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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

/S from March 2014 to the present ( Huffman et al. 2015 ). Table 1. Summary of TRMM and GPM multisatellite products, resolutions, availability, and latency. The TRMM level-3 multisatellite product TMPA has a near-real-time version that is calibrated with a gauge climatology and a research product that uses a global network of gauges to calibrate the product. GPM level-3 IMERG has three versions: the early run is produced with a latency of 4–5 h after satellite acquisition, the late run uses more

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Daniel J. Cecil and Themis Chronis

and Θ 89 from the literature have proven effective over the years. We reexamine them here because it has become convenient to apply our methods to vastly larger sample sizes than were used in the previous studies. Our optimal coefficients (producing the smallest contrast between land and water surfaces, and thus less ambiguity related to surface type) for PCT 37 and PCT 89 are slightly different from those that have already been widely used. Our analysis shows that a broad range of coefficient

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Stephen E. Lang and Wei-Kuo Tao

pairs of rain-normalized convective and stratiform diabatic heating profiles [i.e., Q 1 or the apparent heat source; Yanai et al. (1973) ], one pair for land and one for ocean, obtained from composites of both GCE model ( Tao and Simpson 1993 ) simulations and sounding budget calculations; a single additional pair was later added for shallow heating. Using surface rainfall rates and the proportion of stratiform rain, cloud heating profiles could then be retrieved remotely from satellite or other

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Xiang Ni, Chuntao Liu, and Edward Zipser

geographical distribution of median microphysical parameters at 12 km is shown in Fig. 8 . Consistent with high DFR values in Fig. 3b , values of D m over land are typically larger than those over ocean ( Fig. 8a ). G. M. Heymsfield et al. (2010) examined vertical updrafts of different types of tropical deep convection using airborne observations. Results showed that the maximum updrafts of continental convection are stronger than oceanic convection above 10 km. Figure 8 shows larger particle sizes

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

atmospheric column, the environmental parameters to be used as cloud morphology predictors in the a priori database are chosen to correspond to the time step preceding their coupled precipitation rates. f. Database The above datasets are grouped to build the a priori knowledge for GPROF retrieval. Each of 14 surface types is treated separately. Data count distributions of eight land surface classes occurring over the domain of this study are given in Fig. 3 as a function of TPW and 2-m temperature

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Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

radiometers on board the Soil Moisture Active Passive (SMAP) satellite and the Soil Moisture and Ocean Salinity (SMOS) satellite have a frequency of 1.4 GHz. The Advanced Scatterometer (ASCAT) on board the MetOp satellites operates at ~5.2 GHz. In contrast, the primary frequencies to measure the ice scattering over land from passive microwave radiometers are around 85 GHz and higher (e.g., 150 and 183 GHz). The lower frequencies used for soil moisture measurement can penetrate a thicker layer of soil and

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

land radiation at higher frequencies (e.g., 85 GHz) is used though it is strongly affected by ice scattering near the top of the clouds. ( Petković and Kummerow 2017 ). IR sensors contributing to satellite precipitation products use the information of cloud-top temperature to estimate the surface precipitation. Thus, the top-down view of satellites leads to strong consideration of information in upper atmospheric levels to estimate surface rainfall potentially missing evaporation effects in PMW

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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

estimate the particle size more accurately than a single-frequency radar so that we can improve the estimates of rainfall rate and identify snow precipitation regions. In fact, by using the difference in the scattering and attenuation properties of liquid and solid water particles between Ku- and Ka-band electromagnetic (EM) waves, it is possible to estimate the mean diameter of precipitation particles once an appropriate particle size distribution (PSD) model is chosen. Since the mean particle size

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Wesley Berg, Stephen Bilanow, Ruiyao Chen, Saswati Datta, David Draper, Hamideh Ebrahimi, Spencer Farrar, W. Linwood Jones, Rachael Kroodsma, Darren McKague, Vivienne Payne, James Wang, Thomas Wilheit, and John Xun Yang

calibration differences corresponding to the coldest observable temperatures but also does not rely on the limited availability and regional distribution of coincident observations. c. Double differences over unpolarized vegetated land (window channel warm scenes) To determine sensor calibration differences for warm scenes, double differences were computed for depolarized areas in highly vegetated regions, such as the Amazon rain forest, using variations of the approach developed by Brown and Ruf (2005

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

available only over CONUS, all the results based on this dataset are valid at a regional scale. While all sun synchronous GMI orbits over CONUS have been considered, only ATMS ascending orbits (between 0600 and 1300 UTC), closest in time to the SNODAS reference time (0600 UTC), have been selected. The dataset has been built following the same procedures used for the development and validation datasets, obtaining a snow-cover occurrence index, a land fraction index (since SNODAS provides information only

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