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

al. 2000 ). Data from Manus also included observations from a microwave radiometer (MWR), upper-air soundings, a micropulse lidar (MPL), a ceilometer, and optical rain gauges. Other data used are rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM 3B42v7; 0.25° × 0.25°; Kummerow et al. 2000 ); rainfall, specific humidity, and its physical tendency term from the operational analysis (0.56° × 0.56°) of the European Centre for Medium Range Weather Forecasts (ECMWF) prepared for

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Ji-Eun Kim, Chidong Zhang, George N. Kiladis, and Peter Bechtold

summary and discussion are given in section 5 . 2. Data ECMWF IFS reforecasts (IFS-RF) were made for the DYNAMO period (1 October 2011–31 January 2012) using the model cycle of Cy43r1 (implemented in November 2016) with horizontal resolution of TCo639 (~16 km) and 137 vertical levels. DYNAMO sounding observations were submitted to global telecommunication satellites (GTS) and assimilated into the IFS model. Only microwave all-sky humidity sounders were used for precipitation assimilation. The model

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Weixin Xu, Steven A. Rutledge, Courtney Schumacher, and Masaki Katsumata

provide a rich dataset to investigate cloud population statistics, precipitation processes, and how these processes are related to MJO initiation and development ( Johnson and Ciesielski 2013 ). Observations from multiwavelength radars at Gan Island and Addu Atoll [Gan; Fig. 3 in Yoneyama et al. (2013) ] were employed to examine the full spectrum of MJO convective clouds. Measurements from the three radars (Ka, C, and S band) deployed on Gan were merged into a precipitating and nonprecipitating radar

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Naoko Sakaeda, Scott W. Powell, Juliana Dias, and George N. Kiladis

). However, many details of the relationship between the diurnal cycle of rainfall and the MJO remain to be answered. The physical processes underlying the relationship between the MJO and the diurnal evolution of cloud and rain types were unclear in Sakaeda et al. (2017) because the analysis was limited to satellite estimates of cloud types and rain rates. Here we extend the results of Sakaeda et al. (2017) by using observations collected during the Dynamics of the MJO (DYNAMO; Yoneyama et al. 2013

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Weixin Xu and Steven A. Rutledge

present, from 50°S to 50°N. This dataset has 3-h temporal resolution and 0.25° spatial resolution and uses TRMM PR observations, passive-microwave measurements from low-Earth-orbiting satellites, infrared radiance measurements from geostationary satellites, and rain gauge data when available. 2) TRMM PF dataset We use the version 7 TRMM database, mainly observations from the precipitation radar (PR) ( Kummerow et al. 1998 ), to provide climatological context for the DYNAMO ship radar data. PR

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Brian Mapes, Arunchandra S. Chandra, Zhiming Kuang, Siwon Song, and Paquita Zuidema

heating responses to vertically resolved humidity variations. Another driving impetus for this work is to do so in a way that makes some meaningful, statistically significant use of special field campaign data, in the age of computer modeling. Specifically, we attempt to utilize observations from the several-month 2011/12 ARM MJO Investigation Experiment (AMIE)-Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign ( Yoneyama et al. 2013 ) in the equatorial Indian Ocean to estimate or infer

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

evaluation of the accuracy of those analyses through comparison with independent estimates is important. The analysis products developed in this study have been recently used by Sobel et al. (2014) to investigate the moist static energy budget for the DYNAMO MJOs. Observations collected on Gan Island during AMIE have enabled the computation of the vertical profile of radiative heating in the troposphere at that location as a function of time throughout the experiment ( Feng et al. 2014 ). We use these

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

diagrams of tropical column water vapor anomalies (millimeters of liquid equivalent; kg m −2 ) from ECMWF analysis during October–December 2011 latitudinally averaged over 5°S–5°N. Column-integrated water vapor is well measured by microwave radiometry, over the oceans from a robust constellation of satellites, and from the surface anywhere by microwave radiometry or even by low-cost GPS-meteorology equipment ( Adams et al. 2015 ). This makes it an especially attractive quantity to study observationally

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

rain evolution Cold pools are supposed to originate from convection and to generate subsequent convection. To examine the temporal relationship between the DYNAMO cold pools and precipitation and clouds surrounding them, we composite the observations of rain rate from an optical rain gauge on the ship and the liquid water and water vapor path observed by a microwave radiometer on the ship. Individual cold pool time series of rain ranked by the magnitude of their temperature drop Δ T are shown in

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