Plateau Meteorology

Plateau Meteorology >

2018 , Vol. 37 >Issue 1: 253 - 263

DOI: https://doi.org/10.7522/j.issn.1000-0534.2017.00023

Generalised Variational Assimilation of Cloud-affected Brightness Temperature of AIRS Data based on the Constraint Terms

  • WANG Gen ,
  • ZHANG Jianwei ,
  • WEN Huayang ,
  • YANG Yin
Expand
  • Anhui Meteorological Information Centre Anhui key lab of atmospheric science and satellite remote sensing, Hefei 230031, Anhui, China;The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110000, Liaoning, China;College of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;National Meteorological Center of China, Beijing 100081, China

Received date: 2016-10-26

  Online published: 2018-02-28

Fold

Abstract

Assimilated channel brightness temperature data from infrared sounders accounting for cloud effects has a positive effect on weather forecasting, especially in weather-sensitive areas. When cloud parameters including effective cloud fraction and effective cloud top pressure are considered in the simulation on channel brightness temperature of the infrared sounders, the deviation of brightness temperature follows strong Non-Gaussian. The classical variational assimilation requires the observational errors to follow a Gaussian distribution to apply the least-square principle. The least-squares method is sensitive to outliers; if the analyzed data contain gross errors, the parameter estimation is inaccurate. When processing the cloud-affected brightness temperature, cloud detection or assimilating specific channel brightness temperature with weight function peaks above the cloud top were needed. Useful data were lost through the cloud detection process to eliminate the so-called outliers. The outliers are not always harmful, which may represent new information, such as weather phenomena. At present, the quality control is generally based on a certain threshold value if the subjective uncertainty is too strong. If outliers persist after the quality control, the optimal parametric results obtained by the classical variational assimilation are meaningless. In this paper, Atmospheric Infra-Red Sounder (AIRS) brightness temperature channel which affected by cloud, were assimilated by generalized variation method from the constraint terms of Non-Gaussian model. It combines both the advantages of classical variational assimilation and robust M-estimators. Generalized one is coupled with quality control in the process of assimilation. The main idea is to use weighting factor of M-estimators to re-estimate the contribution rate of the observation items to the objective function in each process of objective function minimization based on the classical variational assimilation. The cost function contains the M-estimators to guarantee the robustness to outliers, thus to improve the assimilation results. Numerical algorithm steps of the generalized variational assimilation are as follows:Firstly, a channel set was formed by dynamically selecting AIRS channels based on the temperature Jacobian matrix in each field-of-view (FOV). Secondly, generalized variational assimilation of cloud parameters, which were input variables in radiative transfer model (such as, RTTOV), were involved in the variational minimisation iteration process, and were used to simulate AIRS brightness temperature of channel set. Assimilation experiment demonstrated that for high clouds, the Cauchy estimator inversion results are the best, whereas for the mid and low clouds, the Huber estimator provides the best results. However, the inversion results for temperature in the high level of model is worse using the Fair estimator, and, on the contrary, the inversion results for humidity are good. These results may reflect an inherent characteristic of Fair distribution. The assimilated results in cloud-affected brightness temperature are better by using generalized variation method than the classical method, but the former depends on selecting M-estimator weight functions. The preliminary results also demonstrated the potential application value of generalized variational assimilation.

Key words： AIRS; cloud parameters; non-Gaussian; constraint terms; generalized variatio

Cite this article

WANG Gen , ZHANG Jianwei , WEN Huayang , YANG Yin . Generalised Variational Assimilation of Cloud-affected Brightness Temperature of AIRS Data based on the Constraint Terms[J]. Plateau Meteorology, 2018 , 37(1) : 253 -263 . DOI: 10.7522/j.issn.1000-0534.2017.00023

References

[1]Aumann H H, Chahine M T, Gautier C, et al, 2003. AIRS/AMSU/HSB on the Aqua mission:Design, science objectives, data products, and processing systems[J]. IEEE Trans Geosci Remote Sens, 41(2):253-264.
[2]Bhar L, 2007. Robsut regression[EB/OL]. <a href="http://iasri.res.in/design/ebook/EBADAT/3-Diagnostics%20and%20Remedial%20Measures/5-ROBUST%20REGRESSION1.pdf">http://iasri.res.in/design/ebook/EBADAT/3-Diagnostics%20and%20Remedial%20Measures/5-ROBUST%20REGRESSION1.pdf</a>.
[3]Black M J, Sapiro G, Marimont D H, et al, 1998. Robust anisotropic diffusion[J]. IEEE Trans Image Proc, 7(3):421-432.
[4]Cameron J, Collard A D, English S J, 2005. Operational use of AIRS observations at the met office[EB/OL]. <a href="http://cimss.ssec.wisc.edu/itwg/itsc/itsc14">http://cimss.ssec.wisc.edu/itwg/itsc/itsc14</a>. In: Tech. Proceedings 14<sup>th</sup> Internat. TOVS Study Conference, Beijing, China, 25-31 May 2005.
[5]Chahine M T, 1974. Remote sounding of cloudy atmospheres. Ⅰ. The single cloud layer[J]. J Atmos Sci, 31(1):233-243.
[6]Chevallier F, 2001. Sampled databases of 60-level atmospheric profiles from the ECMWF analyses, Eumetsat/ECMWF SAF program.
[7]Research Report No. 4. Available from: NWP SAF Help desk, Met Office, Fitz Roy Road, Exeter, EX1 3PB, UK.
[8]Chevallier F, Lopez P, Tompkins A M, et al, 2004. The capability of 4D-Var systems to assimilate cloud-affected satellite infrared radiances[J]. Quart J Roy Meteor Soc, 130(598):917-932.
[9]English S J, Eyre J R, Smith J A, 1999. A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction[J]. Quart J Roy Meteor Soc, 125(559):2359-2378.
[10]Eyre J R, Kelly G A, McNally A P, et al, 1993. Assimilation of TOVS radiance information through one-dimensional variational analysis[J]. Quart J Roy Meteor Soc, 119(514):1427-1463.
[11]Eyre J R, Menzel W P, 1989. Retrieval of cloud parameters from satellite sounder data:A simulation study[J]. J Appl Meteor, 28(4):267-275.
[12]Huang H L, Li J, Baggett K, et al, 2005. Evaluation of cloud-cleared radiances for numerical weather prediction and cloud-contaminated sounding applications[C]//Optics &amp; Photonics 2005. International Society for Optics and Photonics, 2005: 589006-589006-7.
[13]Huang H L, Smith W L, Li J, et al, 2004. Minimum local emissivity variance retrieval of cloud altitude and effective spectral emissivity-simulation and initial verification[J]. J Appl Meteor, 43(5):795-809.
[14]Huber P J, 1981. Robust statistics[M]. John Wiley &amp; Sons, New York. 308. (Wiley series in probability and mathematical statistics).
[15]Lsaksen L, 2010. Variational quality control[EB/OL]. <a href="http://www.ecmwf.int/newsevents/training/meteorological_presentations/pdf/DA/VarQC.pdf">http://www.ecmwf.int/newsevents/training/meteorological_presentations/pdf/DA/VarQC.pdf</a>.
[16]Li J, Liu C Y, Huang H L, et al, 2005. Optimal cloud-clearing for AIRS radiances using MODIS[J]. IEEE Trans Geosci Remote Sens, 43(6):1266-1278.
[17]Liu J, Li H, Kalnay E, et al, 2009. Univariate and multivariate assimilation of AIRS humidity retrievals with the local ensemble transform Kalman filter[J]. Mon Wea Rev, 137(11):3918-3932.
[18]Liu Z Q, Qi C L, 2005. Robust variational inversion: with simulated ATOVS radiances. [EB/OL]. <a href="http://cimss.ssec.wisc.edu/itwg/itsc/itsc14/proceedings/A31_Liu.pdf">http://cimss.ssec.wisc.edu/itwg/itsc/itsc14/proceedings/A31_Liu.pdf</a>.
[19]Martinet P, Bell W, Pavelin E, et al, 2013. Evaluation of control variables for the assimilation of cloud-affected infrared radiances[R]. Report of Visiting Scientist mission NWP_VS12_03 Document NWPSAF-MO-VS-048, Version 1. 0, 27, May, 2013.
[20]McNally A P, 2002. A note on the occurrence of cloud in meteorologically sensitive areas and the implications for advanced infrared sounders[J]. Quart J Roy Meteor Soc, 128(585):2551-2556.
[21]McNally A P, Watts P D, 2003. A cloud detection algorithm for high-spectral-resolution infrared sounders[J]. Quart J Roy Meteor Soc, 129(595):3411-3423.
[22]Menzel W P, Smith W L, Stewart T R, 1983. Improved cloud motion wind vector and altitude assignment using VAS[J]. J Climate Appl Meteor, 22(3):377-384.
[23]Pangaud T, Nadia F, Vincent G, et al, 2009. Assimilation of AIRS radiances affected by Mid-to Low-Level Clouds[J]. Mon Wea Rev, 137(12):4276-4292.
[24]Pavelin E G, English S J, Eyre J R, 2008. The assimilation of cloud-affected infrared satellite radiances for numerical weather prediction[J]. Quart J Roy Meteor Soc, 134(632):737-749.
[25]Rabier F, McNally A, Andersson E, et al, 1998. The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Ⅱ:Structure functions[J]. Quart J Roy Meteor Soc, 124(550):1809-1829.
[26]Rodgers C D, 2000. Inverse methods for atmospheres:Theory and Practice[M]. World Scientific Publishers, Singapore.
[27]Saunders R, Brunel P, English S, et al, 2005. RTTOV-8-Science and validation report. Eumetsat/ECMWF SAF Programme, Document ID NWPSAF-MOTV-007. Available from: NWP SAF Helpdesk, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK.
[28]Wang G, Zhang J W, 2014. Generalised variational assimilation of cloud-affected brightness temperature using simulated hyper-spectral atmospheric infrared sounder data[J]. Adv Space Res, 54(1):49-58. DOI:10. 1016/j. asr. 2014. 03. 009.
[29]Cai M, ZhouY Q, Ou J J, et al, 2015. Study on diagnosing three dimensional cloud region[J]. Plateau Meteor, 34(5):1330-1344. DOI:10. 7522/j. issn. 1000-0534. 2014. 00061.<br/>蔡淼, 周毓荃, 欧建军, 等, 2015.三维云场分布诊断方法的研究[J].高原气象, 34(5):1330-1344.
[30]Dong C H, Li J, Zhang P, et al, 2013. Atmospheric remote sensing principle and application of satellite hyper-spectral infrared[M]. Beijing:science press.<br/>董超华, 李俊, 张鹏, 等, 2013.卫星高光谱红外大气遥感原理和应用[M].北京:科学出版社.
[31]Du H D, Huang S X, Shi H Q, 2008. Method and experiment of channel selection for high spectral resolution data[J]. Acta Physica Sinica, 57(12):7685-7693. DOI:10. 3321/j. issn:1000-3290. 2008. 12. 045.<br/>杜华栋, 黄思训, 石汉青, 2008.高光谱分辨率遥感资料通道最优选择方法及试验[J].物理学报, 57(12):7685-7693.
[32]Li H R, Sun X J, Wang M Y, et al, 2015. Research on different types of cloud and variation characteristics of hydrometeors in cloud over China and its neighborhood in daytime[J]. Plateau Meteor, 34 (6):1625-1635. DOI:10. 7522/j. issn. 1000-0534. 2014. 00129.<br/>李浩然, 孙学金, 王旻燕, 等, 2015.中国及周边地区白天各类云及其水凝物变化特征研究[J].高原气象, 34 (6):1625-1635.
[33]Ma Z S, Liu Q J, Qin Y Y, 2016. Validation and evaluation of cloud and precipitation forecast performance by different moist physical processes schemes in GRPAES_GFS Model[J]. Plateau Meteor, 35(4):989-1003. DOI:10. 7522/j. issn. 1000-0534. 2015. 00069.<br/>马占山, 刘奇俊, 秦琰琰, 2016. GRAPES_GFS不同湿物理过程对云降水预报性能的诊断与评估[J].高原气象, 35(4):989-1003.
[34]Wang G, 2014. FY3B/IRAS data bias correction, cloud detection, quality control and assimilation test[D]. Nanjing: Nanjing University of Information Science and Technology (NUIST).<br/>王根, 2014. FY3B/IRAS资料偏差订正、云检测、质量控制和同化测试[D]. 南京: 南京信息工程大学.
Options
Outlines

/

〈 〉

The system is designed and developed by Beijing magtec Technology Development Co., Ltd. with technical support: support@magtech.com.cn