TY - JOUR
T1 - Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products
AU - Arabzadeh, Alireza
AU - Ehsani, Mohammad Reza
AU - Guan, Bin
AU - Heflin, Stella
AU - Behrangi, Ali
N1 - Publisher Copyright:
©2020. American Geophysical Union. All Rights Reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR-related precipitation using 18 years (2001–2018) of globally gridded AR locations derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi-satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias-adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR]) and reanalysis (MERRA-2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR-related events compared to non-AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN-CDR are fairly high, considerable discrepancies are shown in their estimation of AR-related events (i.e., overall IMERG and CMORPH show a higher fraction of AR-related precipitation). It was found that the degree of consistency between reanalysis and satellite-based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.
AB - Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR-related precipitation using 18 years (2001–2018) of globally gridded AR locations derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi-satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias-adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR]) and reanalysis (MERRA-2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR-related events compared to non-AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN-CDR are fairly high, considerable discrepancies are shown in their estimation of AR-related events (i.e., overall IMERG and CMORPH show a higher fraction of AR-related precipitation). It was found that the degree of consistency between reanalysis and satellite-based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.
KW - IMERG V06
KW - atmospheric river
KW - extreme event
KW - precipitation product
KW - reanalysis
KW - remote sensing
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U2 - 10.1029/2020JD033021
DO - 10.1029/2020JD033021
M3 - Article
AN - SCOPUS:85095819084
SN - 2169-897X
VL - 125
JO - Journal of Geophysical Research Atmospheres
JF - Journal of Geophysical Research Atmospheres
IS - 21
M1 - e2020JD033021
ER -