Search Results

You are looking at 1 - 10 of 14 items for :

  • Regression analysis x
  • Years of the Maritime Continent x
  • Refine by Access: Content accessible to me x
Clear All
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

Open access
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

Open access
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

Full access
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

Full access
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

Full access
Andung Bayu Sekaranom and Hirohiko Masunaga

part of the land ( Qian 2008 ). The environmental characteristics specific to the MC are responsible not only for the high annual precipitation, which is approximately 1500–3000 mm yr −1 over land ( As-Syakur et al. 2013 ), but also for the high frequency of extreme precipitation that can trigger hazards over the MC. An analysis of 11-yr records of global flood frequency in 1998–2008 conducted by Adhikari et al. (2010) shows that Indonesia (which constitutes the largest part of the MC) is listed

Full access
Yan Zhu, Tim Li, Ming Zhao, and Tomoe Nasuno

summary is given in section 5 . 2. Data, methodology, and model description a. Data Primary observational datasets used in the present analysis include 1) interpolated outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites ( Liebmann and Smith 1996 ) and 2) atmospheric three-dimensional fields including zonal and meridional wind ( u and υ ), temperature ( T ), pressure vertical velocity ( ω ), geopotential height ( ϕ ), and specific

Full access
Shijian Hu, Ying Zhang, Ming Feng, Yan Du, Janet Sprintall, Fan Wang, Dunxin Hu, Qiang Xie, and Fei Chai

freedom of 12 is indicated in blue. The significant lagged correlation between the southeastern Indian Ocean salinity anomaly and the PDO index makes it possible to assess its predictability. Here we propose a statistical prediction model based on a regression analysis: (5) S SEIO ′ ⁡ ( τ ) = γ 0 + γ 1 PDO ⁡ ( τ − 10 ) , where S SEIO ′ ⁡ ( τ ) is the mean salinity anomaly in the southeastern Indian Ocean at month τ , and γ 0 and γ 1 are coefficient estimates for a multilinear regression of the

Open access
Jieshun Zhu, Arun Kumar, and Wanqiu Wang

2008 ). The early models used in these MJO predictability studies, however, were generally poor in simulating the MJO (e.g., Zhang et al. 2006 ). During the recent years when extensive hindcast datasets (e.g., the S2S hindcast dataset) became available, the MJO predictability was reevaluated (e.g., Rashid et al. 2011 ; Kim et al. 2014 ; Neena et al. 2014 ; Liu et al. 2017 ). For example, Neena et al. (2014) conducted a comprehensive analysis about the MJO predictability based on hindcasts by

Free access
Wei-Ting Chen, Shih-Pei Hsu, Yuan-Huai Tsai, and Chung-Hsiung Sui

, complex coastlines, and steep topography ( Birch et al. 2015 ). This region is surrounded by islands and continents with complex topography, which cultivates prominent diurnal variability of convection. Periodically and zonally propagating modes of tropical convection at different temporal and spatial scales can be found active over the SCS–MC. These are regarded as convectively coupled tropical waves based on the theoretical study of Matsuno (1966) and the analysis of Wheeler and Kiladis (1999

Full access