Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

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.

Evaluation of Total Precipitable Water over East Asia from FY-3A/VIRR Infrared Radiances

Satellite retrieval of atmospheric water vapor is intended to further understand the role played by the energy and water cycle to determine the Earth's weather and climate.The algorithm for operational retrieval of total precipitable water (TPW) from the visible and infrared radiometer (VIRR) onboard Fengyun 3A (FY-3A) employs a split window technique for clear sky radiances over land and oceans during both day and night.The retrieved TPW is compared with that from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite and data from radiosonde observations (RAOB).During the study period,comparisons show that the FY-3A TPW is in general agreement with the gradients and distributions from the Terra TPW.Their zonal mean difference over East Asia is smaller in the daytime than at night,and the main difference occurs in the complex terrain at mid latitude near 30°N.Compared with RAOB,the zonal FY-3A and the Terra TPW have a moist bias at low latitudes and a dry bias at mid and high latitudes;in addition,the FY-3A TPW performs slightly better in zonal mean biases and the diurnal cycle.The temporal variation of the FY-3A and the Terra TPW generally fits the RAOB TPW with the FY-3A more accurate at night while Terra TPW more accurate during the daytime.Comparisons of correlations,root mean square differences and standard deviations indicate that the FY-3A TPW series is more consistent with the RAOB TPW at selected stations.As a result,the FY-3A TPW has some advantages over East Asia in both spatial and temporal dimensions.