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Jeffrey L. Anderson and Nancy Collins

least squares framework for implementing most variants of ensemble filters that have been described in the literature. In this framework, it is possible to completely describe an ensemble filter by discussing only the impact of a single scalar observation on a single state variable. Several filters have been implemented using this framework in both idealized and large models ( Zhang et al. 2005 ). However, the sequential nature of the algorithm has led to concerns that it cannot be practically

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Mircea Grecu, William S. Olson, Stephen Joseph Munchak, Sarah Ringerud, Liang Liao, Ziad Haddad, Bartie L. Kelley, and Steven F. McLaughlin

uniformly calibrated rain algorithms for all radiometers in the GPM constellation ( Kummerow et al. 2011 ). The constellation of radiometers provides the temporal sampling necessary to achieve the mission objective. During the Tropical Rainfall Measuring Mission (TRMM) era, several algorithms for estimating precipitation from a combination of radar and microwave radiometer observations were developed. The TRMM observatory included a single-frequency (Ku band) cross-track scanning radar and a

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Patrick N. Gatlin and Steven J. Goodman

1. Introduction The purpose of this study is to examine the utility of using trends in total lightning activity to help diagnose the severe weather potential of a thunderstorm. An algorithm derived from total lightning data measurements (both cloud and ground flashes) has been developed to help gauge thunderstorm intensity. This algorithm attempts to predict severe weather without the use of any radar observables. The proposed total lightning algorithm is based upon the observations of rapid

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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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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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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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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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