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Biao Geng and Masaki Katsumata

-dB Z echo-top height in each grid was defined as the maximum height of the specified echo intensity in the column of the grid. Echo areas, echo-top heights, and volumetric rainfall values were derived for both convective and stratiform echoes. Relationships between the radar-derived variables and the MJO, ERW, KW, and MRGW events were investigated by performing a simple linear regression (SLR) analysis and a standardized multiple linear regression (MLR) analysis. Each time series of the radar

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Yun-Lan Chen, Chung-Hsiung Sui, Chih-Pei Chang, and Kai-Chih Tseng

structure of the wind field in the subtropics was crucial in elucidating the negative impact of the MJO on the cold surges in the South China Sea. This information that is revealed in the two-dimensional SVD analysis was lost when the one-dimensional RMM scheme is used to represent the MJO. Since the MJO influences EA wintertime rainfall through a process of meridional interaction, we will use the SVD method to resolve the MJO phase structure. The purpose of this paper is to find out if a two

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Joshua Chun Kwang Lee, Anurag Dipankar, and Xiang-Yu Huang

as statistical linear regression or analytical balance operators) between them. These multivariate relationships can be extracted by inserting a single observation of a specific variable and assessing the resulting analysis increments from all other variables. To illustrate, a pseudo-single observation of θ , which is 1 K above the background is inserted at around 1 km altitude near the center of the domain ( Fig. 1a ) with an observation error of 0.2 K. We focus on the prescribed relationship

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Daehyun Kang, Daehyun Kim, Min-Seop Ahn, and Soon-Il An

IO. Fig . 1. The 20–100-day bandpass-filtered anomalies of OLR (W m −2 ; shaded) and column-integrated moisture tendency (kg m −2 ; contour) regressed onto the reference OLR time series from the IO base point (5°S–5°N, 75°–85°E). The column-integrated moisture tendency anomalies are weighted by 1 / [ τ c ¯ ] . c. MJO moisture budget weighted by the convective moisture adjustment timescale To understand MJO propagation under the moisture mode framework, a moisture budget analysis is conducted. We

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Shuguang Wang and Adam H. Sobel

solver and parameters. c. Rain and column-integrated moisture We derive the linear relationship between precipitation and first moisture mode q 1 using the DYNAMO northern sounding dataset. First, empirical orthogonal analysis is performed on 6-hourly moisture anomalies. The leading empirical orthogonal function (EOF; Fig. A1a ) explains more than 66% of total variances. Second, the column-integrated first EOF of moisture anomalies is regressed to surface precipitation. Regression

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Yuntao Wei and Zhaoxia Pu

the statistical regression and composite analysis documented in previous studies: weak and shallow convection followed by strong and deep convection and finally a stratiform-like mode (e.g., Johnson et al. 1999 ; Kiladis et al. 2005 ; Mapes et al. 2006 ; Benedict and Randall 2007 ; Haertel et al. 2008 ; Kiladis et al. 2009 ). During 21–23 July, another synoptic precipitation episode occurs. In the WRF simulation, the peak of this precipitation episode is somewhat stronger and occurs 1 day

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

experiments. c. Analysis method and observations Anomalies are calculated as departures from seasonal climatology, which is defined as annual mean plus the first four harmonics of long-term average. To focus on the intraseasonal variability, most analyses are based on intraseasonal anomalies obtained by applying 20–100-day bandpass filtering to the raw daily mean anomalies. When evaluating the zonal propagation features of the simulated MJO, lead–lag correlations or regressions are calculated for the 10°S

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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

. From the time series of RMM indices over the analysis period (1998–2017 for rainfall and 1988–2017 for others), the corresponding spatial patterns in other fields are obtained on the basis of regressions of unfiltered daily anomalies of respective fields against daily values of RMM indices. From the spatial regression patterns and RMM indices, daily values of MJO-related components are linearly reconstructed for the fields of interest. Variance percentage associated with MJO variability is then

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Xingwen Jiang, Jianchuan Shu, Xin Wang, Xiaomei Huang, and Qing Wu

. 2003 ); monthly mean rainfall from the Global Precipitation Climatology Project (GPCP), version 2.2, monthly rainfall analysis dataset from 1979 to 2015 ( Adler et al. 2003 ); and the rain gauge data (1979–2015) from the latest version [version 3 (V3)] of surface climatological data compiled by the China National Meteorological Information Center. To investigate different roles of convection over various regions, partial correlations (e.g., Behera and Yamagata 2003 ) and partial regression are

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Dongliang Yuan, Xiang Li, Zheng Wang, Yao Li, Jing Wang, Ya Yang, Xiaoyue Hu, Shuwen Tan, Hui Zhou, Adhitya Kusuma Wardana, Dewi Surinati, Adi Purwandana, Mochamad Furqon Azis Ismail, Praditya Avianto, Dirham Dirhamsyah, Zainal Arifin, and Jin-Song von Storch

2012–14 mooring site). Then, the meridional velocity at M00 is used in a linear regression model to approximate the 2014–16 transports estimated from the three moorings. The correlation coefficients between the regression model and the three mooring transports are 0.80 and 0.84 for the free-slip and nonslip conditions, respectively, above the 99.9% significance level, suggesting the success of the regression model. The root-mean-square differences between the regressed and calculated transports

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