Estimating the Parameters of the Spatial Autocorrelation of Rainfall Fields by Measurements From Commercial Microwave Links

作者：Adam Eshel, Pinhas Alpert, Hagit Messer · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2022 · DOI：10.1109/tgrs.2022.3165309 · 被引用次数：7 · 研究领域：Precipitation Measurement and Analysis、Cryospheric studies and observations、Hydrology and Watershed Management Studies

The spatial structure of rain fields is important to the understanding of their effects on ground-level aspects, such as runoff generation, and is considered crucial information for the accurate reconstruction of these fields. It is commonly characterized by a simplified spatial autocorrelation function (ACF). The near-ground ACF, and—particularly—its decorrelation distance, is evaluated from point measurements (rain gauges and distrometers). However, the spatial representation of such measurements is limited and therefore rarely sufficient for reliable ACF estimation. The emerging use of commercial microwave links (CMLs) for near-ground rain retrieval, and their spatial abundance, suggests using them for ACF estimation. In this study, we propose a method for extracting spatial features of a rain field, and in particular its decorrelation distance, from CML measurements. When sampled by path integration, the rain measurements acquire a distortion as a result of the averaging of a once fluctuating signal, where extreme rain intensities are being smeared. When evaluating the AFC from CMLs’ measurements, this effect needs to be compensated for. We propose methods for retrieving the original parameters characterizing the AFC and validate them on semisynthetic simulated data, based on actual rain events. The error was found to be 5%.