Localizing the impact of satellite radiance observations using a global group ensemble filter

Journal of Advances in Modeling Earth Systems

Volumn 8, Issue 2, 2016, Pages 719-734

  Lei, Lili ^a,b,c   Anderson, Jeffrey L ^d   Whitaker, Jeffrey S ^c  

^a NANJING UNIVERSITY   (China)

^b UNIVERSITY OF COLORADO   (United States)

^c NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^d NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

data assimilation; ensemble Kalman filter; localization; radiance observation

Indexed keywords

BANDPASS FILTERS; CARRIER CONCENTRATION; ERRORS; FORECASTING; KALMAN FILTERS; SATELLITES; SOUNDING APPARATUS; WEATHER FORECASTING;

BACKGROUND-ERROR COVARIANCES; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; HIERARCHICAL ENSEMBLE; LOCALIZATION; LOCALIZATION FUNCTIONS; NUMERICAL WEATHER PREDICTION; RADIANCE OBSERVATION;

TRACKING (POSITION);

EID: 84971343360     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2016MS000627     Document Type: Article

References (47)

1. Aksoy, A., D. C. Dowell, and C. Snyder (2009), A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses, Mon. Weather Rev., 137, 1805–1824.
2. Anderson, J. L. (2007), Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111.
3. Anderson, J. L. (2009), Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, Ser. A, 61, 72–83.
4. Anderson, J. L. (2012), Localization and sampling error correction in ensemble Kalman filter data assimilation, Mon. Weather Rev., 140, 2359–2371.
5. Anderson, J. L. (2016), Reducing correlation sampling error in ensemble Kalman filter data assimilation, Mon. Weather Rev., 144, 913–925.
6. Anderson, J. L., and S. L. Anderson (1999), A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758.
7. Anderson, J. L., and L. Lei (2013), Empirical localization of observation impact in ensemble Kalman filters, Mon. Weather Rev., 141, 4140–4153.
8. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He (2010a), Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments, Mon. Weather Rev., 138, 1550–1566.
9. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He (2010b), Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations, Mon. Weather Rev., 138, 1567–1586.
10. Burgers, G., P. J. van Leeuwen, and G. Evensen (1998), Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev. 126, 1719–1724.
11. Campbell, W. F., C. H. Bishop, and D. Hodyss (2010), Vertical covariance localization for satellite radiances in ensemble Kalman filters, Mon. Weather Rev., 138, 282–290.
12. Caron, J.-F., and L. Fillion (2010), An examination of background error correlations between mass and rotational wind over precipitation regions, Mon. Weather Rev., 138, 563–578.
13. Collard, A. D., and A. P. McNally (2009), The assimilation of infrared atmospheric sounding interferometer radiances at ECMWF, Q. J. R. Meteorol. Soc., 135, 1044–1058.
14. Derber, J. C., and W.-S. Wu (1998), The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system, Mon. Weather Rev., 126, 2287–2299.
15. Evensen, G. (1994), Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99(C5), 10,143-10,162.
16. Fertig, E. J., B. R. Hunt, E. Ott, and I. Szunyogh (2007), Assimilating non-local observations with a local ensemble Kalman filter, Tellus, Ser. A, 59, 719–730.
17. Gaspari, G., and S. E. Cohn (1999), Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723–757.
18. Hamill, T. M., J. S. Whitaker, and C. Snyder (2001), Distance-dependent filtering of background-error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790.
19. Hamurd, M., M. Bonavita, and L. Isaksen (2015), EnKF and hybrid gain ensemble Kalman filter data assimilation. Part I: EnKF implementation, Mon. Weather Rev., 143, 4847–4864.
20. Han, Y., F. Weng, Q. Liu, and P. van Delst (2007), A fast radiative transfer model for SSMIS upper atmosphere sounding channels, J. Geophys. Res., 112, D11121, doi:10.1029/2006JD008208.
21. Houtekamer, P. L., and H. L. Mitchell (2001), A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137.
22. Houtekamer, P. L., and H. L. Mitchell (2005), Ensemble Kalman filtering, Q. J. R. Meteorol. Soc., 131, 3269–3289.
23. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen (2005), Atmospheric data assimilation with the ensemble Kalman filter: Results with real observations, Mon. Weather Rev., 133, 604–620.
24. Houtekamer, P. L., X. Deng, H. L. Mitchell, S. Baek, and N. Gagnon (2014), Higher resolution in an operational ensemble Kalman filter, Mon. Weather Rev., 142, 1143–1162.
25. Kalman, R. E. (1960), A new approach to linear filtering and prediction problems, J. Basic Eng., 82D, 35–45.
26. Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W. Wu, and S. Lord (2009), Introduction of the GSI into NCEP Global Data Assimilation System, Weather Forecasting, 24, 1691–1705.
27. Lei, L., and J. L. Anderson (2014a), Comparisons of empirical localization techniques for serial ensemble Kalman filters in a simple atmospheric general circulation model, Mon. Weather Rev., 142, 739–754.
28. Lei, L., and J. L. Anderson (2014b), Empirical localization of observations for serial ensemble Kalman filter data assimilation in an Atmospheric General Circulation Model, Mon. Weather Rev., 142, 1835–1851.
29. Lei, L., and J. S. Whitaker (2015), Model space localization is not always better than observation space localization for assimilation of satellite radiances, Mon. Weather Rev., 143(10), 3948–3955.
30. Lei, L., J. L. Anderson, and G. S. Romine (2015), Empirical localization functions for ensemble Kalman filter data assimilation in regions with and without precipitation, Mon. Weather Rev., 143, 3664–3679.
31. Le Marshall, J., et al. (2006), Improving global analysis and forecasting with AIRS, Bull. Am. Meteorol. Soc., 87, 891–894.
32. McCarty, W., G. Jedlovec, and T. L. Miller (2009), Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts, J. Geophys. Res., 114, D18122, doi:10.1029/2008JD011626.
33. McNally, A. P., P. D. Watts, J. A. Smith, R. Engelen, G. A. Kelly, J. N. Thepaut, and M. Matricardi (2006), The assimilation of AIRS radiance data at ECMWF, Q. J. R. Meteorol. Soc., 132, 935–957.
34. Miyoshi, T., and Y. Sato (2007), Assimilating satellite radiances with a local ensemble transform Kalman filter (LETKF) applied to the JMA global model (GSM), SOLA, 3, 37–40.
35. Miyoshi, T., Y. Sato, and T. Kadowaki (2010), Ensemble Kalman filter and 4D-Var intercomparison with the Japanese Operational Global Analysis and Prediction System, Mon. Weather Rev., 138, 2846–2866.
36. Montmerle, T., and L. Berre (2010), Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale, Q. J. R. Meteorol. Soc., 136, 1408–1420.
37. Ota, Y., J. C. Debber, E. Kalnay, and T. Miyoshi (2013), Ensemble-based observation impact estimates using the NCEP GFS, Tellus Ser. A, 65, 20038.
38. Otkin, J. A. (2012), Assessing the impact of the covariance localization radius when assimilating infrared brightness temperature observations using an ensemble Kalman filter, Mon. Weather Rev., 140, 543–561.
39. Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, and A. Weisheimer (2009), Stochastic parameterization and model uncertainty, ECMWF Tech. Memo. 598, 44 pp., Eur. Cent. for Medium-Range Weather Forecasts, Reading, U. K. [Available at http://www.ecmwf.int/sites/default/files/elibrary/2009/11577-stochastic-parametrization-and-model-uncertainty.pdf.]
40. Snyder, C., and F. Zhang (2003), Assimilation of simulated Doppler radar observations with an ensemble Kalman filter, Mon. Weather Rev., 131, 1663–1677.
41. Tong, M., and M. Xue (2005), Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments, Mon. Weather Rev., 133, 1789–1807.
42. Weng, F. (2007), Advances in radiative transfer modeling in support of satellite data assimilation, J. Atmos. Sci., 64, 3799–3807.
43. Whitaker, J. S., and T. M. Hamill (2002), Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924.
44. Whitaker, J. S., and T. M. Hamill (2012), Evaluating methods to account for system errors in ensemble data assimilation, Mon. Weather Rev., 140, 3078–3089.
45. Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth (2008), Ensemble data assimilation with the NCEP Global Forecast System, Mon. Weather Rev., 136, 463–482.
46. Wu, W. S., R. J. Purser, and D. F. Parrish (2002), Three-dimensional variational analysis with spatially inhomogeneous covariances, Mon. Weather Rev., 130, 2905–2916.
47. Zapotocny, T. H., J. A. Jung, J. F. Le Marshall, and R. E. Treadon (2008), A two-season impact study of four satellite data types and rawinsonde data in the NCEP Global Data Assimilation System, Weather Forecasting, 23, 80–100.