Abstract:

Due to a scarcity of observation sites, only a small amount of conventional observation data on the temporal and spatial distribution of temperature and humidity in Central Asia can be obtained, which makes analysis difficult. High-resolution air infrared detector (AIRS) data can effectively fill the gap. In this paper, the radiance temperature of AIRS simulated by the input radiosonde in the Community Radiative Transfer Model was utilized as the reference value, deviations in the brightness temperature of the AIRS observation were analyzed, and the applicability of AIRS satellite data in the Central Asia numerical weather forecast operation system was evaluated. First, it shows that the average value of the maximum positive deviation of brightness temperature over the selected stations was approximately 3.3 K, and the absolute value of the maximum negative deviation was approximately 2.6 K. Second, the average brightness temperature of the AIRS observation in multiple stations was slightly higher than the overall simulated brightness temperature, and its probability density distribution was closer to the normal distribution curve than that of a single station. Finally, the assimilation of AIRS improved the prediction effect of RMAPS-CA on the geopotential height, temperature, specific humidity, and other high-altitude elements, but did not improve the prediction of high-altitude wind speed. For each factor, the assimilation improvement range of AIRS was larger at the lower and higher levels. After assimilation, the root mean square error of the geopotential height, temperature, specific humidity, and wind speed were less than 20 gpm, 2 K, 8×10^-4 kg·kg^-1 and 5 m·s^-1, respectively. It should be noted that, due to the time limitations imposed on the encrypted sounding experiment, only one full month in July 2016 was selected as the research time period in the deviation analysis of this study. Due to the relatively less vegetation coverage on the underlying surface in Central Asia, the surface radiation was large in summer, while the area without snow cover in winter was relatively small. Therefore, the conclusion of the deviation analysis in this paper is not necessarily applicable to other seasons. In addition, considering the possibility of a business transformation of the research results, this study was based on Rapid-Refresh Multiscale Analysis and Prediction System-Central Asia (RMAPS-CA) to carry out assimilation analysis when evaluating the applicability of AIRS. The system uses the three-dimensional variational data assimilation (3DVAR) method. If a more advanced four-dimensional variational data assimilation (4DVAR) assimilation method is adopted, the assimilation effect may improve.

Key words: AIRS, Central Asia, radiative brightness temperature, bias analysis, applicability