Articles | Volume 12, issue 3
https://doi.org/10.5194/amt-12-1755-2019
https://doi.org/10.5194/amt-12-1755-2019
Research article
 | 
18 Mar 2019
Research article |  | 18 Mar 2019

Albedo-Ice Regression method for determining ice water content of polar mesospheric clouds using ultraviolet observations from space

Gary E. Thomas, Jerry Lumpe, Charles Bardeen, and Cora E. Randall

Related authors

Extending the SBUV polar mesospheric cloud data record with the OMPS NP
Matthew T. DeLand and Gary E. Thomas
Atmos. Chem. Phys., 19, 7913–7925, https://doi.org/10.5194/acp-19-7913-2019,https://doi.org/10.5194/acp-19-7913-2019, 2019
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024,https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Contrail altitude estimation using GOES-16 ABI data and deep learning
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024,https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
The Ice Cloud Imager: retrieval of frozen water column properties
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024,https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Supercooled liquid water cloud classification using lidar backscatter peak properties
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024,https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Marine cloud base height retrieval from MODIS cloud properties using machine learning
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024,https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary

Cited articles

AIM CIPS Science Team: Cloud Imaging and Particle Size (CIPS) Instrument Overview, available at: http://lasp.colorado.edu/aim/, last access: 12 March 2019. 
Bailey, S. M., Thomas, G. E., Rusch, D. W., Merkel, A. W., Jeppesen, C., Carstens, J. N., Randall, C. E., McClintock, W. E., and Russell III, J. M.: Phase functions of polar mesospheric cloud ice as observed by the CIPS instrument on the AIM satellite, J. Atmos. Sol.-Terr. Phy., 71, 373–380, https://doi.org/10.1016/j.jastp.2008.09.039, 2009. 
Bardeen, C. G., Toon, O. B., Jensen, E. J., Hervig, M. E., Randall, C. E., Benze, S., Marsh, D. R., and Merkel, A.: Numerical simulations of the three-dimensional distribution of polar mesospheric clouds and comparisons with Cloud Imaging and Particle Size (CIPS) experiment and the Solar Occultation For Ice Experiment (SOFIE) observations, J. Geophys. Res., 115, D10204, https://doi.org/10.1029/2009JD012451, 2010. 
Baumgarten, G., Fiedler, J., and Rapp, M.: On microphysical processes of noctilucent clouds (NLC): observations and modeling of mean and width of the particle size-distribution, Atmos. Chem. Phys., 10, 6661–6668, https://doi.org/10.5194/acp-10-6661-2010, 2010. 
Benze, S., Randall, C. E., DeLand, M. T., Thomas, G. E., Rusch, D. W., Bailey, S. M., Russell III, J. M., McClintock, W., Merkel, A. W., and Jeppesen, C.: Comparison of polar mesospheric cloud measurements from the Cloud Imaging and Particle Size experiment and the Solar Backscatter Ultraviolet instrument in 2007, J. Atmos. Sol.-Terr. Phy., 71, 365–372, 2009. 
Download
Short summary
Polar mesospheric clouds are an upper atmospheric phenomenon of great interest in that they provide information about a previously inaccessible atmospheric region, the coldest of the planet. This paper provides the basis for converting raw radiance measurements of clouds, made by diverse satellite instrumentation, into a physically based quantity, the cloud ice water content. The new algorithm allows intercomparisons of data collected using diverse optical methods.