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Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of 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 a 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.

satellite radiance --- WRF-Hydro --- meteorological radar --- QPE --- microstructure of rain --- TMPA --- evaluation --- precipitation --- volume matching --- CFSR --- GMI --- terminal velocity --- TRMM-TMPA --- surface rain intensity --- retrieval algorithm --- rain gauges --- tropical cyclone --- CMORPH --- T-Matrix --- Global Precipitation Measurement (GPM) --- statistical evaluation --- vertical air velocity --- heavy rainfall prediction --- GPM IMERG v5 --- Tianshan Mountains --- Red River Basin --- precipitation retrieval --- satellite precipitation --- PERSIANN-CCS --- validation network --- PEMW --- satellite rainfall estimate --- high latitude --- Cyprus --- GPM --- wet deposition --- CloudSat --- thundercloud --- GPS --- satellite remote sensing --- assessment --- numerical weather prediction --- mineral dust --- complex terrain --- mesoscale precipitation patterns --- GNSS meteorology --- lumped models --- satellites --- Southern China --- error analysis --- topography --- cloud scavenging --- radar reflectivity–rain rate relationship --- CHAOS --- RADOLAN --- hydrometeor classification --- TRMM --- thunderstorm --- CHIRPS --- satellite precipitation retrieval --- GPM/IMERG --- GSMaP --- bias correction --- Precise Point Positioning --- Mainland China --- supercooled droplets detection --- SEID --- Saharan dust transportation --- Huaihe River basin --- GPM Microwave Imager --- satellite --- TMPA 3B42RT --- forecast model --- quality indexes --- SEVIRI --- radiometer --- triple collocation --- satellite precipitation product --- Mandra --- synoptic weather types --- drop size distribution (DSD) --- Amazon Basin --- weather radar --- X-band radar --- downscaling --- precipitation rate --- neural networks --- rain rate --- CMIP --- GPM-era IMERG --- GR models --- weather --- typhoon --- satellite rainfall retrievals --- TRMM 3B42 v7 --- validation --- low-cost receivers --- rainfall retrieval techniques --- snowfall detection --- GPM satellite --- Zenith Tropospheric Delay --- 3B42 --- hurricane Harvey --- PERSIANN_CDR --- TRMM 3B42 V7 --- snow water path retrieval --- DPR --- satellite precipitation adjustment --- Peninsular Spain --- RMAPS --- daily rainfall estimations --- streamflow simulation --- regional climate models --- Red–Thai Binh River Basin --- Ensemble Precipitation (EP) algorithm --- cloud radar --- disdrometer --- TRMM-era TMPA --- hydrometeorology --- MSG --- radar data assimilation --- dust washout process --- runoff simulations --- geostationary microwave sensors --- radar --- topographical and seasonal evaluation --- goGPS --- XPOL radar --- TMPA 3B42V7 --- telemetric rain gauge --- harmonie model --- tropical storm rainfall --- linear-scaling approach --- Milešovka observatory --- precipitable water vapor --- heavy precipitation --- hydrological simulation --- reflectivity --- Ka-band --- Tibetan Plateau --- satellite rainfall estimates --- regional rainfall regimes --- Lai Nullah --- microwave scattering --- remote sensing --- pre-processing --- rainfall rate --- MSWEP --- climatology --- VIC model --- CMORPH_CRT --- IMERG --- single frequency GNSS --- PERSIANN --- flood-inducing storm --- climate models --- Pakistan --- precipitating hydrometeor --- data assimilation --- rainfall --- kriging with external drift --- dual-polarization --- quantitative precipitation estimates --- flash flood --- Satellite Precipitation Estimates --- gridded radar precipitation --- regional rainfall sub-regimes --- polar systems

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In the Earth sciences, a transition is currently occurring in multiple fields towards an integrated Earth system approach, with applications including numerical weather prediction, hydrological forecasting, climate impact studies, ocean dynamics estimation and monitoring, and carbon cycle monitoring. These approaches rely on coupled modeling techniques using Earth system models that account for an increased level of complexity of the processes and interactions between atmosphere, ocean, sea ice, and terrestrial surfaces. A crucial component of Earth system approaches is the development of coupled data assimilation of satellite observations to ensure consistent initialization at the interface between the different subsystems. Going towards strongly coupled data assimilation involving all Earth system components is a subject of active research. A lot of progress is being made in the ocean–atmosphere domain, but also over land. As atmospheric models now tend to address subkilometric scales, assimilating high spatial resolution satellite data in the land surface models used in atmospheric models is critical. This evolution is also challenging for hydrological modeling. This book gathers papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.

land data assimilation system --- land data assimilation --- rainfall-runoff simulation --- 4D-Var data assimilation --- total water storage --- accuracy --- ocean–atmosphere assimilation --- precipitation --- Earth system models --- numerical weather prediction --- fluorescence --- GRACE --- MCA analysis --- weakly coupled data assimilation --- GPM IMERG --- atmospheric models --- rainfall correction --- remote sensing --- microwave remote sensing --- SMAP --- land surface modeling --- bending angle --- floods soil moisture --- vegetation --- GPSRO --- WRF --- merged CMORPH --- land surface model --- temperature --- 4D-Var --- data assimilation --- data-driven methods --- GSI --- radio occultation data --- rainfall --- soil moisture --- sea level anomaly --- total cloud cover --- land surface models --- Mediterranean basin --- interpolation --- sea surface height --- drought --- TRMM 3B42 --- analog data assimilation --- ocean models

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Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models.

Technology: general issues --- History of engineering & technology --- statistical weather generator --- stochastic process --- Diyala River basin --- Wilks’ technique --- hydrological models --- rainfall --- surface runoff --- linear regression models --- curve number --- SCS.CN model --- mulching --- wildfire --- prescribed fire --- n/a --- CHIRPS --- GPM-IMERG --- rainfall data scarcity --- agro-hydrology --- Rift Valley Lake Basin --- hydrological research basin --- precipitation --- temperature --- long-term trends --- climate change --- evapotranspiration --- groundwater recharge --- thresholds --- seasonality --- spatiotemporal variations --- regional-scale --- long-term --- HydroBudget model --- cold and humid climates --- Quebec (Canada) --- tank cascade system --- dry zone --- water governance --- flood control --- traditional knowledge --- community participation --- Sri Lanka --- Wilks' technique

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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.

Research & information: general --- 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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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