AIRS/AMSU/HSB validation

E. Fetzer, L.M. McMillin, D. Tobin, H.H. Aumann, M.R. Gunson, W.W. McMillan, D.E. Hagan, M.D. Hofstadter, J. Yoe, D.N. Whiteman, J.E. Barnes, R. Bennartz (+12 others)
2003 IEEE Transactions on Geoscience and Remote Sensing  
The Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) instrument suite onboard Aqua observes infrared and microwave radiances twice daily over most of the planet. AIRS offers unprecedented radiometric accuracy and signal to noise throughout the thermal infrared. Observations from the combined suite of AIRS, AMSU, and HSB are processed into retrievals of atmospheric parameters such as temperature, water vapor, and trace gases under all but
more » ... he cloudiest conditions. A more limited retrieval set based on the microwave radiances is obtained under heavy cloud cover. Before measurements and retrievals from AIRS/AMSU/HSB instruments can be fully utilized they must be compared with the best possible in situ and other ancillary "truth" observations. Validation is the process of estimating the measurement and retrieval uncertainties through comparison with a set of correlative data of known uncertainties. The ultimate goal of the validation effort is retrieved product uncertainties constrained to those of radiosondes: tropospheric rms uncertainties of 1.0 C over a 1-km layer for temperature, and 10% over 2-km layers for water vapor. This paper describes the data sources and approaches to be used for validation of the AIRS/AMSU/HSB instrument suite, including validation of the forward models necessary for calculating observed radiances, validation of the observed radiances themselves, and validation of products retrieved from the observed radiances. Constraint of the AIRS product uncertainties to within the claimed specification of 1 K/1 km over well-instrumented regions is feasible within 12 months of launch, but global validation of all AIRS/AMSU/HSB products may require considerably more time due to the novelty and complexity of this dataset and the sparsity of some types of correlative observations.
doi:10.1109/tgrs.2002.808293 fatcat:4h2vubtlrbgqnfegcf5sp26kwu