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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

clouds of extratropical cyclones passing over the windward slopes, high terrain, and lee side of the Olympic Mountains. Observations on the western side of the Olympic Peninsula were concentrated within and near the Quinault River valley, a very wet drainage on the windward side of the Olympic Mountains ( Fig. 1 ). A secondary focus of observations was the Chehalis River valley lying to the south of the Olympic Mountains. On two occasions, when the primary precipitation occurrence was in the Chehalis

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

). This system is also in the process of testing IMERG precipitation estimates. GFMS couples the Variable Infiltration Capacity (VIC) land surface model ( Liang et al. 1994 ) and the Dominant River Tracing Routing (DRTR) model to form the Dominant River routing Integrated with VIC Environment (DRIVE) modeling system. To establish percentile thresholds for flood detection within the GFMS system, the DRIVE model was run retrospectively for 15 years using the TMPA record to provide a history of water

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Daniel J. Cecil and Themis Chronis

(GPROF surface types 3–5, corresponding to “maximum vegetation,” “high vegetation,” and “moderate vegetation”) or ocean (GPROF surface type 1). The “ocean” classification can include large water bodies, for example, the Great Lakes. Sea ice, arid regions, surface snow cover, rivers, coasts, and precipitation scenes are excluded. Each orbit is divided into 5° latitude bins. Statistics are derived separately for each of these bins that has at least 10 land and 10 water pixels without precipitation

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Jackson Tan, Walter A. Petersen, and Ali Tokay

accumulations over a large river basin. As such, for these applications, the random errors are likely to be lower. On the other hand, systematic errors are more pressing as they cannot be removed by statistical methods available to the user. The gauge adjustment, intended to rein in systematic errors, may not be sufficiently resolved to address biases at the pixel level, as it occurs at a monthly time scale over 1° grids. Given the varying performance of each platform, it may be worth investigating whether

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

observed precipitation and gridbox values of the five LSVs across the entire region of China was calculated for all stations and all grid boxes. The results for the Beijing site are shown in Fig. 3a . For the MSLP, the center of the highly correlated areas is located between the lower reaches of both the Yellow River and Yangtze River. This high-correlation zone corresponds to a low pressure system that occurs in boreal summer and is a component of the rain belt that is brought by the summer monsoon

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W.-K. Tao, T. Iguchi, and S. Lang

it began as snow. CalWater ( Ralph et al. 2016 ) has managed a series of field campaigns along the U.S. West Coast to investigate clouds and precipitation, particularly in relation to atmospheric rivers (ARs). The 2015 field campaign occurred over January–March 2015 and captured several offshore AR events. The first case on 5 February 2015 developed offshore and was analyzed by Fairall et al. (2018) . Synoptic-scale weak convective precipitation associated with a midlatitude frontal system

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

, 2936 , . 10.3390/rs11242936 Dowdy , A. J. , and Coauthors , 2019 : Review of Australian east coast low pressure systems and associated extremes . Climate Dyn. , 53 , 4887 – 4910 , . 10.1007/s00382-019-04836-8 ElSaadani , M. , W. F. Krajewski , and D. L. Zimmerman , 2018 : River network based characterization of errors in remotely sensed rainfall products in hydrological applications . Remote Sens. Lett

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

), moderate snow (MDS), low snow (LS), minimal snow (MNS), standing water and rivers (IW), water/land boundary (WLB), water/ice boundary (WIB), and land/ice boundary (LIB). (e),(f) Snow detection scores, i.e., the false alarms (FA), correct rejections (CR), hits (H), and missed detections (MD) over the points that satisfy all the criteria for the comparison. Faint colors indicate points that are further than 110 km from the MRMS radars and are not used in the statistical analysis. The magenta crosses show

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

, 2017 : Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin . Atmos. Res. , 183 , 212 – 223 , . 10.1016/j.atmosres.2016.07.020 Liu , Z. J. , Y. S. Liu , S. S. Wang , X. J. Yang , L. C. Wang , M. H. A. Baig , W. F. Chi , and Z. S. Wang , 2018 : Evaluation of spatial and temporal performances of ERA-Interim precipitation and temperature in Mainland China . J

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

al. (2016) revealed that the IMERG product has more skill in representing daily precipitation than the post-real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) and the ERA-Interim product from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Iran from March 2014 to February 2015. For the midlatitude region of the Ganjiang River basin in southeast China, Tang et al. (2016b) showed that the detection skill of the Day-1 IMERG

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