06469nam 2201753z- 450 991055770490332120231214133355.0(CKB)5400000000046230(oapen)https://directory.doabooks.org/handle/20.500.12854/77037(EXLCZ)99540000000004623020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierRemote Sensing of Precipitation: Part IIBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (594 p.)3-0365-2327-8 3-0365-2328-6 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.Remote Sensing of PrecipitationResearch & information: generalbicsscNorthern Chinaraindrop size distribution (DSD)microphysical processesquantitative precipitation estimation (QPE)satellite-based precipitationelevationextreme eventsIMERG-V05B and V06AMSWEPERA5SM2RAINprecipitation estimationsoil moistureSM2RAIN-CCISM2RAIN-ASCATmulti-satellite precipitation analysis (TMPA)error decompositioncomplex topographydiverse climategauge dataIMERGTAHMOmorphingfield displacementTIGGEprecipitationnumerical weather predictionsatellitefloodspring 2019IranGPM IMERGsatellite precipitationspatiotemporal analysisstatistical distributionvalidationMainland ChinaGSMaP_NRTGSMaP_Gauge_NRTraindrop size distributionradar reflectivityraindrop spectrometersemi-arid areaassessmentTaiwandata assimilationWRF modelhigh-impact rainfall eventsGNSS ZTDoptimum interpolationgeographically weighted regressiondownscalingTianshan Mountainssatellite precipitation productsevaluationdaily rainfallhourly rainfallGPMTRMMGNSSGNSS antennareceiver antenna calibrationrelative calibrationPhase Center VariationU-bloxgoGPSZenith Tropospheric DelayZED-F9PGSMaPNepalcloud radarthunderstormLDRhydrometeorhydrometeor classificationlightningdischargeremote sensingSEVIRIground radarprecipitation interpolationgeographically and temporally weighted regressiontime weight functiongeographically and temporally weighted regression krigingextreme rainfallpolarimetric radar signaturesquantitative precipitation estimationsouthern chinareanalysislinear trendsmainland ChinaEDBF algorithmgeospatial predictorspatial patternweighted precipitationCyprusbias correctionobject-based methodstorm eventsThiesdisdrometerweather circulationsconvectivestratiformrain spectraradar reflectivity-rain rate relationshipgridded precipitation productsabrupt changestrendsstatistical indicatorsagriculturePakistanrainfallradarextreme precipitationspatial bootstrapLouisianaannual maximaResearch & information: generalMichaelides Silasedt1292430Michaelides SilasothBOOK9910557704903321Remote Sensing of Precipitation: Part II3026991UNINA