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multivariate treatment reduce the sample size in some bins below levels that are appropriate for analysis. This marks the limit of what can be gleaned from this bivariate treatment and motivates a more complex multivariate analysis to better isolate signals in the aerosol and meteorological variables. 5. Multivariate statistics a. Multiple linear regression To elucidate additional relationships using multivariate techniques, we first perform least squares multiple linear regression on the cold CTT
multivariate treatment reduce the sample size in some bins below levels that are appropriate for analysis. This marks the limit of what can be gleaned from this bivariate treatment and motivates a more complex multivariate analysis to better isolate signals in the aerosol and meteorological variables. 5. Multivariate statistics a. Multiple linear regression To elucidate additional relationships using multivariate techniques, we first perform least squares multiple linear regression on the cold CTT
Longwave Radiation Corrections for the OMNI Buoy Networkdome temperature measurements. Gradual rise and fall are observed in NIOT measurements except for a few peaks and troughs which are also evident in the comparatively large scatter in the regression plot. The different responses resulted in lower correlation of 0.76 and a standard deviation of 31.13 mV with large offset of 139.43 mV. An analysis of the voltage measurements in NIOT at 1-h intervals revealed a few zero values preceded and succeeded by nonzero values. These zero values were replaced
dome temperature measurements. Gradual rise and fall are observed in NIOT measurements except for a few peaks and troughs which are also evident in the comparatively large scatter in the regression plot. The different responses resulted in lower correlation of 0.76 and a standard deviation of 31.13 mV with large offset of 139.43 mV. An analysis of the voltage measurements in NIOT at 1-h intervals revealed a few zero values preceded and succeeded by nonzero values. These zero values were replaced
, 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
, 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
, 2014 ), the effects of spinup due to initializing the CAM5 model with a “foreign” (ECMWF) analysis would have impacts on the first 24 h (i.e., day-1 hindcast ensembles). Afterward the tropical precipitation in the hindcasts reaches a relative equilibrium state close to the AMIP simulation of CAM5. Therefore in this study we concatenated each hindcast from 24- to 48-h lead time to form the day-2 time series (48–72 h for day 3) of the data stream from 1998 to 2012. We also performed a companion AMIP
, 2014 ), the effects of spinup due to initializing the CAM5 model with a “foreign” (ECMWF) analysis would have impacts on the first 24 h (i.e., day-1 hindcast ensembles). Afterward the tropical precipitation in the hindcasts reaches a relative equilibrium state close to the AMIP simulation of CAM5. Therefore in this study we concatenated each hindcast from 24- to 48-h lead time to form the day-2 time series (48–72 h for day 3) of the data stream from 1998 to 2012. We also performed a companion AMIP
