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Simon P. de Szoeke, Eric D. Skyllingstad, Paquita Zuidema, and Arunchandra S. Chandra

period between t max and t min as the “front.” This front-relative time coordinate is used for compositing fluxes, rain rate, and water vapor and liquid water paths. Figure 6a shows the distribution of the duration of cold pool fronts and the distance traveled along the path of wind. Fig . 5. Mean (a) 10-m air temperature, (b) specific and relative humidity, (c) wind speed, and (d) SST composited on time elapsed from the start ( t max ) and end ( t min ) of the cold pool front and on a

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

, cannot be neglected. Second, the ratio of anomalous water vapor mixing ratio q υ to dry static energy s is much greater than that of CCWs and far greater than the ratio of vertical gradients dq / dp to ds / dp that would characterize adiabatic vertical displacements of ambient stratification ( Mapes et al. 2006 ). Apparently moisture in the MJO is modulated in more complex, presumably cloud population mediated, ways. The MJO’s relatively large amplitude in moisture, and the emergence of new

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Sue Chen, Maria Flatau, Tommy G. Jensen, Toshiaki Shinoda, Jerome Schmidt, Paul May, James Cummings, Ming Liu, Paul E. Ciesielski, Christopher W. Fairall, Ren-Chieh Lien, Dariusz B. Baranowski, Nan-Hsun Chi, Simon de Szoeke, and James Edson

perturbation fields were derived from the analytical expressions determined from Eqs. (11-28) through (11-42) of Holton (1979 , 308–311). These perturbations were superimposed on a horizontally homogeneous mean state consisting of quiescent flow conditions and a vertical potential temperature profile consisting of a constant Brunt–Väisälä frequency ( N = 0.012 s −1 ). The initial vertical distribution of the water vapor was derived from the 12–19 November sounding composite taken from Gan. We utilized

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Adam Sobel, Shuguang Wang, and Daehyun Kim

during the experiment. Plots of the time series of these terms from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim, hereafter ERA-I; Dee et al. 2011 ) are also examined. Discussion of these time series is complemented by synoptic maps of satellite-derived column water vapor, precipitation, and low-level horizontal flow. 2. Data and methods a. Data The averaged atmospheric state variables derived from the Colorado State University array

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Matthew A. Janiga and Chidong Zhang

1. Introduction The Madden–Julian oscillation (MJO) is a planetary-scale phenomenon that modulates convective activity in the tropics on intraseasonal time scales (30–100 days) ( Madden and Julian 1971 , 1972 ). The MJO is characterized by an envelope of increased rainfall and free-tropospheric water vapor that originates over the Indian Ocean, propagates eastward at 5–8 m s −1 , and dissipates over the central Pacific. This convective envelope modulates rainfall and tropical cyclogenesis as

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David M. Zermeño-Díaz, Chidong Zhang, Pavlos Kollias, and Heike Kalesse

radar are susceptible to attenuation because of absorption by water and water vapor ( Matrosov 2005 ; Kollias et al. 2007a ; Feng et al. 2009 , 2014 ). To avoid attenuation errors, MMCR observations were excluded when the surface rain rate exceeded a threshold of 25 mm h −1 . The determination of this threshold is explained in appendix A . 2) Shallow cloud classification The vertically pointing MMCR can detect clouds passing over or standing above. We treated its temporarily adjacent hydrometeor

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Hungjui Yu, Paul E. Ciesielski, Junhong Wang, Hung-Chi Kuo, Holger Vömel, and Ruud Dirksen

.61N algorithm at these higher levels. The implications of these corrections on the climate record are likely small in the lower atmosphere but potentially significant in the upper troposphere/lower stratosphere, where the frequency of detected saturated layers more than doubles using corrected soundings and the role of water vapor in climate change is strongly debated ( Luo et al. 2007 ). Using D3.64- and GRUAN-corrected soundings, the detected saturated layer rates are largely consistent with

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H. Bellenger, K. Yoneyama, M. Katsumata, T. Nishizawa, K. Yasunaga, and R. Shirooka

in accurate moisture fields, even in the upper troposphere, where these corrections are largest ( Ciesielski et al. 2014 ). We then interpolate the sounding observations to obtain a 90-m vertical resolution dataset. The vertical distribution of water vapor is also monitored using a water vapor Raman lidar technique (e.g., Sakai et al. 2003 ). The receiver system to measure the water vapor Raman backscatter signal at 660 nm is added to an HSRL onboard the R/V Mirai ( Nishizawa et al. 2012

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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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