Speaker
Description
Rainfall estimation from commercial microwave links (CMLs) can be derived from existing signal level measurements, collected under various operational sampling protocols. One of the most widely used protocols reports only the extreme values (the minimum and the maximum values per a given interval; min-max in short) of the signal level, usually once per 15-minute interval. In this work, we compared the performance of the rainfall estimation, based on the min-max protocol and the average signal level, with alternative protocols in which instantaneous signal level measurements are reported at different sampling rates. Previous theoretical analyses, conducted under simplified assumptions, have suggested that min-max sampling at 15-minute intervals contains a similar amount of rainfall related information as instantaneous sampling every 50 seconds. However, actual CMLs’ measurements are affected by multiple factors that were not accounted for in the theoretical studies. Thus, we investigated this question using real data, following a development of a dedicated Transformer-based neural network for rainfall estimation, which was trained and applied both on simulated data as well as on real-world CMLs’ measurements collected under different sampling protocols, aimed to minimize the mean square estimation error. The results provide a comprehensive empirical quantified assessment of these protocols and validate the theoretical advantage of using the min-max signal levels collected by CMLs, rather than instantaneous samples at comparable rate, for rainfall estimation under both idealized and real-world conditions. In particular, our analysis shows that under the real-world conditions that were inspected, the min-max sampling protocol is equivalent to instantaneous sampling period of approximately 2 minutes between samples. Under ideal simulated conditions, the min–max protocol corresponds to approximately 18 instantaneous samples per 15-minute interval, matching the theoretical expected value, while real-world measurements indicate an equivalence of about 9 samples per interval, confirming that min-max sampling maintains competitive performance despite its lower temporal resolution even when diverge from ideal conditions. Lastly, this work also serves as a tool to establish a quantitative assessment of the large amount of rainfall information held in the min-max measurements as described in past studies (e.g., by Van der Valk et. al., Pudashine et. al. and Ostrometzky et. al, among others).