Remote Sensing of Precipitation: Volume 2
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 sys...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 07510naaaa2202257uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_58178 | ||
| 005 | 20210212 | ||
| 020 | |a books978-3-03921-288-0 | ||
| 020 | |a 9783039212873 | ||
| 020 | |a 9783039212880 | ||
| 024 | 7 | |a 10.3390/books978-3-03921-288-0 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Michaelides, Silas |4 auth | |
| 245 | 1 | 0 | |a Remote Sensing of Precipitation: Volume 2 |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (318 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a satellite radiance | ||
| 653 | |a WRF-Hydro | ||
| 653 | |a meteorological radar | ||
| 653 | |a QPE | ||
| 653 | |a microstructure of rain | ||
| 653 | |a TMPA | ||
| 653 | |a evaluation | ||
| 653 | |a precipitation | ||
| 653 | |a volume matching | ||
| 653 | |a CFSR | ||
| 653 | |a GMI | ||
| 653 | |a terminal velocity | ||
| 653 | |a TRMM-TMPA | ||
| 653 | |a surface rain intensity | ||
| 653 | |a retrieval algorithm | ||
| 653 | |a rain gauges | ||
| 653 | |a tropical cyclone | ||
| 653 | |a CMORPH | ||
| 653 | |a T-Matrix | ||
| 653 | |a Global Precipitation Measurement (GPM) | ||
| 653 | |a statistical evaluation | ||
| 653 | |a vertical air velocity | ||
| 653 | |a heavy rainfall prediction | ||
| 653 | |a GPM IMERG v5 | ||
| 653 | |a Tianshan Mountains | ||
| 653 | |a Red River Basin | ||
| 653 | |a precipitation retrieval | ||
| 653 | |a satellite precipitation | ||
| 653 | |a PERSIANN-CCS | ||
| 653 | |a validation network | ||
| 653 | |a PEMW | ||
| 653 | |a satellite rainfall estimate | ||
| 653 | |a high latitude | ||
| 653 | |a Cyprus | ||
| 653 | |a GPM | ||
| 653 | |a wet deposition | ||
| 653 | |a CloudSat | ||
| 653 | |a thundercloud | ||
| 653 | |a GPS | ||
| 653 | |a satellite remote sensing | ||
| 653 | |a assessment | ||
| 653 | |a numerical weather prediction | ||
| 653 | |a mineral dust | ||
| 653 | |a complex terrain | ||
| 653 | |a mesoscale precipitation patterns | ||
| 653 | |a GNSS meteorology | ||
| 653 | |a lumped models | ||
| 653 | |a satellites | ||
| 653 | |a Southern China | ||
| 653 | |a error analysis | ||
| 653 | |a topography | ||
| 653 | |a cloud scavenging | ||
| 653 | |a radar reflectivity-rain rate relationship | ||
| 653 | |a CHAOS | ||
| 653 | |a RADOLAN | ||
| 653 | |a hydrometeor classification | ||
| 653 | |a TRMM | ||
| 653 | |a thunderstorm | ||
| 653 | |a CHIRPS | ||
| 653 | |a satellite precipitation retrieval | ||
| 653 | |a GPM/IMERG | ||
| 653 | |a GSMaP | ||
| 653 | |a bias correction | ||
| 653 | |a Precise Point Positioning | ||
| 653 | |a Mainland China | ||
| 653 | |a supercooled droplets detection | ||
| 653 | |a SEID | ||
| 653 | |a Saharan dust transportation | ||
| 653 | |a Huaihe River basin | ||
| 653 | |a GPM Microwave Imager | ||
| 653 | |a satellite | ||
| 653 | |a TMPA 3B42RT | ||
| 653 | |a forecast model | ||
| 653 | |a quality indexes | ||
| 653 | |a SEVIRI | ||
| 653 | |a radiometer | ||
| 653 | |a triple collocation | ||
| 653 | |a satellite precipitation product | ||
| 653 | |a Mandra | ||
| 653 | |a synoptic weather types | ||
| 653 | |a drop size distribution (DSD) | ||
| 653 | |a Amazon Basin | ||
| 653 | |a weather radar | ||
| 653 | |a X-band radar | ||
| 653 | |a downscaling | ||
| 653 | |a precipitation rate | ||
| 653 | |a neural networks | ||
| 653 | |a rain rate | ||
| 653 | |a CMIP | ||
| 653 | |a GPM-era IMERG | ||
| 653 | |a GR models | ||
| 653 | |a weather | ||
| 653 | |a typhoon | ||
| 653 | |a satellite rainfall retrievals | ||
| 653 | |a TRMM 3B42 v7 | ||
| 653 | |a validation | ||
| 653 | |a low-cost receivers | ||
| 653 | |a rainfall retrieval techniques | ||
| 653 | |a snowfall detection | ||
| 653 | |a GPM satellite | ||
| 653 | |a Zenith Tropospheric Delay | ||
| 653 | |a 3B42 | ||
| 653 | |a hurricane Harvey | ||
| 653 | |a PERSIANN_CDR | ||
| 653 | |a TRMM 3B42 V7 | ||
| 653 | |a snow water path retrieval | ||
| 653 | |a DPR | ||
| 653 | |a satellite precipitation adjustment | ||
| 653 | |a Peninsular Spain | ||
| 653 | |a RMAPS | ||
| 653 | |a daily rainfall estimations | ||
| 653 | |a streamflow simulation | ||
| 653 | |a regional climate models | ||
| 653 | |a Red-Thai Binh River Basin | ||
| 653 | |a Ensemble Precipitation (EP) algorithm | ||
| 653 | |a cloud radar | ||
| 653 | |a disdrometer | ||
| 653 | |a TRMM-era TMPA | ||
| 653 | |a hydrometeorology | ||
| 653 | |a MSG | ||
| 653 | |a radar data assimilation | ||
| 653 | |a dust washout process | ||
| 653 | |a runoff simulations | ||
| 653 | |a geostationary microwave sensors | ||
| 653 | |a radar | ||
| 653 | |a topographical and seasonal evaluation | ||
| 653 | |a goGPS | ||
| 653 | |a XPOL radar | ||
| 653 | |a TMPA 3B42V7 | ||
| 653 | |a telemetric rain gauge | ||
| 653 | |a harmonie model | ||
| 653 | |a tropical storm rainfall | ||
| 653 | |a linear-scaling approach | ||
| 653 | |a Milešovka observatory | ||
| 653 | |a precipitable water vapor | ||
| 653 | |a heavy precipitation | ||
| 653 | |a hydrological simulation | ||
| 653 | |a reflectivity | ||
| 653 | |a Ka-band | ||
| 653 | |a Tibetan Plateau | ||
| 653 | |a satellite rainfall estimates | ||
| 653 | |a regional rainfall regimes | ||
| 653 | |a Lai Nullah | ||
| 653 | |a microwave scattering | ||
| 653 | |a remote sensing | ||
| 653 | |a pre-processing | ||
| 653 | |a rainfall rate | ||
| 653 | |a MSWEP | ||
| 653 | |a climatology | ||
| 653 | |a VIC model | ||
| 653 | |a CMORPH_CRT | ||
| 653 | |a IMERG | ||
| 653 | |a single frequency GNSS | ||
| 653 | |a PERSIANN | ||
| 653 | |a flood-inducing storm | ||
| 653 | |a climate models | ||
| 653 | |a Pakistan | ||
| 653 | |a precipitating hydrometeor | ||
| 653 | |a data assimilation | ||
| 653 | |a rainfall | ||
| 653 | |a kriging with external drift | ||
| 653 | |a dual-polarization | ||
| 653 | |a quantitative precipitation estimates | ||
| 653 | |a flash flood | ||
| 653 | |a Satellite Precipitation Estimates | ||
| 653 | |a gridded radar precipitation | ||
| 653 | |a regional rainfall sub-regimes | ||
| 653 | |a polar systems | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1436 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/58178 |7 0 |z DOAB: description of the publication |