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Emmanouil N. Anagnostou and Christian Kummerow

studies have validated their retrieved rainfall with independent ground “truth” measurements, none of them has ever reported validations of their classification estimates. Unlike previous techniques that focus on the radiance information from a single pixel, this study seeks to classify rainfall based upon the spatial variability of rainfall. Convective precipitation regimes are associated with high instabilities and strong updrafts and downdrafts, which result in significant turbulence in the clouds

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Malarvizhi Arulraj and Ana P. Barros

). Rainfall regime detection and classification are therefore a key step in the retrieval work flow prior to rainfall estimation proper (see section S1 of the supplemental material for a review of existing rainfall classification strategies developed for TRMM and GPM precipitation retrieval algorithms). Orographic processes and/or precipitation regimes in regions of complex topographic transitions are not explicitly addressed in operational algorithms because of the complex microphysics of orographic

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M. Thurai, V. N. Bringi, and P. T. May

the methodology a. Monsoon regime case Here, we compare the DSD-based technique with the texture-based algorithm of SHY . The latter uses the intensity and sharpness of the peaks of radar reflectivity relative to the “background” reflectivity, with the peaks indicating the centers of the convective regions. They used the C-band Tropical Ocean Global Atmosphere (TOGA) radar located in Darwin to fine tune their convective rain classification using gridded data for the month of February 1988. In

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David S. Henderson, Christian D. Kummerow, David A. Marks, and Wesley Berg

. Elsaesser et al. (2010) describe such a methodology to classify the level of organization within a precipitating system through the use of a k -means clustering classification. The k -means clustering method classifies precipitation regimes based on a cloud’s properties within 1° × 1° boxes along the PR swath. These precipitating systems exhibit self-similar characteristics across the global oceans and thus were recommended for use in validation exercises because of their applicability across

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Guillaume Penide, Alain Protat, Vickal V. Kumar, and Peter T. May

, Australia. Note that both classification methods were originally built (and therefore are tuned) for the Darwin region and would need to be tested and tuned to other climate regions (other than the tropics) in order to extend the results presented herein to other regions. To extract consistent conclusions about each classification method and rain type, probability density functions (PDFs) of reflectivity ( Z ), rain rate ( R ), median volume diameter ( D 0 ), and generalized intercept parameter ( N w

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T. Narayana Rao, N. V. P. Kirankumar, B. Radhakrishna, D. Narayana Rao, and K. Nakamura

these processes. The algorithm has been applied on 546 h of rain data collected with the LAWP, covering three principal rainy seasons. The rainfall systems are separated into five rain regimes, namely, shallow convection, shallow stratiform, deep convection (simply convection), transition, and deep stratiform (simply stratiform). Simultaneous surface rainfall observations made with an optical rain gauge are also grouped into five rain regimes, based on profiler classification. A summary of the

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Guang Wen, Alain Protat, Peter T. May, Xuezhi Wang, and William Moran

thermodynamic properties of the major tropical western pacific cloud regimes . J. Climate , 18 , 1203 – 1215 , doi: 10.1175/JCLI3326.1 . Keenan, T. , 2003 : Hydrometeor classification with a C-band polarimetric radar . Aust. Meteor. Mag. , 52 , 23 – 31 . Li, Z. , 2011 : Applications of Gaussian mixture model to weather observations. Ph.D. dissertation, University of Oklahoma, 206 pp . Lim, S. , Chandrasekar V. , and Bringi V. N. , 2005 : Hydrometeor classification system using dual

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T. Narayana Rao, N. V. P. Kirankumar, B. Radhakrishna, D. Narayana Rao, and K. Nakamura

the proper Z – R relation for accurate rainfall estimation, unambiguous distinction of precipitating systems is essential. The rationale for classification of precipitating systems into different rain regimes was recognized long ago, and accordingly several classification schemes have been developed using a variety of instruments. Many of these schemes depend on a single rain parameter (rainfall rate R or reflectivity factor Z or vertical air motion w ) for discriminating two principal rain

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Bijoy Vengasseril Thampi, Takmeng Wong, Constantin Lukashin, and Norman G. Loeb

classification and regression tool for compound classification and QSAR modeling . J. Chem. Inf. Sci. , 43 , 1947 – 1958 , doi: 10.1021/ci034160g . 10.1021/ci034160g Tett , S. F. B. , D. J. Rowlands , M. J. Mineter , and C. Cartis , 2013 : Can top-of-atmosphere radiation measurements constrain climate predictions? Part II: Climate sensitivity . J. Climate , 26 , 9367 – 9383 , doi: 10.1175/JCLI-D-12-00596.1 . 10.1175/JCLI-D-12-00596.1 Verikas , A. , A. Gelzinis , and M. Bacauskiene

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Natalie Midzak, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill

accomplish more detailed habit classifications and gain an understanding of their properties, the combination of retrievals from lidar and polarimeter are necessary. In this paper, collocated lidar and polarimeter observations of cirrus during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) campaign collected over the continental United States and Gulf of Mexico are combined and analyzed using a K-means clustering technique. The results of

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