Type of Document	Thesis
Author	O'Connor, Janna Elizabeth
Author's Email Address	joconnor@met.fsu.edu
URN	etd-04072006-174131
Title	A Quality Control Procedure for Assimilating Airs Radiance Data into a Mesoscale Model
Degree	Master of Science
Department	Meteorology, Department of
Advisory Committee	Advisor Name Title Xiaolei Zou Committee Chair Ming Cai Committee Member Robert Ellingson Committee Member
Keywords	Data Assimilation Biweight
Date of Defense	2006-04-04
Availability	unrestricted
Abstract The steps involved in establishing and implementing a quality control procedure for AIRS radiance data prior to its use in a mesoscale model are discussed. The Limited Cloud-Clearing Data Removal (LCCDR) Algorithm utilizes AIRS channel maximum weighting function (WF) height, vertical structure of WF, and cloud height at each pixel to remove cloud-contaminated data points. Biweight statistics, which include a weighted average and weighted standard deviation, are implemented to remove remaining data with large model deviations from observations. Two test cases are examined: a non-precipitation case and a case involving Hurricane Charley. The LCCDR algorithm effectively removes outliers due to cloud-contamination, producing a reduction in the mean difference between simulated and observed brightness temperatures. Biweight statistics further exclude points where simulated values disagree with AIRS data. For both cases, large discrepancies are found between AIRS observations and simulated surface brightness temperatures in terms of the range of brightness temperature change and intensity. Overall, AIRS radiances compared more favorably with simulated data shown by the reduction in root mean square error values after quality control and the increase in correlation coefficient values. Future work includes improving model ability to predict dry versus moist air boundaries and cyclone intensity through assimilation of AIRS radiance observations.
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