Observation impact in a convective-scale localized ensemble transform Kalman filter

Quarterly Journal of the Royal Meteorological Society

Volumn 140, Issue 685, 2014, Pages 2672-2679

  Sommer, Matthias ^a   Weissmann, Martin ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Data assimilation; Forecast sensitivity to observations; FSO

Indexed keywords

EARTH ATMOSPHERE; METEOROLOGY;

ADJOINT MODELS; APPROXIMATION METHODS; DATA ASSIMILATION; FORECAST ERRORS; FORECAST SENSITIVITY; MODELLING SYSTEMS; NUMBER OF DATUM; OBSERVATION IMPACTS;

FORECASTING;

EID: 84922837566     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2343     Document Type: Article

References (28)

1. Ancell B, Hakim GJ. 2007. Comparing adjoint- and ensemble-sensitivity analysis with applications to observation targeting. Mon. Weather Rev. 135: 4117-4134.
2. Baker NL, Daley R. 2000. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q. J. R. Meteorol. Soc. 126: 1431-1454.
3. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendorfer M, Reinhardt T. 2011. Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Weather Rev. 139: 3887-3905.
4. Bouttier F, Kelly G. 2001. Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q. J. R. Meteorol. Soc. 127: 1469-1488.
5. Brousseau P, Desroziers G, Bouttier F, Chapnik B. 2013. A posteriori diagnostics of the impact of observations on the AROME-France convective-scale data assimilation system. Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2179.
6. Cardinali C. 2009. Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc. 135: 239-250.
7. Cardinali C, Pezzulli S, Andersson E. 2004. Influence-matrix diagnostic of a data assimilation system. Q. J. R. Meteorol. Soc. 130: 2767-2786.
8. Desroziers G, Brousseau P, Chapnik B. 2005. Use of randomization to diagnose the impact of observations on analyses and forecasts. Q. J. R. Meteorol. Soc. 131: 2821-2837.
9. English SJ, Saunders R, Candy B, Forsythe M, Collard AD. 2004. Met Office satellite data OSEs. In Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, Böttger, H, Menzel, P, Pailleux, J. (eds.), WMO/TD No1228: 146-156, World Meteorological Organization: Geneva, Switzerland.
10. Gelaro R, Langland RH, Pellerin S, Todling R. 2010. The THORPEX observation impact intercomparison experiment. Mon. Weather Rev. 138: 4009-4025.
11. Hakim GJ, Torn RD. . 2008. Ensemble synoptic analysis. In Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting, Lance, FB, Howard, BB. (eds.): 147-161. Springer: Berlin.
12. Hamill TM, Whitaker JS, Snyder C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Weather Rev. 129: 2776-2790.
13. Harnisch F, Weissmann M, Cardinali C, Wirth M. 2011. Experimental assimilation of dial water vapour observations in the ECMWF global model. Q. J. R. Meteorol. Soc. 137: 1532-1546.
14. Hunt BR, Kostelich EJ, Szunyogh I. 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230: 112-126.
15. Kalnay E, Ota Y, Miyoshi T, Liu J. 2012. A simpler formulation of forecast sensitivity to observations: Application to ensemble Kalman filters. Tellus A 64: 18462, doi: 10.3402/tellusa.v64i0.18462.
16. Kunii M, Miyoshi T, Kalnay E. 2012. Estimating the impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Weather Rev. 140: 1975-1987.
17. Langland RH. 2005. Observation impact during the North Atlantic TReC-2003. Mon. Weather Rev. 133: 2297-2309.
18. Langland RH, Baker NL. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A 56: 189-201.
19. Li H, Liu J, Kalnay E. 2010. Correction of 'Estimating observation impact without adjoint model in an ensemble Kalman filter'. Q. J. R. Meteorol. Soc. 136: 1652-1654.
20. Liu J, Kalnay E. 2008. Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 134: 1327-1335, doi: 10.1002/qj.280.
21. Liu J, Kalnay E, Miyoshi T, Cardinali C. 2009. Analysis sensitivity calculation in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 135: 1842-1851.
22. Lorenc AC, Marriott RT. 2014. Forecast sensitivity to observations in the Met Office global numerical weather prediction system. Q. J. R. Meteorol. Soc. 140: 209-224.
23. Ota Y, Derber J, Kalnay E, Miyoshi T. 2013. Ensemble-based observation impact estimates using the NCEP GFS. Tellus A 65: 20038, doi: 10.3402/tellusa.v65i0.20038.
24. Rabier F. 2005. Overview of global data assimilation developments in numerical weather-prediction centres. Q. J. R. Meteorol. Soc. 131: 3215-3233.
25. Reich H, Rhodin A, Schraff C, 2011 'LETKF for the nonhydrostatic regional model COSMO-DE' Newsletter11, Consortium for Small-scale Modelling. http://www.cosmo-model.org/content/model/documentation/newsLetters(accessed 13 February 2014).
26. Torn RD, Hakim GJ. 2008. Ensemble-based sensitivity analysis. Mon. Weather Rev. 136: 663-677.
27. Weissmann M, Harnisch F, Wu CC, Lin PH, Ohta Y, Yamashita K, Kim YH, Jeon EH, Nakazawa T, Aberson S. 2011. The influence of assimilating dropsonde data on typhoon track and midlatitude forecasts. Mon. Weather Rev. 139: 908-920.
28. Weissmann M, Langland RH, Cardinali C, Pauley P, Rahm S. 2012. Influence of airborne Doppler wind lidar profiles near typhoon Sinlaku on ECMWF and NOGAPS forecasts. Q. J. R. Meteorol. Soc. 138: 118-130.