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Yanting Wang and V. Chandrasekar

summarized first in section 2 with the state-of-the-art Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) algorithm as an example. Then, the new estimator is described in section 3 to deal with wrapped phases, and an adaptive algorithm for its implementation is developed in section 4 . Its application to radar observations from CSU–CHILL S-band radar and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) X

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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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Jonathan J. Gourley, Pierre Tabary, and Jacques Parent du Chatelet

forecasts of streamflow ( Faures et al. 1995 ; Frank et al. 1999 ; Ogden et al. 2000 ; Droegemeier et al. 2000 ). The accuracy and timeliness of flash flood and river flood forecasts are limited by the accuracy of radar-derived precipitation. Steiner and Smith (2002) provide an excellent summary of existing techniques to mitigate radar returns from ground clutter. They conclude that their algorithm yields improvement but fails in situations with strong, widespread clear air echoes and when anomalous

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Keith D. Hutchison, Robert L. Mahoney, Eric F. Vermote, Thomas J. Kopp, John M. Jackson, Alain Sei, and Barbara D. Iisager

products ( Savtchenko et al. 2004 ). VIIRS will collect data in 22 bands that will be used to create 23 data products ( Hutchison and Cracknell 2006 ). A key product created with both MODIS and VIIRS sensors is the cloud mask, which is generated using sophisticated logic that includes a series of cloud detection tests. Although the MODIS cloud mask (MCM) algorithm has evolved since the launch of MODIS on the Terra spacecraft in December 1999 ( Ackerman et al. 1997 , 2002 ), the VIIRS cloud mask (VCM

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Andrei Bourchtein

includes also the reference to BDM balance equations. One of the problems of NMI method, discovered by Daley (1978) , is divergence of iterative algorithm for solution of balance equations in the case when the geopotential field is held unchanged (the so-called geopotential constrained initialization). The discussions about the reasons behind this problem were centered about two possibilities: convergence properties of the applied iterative algorithm and mathematical nature of the balance

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

regimes, the stratiform and convection. For instance, to delineate these rain regimes, Baldwin et al. (2005) used a threshold rain rate of 5 mm h −1 , following Johnson and Hamilton (1988) . Similarly, reflectivity thresholds of 39 and 40 dB Z are employed to distinguish convection from widespread stratiform rain in the Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) algorithm ( Awaka 1998 ) and in texture algorithms based on scanning radar data ( Steiner et al. 1995 ). The

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Evan Ruzanski, John C. Hubbert, and V. Chandrasekar

SMPRF algorithm, originally developed for ionospheric measurement applications, for range–velocity mitigation. In the SMPRF algorithm, several different PRTs are chosen and are concatenated to form a block of PRTs, which is repeated in time. Thus, the time length of the block of PRTs is (neglecting the transmit pulse width) T = T 1 + T 2 + . . . + T i , where i is the number of unique PRTs in the block and T is typically chosen to be equal to or to exceed the desired maximum unambiguous

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Brenda Dolan and Steven A. Rutledge

-based hydrometeor identification (HID) algorithms have been applied to data from primarily S-band (10–11 cm) radars ( Vivekanandan et al. 1999 ; Liu and Chandrasekar 2000 ; Straka et al. 2000 , hereafter S00 ; Ryzhkov et al. 2005 ; Tessendorf et al. 2005 ). S00 provide an extensive overview of what has been accomplished in terms of bulk hydrometeor classification, particularly at S band. Their overview presents expected variable ranges for different hydrometeor types based on previous modeling and

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Shanghong Li and Robert Lund

many variants of the general subsegmentation algorithm ( Hawkins 1976 discusses an attractive one), it is usually easy to construct multiple changepoint configurations that evade detection by any specific subsegmenting algorithm. In particular, subsegmentation algorithms have difficulty identifying two changepoint times that occur close together, especially when the mean shifts induced by the two changepoints take opposite signs as this mimics a “run of outliers.” Also, as the subsegmented series

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Bin Pei and Firat Y. Testik

1. Introduction The improvement of radar rainfall estimation has long been an active topic, as radar measurements play an important role in various applications related to meteorology, hydrology, and agriculture, among others ( Sene 2009 ; Testik and Gebremichael 2010 ). Over the years a large number of dual-polarization radar algorithms for rain-rate estimations have been developed and tested (e.g., Seliga and Bringi 1976 ; Bringi and Chandrasekar 2001 ). These algorithms can be classified

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