HR: 0800h
AN: H31H-0767    [Abstracts]
TI: Evaluation and Assimilation of Cloud Cleared Radiances for AIRS in GEOS-5
AU: * Liu, H E
EM: eliu@gmao.gsfc.nasa.gov
AF: Global Modeling and Assimlation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: * Liu, H E
EM: eliu@gmao.gsfc.nasa.gov
AF: SAIC, 4600 Powder Mill Road, Beltsville, MD 20705, United States
AU: Rienecker, M
EM: Michele.Rienecker@nasa.gov
AF: Global Modeling and Assimlation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: Rienecker, M
EM: Michele.Rienecker@nasa.gov
AF: Joint Center for Satellite Data Assimilation, 5200 Auth Road, Camp Springs, MD 20746, United States
AU: Riishojgaard, L
EM: larspr@gmao.gsfc.nasa.gov
AF: Global Modeling and Assimlation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: Riishojgaard, L
EM: larspr@gmao.gsfc.nasa.gov
AF: Joint Center for Satellite Data Assimilation, 5200 Auth Road, Camp Springs, MD 20746, United States
AU: Huang, H
EM: allen.huang@ssec.wisc.edu
AF: CIMSS/SSEC University of Wisconsion, 1225 West Dayton Street, Madison, WI 53706, United States
AU: Zhang, H
EM: hong.zhang@ssec.wisc.edu
AF: CIMSS/SSEC University of Wisconsion, 1225 West Dayton Street, Madison, WI 53706, United States
AB: The use of clear (cloud-free) channels for AIRS in GEOS-5 had shown positive impact on forecast skills in both hemispheres. However, improvements in forecast skills due to the assimilation of AIRS data are less impressive since the number of assimilated channels from AIRS is much larger than that from other Infrared sounders such as HIRS-3 onboard NOAA 15-17 satellites. This limitation of AIRS radiance data to improve the forecast skill is mainly due to the fact that channels capable of peaking below clouds are not used in the assimilation and yet those have highest vertical resolving capability of AIRS instrument are concentrated in the lower troposphere. On average, the percentage of AIRS footprints completely clear for all channels is less than 10%. The percentage of assimilated AIRS channel radiances however ranges from 100% for channels peaking in the upper stratosphere, above the cloud, to no more that 5% in the lower atmosphere due to cloud contamination. Our current ability to model and predict clouds accurately in global model, and to fully characterize and parameterize optical properties of cloud particles in radiative transfer model are the two major obstacles prohibiting us to use cloudy radiance directly in the assimilation. To further improve forecast skill using AIRS data, we ought to use the channels peaking below the clouds in the troposphere, which can be accomplished by assimilating cloud-cleared radiance. The cloud-cleared radiance data for AIRS used in this study were obtained from optimal cloud clearing procedures developed by researchers at CIMSS of University of Wisconsin at Madison to retrieve clear column radiances for all AIRS channels by collocating multi-band MODIS IR clear radiance observations with the AIRS cloudy radiances on a single footprint basis. Two adjacent AIRS cloudy footprints are used to retrieve one AIRS cloud-cleared radiance spectrum and no background information (first guess) is needed. To assimilate the cloud-cleared radiance data, the errors of the cloud-cleared radiances need to be addressed. The details of convolving AIRS radiances with MODIS spectral response function and comparison with MODIS-measured cloud- free radiance will be presented. The range of errors of cloud-cleared radiances for AIRS using collocated MODIS clear and near-by AIRS clear data will be shown. The NASA global data assimilation model, GEOS-5, is used to evaluate and assimilate the cloud-cleared radiance for AIRS. The residues between the cloud-cleared brightness temperature and the simulated brightness temperature from background (i.e., OMFs) will be investigated. The quality control procedures will be documented based on error estimation and the OMFs. Finally, the impacts between assimilation of clear channel radiances and cloud-cleared radiances will be addressed.
DE: 1805 Computational hydrology
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting