Development of Regionally Focused Algorithm for AIRS Temperature and Humidity Retrievals Using a Moving-Window Technique

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 지구환경과학부, 2017. 8. 손병주.Regionally focused algorithm for Atmospheric Infrared Sounder (AIRS) temperature and humidity retrievals was developed. We first employed regression model with a moving window technique. This is done by relating the AIRS measurements to temperature and humidity profiles with consideration of regionally and seasonally changing local climatology. Regression coefficients were obtained from four-year (2006-2009) of ECMWF interim data over East Asia and simulated AIRS radiances. Result showing a notable improvement of mean biases, compared to the regression retrieval which does not consider local features, suggests that the moving-window technique can produce better regression retrievals by including the local climatology in the regression model. For further improvement of the regression retrieval, one dimensional variational (1DVAR) physical model was also included in our algorithm. Error covariance matrix for the moving-window regression was obtained by using pre-developed regression retrieval and its error covariance. To assess the performance of 1DVAR using the mowing-window regression as a priori, error statistics of the physical retrievals from clear-sky AIRS measurements during four months of observation (March, June, September, and December of 2010) were comparedthe results obtained using new a priori information were compared with those using a priori information from a global set of training data which are classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the physical retrieval from the moving-window regression shows better result in terms of the root mean square error (RMSE) improvement. For temperature, RMSE improvements of 0.1 – 0.2 K and 0.25 – 0.5 K were achieved over the 150 – 300 hPa and 900 – 1000 hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5 – 3.5% above the 300 hPa level and by 0.5 – 1% within the 700 – 950 hPa layer. As most of improvements due to use of the moving-window technique were shown in situations in which the relationship between measured radiances and atmospheric state is not clear, we investigated a possible use of surface data for further improving AIRS temperature and humidity retrievals over the boundary layer. Surface data were statistically and physically used for our AIRS retrieval algorithm. Results showing reduced RMSEs at both the surface level and the boundary layer, suggest that the use of surface data can help better resolve vertical structure of temperature and moisture near the surface layer by alleviating the influences of incomplete channel weighting function near the surface on the retrieval. In conclusion, developing regionally focused algorithm, the inclusion of climate features in the AIRS retrieval algorithm can result in better temperature and humidity retrievals. Further improvement was also demonstrated by adding surface station data to the channel radiances as pseudo channels. Since the hyperspectral sounder is available on the geostationary platform, the development of regionally focused algorithm could enhance its applicability to enhance our ability to monitor and forecast severe weather.1. Introduction 1 2. Review of previous satellite-based temperature and humidity soundings 7 3. Infrared hyperspectral measurements 18 4. Development of regionally focused regression model 24 4.1. Construction of training data 24 4.2. Moving-window regression model 32 4.3. Detecting clear-sky FOVS from MODIS measurements 35 4.4. Error analysis 37 4.4.1. Validation by using independent simulation dataset 37 4.4.2. Case study 50 4.4.3. Comparison retrievals from real observation with reanalysis data 55 5. Impact of a priori information improvement on accuracy of 1DVAR 62 5.1. 1DVAR model 62 5.1.1. Background error covariance 63 5.1.2. Averaging kernel 68 5.1.3. Residual analysis for convergence criteria and quality control 68 5.2. Error analysis 74 5.2.1. Validation by using independent simulation dataset 74 5.2.2. Case study 83 5.2.3. Comparison retrievals from real observation with reanalysis data 87 6. Synergetic use of AWS data for AIRS T/q retrievals 94 6.1. Impact of AWS data on AIRS T/q soundings: Statistical perspective 98 6.1.1. Pseudo-AWS data for training 98 6.1.2. Retrieval sensitivity related to error of AWS data 101 6.1.3. Change of regression coefficient due to use of AWS data 108 6.1.4. Application 113 6.2. Impact of AWS data on AIRS T/q soundings: Physical perspective 115 6.2.1. 1DVAR with AWS observation 115 6.2.2. Result 117 7. Summary and discussion 120 References 125 국문초록 135Docto