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Junhong Wang, Jianchun Bian, William O. Brown, Harold Cole, Vanda Grubišić, and Kate Young

detail. The technique is applied to the T-REX sounding data to produce a carefully quality-controlled VV-sounding dataset. The VV data are judged against other collocated aircraft and in situ data and are utilized to study several types of atmospheric phenomena with strong vertical motions sampled during T-REX. The methods for estimating the VV from the T-REX data are presented in section 2 . The methods are validated in section 3 . The scientific highlights of sounding-derived VV data are provided

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Qingfang Jiang and James D. Doyle

Harshvardhan et al. (1987) . The initial fields for the model are created from multivariate optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Analysis and Prediction System (NOGAPS) forecast fields. The computational domain contains four horizontally nested grid meshes (i.e., one

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Shiyuan Zhong, Ju Li, C. David Whiteman, Xindi Bian, and Wenqing Yao

meteorological stations varied from site to site, ranging from 3 to 20 yr ( Table 1 ). At all sites, winds were measured on 10-m masts at a sampling rate of one sample every 2 s. Data at each site have gone through automated quality control procedures to remove erroneous values. Additional quality control procedures were applied to remove suspect values from the climatological analyses. Despite occasional missing data caused mostly by severe winter weather, the data quality was generally good. Hourly vector

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Susanne Drechsel, Georg J. Mayr, Michel Chong, Martin Weissmann, Andreas Dörnbrack, and Ronald Calhoun

Kingdom. Thanks to Ralph Burton and his team of the University of Leeds for helping with all our concerns of the radiosonde. We are grateful to William Brown and his team from NCAR for providing the wind profiler data, with special thanks for spending a lot of time on extra quality control. We are indebted to Alexander Gohm from the University of Innsbruck for providing the VAD analysis code. REFERENCES Angevine, W. M. , 1997 : Errors in mean vertical velocities measured by boundary layer wind

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Qingfang Jiang, Ming Liu, and James D. Doyle

optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data sources that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast fields. The 36-h forecast period runs from 0000 UTC 25 March to 1200 UTC 26 March with a data output frequency of 5 min. b. Aerosol model A dust microphysical aerosol model

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Michael Hill, Ron Calhoun, H. J. S. Fernando, Andreas Wieser, Andreas Dörnbrack, Martin Weissmann, Georg Mayr, and Robert Newsom

relatively less tolerant of pointing and synchrony errors. c. Quality control The SNR fields associated with the radial velocity data for the morning of 25 March show the decay of data quality with increasing radial distance ( Fig. 5 ). SNR, as shown, is measured as the base 10 logarithm of the signal power to noise power ratio. The average SNR for both lidars drops to below −10 dB at radial distances beyond 4 km; SNR values below this level in the decibel scale indicate that the ratio of returned signal

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Peter Sheridan and Simon Vosper

through a common quality-control procedure and averaged to 5-min intervals, are used to supplement the main T-REX surface data over a broader area. 3. Model simulations The numerical simulations were performed using MetUM on nested grids, the outermost of which was that of the operational global NWP configuration of the model. The horizontal grid spacing of the global model used was 0.5625° and 0.375° in the zonal and meridional directions, respectively. Forecast data from the global model were used

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Bryan K. Woods and Ronald B. Smith

height proportional to the wind speed and the two fluxes are related by the formula EF = − U · MF . Bretherton (1969) and others showed how waves might be absorbed in turbulent patches and critical layers. Lilly and Kennedy (1973 , henceforth LK73) first measured wave momentum fluxes over the Rockies, using aircraft leg altitudes all the way up to 60 hPa. While LK73 ’s measurements were generally consistent with EP60 , data quality issues limited the interpretation of their measurements. Gust

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Stefano Serafin, Lukas Strauss, and Vanda Grubišić

Western Regional Climate Center; Greg McCurdy and David Simeral are thanked for their efforts in building and maintaining the network. We thank all those involved in the Sierra Rotors and T-REX campaigns for their individual contributions. Ming Xiao (formerly of DRI) and Maria Wind (University of Vienna) are thanked for data quality control and preliminary analyses. Maria Wind’s work was carried out as part of her B.Sc. thesis project at the University of Vienna. The Sierra Rotors and T-REX projects

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Qingfang Jiang and James D. Doyle

water vapor ( Rutledge and Hobbs 1983 ) and the subgrid-scale moist convective processes are parameterized using an approach following Kain and Fritsch (1993) . The short- and longwave radiation processes are parameterized following Harshvardhan et al. (1987) . The initial fields for the model are created from multivariate optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data that are quality controlled and blended with the 12-h COAMPS forecast

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