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  • Data quality control x
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Denny P. Alappattu and Qing Wang

correct the depth error. Such depth bias does not exist in the processed AXBT profiles. This depth bias in the AXCTD measurements produces misleading results, especially when the ocean measurements come from a mixture of both AXBT and AXCTD probes. An important step in quality control of the AXCTD data is thus to remove the bias using an objective and consistent method, which is the focus of this work. In addition, since AXBT and AXCTD data are used together, a comparison of the temperature

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

humidity data during the eighth WMO Intercomparison of High Quality Radiosonde Systems, held in Yangjiang, China, in July 2010 ( Nash et al. 2011 ). It was shown to greatly improve the RS92 humidity measurements, especially in the upper troposphere and during daytime ( Nash et al. 2011 ). The new algorithm was implemented in the DigiCORA sounding software, version 3.64, in early 2011 (see the appendix for details). However, because of the proprietary nature of the DigiCORA algorithm and a lack of

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Jennifer L. Davison

) 40°. The beamwidth is 0.92°. Data taken at elevation angles lower than 5° were excluded in this analysis 1) for having too shallow a vertical cross section for good identification of BSLs above the one associated with the mixed layer top and 2) due to the presence of significant interfering ground clutter at elevation angles less than 3.5° ( Rilling et al. 2013 ). Note that for the BSL analysis presented here, the quality-controlled in-field version of the data, DBZ_F, is used, rather than the

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Zhe Feng, Sally A. McFarlane, Courtney Schumacher, Scott Ellis, Jennifer Comstock, and Nitin Bharadwaj

recommendations of how to use the comprehensive suite of radar data collectively to address the DYNAMO science goal related to the initiation of the MJO will be provided. The paper is organized as follows. Section 2 describes the three radar systems and other ancillary data used in this study, and the collocation of data and quality control; section 3 provides cloud statistics and comparison results; section 4 describes a procedure to produce a merged cloud–precipitation dataset for cloud microphysics

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

resolution (every 1–2 s) during the IOP (1 October 2011–15 January 2012) of DYNAMO ( Ciesielski et al. 2014 ). Soundings and associated derived products have been rigorously quality controlled ( Ciesielski et al. 2014 ). Vertical velocity was derived from sounding array data and supplemented with European Centre for Medium-Range Weather Forecasts (ECMWF) Operational Analysis when one or both research ships were offsite ( ). Table 1

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Emily M. Riley Dellaripa, Eric Maloney, and Susan C. van den Heever

section 5 compares the convective cluster characteristics from the control simulations with the sensitivity simulations. Section 6 compares convective organization in the control and sensitivity experiments to assess the importance of LHFLX feedbacks for convective organization. Finally, section 7 provides a discussion of our results, while section 8 summarizes the paper. 2. Model and data description a. Model description CRM simulations were completed using the Regional Atmospheric Modeling

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Elizabeth J. Thompson, Steven A. Rutledge, Brenda Dolan, Merhala Thurai, and V. Chandrasekar

al. 2005b ). It is also important to determine how often Z dr and K dp are below data quality thresholds during tropical oceanic rain, which would foil the opportunity to use these variables for radar R estimation. In the midlatitudes, Z dr is used for rainfall estimation when it is above 0.5 dB (i.e., when R > 6 mm h −1 ), and K dp is used if it is above 0.3° km −1 , that is, when Z h > 38 dB Z and R > 50 mm h −1 (for S band; Ryzhkov et al. 2005b , c ; Matrosov et al. 2006

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

. 2011 ) will be explored based on computations of column-integrated radiative and convective heating rates for the two MJOs and comparing them to the normalized gross moist stability ( Neelin and Held 1987 ; Raymond et al. 2009 ; Sobel and Maloney 2012 , 2013 ). Further work is underway to relate the budget results to cloud populations as determined by the research radars, but that effort is awaiting additional quality control and evaluation of the radar products. 2. Data and analysis procedures

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Brandon W. Kerns and Shuyi S. Chen

considered in this study. This study uses the Southern Hemisphere quadrilateral, also known as the southern array. The sounding data at Gan (0.6°S, 73.1°E), Diego Garcia (7.3°S, 72.4°E), and the RV Revelle (stationed at 0°, 80°E during the period of this study) are level-3 quality controlled data processed by the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL). See Wang et al. (2012) for details regarding the quality control process used. The RV Mirai (stationed at

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Kai-Chih Tseng, Chung-Hsiung Sui, and Tim Li

is strongly influenced by in situ observations. Before doing budget analysis, we compare the ECMWF operational data and sounding data. The result (not shown) reveals that operational data capture synoptic-scale and intraseasonal-scale signal well. Ciesielski et al. (2014) carried out an assessment of moisture fields from the ECMWF operational products using quality-controlled upper-air sounding data. They found an overall good agreement, with the exception at upper levels, where the assimilated

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