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Matthew A. Janiga and Chidong Zhang

the assumption of diabatic balance. The numerical model, physics parameterizations, and simulation setup are described in section 2 . In section 3 , the results of the simulation are described. This begins with an overview of the moisture variability during the simulation and analyses of simulated diabatic tendencies. This is followed by an analysis of the validity of the diabatic balance framework used for diagnosing the moisture budget and concludes with an examination of the roles that

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Richard H. Johnson, Paul E. Ciesielski, James H. Ruppert Jr., and Masaki Katsumata

; Johnson 1980 ), particularly over the NSA for the October MJO. During the period of increasing precipitation between 15 and 22 October, the budget rainfall rate exceeds all of the satellite estimates, but the reverse is true toward the end of October. The discrepancy for the 15–22 October period may be related to the storage of water in clouds as the cloud area increased rapidly (as shown in the next section), whereas the reverse situation at the end of the month is presumably a result of evaporation

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Adam Sobel, Shuguang Wang, and Daehyun Kim

et al. 2003 ; Huffman et al. 2009 ). Total precipitable water estimated from satellite observations—a combination of the Special Sensor Microwave Imager (SSM/I) and TRMM Microwave Imager (TMI)—is compared against sounding array values and the ERA-I dataset. b. Methods The budget of the column-integrated MSE is computed as where h denotes moist static energy, where T is temperature; q is specific humidity; c p is dry air heat capacity at constant pressure (1004 J K −1 kg −1 ); L υ is

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Eric D. Skyllingstad and Simon P. de Szoeke

h of day 6 for experiments with heat flux removed for melting (small dash) and both melting and sublimation (large dash), along with the control experiment (solid). The total column integrated moisture budget can be calculated as the sum: where is the total water storage with q υ defined as the water vapor and q twc defined as the nonvapor water specific humidity in the atmosphere (rain, snow, graupel, cloud water, and cloud ice), E is the average evaporation, P is the average rainfall

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Michael S. Pritchard and Christopher S. Bretherton

associated with SPCAM's MJO. The approximate budget equations governing intraseasonal anomalies of column dry static energy 〈 s 〉 and latent energy due to column water vapor L υ 〈 q 〉 in our experiments are where 〈LW〉 is the column-integrated longwave heating rate, L υ is the latent heat of vaporization, LH is the latent heat flux, and P is the precipitation. The first term on the right-hand side of Eq. (5) represents our interference in the moisture budget F α . All terms are in watts per square

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Walter M. Hannah, Brian E. Mapes, and Gregory S. Elsaesser

in our analysis are given in section 3 . Comparison of the LCT in analysis and sounding data is presented in section 4 , followed by analysis of radar data in the context of the LCT in section 5 . Summary and conclusions are discussed in section 6 . 2. Framework: The Lagrangian tendency of CWV The Eulerian budget of water vapor in advective form, in a hydrostatic pressure coordinate, integrated over column mass as indicated by angle brackets, may be written as follows: where q υ is specific

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Simon P. de Szoeke

1. Introduction Processes that control the moist static energy (MSE) of the boundary layer (BL) determine updraft buoyancy, affecting deep convection that drives the atmospheric circulation ( Raymond 1995 ; Neelin and Zeng 2000 ; DeMott et al. 2015 ). Climate models with stronger mixing of water vapor between the atmospheric BL and the free troposphere have stronger positive low-cloud feedback and stronger climate sensitivity to greenhouse gases ( Sherwood et al. 2014 ), yet the mechanism for

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Tomoe Nasuno, Tim Li, and Kazuyoshi Kikuchi

) was diagnosed from the advection terms and the net tendency (i.e., the residual term). The net tendency was estimated from the temporal difference in the daily data divided by the time interval. In the tropics, vertical advection and a diabatic heating/moisture sink (residual) are the major terms in balance ( Yanai et al. 1973 ). Zhao et al. (2013) and Hsu et al. (2011) investigated the interactions between intraseasonal- and synoptic-scale disturbances by a budget analysis of heat, moisture

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James H. Ruppert Jr. and Richard H. Johnson

. (2015) for more detailed budget validation]. Presented next is the column-integrated water vapor budget ( Fig. 5 ), which is employed in lieu of the column-integrated MSE budget ( Sobel et al. 2014 ) since the latter hides the direct effects of clouds (i.e., the moistening–drying due to liquid–vapor phase changes). Furthermore, in the column-integrated MSE budget, vertical dry static energy advection typically cancels with moisture advection in the vertical advection term, therefore potentially

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Jianhao Zhang, Paquita Zuidema, David D. Turner, and Maria P. Cadeddu

heat budgets ( Johnson et al. 2015 , and references therein). Such networks possess a 3-hourly temporal resolution at best, and the spatial sampling is sparse enough that significant moisture filaments can be missed ( Hannah et al. 2016 ). Satellite measurements of column water vapor path provide improved spatial resolution, but they have more difficulty resolving moisture variations in the lower free troposphere because of contributing surface emission, especially over land. Another approach

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