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Mircea Grecu, Lin Tian, William S. Olson, and Simone Tanelli

1. Introduction Knowledge regarding the three-dimensional variability of precipitation is essential in the development of precipitation retrieval algorithms from satellite radiometer observations. This is because satellite radiometer observations cannot be uniquely associated with precipitation, and statistical information is required to determine optimal precipitation estimates. Spaceborne radar observations may be used to derive such information. For example, it is anticipated that in the

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Hao Yan and Song Yang

budgets, verification and data assimilation in general circulation model simulations, and hydrologic applications. The accumulated rainfall estimations are required for precipitation climatological research ( Huffman et al. 1997 ; Kidd 2001 ; Mark et al. 2001 ). The methods for estimating precipitation from satellite observations have been evolving and improving for decades. The early rain retrieval algorithms with satellite visible (VIS) and infrared (IR) measurements applied statistical regression

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Shashi K. Gupta, David P. Kratz, Paul W. Stackhouse Jr., Anne C. Wilber, Taiping Zhang, and Victor E. Sothcott

) measurements of reflected and earth-emitted radiation in three broadband channels: a shortwave (SW) channel (0.2–5.0 μ m), a total channel (from 0.2 to >100 μ m), and a thermal infrared (IR) window channel (8–12 μ m). An extensive modeling effort is subsequently used with TOA measurements for deriving surface SW and longwave (LW) fluxes and corresponding flux profiles at multiple levels in the atmosphere. The three LW algorithms discussed in this study are part of the surface-only flux algorithms (SOFA

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Niilo Siljamo and Otto Hyvärinen

the global community, but to the authors’ best knowledge, no detailed information about the algorithm has been published in English. The major weakness of algorithms that use a combination of visual and IR channels for snow detection is that they can be used only during daytime and in cloud-free conditions. The high temporal resolution of the instruments on board a geostationary satellite helps to mitigate this to some extent, as it is much more likely that, for a certain area during one day, at

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

of CloudSat to light rainfall. Described herein is an algorithm and methodology for quantifying profiles of rain rate from measurements of radar backscatter. The technique is designed to augment the existing suite of level-2 environmental data records produced by CloudSat . In light of the multiple challenges (both algorithmic and sensor hardware) associated with harnessing the potential of this new sensor dataset, the results presented here are regarded as preliminary. 2. CloudSat’s 94-GHz

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Donglian Sun, Yunyue Yu, Li Fang, and Yuling Liu

long been an interesting and challenging research area in thermal remote sensing ( Lorenz 1986 ; Nerry et al. 1990 ). During the past decades, significant satellite-based LST efforts have been focused on polar-orbiting satellites by using several types of split-window (11 and 12 μ m) regression algorithms, which are largely statistical–empirical solutions used to determine the relationship between surface skin and at-sensor brightness temperatures ( Price 1984 ; Ulivieri and Cannizzaro 1985

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Lance O’Steen and David Werth

needed that can optimize an extremely complex model with a large number of adjustable parameters. Evolutionary methods provide such an optimization procedure ( Fogel 2000 ). We use “evolutionary” here instead of “genetic” because only mutation operators are employed in the computational algorithm (genetic methods typically allow mating or recombination processes). In addition, real-valued parameters are being perturbed as opposed to the binary string sequences typically employed in genetic algorithms

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Munehisa K. Yamamoto, Shoichi Shige, Cheng-Ku Yu, and Lin-Wen Cheng

. Additionally, the great success of the Tropical Rainfall Measuring Mission (TRMM) has accelerated the development of rain retrieval algorithms such as the Goddard profiling algorithm (GPROF; Kummerow et al. 2015 ) and the Global Satellite Mapping of Precipitation (GSMaP) algorithm ( Aonashi et al. 2009 ). The MWR algorithms used for estimating the rainfall rate over land are based on the scattering effect in the high-frequency channels. This is due to vertically integrated solid hydrometeors above the

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Anita D. Rapp, M. Lebsock, and C. Kummerow

to a common resolution, shows that inhomogeneity effects are still very large. In this paper, we examine the consequences of data convolution and deconvolution on an optimal estimation (OE) retrieval algorithm that uses microwave radiometer measurements to retrieve cloud LWP, wind speed, and total precipitable water (TPW). Results show that data resampling has a substantial effect on the retrieved parameters when compared with retrievals performed on microwave radiometer observations at their

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Shoichi Shige, Yukari N. Takayabu, Wei-Kuo Tao, and Chung-Lin Shie

estimate the four-dimensional latent heating structure over the global Tropics for one month (February 1998). Three different latent heating algorithms, the hydrometeor heating (HH; Yang and Smith 1999a , b , 2000 ), the convective–stratiform heating (CSH; Tao et al. 1993 , 2000 ), and the Goddard profiling (GPROF) heating ( Olson et al. 1999 ) algorithms were used, and their results were intercompared. The HH and GPROF algorithms are microwave radiometer based for the TMI. Only one of the three

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