Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

Abstract

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications. , 2012: Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval.

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

“…To examine the moistness dependency, the error statistics of the UM analysis and ERA-I reanalysis are examined as a function of the total precipitable water (TPW) between the surface and 100 hPa. Similar analyses were taken for the temperature and moisture retrievals from satellite-based hyperspectral sounder measurements [23,24].…”

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

“…The study uses observation errors that are dynamically calculated according to the scene temperature but employs fixed background errors provided for 3 latitudinal bands. However, previous study [38] suggests that using a priori errors dynamically updated based on the atmospheric conditions can improve the accuracy of water vapor retrievals in the boundary layer in the physical retrieval results. Future work can consider dynamic background errors classified by TPW to improve the boundary layer retrieval.…”

Applications of Full Spatial Resolution Space-Based Advanced Infrared Soundings in the Preconvection Environment

“…Radiance measurements from all good IR channels are used in the sounding retrieval process. The retrieved profiles have root-mean-square differences (RMSD) of 1 K in temperature and less than 2 g kg 21 in moisture ratio when compare with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis dataset (Kwon et al 2012), The CHISR algorithm can be applied to hyperspectral IR sounder radiances for atmospheric profiles in noncloudy preconvection environments with full spatial resolution (approximately 12-14 km at nadir) for nowcasting purposes. These full spatial resolution soundings are crucial for measuring the degree of atmospheric instability, which is highly related to the storm genesis, and can be used to analyze the preconvection environment.…”