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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

North America. Thus, if the goal is to have a global snowfall retrieval, future iterations of the retrieval should look to included additional field campaign measurements of PSDs collected in other precipitation regimes across the globe. A sensitivity analysis using different sources for the temperature input to the retrieval does affect the retrieval by around 10–25%. Thus, for optimal performance of this NN retrieval, users should use the most accurate available temperature input (e.g., Sounding

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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

location, 18° farther north, in the Arctic. First, we evaluate in detail the retrieved snowfall rate on 7 February 2018 and also compare our results to results of other Z e –S relationships from the literature. As observational reference, we use the snowfall rate and accumulated snowfall from Pluvio. For the latter, low wind speeds are of great importance due to otherwise possible undercatchment. Thus, to be sure that we are looking at pure snowfall events and to reduce wind effects in the Pluvio

Open access
Jackson Tan, Walter A. Petersen, and Ali Tokay

Abstract

The comparison of satellite and high-quality, ground-based estimates of precipitation is an important means to assess the confidence in satellite-based algorithms and to provide a benchmark for their continued development and future improvement. To these ends, it is beneficial to identify sources of estimation uncertainty, thereby facilitating a precise understanding of the origins of the problem. This is especially true for new datasets such as the Integrated Multisatellite Retrievals for GPM (IMERG) product, which provides global precipitation gridded at a high resolution using measurements from different sources and techniques. Here, IMERG is evaluated against a dense network of gauges in the mid-Atlantic region of the United States. A novel approach is presented, leveraging ancillary variables in IMERG to attribute the errors to the individual instruments or techniques within the algorithm. As a whole, IMERG exhibits some misses and false alarms for rain detection, while its rain-rate estimates tend to overestimate drizzle and underestimate heavy rain with considerable random error. Tracing the errors to their sources, the most reliable IMERG estimates come from passive microwave satellites, which in turn exhibit a hierarchy of performance. The morphing technique has comparable proficiency with the less skillful satellites, but infrared estimations perform poorly. The approach here demonstrated that, underlying the overall reasonable performance of IMERG, different sources have different reliability, thus enabling both IMERG users and developers to better recognize the uncertainty in the estimate. Future validation efforts are urged to adopt such a categorization to bridge between gridded rainfall and instantaneous satellite estimates.

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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

different set of years used in each, and the less robust sample size in Fig. 4b . The largest concentration of hailstorms is indicated in central Africa, with other hot spots in the central and southeastern United States and in Mexico, Argentina, Bangladesh, and Pakistan. In midlatitude regions ( Fig. 4b ), there are fewer storms satisfying these criteria than in tropical and subtropical regions but there are considerable concentrations in Europe, eastern Eurasia, and central North America. There are

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Stephen E. Lang and Wei-Kuo Tao

split echo-top LUTs. At upper levels (e.g., 7 km, Fig. 16 ), the heating patterns and mean intensities are fairly similar between the GPM and TRMM retrievals with strong areas of heating over equatorial Africa, northern South America, the Maritime Continent, and within a sharp, well-defined ITCZ extending across the central and east Pacific. Other areas of prominent heating in the equatorial Atlantic, the SPCZ, and the equatorial Indian Ocean are also similar in shape and magnitude when allowing

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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

. Wick , J. D. Lundquist , and M. D. Dettinger , 2008 : Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations . J. Hydrometeor. , 9 , 22 – 47 , doi: 10.1175/2007JHM855.1 . 10.1175/2007JHM855.1 Painter , T. H. , and Coauthors , 2016 : The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

: Intercalibrating the GPM constellation using the GPM microwave imager (GMI). Int. Geoscience and Remote Sensing Symp. , Milan, Italy, IEEE, 5162–5165, doi: 10.1109/IGARSS.2015.7326996 . 10.1109/IGARSS.2015.7326996 Xia , Y. , and Coauthors , 2012 : Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products . J. Geophys. Res. , 117 , D03109 , doi: 10.1029/2011JD016048

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Xiang Ni, Chuntao Liu, and Edward Zipser

(a). The median values of DFR at 12 km from these DCCs are calculated in each grid and shown in Fig. 2b . A higher value of DFR indicates the existence of relatively larger ice particle sizes. The median values of DFR at 12 km are higher over central North America, Argentina, central Africa, and northeast China, which is similar to the global distribution pattern of maximum height of 40 dB Z ( Liu and Zipser 2015 ). The median DFR values in central Africa is generally larger than that in the

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Xinxuan Zhang and Emmanouil N. Anagnostou

Central Mountains in France ( Zhang et al. 2013 ), the southern Appalachian Mountains in North America ( Zhang et al. 2016 ), and the Rocky Mountains in Colorado in the western United States ( Nikolopoulos et al. 2015 ). Results based on these studies have shown that the NWP-based adjustments can reduce the CMORPH underestimation of high rain rates and moderate the magnitude-dependent bias. Authors have argued that although the NWP-based adjustment is independent of any ground observation, the

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

phase detection capabilities over snow-covered surfaces ( Fig. 8 ). The probability of hit for the liquid, mixed, and the solid phase is mostly greater than 0.85 and reaches 0.95 over the high altitudes of North America. However, we observe a relatively lower detection rate of around 0.74 for liquid precipitation over the tropical and subtropical regions such as the rain forest of Amazonian and central Africa. The results show that the low probability of detection for the liquid phase is mostly

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