Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation

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dc.contributor.authorWang, Chenxi
dc.contributor.authorPlatnick, Steven
dc.contributor.authorZhang, Zhibo
dc.contributor.authorMeyer, Kerry
dc.contributor.authorWind, Gala
dc.contributor.authorYang, Ping
dc.date.accessioned2018-09-19T20:14:07Z
dc.date.available2018-09-19T20:14:07Z
dc.date.issued2016-05-17
dc.descriptionAn edited version of this paper was published by AGU. Copyright 2016, American Geophysical Union.en_US
dc.description.abstractAn infrared-based optimal estimation ( OE-IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A-Train) with Aqua. The results show that OE-IR cloud optical thickness (τ) and effective radius (r ₑ𝒻𝒻 ) retrievals perform best for ice clouds having 0.5 < τ < 7 and r ₑ𝒻𝒻 < 50 μm. For global ice clouds, the averaged retrieval uncertainties of τ and r ₑ𝒻𝒻 are 1 9% and 33%, respectively. For optically thick ice clouds with τ larger than 10, however, the τ and r ₑ𝒻𝒻 retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height ( h), the averaged global uncertainty is 0.48 km. Relatively large h uncertainty (e.g., > 1 km) occurs for τ < 0.5. Analysis of 1 month of the OE-IR retrievals shows large τ and r ₑ𝒻𝒻 uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high-latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE-IR and the operational products show consistent τ and h retrievals. However, obvious differences between the OE-IR and the MODIS Collection 6 r ₑ𝒻𝒻 are found.en_US
dc.description.sponsorshipThe authors are grateful for support from the NASA Radiation Sciences Program. Chenxi Wang would like to thank Quanhua Liu, David Groff, and Eva E. Borbas for their help on CRTM. The computations in this study were performed on the UMBC High Performance Computing Facility (HPCF); this facility is supported by the U.S. National Science Foundation through the MRI program (grant CNS-0821258 and CNS-1228778) and the SCREMS program (grant DMS 0821311), with additional substantial support from UMBC. The Collection 6 MODIS and CALIPSO/CALIOP products are publicly availab le at NASA/LAADS (ftp://ladsweb.nascom.nasa.gov/allData/6/) and NASA/ASDC (https://eosweb.larc.nasa.gov/).en_US
dc.description.urihttps://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2015JD024528en_US
dc.format.extent19 pagesen_US
dc.genrejournal articleen_US
dc.identifierdoi:10.13016/M2Z31NS3C
dc.identifier.citationChenxi Wang, Steven Platnick, Zhibo Zhang, Kerry Meyer, Gala Wind, Ping Yang, Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation, Journal of Geophysical Research: Atmospheres, 121, 5827–5845, 2016, doi:10.1002/2015JD024528.en_US
dc.identifier.uri10.1002/2015JD024528.
dc.identifier.urihttp://hdl.handle.net/11603/11323
dc.language.isoen_USen_US
dc.publisherAGUen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectice cloud propertiesen_US
dc.subjectn infrared-based optimal estimation (OE-IR)en_US
dc.subjectMODerate resolution Imaging Spectroradiometer (MODIS)en_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.subjectAqua satellite (MYD06)
dc.subjectCloud-Aerosol Lidar with Orthogonal Polarization (CALIOP),
dc.subjectImaging Infrared Radiometer (IIR);
dc.titleRetrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluationen_US
dc.typeTexten_US

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