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Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation
Alternative TitleAssimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation
Wang Gen1; Zhang ZhengQuan4; Deng ShuMei5; Liu HuiLan1
2019
Source PublicationJOURNAL OF INFRARED AND MILLIMETER WAVES
ISSN1001-9014
Volume38Issue:4Pages:464-472
AbstractHyper-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 shown 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 similar to 750 hPa was relatively significant. The retrieval temperature at the surface and around the tropopause (80 similar to 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.
KeywordCHANNEL SELECTION RADIANCES DIAGNOSIS hyper-spectral non-gaussian generalized variational assimilation observation error re-estimation degrees of freedom for signal
Indexed ByCSCD
Language英语
Funding Project[National Natural Science Foundation of China] ; [Natural Science Foundation of Anhui province] ; [Shenyang Institute of Atmospheric Environment of China Meteorological Administration]
CSCD IDCSCD:6567862
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Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/156677
Collection中国科学院金属研究所
Affiliation1.Anhui Meteorol Informat Ctr, Key Lab Strong Weather Anal & Forecast, Hefei 230031, Anhui, Peoples R China
2.中国科学院金属研究所
3.Anhui Institute Meteorol, Hefei 230031, Anhui, Peoples R China
4.University Sci & Technol China, Sch Math, Hefei 230022, Peoples R China
5.AnhuiJianzhu University, Sch Environm & Energy Engn, Hefei 230601, Anhui, Peoples R China
Recommended Citation
GB/T 7714
Wang Gen,Zhang ZhengQuan,Deng ShuMei,et al. Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation[J]. JOURNAL OF INFRARED AND MILLIMETER WAVES,2019,38(4):464-472.
APA Wang Gen,Zhang ZhengQuan,Deng ShuMei,&Liu HuiLan.(2019).Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation.JOURNAL OF INFRARED AND MILLIMETER WAVES,38(4),464-472.
MLA Wang Gen,et al."Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation".JOURNAL OF INFRARED AND MILLIMETER WAVES 38.4(2019):464-472.
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