Abstract
The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links.
Highlights
Published: 27 July 2021Underlying the efforts to improve gridded precipitation data are the needs of weather now-casting and flood forecasting
Building on the high density of Commercial Microwave Link (CML) networks, this work focuses on identifying microwave links that were unreliable for measuring rainfall, and demonstrates that precipitation grids can be improved by filtering those links from the CML network
When the resulting precipitation grids were correlated to independent sets gauge observations, a higher match was found over several storm events to precipitation derived from the filtered, Reliable set of CML data
Summary
Underlying the efforts to improve gridded precipitation data are the needs of weather now-casting and flood forecasting. Accurate hydrological modeling can assist in avoiding these negative effects, which, even a few hours of early warning, in the framework of now-casting (Franch et al [5], Heuvelink et al [6]), are enough to mobilize emergency staff and to evacuate populations when necessary. This level of forecast accuracy is attained only when the model inputs are accurate, where first and foremost are detailed and precise precipitation data. Gosset et al [10] discussed the use of CML data for rainfall measurements in gauge-poor regions of Africa
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