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Ensemble-based approximation of observation impact using an observation-based verification metric Cover

Ensemble-based approximation of observation impact using an observation-based verification metric

Open Access
|Dec 2016

References

  1. Baker N. L. , Daley R . Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q. J. Roy. Meteorol. Soc. 2000; 126(565): 14311454.
  2. Baker W. E. , Atlas R. , Cardinali C. , Clement A. , Emmitt G. D. , co-authors . Lidar-measured wind profiles: the missing link in the global observing system. Bull. Am. Meteorol. Soc. 2014; 95(4): 543564.
  3. Baldauf M. , Seifert A. , Förstner J. , Majewski D. , Raschendorfer M. , co-authors . Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mon. Weather Rev. 2011; 139(12): 38873905.
  4. Brousseau P., Desroziers G., Bouttier F., Chapnik B. A posteriori diagnostics of the impact of observations on the AROME-France convective-scale data assimilation system. Q. J. Roy. Meteorol. Soc. 2013; 140(680): 982994. DOI: http://dx.doi.org/10.1002/qj.2179.
  5. Cardinali C . Monitoring the observation impact on the short-range forecast. Q. J.Roy. Meteorol. Soc. 2009; 135(638): 239250.
  6. Cardinali C. , Pezzulli S. , Andersson E . Influence-matrix diagnostic of a data assimilation system. Q. J. Roy. Meteorol. Soc. 2004; 130(603): 27672786.
  7. Gasperoni N. A. , Wang X . Adaptive localization for the ensemble-based observation impact estimate using regression confidence factors. Mon. Weather Rev. 2015; 143(6): 19812000.
  8. Gelaro R. , Langland R. H. , Pellerin S. , Todling R . The THORPEX observation impact intercomparison experiment. Mon. Weather Rev. 2010; 138(11): 40094025.
  9. Gelaro R. , Zhu Y . Examination of observation impacts derived from observing system experiments (OSES) and adjoint models. Tellus A. 2009; 61(2): 179193.
  10. Harnisch F. , Keil C . Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation. Mon. Weather Rev. 2015; 143(5): 15831600.
  11. Harnisch F. , Weissmann M. , Cardinali C. , Wirth M . Experimental assimilation of dial water vapour observations in the ECMWF global model. Q. J. Roy. Meteorol. Soc. 2011; 137(659): 15321546.
  12. Hotta D . Proactive Quality Control Based on Ensemble Forecast Sensitivity to Observations. 2014; Graduate School of the University of Maryland, College Park. Doctoral thesis.
  13. Hunt B. R. , Kostelich E. J. , Szunyogh I . Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys. D: Nonlin. Phenom. 2007; 230(1): 112126.
  14. Kalnay E., Ota Y., Miyoshi T., Liu J. A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters. Tellus A. 2012; 64Online at: http://www.tellusa.net/index.php/tellusa/article/view/18462.
  15. Kostka P. M. , Weissmann M. , Buras R. , Mayer B. , Stiller O . Observation operator for visible and near-infrared satellite reflectances. J. Atmos. Ocean. Technol. 2014; 31(6): 12161233.
  16. Kunii M. , Miyoshi T. , Kalnay E . Estimating the impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Weather Rev. 2012; 140(6): 19751987.
  17. Langland R. H . Observation impact during the north Atlantic TReC-2003. Mon. Weather Rev. 2005; 133(8): 22972309.
  18. Langland R. H. , Baker N. L . Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A. 2004; 56(3): 189201.
  19. Langland R. H. , Rohaly G. D . Adjoint-Based Targeting of Observations for FASTEX Cyclones 9–13 September 1996. . 1996. Technical Report, DTIC Document, Seventh Mesoscale processes conference, University of Reading, UK.
  20. Li H. , Liu J. , Kalnay E . Correction of estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. Roy. Meteorol. Soc. 2010; 136(651): 16521654.
  21. Liu J., Kalnay E. Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. Roy. Meteorol. Soc. 2008; 134(634): 13271335. DOI: http://dx.doi.org/10.1002/qj.280.
  22. Liu J. , Kalnay E. , Miyoshi T. , Cardinali C . Analysis sensitivity calculation in an ensemble Kalman filter. Q. J. Roy. Meteorol. Soc. 2009; 135(644): 18421851.
  23. Lorenc A. C. , Marriott R. T . Forecast sensitivity to observations in the met office global numerical weather prediction system. Q. J. Roy. Meteorol. Soc. 2013; 140(678): 209224.
  24. Ota Y. , Derber J. , Kalnay E. , Miyoshi T . Ensemble-based observation impact estimates using the NCEP GFS. Tellus A. 2013; 65: 20038.
  25. Rabier F. , Järvinen H. , Klinker E. , Mahfouf J-F. , Simmons A . The ECMWF operational implementation of four-dimensional variational assimilation. I: experimental results with simplified physics. Q. J. Roy. Meteorol. Soc. 2000; 126(564): 11431170.
  26. Rabier F. , Klinker E. , Courtier P. H. , Hollingsworth A . Sensitivity of forecast errors to initial conditions. Q. J. Roy. Meteorol. Soc. 1996; 122(529): 121150.
  27. Schomburg A., Schraff C., Potthast R. A concept for the assimilation of satellite cloud information in an Ensemble Kalman Filter: single-observation experiments. Quarterly Journal of the Royal Meteorological Society. 2015; 141(688): 893908. DOI: http://dx.doi.org/10.1002/qj.2407.
  28. Schraff C. , Reich H. , Rhodin A. , Schomburg A. , Stephan K. , co-authors . Kilometre-scale ensemble data assimilation for the COSMO Model (KENDA). Q. J. Roy. Meteorol. Soc. 2016; 142(696): 14531472.
  29. Simmer C., Adrian G., Jones S., Wirth V., Göber M., co-authors. HErZ – The German Hans-Ertel Centre for weather research. Bull. Am. Meteorol. Soc. 2016. DOI: http://dx.doi.org/10.1175/BAMS-D-13-00227.1.
  30. Sommer M., Weissmann M. Observation impact in a convective-scale localized ensemble transform Kalman filter. Q. J. Roy. Meteorol. Soc. 2014; 140(685): 26722679. DOI: http://dx.doi.org/10.1002/qj.2343.
  31. Wahba G. , Johnson D. R. , Gao F. , Gong J . Adaptive tuning of numerical weather prediction models: randomized GCV in three-and four-dimensional data assimilation. Mon. Weather Rev. 1995; 123(11): 33583370.
  32. Weissmann M. , Folger K. , Lange H . Height correction of atmospheric motion vectors using airborne lidar observations. J. Appl. Meteorol. Climatol. 2013; 52(8): 18681877.
  33. Weissmann M. , Göber M. , Hohenegger C. , Janjic T. , Keller J. , co-authors . Initial phase of the Hans-Ertel Centre for weather research – a virtual centre at the interface of basic and applied weather and climate research. Meteorol. Z. 2014; 23(3): 193208.
  34. Weissmann M. , Harnisch F. , Wu C.-C. , Lin P.-H. , Ohta Y. , co-authors . The influence of assimilating dropsonde data on typhoon track and midlatitude forecasts. Mon. Weather Rev. 2011; 139(3): 908920.
  35. Weissmann M. , Langland R. H. , Cardinali C. , Pauley P. M. , Rahm S . Influence of airborne doppler wind lidar profiles near typhoon Sinlaku on ECMWF and NOGAPS forecasts. Q. J. Roy. Meteorol. Soc. 2012; 138(662): 118130.
Language: English
Page range: 27885 - 27885
Submitted on: Mar 16, 2016
Accepted on: Jun 1, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2016 Matthias Sommer, Martin Weissmann, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.