Infrared Precipitation Estimation (NIPE). All rainfall products are compared against gauge-adjusted radar data, considered as the ground truth given its high quality, resolution, and availability. The evaluation is done for seven months at 30 min and 24h. Overall, we found that link-derived rainfall maps outperform the satellite products and that IMERG outperforms CPP and NIPE. We also explore the potential of a CML network to validate satellite rainfall products. Usually, satellite derived products are validated against radar or rain gauge networks. If data from CMLs would be available, this would be highly relevant for ground validation in areas with scarce rainfall observations, since link-derived rainfall is truly independent of satellite-derived rainfall. The large worldwide coverage of CMLs potentially offers a more extensive platform for the ground validation of satellite estimates over the land surface of the Earth.
Highlights
R AINFALL is the key input in environmental applications such as hydrological modeling, flash-flood and crop forecasting, landslide triggering, fresh water availability, and waterborne disease propagation
We evaluate the accuracy of four gridded rainfall products: Cloud Physical Properties (CPP) and the experimental Nighttime IR Precipitation Estimation (NIPE), both from the geostationary earth orbit (GEO) platform Meteosat Second Generation (MSG); Integrated MultisatellitE Retrievals for global precipitation measurement mission (GPM) (IMERG), of which the core satellite is an LEO platform; and rainfall maps from a Dutch commercial microwave link (CML) network
We evaluate the performance of rainfall estimates from satellites and CMLs through the relative bias, the coefficient of variation (CV), and the coefficient of determination (r 2)
Summary
R AINFALL is the key input in environmental applications such as hydrological modeling, flash-flood and crop forecasting, landslide triggering, fresh water availability, and waterborne disease propagation. Devices and techniques have been developed to measure or estimate rainfall quantities and to fill the spatiotemporal gaps in the data collected. Rain gauges are the only devices that directly measure rainfall. They are a relatively low-cost technology due to their compact size and simplicity; large networks of rain gauges are found in many places over the land surface of the Earth [3]. They are point measurements only representative of the area in their immediate vicinity. The spatial variability of rainfall is not well captured by gauge networks given their sparsity, especially over the oceans, where practically no gauges are placed. Rain gauges are the main and most reliable devices to validate more advanced platforms such as weather radars and meteorological satellites
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Geoscience and Remote Sensing
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.
