Title Detection of dust aerosol by combining CALIPSO active lidar and passive IIR measurements
Author Chen, B.; Huang, J.; Minnis, P.; Hu, Y.; Yi, Y.; Liu, Z.; Zhang, D.; Wang, X.
Author Affil Chen, B., Lanzhou University, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou, China. Other: NASA, Langley Research Center; Science Systems and Applications Incorporated; National Institute of Aerospace
Source Atmospheric Chemistry and Physics, 10(9), p.4241-4251, . Publisher: Copernicus, Katlenburg-Lindau, International. ISSN: 1680- 7316
Publication Date 2010
Notes In English. Includes Corrigendum, http://www.atmos-chem- phys.net/10/5359/2010/acp-10-5359-2010.html; published in Atmospheric Chemistry and Physics Discussions: 9 February 2010, http://www.atmos-chem-phys- discuss.net/10/3423/2010/acpd-10-3423-2010.ht ml; accessed in May, 2011. 46 refs. GeoRef Acc. No: 310053
Index Terms absorption; aerosols; backscattering; brightness; clouds (meteorology); dust; ice; lasers; lidar; radar; remote sensing; sediments; solar radiation; temperature; water; water temperature; China--Gansu; China- -Xinjiang--Taklimakan Desert; algorithms; Asia; case studies; China; clastic sediments; climate forcing; Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations; clouds; Far East; Gansu China; geophysical methods; infrared methods; laser methods; lidar methods; MODIS; particulate materials; radar methods; satellite methods; Taklimakan Desert; three-dimensional models; transport; wind transport; Xinjiang China
Abstract The version 2 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) dust layer detection method, which is based only on lidar measurements, misclassified about 43% dust layers (mainly dense dust layers) as cloud layers over the Taklamakan Desert. To address this problem, a new method was developed by combining the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and passive Infrared Imaging Radiometer (IIR) measurements. This combined lidar and IR measurement (hereafter, CLIM) method uses the IIR tri-spectral IR brightness temperatures to discriminate between ice cloud and dense dust layers, and lidar measurements alone to detect thin dust and water cloud layers. The brightness temperature difference between 10.60 and 12.05 Ám (BTD11-12) is typically negative for dense dust and generally positive for ice cloud, but it varies from negative to positive for thin dust layers, which the CALIPSO lidar correctly identifies. Results show that the CLIM method could significantly reduce misclassification rates to as low as ~7% for the active dust season of spring 2008 over the Taklamakan Desert. The CLIM method also revealed 18% more dust layers having greatly intensified backscatter between 1.8 and 4 km altitude over the source region compared to the CALIPSO version 2 data. These results allow a more accurate assessment of the effect of dust on climate.
URL http://www.atmos-chem-phys.net/10/4241/2010/acp-10-4241-2010.pdf
Publication Type journal article
Record ID 65006799