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Kimberly L. Elmore

hydrometeor classification algorithm (HCA; Zrnić et al. 2001 ; Straka et al. 2000 ; Park et al. 2009 ) for use in warm-season convective weather; it, along with a melting-layer detection algorithm (MDA) and a rainfall-estimation algorithm, is the only such algorithm currently scheduled for the initial deployment of the dual-polarization (dual pol) WSR-88D network. Yet, a top-ranked expectation recently expressed by the operational forecast community is the ability to “determine the precipitation type

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Laura Bianco, James M. Wilczak, and Allen B. White

clearly defined PBL at most hours, ambiguities may sometimes exist. These ambiguities can arise for example from weak levels of turbulence within the PBL, intermittent turbulence, and clouds. A particularly difficult period of the day is in the late afternoon with the collapse of the boundary layer. At this time the layer below the inversion is intermittently turbulent before it becomes fully decoupled from the inversion, and the depth of the PBL is nebulous. An automatic algorithm for detecting PBL

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Martin P. Tingley and Peter Huybers

by the regularized expectation–maximization (RegEM) algorithm of Schneider (2001) , which has been used in a number of climate field reconstruction studies ( Rutherford et al. 2003 ; Zhang et al. 2004 ; Rutherford et al. 2005 ; Mann et al. 2007 , 2008 ; Steig et al. 2009 ). There are both benefits and limitations to this methodology, which we will partly address here and in more detail in Tingley and Huybers (2010 , hereafter Part II) . An alternative analysis strategy can be formulated

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Francesco Nencioli, Changming Dong, Tommy Dickey, Libe Washburn, and James C. McWilliams

on the statistical characterization of mesoscale eddy activity within specific regions through the analysis of satellite measurements or results from numerical models (i.e., Isern-Fontanet et al. 2003 ; Morrow et al. 2004 ; Chelton et al. 2007 ; Chaigneau et al. 2008 ; Doglioli et al. 2007 ). A suitable definition of an eddy and the implementation of an algorithm to automatically identify and track mesoscale and submesoscale features are fundamental to study eddy activity from large datasets

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Mariusz Starzec, Cameron R. Homeyer, and Gretchen L. Mullendore

-altitude levels from three-dimensional composites of multiple ground-based radars to distinguish between convective, stratiform, and anvil clouds. While the aforementioned studies incorporated vertical storm information in the SHY procedure, the primary classification between convective and stratiform precipitation in SHY-based algorithms and similar approaches is completed using a single low-altitude map of . For research purposes such as quantitative precipitation estimation, SHY-based methods applied to

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John R. Walker, Wayne M. MacKenzie Jr., John R. Mecikalski, and Christopher P. Jewett

cooling rates, CI forecast lead times of ≥30 min can be provided before the first 35-dB Z radar reflectivity echo is detected. That study provided a framework for the development of a satellite-only CI forecasting system that could provide near-term CI nowcasts in radar-void areas; this algorithm was named the Satellite Convection Analysis and Tracking (SATCAST) system [based on Mecikalski and Bedka (2006) ]. The original SATCAST had three main components. The first component identified only cumulus

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Ana P. Barros and Kun Tao

the formulation of satellite-based rainfall estimation algorithms (e.g., Richards and Arkin 1981 ; Adler and Negri 1988 ; Arkin and Ardanuy 1989 ; Ferriday and Avery 1994 ; Kummerow and Giglio 1995 ; Levizzani et al. 1996 ; Ferraro 1997 ; Miller et al. 2001 ; Kummerow et al. 2001 ; Marzano et al. 2004 ; Joyce et al. 2004 ; Hong et al. 2004 , 2005 ; Huffman et al. 2007 ; among others). The goal of this work is to address the challenge posed by near-instantaneous narrow

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Igor Polonsky and D. M. O’Brien

mixing in the weak CO 2 band ( Hartmann et al. 2009 ). Whereas the benchmark retrieval algorithm for OCO is the “full physics” algorithm ( Bösch et al. 2006 ), the approach advocated by O’Brien and Rayner (2002) might be described as “minimal physics,” meaning that the number of retrieved parameters is comparable to the information content of the spectra. The algorithm described in this paper adapts just the correlation component of the approach outlined above, and therefore might be termed the

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Jari-Petteri Tuovinen, Harri Hohti, and David M. Schultz

was obtained with greater detail than was possible from traditional operational datasets. In another example, Hyvärinen and Saltikoff (2010) compared hail photos in Finland from the photo-sharing service Flickr to output from algorithms for hail detection from dual-polarimetric radar, showing the possible utility of such nontraditional online data. Finally, crowd-sourcing applications, like mobile weather apps, are the newest approach allowing anyone to send their real-time weather observations

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Arthur A. Small III, Jason B. Stefik, Johannes Verlinde, and Nathaniel C. Johnson

1. Introduction This paper presents a decision algorithm developed to improve the efficiency of scientific data collection in stochastic environments. The Atmospheric Radiation Measurement (ARM) Program within the climate science programs of the U.S. Department of Energy has objectives involving the routine collection of data in situ from particular cloud formations by means of specially equipped aircraft (more information available online at http

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