Ensemble kalman filter data assimilation in a 1d numerical model used for fog forecasting

Monthly Weather Review

Volumn 138, Issue 5, 2010, Pages 1792-1810

  Rémy, Samuel ^a   Bergot, Thierry ^a  

^a CENTRE NATIONAL DE RECHERCHES MÉTÉOROLOGIQUES   (France)

Author keywords

Data assimilation; Ensemble; Fog; Kalman filters; Numerical weather prediction forecasting

Indexed keywords

AIRPORT TRAFFIC; ASSIMILATION SYSTEM; BACKGROUND ERRORS; CLOUD FORECASTS; CROSS CORRELATIONS; DATA ASSIMILATION; ENSEMBLE; ENSEMBLE KALMAN FILTER; FOG FORECASTING; FOG FORMATION; FORECAST SYSTEMS; INITIAL CONDITIONS; NUMERICAL MODELS; POOR VISIBILITY; SHORT-TERM FORECASTS; SPECIFIC HUMIDITY;

ADAPTIVE ALGORITHMS; ATMOSPHERIC BOUNDARY LAYER; CLOUDS; DATA PROCESSING; ERROR STATISTICS; FOG; GEOLOGIC MODELS; KALMAN FILTERS; MOISTURE; NUMERICAL METHODS;

WEATHER FORECASTING;

ALGORITHM; ATMOSPHERIC MODELING; BOUNDARY LAYER; CLIMATE PREDICTION; DATA ASSIMILATION; ENSEMBLE FORECASTING; ESTIMATION METHOD; FOG; KALMAN FILTER; NUMERICAL MODEL; WEATHER FORECASTING;

EID: 77955577803     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009MWR3110.1     Document Type: Article

References (31)

1. Anderson, L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59, 210-224.
2. Anderson, L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83.
3. Anderson, L., and S. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.
4. Bergot, T., 1993: Modé lisation du brouillard á l'aide d'un mode' le 1d forcé par des champs mésoé chelle: Application á la pré - vision. Ph.D. thesis, Université Paul Sabatier, 192 pp.
5. Bergot, T., and D. Guédalia, 1994a: Numerical forecasting of radiation fog. Part I: Numerical model and sensitivity tests. Mon. Wea. Rev., 122, 1218-1230.
6. Bergot, T., and D. Guédalia, 1994b: Numerical forecasting of radiation fog. Part II: A comparison of model simulation with several observed fog events. Mon. Wea. Rev., 122, 1231-1246.
7. Bergot, T., D. Carrer, J. Noilhan, and P. Bougeault, 2005: Improved sitespecific numerical prediction of fog and low clouds: A feasibility study. Wea. Forecasting, 20, 627-646.
8. Boone, A., 2000: Modé lisation des processus hydrologiques dans le schéma de surface isba: Inclusion d'un réservoir hydrologique, du gel et modé lisation de la neige. Ph.D. thesis, Université Paul Sabatier, 207 pp.
9. Bougeault, P., and P. Lacarrere, 1989: Parameterization of orographyinduced turbulence in a mesoscale model. Mon. Wea. Rev., 117, 1872-1890.
10. Burgers, G., P. V. Leuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
11. Clark, D., 2002: The 2001 demonstration of automated cloud forecast guidance products for San Francisco International Airport. Preprints, 10th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., JP1.26. [Available online at http://ams.confex.com/ams/13ac10av/techprogram/paper_38862.htm.]
12. Clark, D., 2006: Terminal ceiling and visibility product development for northeast airports. Preprints, 12th Conf. on Aviation, Range, and Aerospace Meteorology, Atlanta, GA, Amer. Meteor. Soc., P1.15. [Available online at http://ams.confex.com/ams/Annual2006/techprogram/paper_103710.htm.]
13. Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385-3396.
14. Estournel, C., 1988: Etude de la phase nocturne de la couche limite atmospherique. Ph.D. thesis, Université Paul Sabatier, 161 pp.
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, 10 142-10 162.
16. Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
17. Hacker, J., and C. Snyder, 2005: Ensemble kalman filter assimilation of fixed screen-height observations in a parametrized PBL. Mon. Wea. Rev., 133, 3260-3275.
18. Hacker, J., and D. Rostkier-Edelstein, 2007: PBL state estimation with surface observations, a column model, and an ensemble filter. Mon. Wea. Rev., 135, 2958-2972.
19. Hamill, M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560.
20. Hamill, M., J. Whitaker, and C. Snyder, 2001: Distance-dependant filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
21. Herzegh, P., S. Benjamin, R. Rasmussen, T. Tsui, G. Wiener, and P. Zwack, 2003: Development of automated analysis and forecast products for adverse ceiling and visibility conditions. Preprints, 19th Int. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Long Beach, CA, Amer. Meteor. Soc., 9.3. [Available online at http://ams.confex.com/ams/pdfpapers/57911.pdf.]
22. Hou, D., E. Kalnay, and K. Droegemeier, 2001: Objective verification of the SAMEX'98 ensemble forecasts. Mon. Wea. Rev., 129, 73-91.
23. Houtekamer, P., and H. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
24. Houtekamer, P., H. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
25. Huang, X., H. Wang, Y. Chen, X. Zhang, S. Tjernkes, and R. Stuhlmann, 2007: An observing system simulation experiment using both MM5 andWRF: Experiment configuration and preliminary results. Extended Abstracts, Eighth Annual WRF User's Workshop, Boulder, CO, University Corporation for Atmospheric Research. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/p2-2_Huang.pdf.]
26. Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.
27. Parrish, D., and J. Derber, 1992: The National Meteorological Center spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
28. Rémy, S., and T. Bergot, 2009: Assessing the impact of observations on a local numerical fog prediction system. Quart. J. Roy. Meteor. Soc., 135, 1248-1265.
29. Roquelaure, S., and T.Bergot, 2007: Seasonal sensitivity onCOBELISBA local forecast system for fog and low clouds. Pure Appl. Geophys., 164, 1283-1301.
30. Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proc. ECMF Workshop on Predictability, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 1-26.
31. Zhang, F., 2005: Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev., 133, 2876-2893.