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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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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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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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Stephen E. Lang and Wei-Kuo Tao

great importance, LH is hard to measure directly. However, the launch of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al. 1996 ; Kummerow et al. 2000 ) in November of 1997 made it possible to obtain quantitative precipitation measurements over the global tropics and, as a consequence of their close connection, estimates of tropical LH as well. In support of this effort, five different LH algorithms were developed to retrieve profiles of LH using TRMM rainfall products

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Matthew J. Menne and Claude N. Williams Jr.

, the test results can then be used to adjust a series so that it more closely reflects only variations in weather and climate. Numerous approaches have been employed to detect discontinuities in climate series ( Peterson et al. 1998a ), and comparison studies have recently proliferated (e.g., Ducré-Robitaille et al. 2003 ; DeGaetano 2006 ; Reeves et al. 2007 , hereafter R07 ). The goal of this work is to describe an automated homogenization algorithm for monthly data that builds on the most

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Kwang-Y. Kim and Gerald R. North

-dimensional statistical predictor. Prediction schemes may be classified into one of the three categories—statistical, dynamical, and statistical–dynamical. In El Niño predictions, examples of statistical schemes include Barnett et al. (1988) and Xu and von Storch (1990) . Examples of dynamical schemes include Cane et al. (1986) and Latif and Flügel (1991) . Inoue and O’Brien (1984) used a combination of the two called the statistical–dynamical scheme. This study presents a statistical prediction algorithm

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Byung-Ju Sohn and Eric A. Smith

July 1987, a number of PW retrieval algorithms have been developed for various combinations of passive microwave (PMW) brightness temperatures ( T B ) at frequencies in the vicinity of the 22.235-GHz line, the actual SSM/I channel-3 frequency. These consist of various statistical and physical methods in which the former use observed PW or related measurements to produce empirically derived coefficients within the algorithm’s PW formulation, while the latter are formulated in terms of substantiated

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Kwang-Y. Kim and Gerald R. North

1. Introduction This study examines the performance of a general statistical prediction algorithm developed in Kim and North (1998) . In particular, the algorithm is applied to detecting the spatiotemporal patterns of El Niño. El Niño is the largest, short-term interannual climatic fluctuation over the tropical Pacific Ocean characterized by the occurrence of a warm water mass in the eastern Pacific. Prediction of El Niños is of great importance, since they have significant impacts on the

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Ehud Strobach and Golan Bel

regression method also relies on a few inherent assumptions, such as the normal distribution of the prediction errors (therefore, defining the optimal coefficients as those minimizing the sum of squared errors) and the independence of the ensemble member predictions. Sequential learning algorithms (SLAs, also known as online learning) ( Cesa-Bianchi and Lugosi 2006 ) weight ensemble members based on their past performances. These algorithms were shown to improve long-term climate predictions ( Monteleoni

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L. L. Stowe, C. G. Wellemeyer, H. Y. M. Yeh, T. F. Eck, and The Nimbus-7 CLOUD DATA PROCecessing TEAM

VOLUMEI JOURNAL OF CLIMATE MAY 1988Nimbus-7 Global Cloud Climatology. Part I: Algorithms and Validation L. L. STOWE,* C. G. WELLEMEYER,** T. F. ECK,t H. Y. M. YEH**'~ AND THE NIMBUS-7 CLOUD DATA PROCESSING TEAM****NOAA/National Environmental Satellite, Data, and Information Service, Washington, DC**ST Systems Corporation, Lanham, Maryland*Science Applications Research, Lanham, Maryland

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