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

.1175/1520-0469(1993)050<2922:TIOIAS>2.0.CO;2 Blumberg , W. G. , D. D. Turner , U. Löhnert , and S. Castleberry , 2015 : Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions . J. Appl. Meteor. Climatol. , 54 , 2305 – 2319 , https://doi.org/10.1175/JAMC-D-15-0005.1 . 10.1175/JAMC-D-15-0005.1 Cadeddu , M. P. , J. C. Liljegren , and D. D. Turner , 2013 : The Atmospheric Radiation

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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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Shuguang Wang, Adam H. Sobel, Fuqing Zhang, Y. Qiang Sun, Ying Yue, and Lei Zhou

in CMORPH. Radiative fluxes are composited from the 1° × 1° daily Clouds and the Earth’s Radiant Energy System (CERES; Wielicki et al. 1996 ; Loeb et al. 2012 ) 1° synoptic (SYN1deg) data. Total precipitable water vapor estimated from microwave satellite observations—Special Sensor Microwave Imager (SSM/I) and Tropical Rainfall Measuring Mission Microwave Imager (TMI)—is also used for comparison with the simulations. 3. Comparison of the WRF simulation with observations a. The spatial and

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

data at these sites. These sites were chosen because 1) both DigiCORA and GRUAN corrections were applied to their sounding data and 2) being Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program sites, they were equipped with collocated GPS and microwave radiometer (MWR) total-column precipitable water (TPW) observations to help validate RH corrections. Also shown in Fig. 1 is the horizontal distribution of mean TPW from 1 October to 31 December 2011, the period of available

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Brandon W. Kerns and Shuyi S. Chen

destabilization phase in advance of the next MJO event. Recent work suggests that the triggering of an MJO event may be influenced by the extratropics ( Ray and Zhang 2010 ; Zhao et al. 2013 ; Ray and Li 2013 ). In the tropical IO, the characteristics of convection and wind associated with the MJO differ from the western Pacific. In the western Pacific, Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) provided the most detailed observations of the MJO. The

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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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Simon P. de Szoeke, James B. Edson, June R. Marion, Christopher W. Fairall, and Ludovic Bariteau

15 W m −2 are shown with a contour interval of 3 W m −2 , yellow contour is 21 W m −2 ]. (b) Zonal wind stress (shaded) and wind stress vectors from SCOW. The zero zonal wind stress contour is gray. The standard deviation of intraseasonal zonal wind stress is contoured at 0.015 (dashed), 0.02 (light), and 0.025 (thick) N m −2 . Locations of DYNAMO (80.5°E) and TOGA COARE (156°E) ship observations used in this paper are marked with yellow stars. Three MJO convective events and their accompanying

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Kunio Yoneyama, Chidong Zhang, and Charles N. Long

-band scanning Doppler radars and vertically pointing W-band (3.2-mm wavelength) Doppler radars were deployed. Radar observations from Revelle and Mirai captured cloud population during contrasting large-scale convective conditions (see “general conditions during the campaign” section). At Addu Atoll, a multichannel scanning microwave radiometer was deployed next to the S-PolKa for humidity and liquid water retrievals during the IOP. Microwave radiometers of two and three channels operating in

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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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