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Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products.

Northern China --- raindrop size distribution (DSD) --- microphysical processes --- quantitative precipitation estimation (QPE) --- satellite-based precipitation --- elevation --- extreme events --- IMERG-V05B and V06A --- MSWEP --- ERA5 --- SM2RAIN --- precipitation estimation --- soil moisture --- SM2RAIN-CCI --- SM2RAIN-ASCAT --- multi-satellite precipitation analysis (TMPA) --- error decomposition --- complex topography --- diverse climate --- gauge data --- IMERG --- TAHMO --- morphing --- field displacement --- TIGGE --- precipitation --- numerical weather prediction --- satellite --- flood --- spring 2019 --- Iran --- GPM IMERG --- satellite precipitation --- spatiotemporal analysis --- statistical distribution --- validation --- Mainland China --- GSMaP_NRT --- GSMaP_Gauge_NRT --- raindrop size distribution --- radar reflectivity --- raindrop spectrometer --- semi-arid area --- assessment --- Taiwan --- data assimilation --- WRF model --- high-impact rainfall events --- GNSS ZTD --- optimum interpolation --- geographically weighted regression --- downscaling --- Tianshan Mountains --- satellite precipitation products --- evaluation --- daily rainfall --- hourly rainfall --- GPM --- TRMM --- GNSS --- GNSS antenna --- receiver antenna calibration --- relative calibration --- Phase Center Variation --- U-blox --- goGPS --- Zenith Tropospheric Delay --- ZED-F9P --- GSMaP --- Nepal --- cloud radar --- thunderstorm --- LDR --- hydrometeor --- hydrometeor classification --- lightning --- discharge --- remote sensing --- SEVIRI --- ground radar --- precipitation interpolation --- geographically and temporally weighted regression --- time weight function --- geographically and temporally weighted regression kriging --- extreme rainfall --- polarimetric radar signatures --- quantitative precipitation estimation --- southern china --- reanalysis --- linear trends --- mainland China --- EDBF algorithm --- geospatial predictor --- spatial pattern --- weighted precipitation --- Cyprus --- bias correction --- object-based method --- storm events --- Thies --- disdrometer --- weather circulations --- convective --- stratiform --- rain spectra --- radar reflectivity–rain rate relationship --- gridded precipitation products --- abrupt changes --- trends --- statistical indicators --- agriculture --- Pakistan --- rainfall --- radar --- extreme precipitation --- spatial bootstrap --- Louisiana --- annual maxima --- n/a --- radar reflectivity-rain rate relationship