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Haobo Tan, Jietai Mao, Huanhuan Chen, P. W. Chan, Dui Wu, Fei Li, and Tao Deng

on the Stuttgart neural network [the recurrent neural network (RNN) method]. This paper simulates the brightness temperatures at the 35 frequency channels using upper-air ascent data of 6 yr in combination with the monochromatic radiative transfer model (MonoRTM), and establishes their relationship with the vertical profiles of temperature and humidity based on principal component analysis (PCA) and the stepwise regression method. The accuracy of this retrieval method would be determined by

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Allan J. Clarke and Stephen Van Gorder

Rohlf 1995 ), it is known as the geometric mean regression coefficient. The latter nomenclature follows because is the geometric mean of the estimates and . Henceforth, we write Note that is also the regression coefficient obtained when x is normalized by s x , y is normalized by s y , and the perpendicular distance from the regression line is minimized rather than the vertical distance as in an ordinary least squares fit. A principal component analysis of the normalized variables

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Pascal Matte, David A. Jay, and Edward D. Zaron

water level variations can be captured in analytical solutions of the one-dimensional St. Venant equations. The solutions are based on a decomposition of the nonlinear friction term (e.g., Dronkers 1964 ; Godin 1999 ) into contributions caused by external parameters and nonlinear interactions. Simple regression models, exploiting the results of tidal analysis [whether HA, continuous wavelet transform (CWT), or some other form], can be used to identify and predict the relative importance of these

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Scott J. Richter and Robert H. Stavn

Rohlf 1995 ; Warton et al. 2006 ). The literature is substantial regarding estimating a linear functional relationship, dating back to at least Pearson (1901) , and several different methods have been proposed. A model II analysis was applied by Sverdrup (1916) in analyzing meteorological variables as early as 1916. Ricker (1973) proposed the use of model II regression in fishery studies. Laws and Archie (1981) asserted the advisability of using model II regression for various field studies

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Shin-Hoo Kang, Tae-Young Goo, and Mi-Lim Ou

essential to increase the accuracy of an initial (or first) guess profile as a proper constraint. To construct a physically reasonable initial guess profile, we used the original statistical regression ( Feltz et al. 2007 ), the KLAPS analysis data, and automated weather station (AWS) data. Before the regression, the original static bias spectrum ( Feltz et al. 2007 ) was subtracted from observed spectra. Then, original regressions were made with the regression coefficients ( Feltz et al. 2007 ), and

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Chris T. Jones, Todd D. Sikora, Paris W. Vachon, and John Wolfe

et al. (2006) . Therein, the classification of features in overland SAR images begins with binomial classification using logistic regression. Features are classified as being either 1) forests or hedges or 2) other. A multinomial classifier is subsequently applied to features that fall into the second of these classes. In a similar way, we aimed to classify features in over-ocean SAR images as being either 1) SST front signatures or 2) other. The subjects of our analysis are candidate SST front

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Mark L. Morrissey, Howard J. Diamond, Michael J. McPhaden, H. Paul Freitag, and J. Scott Greene

). In addition, rain gauges are also deployed in the Atlantic on the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoy network ( Bourlès et al. 2008 ) and in the Indian Ocean as part of the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA; McPhaden et al. 2009 ). These buoy-mounted rain gauges ( Serra et al. 2001 , hereafter S01 ) are R. M. Young self-siphoning, capacitance-type gauges and are mounted 3.5 m above the ocean surface

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R. Mínguez, B. G. Reguero, A. Luceño, and F. J. Méndez

more than one function of X in the regression Eqs. (1) or (22) . Consequently, we have investigated some of these more complex models, but we will only show results for those models we have found to work best. Before performing the analysis, the particular regression models we have chosen are presented: For the WLS method ( section 3a ), the response variable is transformed using Eq. (19) and the estimate is calculated based on Eq. (20) . Because the relationship between X and Y is

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Sid-Ahmed Boukabara, Kayo Ide, Narges Shahroudi, Yan Zhou, Tong Zhu, Ruifang Li, Lidia Cucurull, Robert Atlas, Sean P. F. Casey, and Ross N. Hoffman

1. Introduction Observing system experiments (OSEs) are data assimilation (DA) and forecast experiments that measure the impact of an observing system by comparing analysis and forecast results with and without the particular observing system. As described by Boukabara et al. (2016) , observing system simulation experiments (OSSEs) extend the concept of OSEs to proposed future sensors by using observations simulated from the nature run (NR). OSSEs support (i) decision-makers by providing

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Xiao-yong Zhuge, Fan Yu, and Ye Wang

method for the cloud objects (the albedo of more than 0.3) based on the previous work. Because the radiation of ground objects can be strongly affected by aerosols located in the lower atmosphere, for which normalization is difficult because of variations in aerosol phase function, the scope of our method is limited to the normalization with albedos greater than 0.3. In this paper, section 2 introduces the model and data. Section 3 describes the algorithm. The analysis of application scope is

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