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Remote Sensing of Precipitation: Volume 1

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ISBN: 9783039212859 9783039212866 Year: Pages: 480 DOI: 10.3390/books978-3-03921-286-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

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.

Keywords

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

Remote Sensing of Precipitation: Volume 2

Author:
ISBN: 9783039212873 9783039212880 Year: Pages: 318 DOI: 10.3390/books978-3-03921-288-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

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.

Keywords

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

Assimilation of Remote Sensing Data into Earth System Models

Authors: --- ---
ISBN: 9783039216406 9783039216413 Year: Pages: 236 DOI: 10.3390/books978-3-03921-641-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:16
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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.

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Author:
ISBN: 9783039216642 9783039216659 Year: Pages: 294 DOI: 10.3390/books978-3-03921-665-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2019-12-09 16:10:12
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Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

Keywords

rainfall --- monsoon --- high resolution --- TRMM --- drought prediction --- APCC Multi-Model Ensemble --- seasonal climate forecast --- machine learning --- sparse monitoring network --- Fiji --- drought analysis --- ANN model --- drought indices --- meteorological drought --- SIAP --- SWSI --- hydrological drought --- discrete wavelet --- global warming --- statistical downscaling --- HBV model --- flow regime --- uncertainty --- reservoir inflow forecasting --- artificial neural network --- wavelet artificial neural network --- weighted mean analogue --- variation analogue --- streamflow --- artificial neural network --- simulation --- forecasting --- support vector machine --- evolutionary strategy --- heavy storm --- hyetograph --- temperature --- clausius-clapeyron scaling --- climate change --- the Cauca River --- climate variability --- ENSO --- extreme rainfall --- trends --- statistical downscaling --- random forest --- least square support vector regression --- extreme rainfall --- polynomial normal transform --- multivariate modeling --- sampling errors --- non-normality --- extreme rainfall analysis --- statistical analysis --- hydrological extremes --- stretched Gaussian distribution --- Hurst exponent --- INDC pledge --- precipitation --- extreme events --- extreme precipitation exposure --- non-stationary --- extreme value theory --- uncertainty --- flood regime --- flood management --- Kabul river basin --- Pakistan --- extreme events --- innovative methods --- downscaling --- forecasting --- compound events --- satellite data

Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia

Authors: ---
ISBN: 9783039212354 9783039212361 Year: Pages: 384 DOI: 10.3390/books978-3-03921-236-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-08-28 11:21:27
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To promote scientific understanding of surface processes in East Asia, we have published details of the CMADS dataset in the journal, Water, and expect that users around the world will learn about CMADS datasets while promoting the development of hydrometeorological disciplines in East Asia. We hope and firmly believe that scientific development in East Asia and our understanding of this typical region will be further advanced.

Keywords

East Asia --- CMADS --- meteorological input uncertainty --- hydrological modelling --- SWAT --- non-point source pollution models --- CMADS --- Qinghai-Tibet Plateau (TP) --- SWAT --- CFSR --- TRMM --- PERSIANN --- PERSIANN-CDR --- CMADS --- satellite-derived rainfall --- streamflow simulation --- SWAT --- Han River --- GLUE --- hydrological model --- ParaSol --- SUFI2 --- uncertainty analysis --- SWAT model --- CMADS --- Lijiang River --- runoff --- uncertainty analysis --- hydrological elements --- statistical analysis --- SWAT --- CMADS --- climate variability --- land use change --- streamflow --- potential evapotranspiration --- Penman-Monteith --- CMADS --- China --- CMADS dataset --- parameter sensitivity --- SUFI-2 --- Yellow River --- reanalysis products --- satellite-based products --- hydrological model --- bayesian model averaging --- Xiang River basin --- total nitrogen --- accumulation --- SWAT model --- CMADS --- Biliuhe reservoir --- CMADS --- SWAT --- East Asia --- meteorological --- hydrological --- precipitation --- TMPA-3B42V7 --- CMADS --- hydrologic model --- uncertainty --- reservoirs --- operation rule --- Noah LSM-HMS --- capacity distribution --- aggregated reservoir --- CMADS --- CMADS --- IMERG --- statistical analysis --- SWAT hydrological simulation --- Jinsha River Basin --- blue and green water flows --- climate variability --- sensitivity analysis --- Erhai Lake Basin --- CMADS --- SWAT --- JBR --- soil moisture --- hydrological processes --- spatio-temporal --- sloping black soil farmland --- soil moisture content --- freeze–thaw period --- soil temperature --- CMADS-ST --- reservoir parameters --- runoff --- CMADS --- SWAT --- Yalong River --- CMADS --- impact --- hydrological modeling --- SWAT --- runoff --- sediment yield --- land-use change --- SWAT --- CMADS

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Authors: ---
ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

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2019 (6)