References
- Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc., 135, 239-250. https://doi.org/10.1002/qj.366
- Gelaro, R., R. H. Langland, S. Pellerin, and R. Todling, 2010: The THORPEX Observation Impact Intercomparison Experiment. Mon. Wea. Rev., 138, 4009-4025, doi:10.1175/2010MWR3393.1.
- Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Q. J. R. Meteorol. Soc., 127, 1453-1468. https://doi.org/10.1002/qj.49712757418
- Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting. Asia-Pac. J. Atmos. Sci., 54, 267-292, doi:10.1007/s13143-018-0028-9.
- Hotta, D., T.-C. Chen, E. Kalnay, Y. Ota, and T. Miyoshi, 2017: Proactive QC: a fully flow-dependent quality control scheme based on EFSO. Mon. Wea. Rev., 145, 3331-3354, doi:10.1175/MWR-D-16-0290.1.
- Kalnay, E., Y. Ota, T. Miyoshi, and J. Liu, 2012: A simpler formulation of forecast sensitivity to observations: Application to ensemble Kalman filters. Tellus A, 64, 18462, doi:10.3402/tellusa.v64i0.18462.
- Kelly, G., J.-N. Thepaut, R. Buizza, and C. Cardinali, 2007: The value of observations. I: Data denial experiments for the Atlantic and the Pacific. Q. J. R. Meteorol. Soc., 133, 1803-1815. https://doi.org/10.1002/qj.150
- Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A, 56, 189-201. https://doi.org/10.1111/j.1600-0870.2004.00056.x
- Lorenc, A. C., and R. T. Marriott, 2014: Forecast sensitivity to observations in the Met Office global numerical weather prediction system. Q. J. R. Meteorol. Soc., 140, 209-224, doi:10.1002/qj.2122.
- Miyoshi, T., S. Yamane, and T. Enomoto, 2007: Localizing the error covariance by physical distances within a Local Ensemble Transform Kalman Filter (LETKF). SOLA, 3, 89-92. https://doi.org/10.2151/sola.2007-023
- Ota, Y., J. C. Derber, E. Kalnay, and T. Miyoshi, 2013: Ensemble-based observation impact estimates using the NCEP GFS. Tellus A, 65, 20038, doi:10.3402/tellusa.v65i0.20038.
- Schulze, G. C., 2007: Atmospheric observations and numerical weather prediction. S. Afr. J. Sci., 103, 318-323.
- Shin, S., J.-S. Kang, and Y. Jo, 2016: The local ensemble transform Kalman filter (LETKF) with a global NWP model on the cubed sphere. Pure Appl. Geophys., 173, 2555-2570, doi:10.1007/s00024-016-1269-0.
- Shin, S., and Coauthors, 2018: Real data assimilation using the Local Ensemble Transform Kalman Filter (LETKF) system for a global non-hydrostatic NWP model on the cubed-sphere. Asia-Pac. J. Atmos. Sci., 54, 351-360, doi:10.1007/s13143-018-0022-2.
- Sienkiewicz, J. M., 1990: An example of the importance of ship observations. Wea. Forecasting, 5, 683-687. https://doi.org/10.1175/1520-0434(1990)005<0683:AEOTIO>2.0.CO;2
- Sommer, M., and M. Weissmann, 2014: Observation impact in a convective-scale localized ensemble transform Kalman filter. Q. J. R. Meteorol. Soc., 140, 2672-2679, doi:10.1002/qj.2343.
- Zhu, Y., and R. Gelaro, 2008: Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Rev., 136, 335-351. https://doi.org/10.1175/MWR3525.1