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

blizzard storm with the mesoscale MM5 model and a delta-Eddington-type radiative transfer (RT) model to produce a storm-scale database for snowfall retrieval using AMSU-B observations. Noh et al. (2009) used a large number of snowfall profiles from airborne, surface, and satellite radars, as well as an atmospheric RT model ( Liu 1998 ) to generate a regional database for snowfall retrievals using the AMSU-B data. The study used the NESDIS Microwave Land Surface Emissivity Model ( Weng et al. 2001

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

1. Introduction Numerical climate and land–atmosphere models are widely used for providing land–atmospheric predictions at different time scales. These models typically capture both atmospheric thermodynamic processes and cloud microphysics to predict the dynamics of land–atmosphere water and energy fluxes. To improve the predictions of land–atmosphere state variables and parameters, a common practice is to assimilate observations from in situ gauges, radiosondes, and satellite measurements

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