Type of Document	Thesis
Author	Carrier, Matthew James
URN	etd-11152004-142619
Title	Preliminary Steps towards Improving Short-Term QPF using Airs Observation through 4D-VAR Data Assimilation
Degree	Master of Science
Department	Meteorology, Department of
Advisory Committee	Advisor Name Title Xiaolei Zou Committee Chair Kwang-Yul Kim Committee Member Paul Ruscher Committee Member
Keywords	Data Assimilation 4D-Var QPF AIRS
Date of Defense	2004-10-20
Availability	unrestricted
Abstract The varying tasks required for assimilating satellite radiance observations from the Atmospheric Infrared Sounder (AIRS) into the initial conditions of a numerical weather prediction model are discussed. The tangent linear and adjoint observation operators were developed from a fast and computationally inexpensive radiative transfer model (RTM). The adjoint model was used to conduct a sensitivity analysis of the RTM-produced radiances at each of the 2378 AIRS spectral channels. The results of this study confirm that the RTM is simulating the radiative transfer process well when using forecast model data as input. This analysis will be used to conduct an efficient channel selection study prior to assimilating AIRS radiances. The errors associated with both the AIRS observations and the RTM calculations are discussed. The error in the AIRS observations for the selected test case compare favorably with expected values obtained by the AIRS science team from both pre-flight and early orbit testing. Errors associated with the RTM when using forecast model fields as input were investigated. The bias of the model was computed and the results of this study will be used to correct the bias in the model solution prior to assimilation. The variance of the model was also computed and will be used to identify outliers and to construct the estimated model error covariance matrix, which will be used in the cost function calculation. The models developed and the errors calculated for all fields will be used in future work to assimilate radiance values into numerical weather forecasts.
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