Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4305-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-4305-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA Aeolus calibration and validation (Cal/Val) test flight campaign
Kristopher M. Bedka
CORRESPONDING AUTHOR
NASA Langley Research Center, Hampton, VA, USA
Amin R. Nehrir
NASA Langley Research Center, Hampton, VA, USA
Michael Kavaya
NASA Langley Research Center, Hampton, VA, USA
Rory Barton-Grimley
NASA Langley Research Center, Hampton, VA, USA
Mark Beaubien
Yankee Environmental Systems, Inc., Turners Falls, MA, USA
Brian Carroll
NASA Postdoctoral Fellowship Program, Universities Space Research Association, NASA Langley Research Center, Hampton, VA, USA
James Collins
Science Systems and Applications, Inc., Hampton, VA, USA
John Cooney
NASA Postdoctoral Fellowship Program, Universities Space Research Association, NASA Langley Research Center, Hampton, VA, USA
G. David Emmitt
Simpson Weather Associates, Charlottesville, VA, USA
Steven Greco
Simpson Weather Associates, Charlottesville, VA, USA
Susan Kooi
Science Systems and Applications, Inc., Hampton, VA, USA
Tsengdar Lee
NASA Headquarters, Washington, DC, USA
Zhaoyan Liu
NASA Langley Research Center, Hampton, VA, USA
Sharon Rodier
Science Systems and Applications, Inc., Hampton, VA, USA
Gail Skofronick-Jackson
NASA Headquarters, Washington, DC, USA
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Liqiao Lei, Timothy A. Berkoff, Guillaume Gronoff, Jia Su, Amin R. Nehrir, Yonghua Wu, Fred Moshary, and Shi Kuang
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Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
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not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Brian J. Carroll, Amin R. Nehrir, Susan A. Kooi, James E. Collins, Rory A. Barton-Grimley, Anthony Notari, David B. Harper, and Joseph Lee
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HALO is a recently developed lidar system that demonstrates new technologies and advanced algorithms for profiling water vapor as well as aerosol and cloud properties. The high-resolution, high-accuracy measurements have unique advantages within the suite of atmospheric instrumentation, such as directly trading water vapor measurement resolution for precision. This paper provides the methodology and first water vapor results, showing agreement with in situ and spaceborne sounder measurements.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech., 14, 1593–1613, https://doi.org/10.5194/amt-14-1593-2021, https://doi.org/10.5194/amt-14-1593-2021, 2021
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We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
Laura M. Judd, Jassim A. Al-Saadi, James J. Szykman, Lukas C. Valin, Scott J. Janz, Matthew G. Kowalewski, Henk J. Eskes, J. Pepijn Veefkind, Alexander Cede, Moritz Mueller, Manuel Gebetsberger, Robert Swap, R. Bradley Pierce, Caroline R. Nowlan, Gonzalo González Abad, Amin Nehrir, and David Williams
Atmos. Meas. Tech., 13, 6113–6140, https://doi.org/10.5194/amt-13-6113-2020, https://doi.org/10.5194/amt-13-6113-2020, 2020
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This paper evaluates Sentinel-5P TROPOMI v1.2 NO2 tropospheric columns over New York City using data from airborne mapping spectrometers and a network of ground-based spectrometers (Pandora) collected in 2018. These evaluations consider impacts due to cloud parameters, a priori profile assumptions, and spatial and temporal variability. Overall, TROPOMI tropospheric NO2 columns appear to have a low bias in this region.
Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.
Atmos. Meas. Tech., 13, 5491–5511, https://doi.org/10.5194/amt-13-5491-2020, https://doi.org/10.5194/amt-13-5491-2020, 2020
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F. Joseph Turk, Svetla Hristova-Veleva, Stephen L. Durden, Simone Tanelli, Ousmane Sy, G. David Emmitt, Steve Greco, and Sara Q. Zhang
Atmos. Meas. Tech., 13, 4521–4537, https://doi.org/10.5194/amt-13-4521-2020, https://doi.org/10.5194/amt-13-4521-2020, 2020
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The mechanisms linking convection and air motion are major factors in much of the uncertainty in weather prediction, but complementary measurements of these quantities are rarely taken in close proximity. These quantities are shown from the 2017 Convective Processes Experiment (CPEX), wherein cloud and vertical air motion winds derived from the APR-2 airborne Doppler radar are combined with joint Doppler wind lidar (DAWN) measurements in the aerosol-rich regions surrounding the convection.
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Short summary
This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar Observatory (HALO) measurement capabilities across a range of atmospheric conditions, compares DAWN and HALO measurements with Aeolus satellite Doppler wind lidar to gain an initial perspective of Aeolus performance, and discusses how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, water vapour, and aerosol profile observations.
This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar...