Differences in scale-dependent, climatological variation of mean areal precipitation based on satellite and radar-gauge observations Yu Zhang a,⇑ , Dong-Jun Seo b , Emad Habib c , Jeffrey McCollum d a Office of Hydrologic Development, NOAA National Weather Service, Silver Spring, MD, United States b University of Texas at Arlington, Arlington, TX, United States c University of Louisiana, Lafayette, LA, United States d FM Global, Center for Property Risk Solutions, Research Division, Norwood, MA, United States article info Article history: Received 2 August 2014 Received in revised form 27 November 2014 Accepted 29 November 2014 Available online 27 December 2014 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Marco Borga, Associate Editor Keywords: Satellite Precipitation Variability Error summary 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 analyt- ical 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 mag- nitude. 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 indi- cator 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. 1. Introduction Satellite-based Quantitative Precipitation Estimates (QPEs), for their wide coverage and spatial continuity, have seen applications in water budget analysis, flood forecasting, soil moisture predic- tions and hydrologic model calibration for regions where ground sensors are lacking or deemed inadequate (Scofield and Kuligowski, 2003; Su et al., 2008; Tobin and Bennett, 2010; Habib et al., 2012b; Zhang et al., 2013; Wu et al., 2014). Evolving space-borne sensor technology and precipitation estimation techniques promise further refinement in the spatio-temporal res- olutions of Satellite-based QPEs (henceforth referred to as SQPEs) and enhancements in their accuracy. For example, the recent launch of the Global Precipitation Measurement (GPM, Kidd and Huffman, 2011) satellite will refine the grid mesh of the multi-satellite products from 1/4 degree to 10 km, and improve their quality through cross-calibration of satellite sensors. Equally notable is that several satellite QPEs (e.g., the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis, or TMPA; Huffman et al., 2007) have accumulated relatively long archives (>10 years), making them a potentially viable source of climate information, especially where a long-term, high resolution precipitation archive is absent. http://dx.doi.org/10.1016/j.jhydrol.2014.11.077 0022-1694/Published by Elsevier B.V. ⇑ Corresponding author. E-mail address: yu.zhang@noaa.gov (Y. Zhang). Journal of Hydrology 522 (2015) 35–48 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol