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Tammy M. Weckwerth, Kristy J. Weber, David D. Turner, and Scott M. Spuler

during its ascent. These soundings are launched operationally worldwide but typically only twice a day (normally at 0000 and 1200 UTC) per site and the sites are separated by hundreds of kilometers. They are often not representative of the mesoscale environment needed for improving forecasts of thunderstorms. Satellite retrievals provide global coverage but have poor vertical resolution and limited temporal resolution. Furthermore, satellites commonly use infrared spectral methods to derive water

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W. G. Blumberg, T. J. Wagner, D. D. Turner, and J. Correia Jr.

. Atmospheric Emitted Radiance Interferometer a. Instrument description The AERI is a ground-based remote sensor that measures downwelling infrared radiation between 520 and 3300 cm −1 (3.0–19.2 μ m) at a resolution of 0.5 cm −1 (unapodized; see Knuteson et al. 2004a , b ). Early versions of the AERI, such as those used in this study, record these observations at a frequency of every 7.5 min, whereas today’s AERIs record observations every 20–30 s ( Turner et al. 2016 ). AERI instruments maintain their

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Kevin R. Haghi, Bart Geerts, Hristo G. Chipilski, Aaron Johnson, Samuel Degelia, David Imy, David B. Parsons, Rebecca D. Adams-Selin, David D. Turner, and Xuguang Wang

; Mueller et al. 2017 ) and microwave radiometers ( Knupp 2006 ; Coleman and Knupp 2011 ) are quite useful. The AERIs measure downwelling spectral infrared radiance, from which profiles of temperature and humidity are retrieved ( Turner and Löhnert 2014 ). Similarly, thermodynamic profiles can also be retrieved from the observations made by microwave radiometers, which measure downwelling microwave radiation at multiple microwave frequencies ( Solheim et al. 1998 ). Both of these passive remote sensors

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Manda B. Chasteen, Steven E. Koch, and David B. Parsons

of a hail-like microphysical class allowed for the depiction of more distinct convective cores ( Bryan and Morrison 2012 ). Shortwave and longwave radiation were parameterized using the Dudhia ( Dudhia 1989 ) and RRTM ( Mlawer et al. 1997 ) schemes, respectively. Table 1. WRF-ARW Model, version 3.6.1, configuration and physics parameterizations. b. Verification of simulation and overview of the simulated MCS The evolution of the WRF simulated radar reflectivity is shown in Fig. 8 . In terms of

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Aaron Johnson and Xuguang Wang

particular, at least one time–height cross section through each of the bores is available from an Atmospheric Emitted Radiance Interferometers (AERI; Turner and Löhnert 2014 ; Turner 2016 , 2017 ). In short, the AERI instrument uses downwelling infrared radiation at wavelengths ranging from 3.3 to 19.2 μ m to retrieve the vertical profile of temperature and water vapor in the lowest ~3 km of the atmosphere. While it can sometimes be challenging to distinguish bores from other features using only

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David M. Loveless, Timothy J. Wagner, David D. Turner, Steven A. Ackerman, and Wayne F. Feltz

1. Introduction Convective weather produces as much as 70% of warm season precipitation in the central Great Plains of the United States ( Fritsch et al. 1986 ), much of which happens at night ( Wallace 1975 ; Heideman and Fritsch 1988 ; Colman 1990 ). Nocturnal convection typically occurs with a stable boundary layer and elevated instability. Infrared radiative cooling of the surface at night creates a stable nocturnal boundary layer, which reduces the amount of energy a convective cell is

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Aaron Johnson, Xuguang Wang, Kevin R. Haghi, and David B. Parsons

source of initial and lateral boundary condition perturbations. All members use Noah land surface scheme ( Ek et al. 2003 ) and RRTMG radiation ( Mlawer et al. 1997 ). In this study, assimilation of surface and upper-air observations from the operational National Centers for Environmental Prediction (NCEP) data stream on the 12-km domain is conducted every 3 h from 0300 UTC 10 July through 0000 UTC 11 July. NEXRAD observations, together with the North American Mesoscale Forecast System (NAM) model

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Samuel K. Degelia, Xuguang Wang, and David J. Stensrud

authors also thank the members of the Multi-Scale Data Assimilation and Predictability Laboratory (MAP; http://weather.ou.edu/~map/index.html ) at the University of Oklahoma, especially Hristo Chipilski and Aaron Johnson, for many thoughtful discussions related to this work. APPENDIX Further Details and Preprocessing of the PECAN Dataset The AERI instrument observes a “spectrally resolved downwelling radiance emitted by the atmosphere in the infrared portion of the electromagnetic spectrum” before

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Coltin Grasmick, Bart Geerts, David D. Turner, Zhien Wang, and T. M. Weckwerth

instruments (although usually not all of them) operated aboard many of the mobile PISAs (MPs). Table 1. Surface network of PECAN platforms and their instruments used in this study with the time of the leading outflow boundary passage. See Geerts et al. (2017) for a more complete description of the PISA network. Of particular interest are the AERIs, which measure downwelling thermal infrared spectra ( Knuteson et al. 2004 ), from which profiles of temperature and water vapor are retrieved ( Turner and

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