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). 3 Quality control includes examining raw data (D-files) for data completeness and atypical features, and then a unique pressure correction is applied to each dropsonde profile (typically 1 hPa or less). Following the correction, geopotential altitude is computed from the GPS altitude, and the raw soundings are processed through the Atmospheric Sounding Processing Environment (ASPEN) software ( http://www.eol.ucar.edu/software/aspen ), which applies final smoothing and removes suspicious data
). 3 Quality control includes examining raw data (D-files) for data completeness and atypical features, and then a unique pressure correction is applied to each dropsonde profile (typically 1 hPa or less). Following the correction, geopotential altitude is computed from the GPS altitude, and the raw soundings are processed through the Atmospheric Sounding Processing Environment (ASPEN) software ( http://www.eol.ucar.edu/software/aspen ), which applies final smoothing and removes suspicious data
dropsondes ( Hock et al. 2017 ) deployed from ~18 km and data from the University of Wisconsin’s Scanning High-Resolution Interferometer Sounder (S-HIS; Revercomb and Taylor 2017 ), contain information about the inner-core temperature structure of Edouard. The dropsondes have been quality controlled and postprocessed at the NCAR Earth Observing Laboratory (EOL) using NCAR’s Atmospheric Sounding Processing Environment (Aspen) software ( Hock et al. 2017 ). None of the HS3 observations were assimilated in
dropsondes ( Hock et al. 2017 ) deployed from ~18 km and data from the University of Wisconsin’s Scanning High-Resolution Interferometer Sounder (S-HIS; Revercomb and Taylor 2017 ), contain information about the inner-core temperature structure of Edouard. The dropsondes have been quality controlled and postprocessed at the NCAR Earth Observing Laboratory (EOL) using NCAR’s Atmospheric Sounding Processing Environment (Aspen) software ( Hock et al. 2017 ). None of the HS3 observations were assimilated in
undergone significant advances that included improved signal processing and reduced side-lobe interference. The processing of HIWRAP data begins with calibration, de-aliasing, and quality control editing. The three-dimensional wind fields are retrieved using the technique developed by Guimond et al. (2014) . This technique is a variational scheme similar to that used for the TA radar except that it is modified to fit the HIWRAP scanning geometry. Results from this retrieval were compared with other
undergone significant advances that included improved signal processing and reduced side-lobe interference. The processing of HIWRAP data begins with calibration, de-aliasing, and quality control editing. The three-dimensional wind fields are retrieved using the technique developed by Guimond et al. (2014) . This technique is a variational scheme similar to that used for the TA radar except that it is modified to fit the HIWRAP scanning geometry. Results from this retrieval were compared with other
) are also used for model performance evaluation. AERONET is a ground-based network for remote sensing of aerosol optical, microphysical, and radiative properties. It imposes standardized instruments, calibration, processing, and distribution across the network. The quality-assured level 2 data have been used in this investigation. The NU-WRF Model is used to simulate this SAL event and to investigate the role of dust and other aerosols in the local atmospheric structure. NU-WRF is a regional Earth
) are also used for model performance evaluation. AERONET is a ground-based network for remote sensing of aerosol optical, microphysical, and radiative properties. It imposes standardized instruments, calibration, processing, and distribution across the network. The quality-assured level 2 data have been used in this investigation. The NU-WRF Model is used to simulate this SAL event and to investigate the role of dust and other aerosols in the local atmospheric structure. NU-WRF is a regional Earth
( Braun et al. 2013 ), HS3 made it possible to not only sample the entire depth of the outflow layer, but also to sample the region in which the radial outflow originates over the deepest convection, hereafter referred to as the outflow roots . Several studies have already examined data from GRIP, focusing on both genesis and rapid intensification (e.g., Davis and Ahijevych 2013 ; Zawislak and Zipser 2014 ; Helms and Hart 2015 ; Rogers et al. 2015 ). In this study, we focus on the full quality-controlled
( Braun et al. 2013 ), HS3 made it possible to not only sample the entire depth of the outflow layer, but also to sample the region in which the radial outflow originates over the deepest convection, hereafter referred to as the outflow roots . Several studies have already examined data from GRIP, focusing on both genesis and rapid intensification (e.g., Davis and Ahijevych 2013 ; Zawislak and Zipser 2014 ; Helms and Hart 2015 ; Rogers et al. 2015 ). In this study, we focus on the full quality-controlled
Storm Sentinel (HS3) 2014 dropsonde data quality report. Version 3.0, National Center for Atmospheric Research (NCAR) Earth Observing Lab (EOL), accessed 16 September 2016. [Available online at http://data.eol.ucar.edu/datafile/nph-get/348.004/readme.V3.HS3-2014.GHdropsonde.pdf .] Zhang , F. , and J. A. Sippel , 2009 : Effects of moist convection on hurricane predictability . J. Atmos. Sci. , 66 , 1944 – 1961 , doi: 10.1175/2009JAS2824.1 . 10.1175/2009JAS2824.1 Zhang , F. , and D. Tao
Storm Sentinel (HS3) 2014 dropsonde data quality report. Version 3.0, National Center for Atmospheric Research (NCAR) Earth Observing Lab (EOL), accessed 16 September 2016. [Available online at http://data.eol.ucar.edu/datafile/nph-get/348.004/readme.V3.HS3-2014.GHdropsonde.pdf .] Zhang , F. , and J. A. Sippel , 2009 : Effects of moist convection on hurricane predictability . J. Atmos. Sci. , 66 , 1944 – 1961 , doi: 10.1175/2009JAS2824.1 . 10.1175/2009JAS2824.1 Zhang , F. , and D. Tao
NOAA IFEX teams, as well as personnel at the NOAA Aircraft Operations Center. HS3 dropsonde data were quality controlled by staff at NCAR EOL, with support from the National Science Foundation, and NOAA/AOML/HRD. Comments from Drs. Paul Reasor and Hua Chen of NOAA/AOML/HRD and three anonymous reviewers helped to improve the manuscript. The authors wish to thank the World Wide Lightning Location Network ( http://wwlln.net ), a collaboration among over 50 universities and institutions, for providing
NOAA IFEX teams, as well as personnel at the NOAA Aircraft Operations Center. HS3 dropsonde data were quality controlled by staff at NCAR EOL, with support from the National Science Foundation, and NOAA/AOML/HRD. Comments from Drs. Paul Reasor and Hua Chen of NOAA/AOML/HRD and three anonymous reviewers helped to improve the manuscript. The authors wish to thank the World Wide Lightning Location Network ( http://wwlln.net ), a collaboration among over 50 universities and institutions, for providing
1999 ), as well as the NASA Cloud Physics Lidar (CPL), which is able to detect multiple layers of aerosol throughout the troposphere. Dropsondes were postprocessed through the automatic sounding quality control software, the Atmospheric Sounding Processing Environment (ASPEN; Wang et al. 2010 ) and reprocessed to correct for an upper-level dry bias ( Vomel et al. 2016 ). Observed precipitation rates are obtained from the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation
1999 ), as well as the NASA Cloud Physics Lidar (CPL), which is able to detect multiple layers of aerosol throughout the troposphere. Dropsondes were postprocessed through the automatic sounding quality control software, the Atmospheric Sounding Processing Environment (ASPEN; Wang et al. 2010 ) and reprocessed to correct for an upper-level dry bias ( Vomel et al. 2016 ). Observed precipitation rates are obtained from the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation
