Assessment of Various Precipitation Products in Capturing Atmospheric Rivers and their Performance as a Function of Near-Surface Conditions
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PublisherThe University of Arizona.
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AbstractAccurate estimation of precipitation is critical for hydrology, study of the Earth system, and various water-related applications. This study investigates the performance of various precipitation products in two challenging areas. First, their performance in capturing Atmospheric rivers (ARs) precipitation and extreme events related to ARs and, second, precipitation estimation over cold surfaces with snow and ice on the surface. The first chapter of our study comprises investigation of AR‐related precipitation using 18 years (2001–2018) of globally gridded AR locations. 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], 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 second chapter includes assessment of IMERG’s various products (IMERG products include precipitation estimates from infrared (IR), combined PMW, and their combination) with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the CONUS and using Stage-IV product as reference precipitation.The main results include: (1) Based on global AR-related precipitation analysis: most of the world (except the tropics) experience more intense precipitation from AR‐related events compared to non‐AR events. 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. There is a better agreement among the products over the tropics than in higher latitudes. The largest inconsistencies occur over the Southern Ocean where IMERG shows the highest percentage of contribution of ARs to total precipitation and extreme events and consequently the highest deviation from other products used in this study. It is shown that, overall, pairs of IMERG/CMORPH and GPCP/PERSIANN‐CDR have higher spatial correlations globally, which is expected given the similarities in their retrieval methods. (2) Based on investigation of various products of IMERG for precipitation retrieval over surfaces with and without snow and ice cover: PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow and ice surfaces (1) PMW products (except AMSR-2) show a higher correlation coefficient than IR, (2) IR and PMW precipitation products tend to overestimate precipitation, but at colder temperatures (e.g., Tw<-10oC) PMW products tend to underestimate and IR product continues to show large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.
Degree ProgramGraduate College