Abstract
Abstract. There is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, 5 km×5 km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15 min single-link validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (R2 of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (R2 of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of −0.26) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33 h for CML against 1 h for the adjusted radar and 24 h for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.
Highlights
High spatial and temporal variability make precipitation one of the most difficult geophysical observables to measure and monitor
The first objective of the present work is to make a validation of the precipitation amounts and distributions estimated only from commercial microwave link (CML) attenuation data, using a well-established, freely available algorithm (i.e. RAINLINK; Overeem et al, 2016a), over two areas of interest in the Po Valley, where CML data have been obtained from Vodafone
We show a case when the link retrievals accurately match the measurements of the nearby rain gauge and a case with markedly low performance
Summary
High spatial and temporal variability make precipitation one of the most difficult geophysical observables to measure and monitor. Its accurate measurement would benefit a wide range of applications in meteorology, hydrology, climatology, and agriculture, just to name the most directly related fields where rainfall plays a key role. The precipitation rate can be measured or estimated directly at the ground or using different remote sensing approaches. Ground-based weather radars, often deployed in large-scale networks (Serafin and Wilson, 2000; Huuskonen et al, 2014; Saltikoff et al, 2019), are widely used by hydrometeorological services to quantitatively monitor precipitation fields, being an effective trade-off between spatial temporal coverage and accuracy in the measurements. Radar estimates are affected by several errors, which the last generation of polarimetric systems have only partially mitigated
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