Remote Sensing of Water-Related Hazards. Группа авторов
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Название: Remote Sensing of Water-Related Hazards

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: География

Серия:

isbn: 9781119159148

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СКАЧАТЬ based on GPCC to daily‐scale adjustment because (1) the monthly‐scale adjustment has limited effect on metrics such as CC, RMSE, and CSI at the hourly scale, (2) IMERG_uncal shows similar performance with IMERG_cal in detecting precipitation occurrence at daily and hourly scales, and (3) GSMaP outperforms IMERG, benefiting from its daily‐scale gauge adjustment. Moreover, IMERG shows weak capability in estimating precipitation under cold climate (e.g., during winter and over high latitudes) due to the effect of snowfall, which could be improved through better rainfall‐snowfall classification or physically based retrieval algorithms. The trend of snowfall is most notable in TP, whereas different data sets show contradictory trends. The measurement or estimation of snowfall remains a challenging problem.

      APPENDIX: ABBREVIATIONS

Abbreviation Full phrase
CC Pearson correlation coefficient
CGDPA Chinese Rain Gauge‐based Daily Precipitation Analysis
CHIRPS Climate Hazards group Infrared Precipitation with Stations
CMA China Meteorological Administration
CMORPH Climate Prediction Center (CPC) MORPHing technique bias corrected (CRT)
CPC Climate Prediction Center
CR critical rainfall
CSI Critical success index
ERA‐Interim ECMWF ReAnalysis Interim
ERA5 Fifth generation of ECMWF atmospheric reanalyses of the global climate
FAR False alarm ratio
FFG Flash Flood Guidance
GPM Global Precipitation Measurement
GSMaP Global Satellite Mapping of Precipitation
GSMaP Gauge‐adjusted Global Satellite Mapping of Precipitation V6/V7
IMERG Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement
IMERG_cal IMERG calibrated precipitation
IMERG_uncal IMERG uncalibrated precipitation
IMERG‐E IMERG Early run
IMERG‐F IMERG Final run
KGE’ Kling‐Gupta efficiency
ME Mean error
MERRA2 The Modern‐Era Retrospective Analysis for Research and Applications, Version 2
MTC Multiplicative TC
NE Northeastern region
PCDR PERSIANN‐Climate Data Record
PERSIANN Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks
PERSIANN‐CCS PERSIANN‐ Cloud Classification System
POD Probability of detection
RMSE Root mean square error
RTI Rain Trigger Index
SM2RAIN SM2RAIN based on ESA Climate Change Initiative (CCI)
SMI Soil Moisture Index
T3B42 TRMM Multi‐satellite Precipitation Analysis (TMPA) 3B42 V7
TC Triple collocation
TMI TRMM microwave imager
TMPA TRMM multi‐satellite precipitation analysis
TP Qinghai‐Tibet Plateau
XJ Xinjiang Province

      We appreciate the extensive efforts by the developers of the ground, satellite, and reanalysis precipitation datasets to make their products available. The study is funded by the Global Water Futures program in Canada, the National Natural Science Foundation of China (grant 71461010701 and 41471430), and the National Key R&D Program of China (2018YFC1508105).

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      3 Behrangi, A., Yin, X., Rajagopal, S., Stampoulis, D., & Ye, H. (2018). On distinguishing snowfall from rainfall using near‐surface atmospheric information: Comparative analysis, uncertainties, and hydrologic importance. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.3240

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