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Hristo G. Chipilski, Xuguang Wang, and David B. Parsons

et al. 2017 ). The dynamical significance of convective outflow boundaries has prompted the scientific community to create automated algorithms for identifying and tracking these features. The earliest algorithm developed for this purpose was entirely based on observational data and closely connected to the procurement plans for the Next Generation Weather Radar (NEXRAD) system (e.g., Crum and Alberty 1993 ). In particular, Uyeda and Zrnić (1986) as well as Smith et al. (1989) were the first

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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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Grant W. Petty and Ke Li

1. Introduction a. Historical overview Passive microwave observations from space have been utilized to estimate precipitation on at least an experimental basis since the 1970s ( Wilheit 1976 ; Weinman and Guetter 1977 ; Wilheit et al. 1977 ; Spencer et al. 1983 ; Spencer 1984 ). More sophisticated and better-calibrated rain-rate retrieval algorithms began to emerge after the launch of the first Special Sensor Microwave Imager (SSM/I) in 1987 ( Adler et al. 1991 ; Barrett et al. 1994

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V. S. Komarov, A. V. Lavrinenko, A. V. Kreminskii, N. Ya Lomakina, Yu B. Popov, and A. I. Popova

interpolation point ( Gandin and Kagan 1976 ). Nowadays two approaches to the problem of data assimilation can be identified, namely, variational ( Le Dimet and Talagrand 1985 ; Courtier 1997 ) and dynamic–stochastic, based on a Kalman filter algorithm ( Ghil and Malanotte-Rizzolli 1991 ; Cohn 1997 ). The first approach is based on the general variational problem formulation and is solved for conjugate models to minimize the corresponding functional using the well-known results of optimal estimation

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Stuart A. Young and Mark A. Vaughan

level 2 data products from the lidar are the locations of atmospheric regions containing particulate matter (clouds and aerosols), the identification of these particles according to type, and profiles and layer integrals of particulate backscatter and extinction in these regions. This paper focuses on the fully automated retrieval of profiles of particulate backscatter and extinction. Note that the level 2 algorithms covered here are applied to measurements made by a single instrument (CALIOP

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Martin P. Tingley and Peter Huybers

2006 ; Jansen et al. 2007 ; Jones et al. 2009 ). Tingley and Huybers (2010 , hereafter Part I) developed a hierarchical Bayesian approach to reconstructing climate fields, referred to as BARCAST for “A Bayesian algorithm for reconstructing climate anomalies in space and time.” This approach is based on specifying parametric forms for the spatial covariance and temporal evolution of the field as well as the relationships between the data types and the field. (See Part I for a detailed

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Yalei You, Nai-Yu Wang, Ralph Ferraro, and Patrick Meyers

). Researchers have historically focused more on the precipitation retrieval algorithm development for imagers. For example, numerous algorithms, either using regression model or Bayesian technique, have been developed for the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS; e.g., Spencer et al. 1989 ; Liu and Curry 1992 ; Petty 1994 ; Ferraro and Marks 1995 ; McCollum and Ferraro 2003 ; Sanò et al. 2013 ; You et al. 2015 ). Similarly, a variety of the

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R. Andrew Weekley, R. Kent Goodrich, and Larry B. Cornman

-processing algorithms is to mimic the clustering and classification processes that the human expert performs. Examples of such algorithms used in the atmospheric sciences include Cornman et al. (1998) and Weekley et al. (2003 , 2004 , 2010) . In the former, the images consisted of Doppler wind profiler radial velocity versus range; in the latter, the images were the time series of anemometer data. These papers described feature detection and quality control algorithms based on image analysis and fuzzy logic

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Jackson Tan, George J. Huffman, David T. Bolvin, and Eric J. Nelkin

1. Introduction The Global Precipitation Measurement (GPM) mission is a joint satellite mission led by NASA and JAXA with contributions from U.S. and international partners “to unify and advance precipitation measurements from space for scientific research and societal applications” ( Hou et al. 2014 ). GPM mission efforts include instrument calibration, algorithm development, data production, ground validation, and science and societal applications. Central to these efforts is the GPM Core

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Shinju Park and Frédéric Fabry

) identified the following factors as possible sources of uncertainty of the retrieval: 1) the extreme noisiness of the measured phase field and 2) simple assumptions used in the retrieval algorithm ( section 2 ). Understanding and quantifying the noise introduced by the different sources of uncertainty affecting the refractivity retrieval will enable the development of an improved algorithm. By means of a phase simulator, we intend to assess in this study 1) which types of phase errors have a

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