Differences in scale-dependent, climatological variation of mean areal precipitation based on satellite and radar-gauge observations

摘要

This study compares the scale-dependent variation in hourly Mean Areal Precipitation (MAP) derived from a satellite (S) and a radar-gauge (R) Quantitative Precipitation Estimate (QPE), and seeks to explain the S-R differences on the basis of errors in the satellite QPE. This study employs an analytical framework to estimate the coefficient of variation (CV) of MAP for window sizes ranging from 4 km to 512 km, using the rainfall fields of the CPC MORPHing (CMORPH) satellite QPE and a radar-gauge Multisensor QPE (MQPE) over five domains centered in Texas, Oklahoma and New Mexico. CV values based on the analytical framework are first corroborated using empirical estimates. Then, S-R differences in CV are analyzed to determine the contributions of the S-R differences from empirical fractional coverage (FC) and spatial correlograms. Subsequently, sensitivity analyses are performed to isolate the impacts of false detections and long-term, magnitude-dependent bias in CMORPH on the inaccuracies in FC and correlograms. The results are stratified by domain and season (winter and summer) to highlight the impacts of differential accuracy of CMORPH under diverse rainfall regimes. Our analyses reveal that CMORPH-based CV tends to plateau at larger window sizes (referred to as critical window size, or CWS), and is broadly higher in magnitude. The mechanisms underlying the CV differences, however, differ between winter and summer. Over the winter, CMORPH suffers from severe underdetection, which yields suppressed FC across window sizes. This underestimation of FC, together with the lack of resolution of internal rainfall structure by CMORPH, leads to an magnification of both CWS and the magnitude of CV. By contrast, over the summer, widespread false detections in CMORPH lead to inflated FC, which tends to suppress CWS but this effect is outweighed by the opposing impacts of inflated outer and inner scales (i.e., distance parameters of indicator and conditional correlograms). Moreover, it is found that introducing false detection to MQPE via a simple expansion scheme is effective in increasing the FC and inner scale in tandem, and that histogram differences are a rather minor contributor to the S-R difference in inner scale. The implications of the findings for disaggregating climate model projection and data fusion are discussed. Published by Elsevier B.V.