Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation
Received:December 04, 2018  Revised:March 05, 2019  download
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Author NameAffiliationE-mail
WANG Gen Key Lab. of Strong Weather Analysis and Forecast, Anhui Meteorological Information Centre 
ZHNAG Zheng-Quan School of Mathematics, University of Science and Technology of China, Heifei 230022,China 
DENG Shu-Mei School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China 
LIU Hui-Lan Anhui Meteorological Information Centre Anhui Key Lab of Strong weather analysis and forecast, Hefei 230031, China 
Abstract:Hyper-spectral Atmospheric Infrared Sounder (AIRS) mainly covers the CO2 and H2O absorption bands. Different from CO2 channels, the brightness temperature bias of water vapor channel follows non-Gaussian statistics. In order to use AIRS channel spectral information effectively, new algorithm research is needed, two methods are presented in this paper: (1)Different from the observation error of the given spectral channel remains unchanged during the classical variational assimilation minimization iteration, the paper based on the posterior estimate of variational assimilation, namely, observation error re-estimation, re-estimating the channel observation error, which is then regarded as the weight of observation to the objective function of classical variational assimilation; Observation error re-estimation can be used to identify the reasonable observation errors which can fit variational assimilation model better. By using the weight function of M-estimators (L2-estimator, Huber-estimator, Fair-estimator and Cauchy-estimator) to couple the classical variational assimilation, and then obtain the generalized variational assimilation, make it Non-Gaussian. Re-estimated the contribution rate of observation terms to the objective function during each minimization iteration. The simulated brightness temperatures of AIRS are used to conduct ideal experiments. It is show that two methods of observation error re-estimation and Huber-estimator can provide better results than the classical method. We diagnose the impact of observations on the analysis with degrees of freedom for signal (DFS). The result of diagnosis shows that two methods can increase the available information of brightness temperatures of water vapour channels during the assimilation process. Furthermore, the analysis field obtained by using the algorithm (observation error re-estimation and Huber-estimator) in this paper is compared with the temperature field of sounding data, and it is obtained that the Huber-estimator, which generalized scale is set as 1.345 K with the best effect, which is set as 2.5 K latter, and the observation error re-estimation is better than classical variational assimilation. The effect of 200~750 hPa was relatively significant. The retrieval temperature at the surface and around the tropopause (80~200 hPa)is less than 2 K based on Huber-estimator variational assimilation. The results of this paper can lay the theoretical foundation and provide the algorithm reference for the variational assimilation of hyper-spectral data of Feng-Yun 4A and Feng-Yun 3D satellite.
keywords:hyper-spectral  non-gaussian  generalized variational assimilation  observation error re-estimation  degrees of freedom for signal
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Copyright:《Journal of Infrared And Millimeter Waves》