Measuring information content from observations for data assimilations: Connection between different measures and application to radar scan design

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 61 A, Issue 1, 2009, Pages 144-153

  Xu, Qin ^a   Wei, Li ^b   Healy, Sean ^c  

^a NATIONAL SEVERE STORMS LABORATORY   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

^c EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ASSIMILATION; RADAR; STATISTICAL ANALYSIS; WEATHER FORECASTING;

EID: 58049104463     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2008.00373.x     Document Type: Article

References (31)

1. Bennett, A. F. 1992. Inverse Method in Physical Oceanography. Cambridge University Press, New York, 346 pp.
2. Cohn, S. 1997. An introduction to estimation theory. J. Metrorol. Soc. Japan 75, 257-288.
3. Courtier, P. 1997. Dual formulation of four dimensional variational assimilation. Quart. J. Roy. Meteorol. Soc. 123, 2449-2461.
4. Daley, R. and Barker, E. 2001. NAVDAS: Formulation and diagnostics. Mon. Wea. Rev. 129, 869-883.
5. Doviak, J. D. and Zrnic, D. S. 1993. Doppler Radar and Weather Observations. 2nd Edition. Academic Press, New York, 562 pp.
6. Eyre, J. R. 1990. The information content of data from satellite sounding systems. A simulation study. Quart. J. Roy. Meteorol. Soc. 116, 401-434.
7. Fisher, M. 2003. Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems. Technical Memorandum No. 397. ECMWF, Shinfield Park, Reading. [available on line http://www.ecmwf.int/publications/library/do/references/show?id=83951]
8. Healy, S. B. and White, A. A. 2005. Use of discrete Fourier transforms in the 1D-Var retrieval problem. Quart. J. Roy. Meteorol. Soc. 131, 63-72.
9. Healy, S. B. and Thépaut, J.-N. 2006. Assimilation experiments with CHAMP GPS radio occultation measurements. Quart. J. Roy. Meteorol. Soc. 132, 605-623.
10. Hodur, R. M. 1997. The Naval Research Laboratory's coupled ocean/ atmosphere mesoscale prediction system (COAMPS). Mon. Wea. Rev. 125, 1414-1430.
11. Huang, H.-L. and Purser, R. J. 1996. Objective measures of the information density of satellite data. Meteorol. Atmos. Phys. 60, 105-117.
12. Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory. Academic Press, San Diego, 376 pp.
13. Kleeman, R. 2002. Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci. 59, 2057-2072.
14. LeDimet, F. X. and Talagrand, O. 1986. Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus 38 A, 97-111.
15. Lewis, J. and Derber, J. 1985. The use of adjoint equations to solve a variational adjustment problem with advective constraint. Tellus 37 A, 309-322.
16. Lorenc, A. 1981. A global three-dimensional multivariate statistical analysis system. Mon. Wea. Rev. 109, 701-721.
17. Lorenc, A. C. 2003. Modeling of error covariances by 4D-Var data assimilation. Quart. J. Roy. Meteorol. Soc. 129, 3167-3182.
18. Majda, A. J., Kleeman, R. and Cai, D. 2002. A mathematical framework for quantifying predictability through relative entropy. Methods Appl. Anal. 9, 425-444.
19. Peckham, G. 1974. The information content of remote measurements of atmospheric temperature by satellite infra-red radiometry and optimum radiometer configurations. Quart. J. Roy. Meteorol. Soc. 100, 406-419.
20. Purser, R. J., Parrish, D. F. and Masutani, M. 2000. Meteorological observational data compression; An alternative to conventional "super-Obbing". Office Note 430, National Centers for Environmental Prediction, Camp Springs, MD, 12 pp. [available on line http://www.emc.ncep.noaa.gov/officenotes/FullTOC.html#2000]
21. Qiu, C. and Xu, Q. 1992. A simple adjoint method of wind analysis for single-Doppler data. J. Atmos. Oceanic Technol. 9, 588-598.
22. Rodgers, C. D. 2000. Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific Publishing Co. Ltd., London, 256 pp.
23. Shannon, C. E. 1949. Communication in the presence of noise. Proc. I.C.E. 37, 10-21.
24. Strikwerda, J. C. 2004. Finite Difference Schemes and Partial Differential Equations. Chapman & Hall, Philadelphia, 450 pp.
25. Wahba, G., Johnson, D. R., Gao, F. and Gong, J. 1995. Adaptive tuning of numerical weather prediction models: Randomized GCV in three- and four-dimensional data assimilation. Mon. Wea. Rev. 123, 3358-3370.
26. Xu, Q. 2007. Measuring information content from observations for data assimilation: Relative entropy versus Shannon entropy difference. Tellus 59A, 198-209.
27. Xu, Q., Gu, H. and Qiu, C. J. 2001a. Simple adjoint retrievals of wet-microburst winds and gust-front winds from single-Doppler radar data. J. Appl. Meteorol. 40, 1485-1499.
28. Xu, Q., Gu, H. and Yang, S. 2001b. Simple adjoint method for three-dimensional wind retrievals from single-Doppler radar. Quart. J. Roy. Meteorol. Soc. 127, 1053-1067.
29. Xu, Q., Nai, K. and Wei, L. 2007. An innovation method for estimating radar radial-velocity observation error and background wind error covariances. Quart. J. Roy. Meteorol. Soc. 133, 407-415.
30. Xu, Q., Qiu, C., Gu, H. D. and Yu, J. 1995. Simple adjoint retrievals of microburst winds from single-Doppler radar data. Mon. Wea. Rev. 123, 1822-1833.
31. Zrnic, D. S., Kimpel, J. F., Forsyth, D. F., Shapiro, A., Crain, G. and co-authors. 2007. Agile-beam phased array radar for weather observations. Bull. Amer. Meteorol. Soc. 88, 1753-1766.