Assimilated Application Study Of ATOVS Microwave Humidity Sounder Radiance In GRAPES-GFS

Remote sensing data of meteorological satellites can provide global and high space-time resolution atmospheric observation information and improve the level of numerical weather prediction greatly, so the assimilation of satellite observation is getting more and more attention. Microwave radiation can launch through the disprecipitated cloud and detect the inside humidity information of cloud and get the water vapor content and distribution information. This is the key point of detection of cloud and rain, so that the study of assimilation on microwave humidity sounder data is very important. Humidity is the variable of least Gaussian and the least homogeneous background errors and sensitive to temperature, making the study and analysis of humidity is full of challenges. The model of NWP developed by our own country-GRAPES hasn’t do research of humidity deeply.This paper focuses on the study of the data assimilation of Microwave Humidity Sounder-MHS in GRAPES-GFS. In the view of cloud detection of MHS data, the article introduces the Liquid Water Path used in GSI assimilation system and the Bennarte Scattering Factor into GRAPES-GFS, do tests to determine thresholds of the two methods. Connected with the current method used in GRAPES and based on the theme permutation and combination in mathematics, the author expands the methods of cloud-detection and analyses the effect of each combination. The author puts forward a new and objective assessment frame on the basis of the subjective analysis of comparing with cloud image. The paper explores the technology of cloud-detection effect. Secondly, the bias correction scheme used in the paper is based on the methods of Harris and Kelly, combined with the characteristics of microwave humidity sounder radiances and impact factors, three schemes are designed in air-mass bias correction. The results show that total-column precipitable water plays positive role as the factor of prediction in bias correction for MHS data. The article establishes a suitable bias correction system in GRAPES for the microwave humidity sounder radiances. At the same time, the paper introduces the bi-weight algorithm into GRAPES-GFS assimilation system for quality control of MHS data. Finally, the article explores the impact of humidity data on GRAPES-GFS, the author designs a control experiment and two groups of contrast experiments. The findings show MHS data has positive effect on the relative humidity and the pressure layers of the main detected channels of MHS have the obvious effect. The paper’s work on MHS data improve the quality of analysis field assimilated in GRAPES and has important significance on application of MHS.