Skip to main content
Have a personal or library account? Click to login
Flow-Dependent Large-Scale Blending for Limited-Area Ensemble Data Assimilation Cover

Flow-Dependent Large-Scale Blending for Limited-Area Ensemble Data Assimilation

Open Access
|Feb 2025

References

  1. Baxter, G.M., Dance, S.L., Lawless, A.S. and Nichols, N.K. (2011) Four-dimensional variational data assimilation for high resolution nested models. Computers & Fluids, 46(1): 137141. DOI: 10.1016/j.compfluid.2011.01.023
  2. Berre, L. (2000) Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev., 128(3): 644667. DOI: 10.1175/1520-0493(2000)128<;0644:EOSAMF>2.0.CO;2
  3. Berry, T. and Sauer, T. (2018) Correlation between system and observation errors in data assimilation. Mon. Wea. Rev., 146(9): 29132931. DOI: 10.1175/MWR-D-17-0331.1
  4. Bučánek, A. and Brožková, R. (2017) Background error covariances for a BlendVar assimilation system. Tellus A, 69(1): 1355718. DOI: 10.1080/16000870.2017.1355718
  5. Caron, J.-F. (2013) Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting: A case study. Mon. Wea. Rev., 141(1): 356374. DOI: 10.1175/MWR-D-12-00051.1
  6. Dahlgren, P. and Gustafsson, N. (2012) Assimilating host model information into a limited area model. Tellus A, 64(1): 15836. DOI: 10.3402/tellusa.v64i0.15836
  7. Davies, H.C. (1976) A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102(432): 405418. DOI: 10.1002/qj.49710243210
  8. Denis, B., Côté, J. and Laprise, R. (2002) Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the discrete cosine transform (DCT). Mon. Wea. Rev., 130(7): 18121829. DOI: 10.1175/1520-0493(2002)130<;1812:SDOTDA>2.0.CO;2
  9. Enomoto, T. and Nakashita, S. (2024) Application of exact newton optimisation to the maximum likelihood ensemble filter. Tellus A, 76(1): 4256. DOI: 10.16993/tellusa.3255
  10. Feng, J., Sun, J. and Zhang, Y. (2020) A dynamic blending scheme to mitigate large-scale bias in regional models. J. Adv. Model. Earth Syst., 12(3): e2019MS001754. DOI: 10.1029/2019MS001754
  11. Fukui, S. and Murata, A. (2021) Sensitivity to horizontal resolution of regional climate model in simulated precipitation over Kyushu in Baiu season. SOLA, 17: 207212. DOI: 10.2151/sola.2021-036
  12. Gainford, A., Gray, S.L., Frame, T.H.A., Porson, A.N. and Milan, M. (2024) Improvements in the spread-skill relationship of precipitation in a convective-scale ensemble through blending. Quart. J. Roy. Meteor. Soc., 150(762): 31463166. DOI: 10.1002/qj.4754
  13. Gaspari, G. and Cohn, S.E. (1999) Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125(554): 723757. DOI: 10.1002/qj.49712555417
  14. Guidard, V. and Fischer, C. (2008) Introducing the coupling information in a limited-area variational assimilation. Quart. J. Roy. Meteor. Soc., 134(632): 723735. DOI: 10.1002/qj.215
  15. Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y. and Fujita, T. (2018) Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144(713): 12181256. DOI: 10.1002/qj.3179
  16. Harville, D.A. (1997) Matrix algebra from a statistician’s perspective. 1st ed. New York: Springer. DOI: 10.1007/b98818
  17. Hsiao, L.-F., Huang, X.-Y., Kuo, Y.-H., Chen, D.-S., Wang, H., Tsai, C.-C., Yeh, T.-C., Hong, J.-S., Fong, C.-T. and Lee, C.-S. (2015) Blending of global and regional analyses with a spatial filter: Application to typhoon prediction over the western North Pacific Ocean. Wea. Forecasting, 30(3): 754770. DOI: 10.1175/WAF-D-14-00047.1
  18. Hu, G., Dance, S.L., Bannister, R.N., Chipilski, H.G., Guillet, O., Macpherson, B., Weissmann, M. and Yussouf, N. (2023) Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021. Atmos. Sci. Lett., 24(1): e1130. DOI: 10.1002/asl.1130
  19. Hunt, B.R., Kostelich, E.J. and Szunyogh, I. (2007) Efficient data assimilation for spatiotemporal chaos: A local ensemble transform kalman filter. Physica D., 230(1): 112126. DOI: 10.1016/j.physd.2006.11.008
  20. Johnson, A., Wang, X., Carley, J.R., Wicker, L.J. and Karstens, C. (2015) A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143(8): 30873108. DOI: 10.1175/MWR-D-14-00345.1
  21. Juang, H.-M.H. and Kanamitsu, M. (1994) The NMC nested regional spectral model. Mon. Wea. Rev., 122(1): 326. DOI: 10.1175/1520-0493(1994)122<;0003:TNNRSM>2.0.CO;2
  22. Kanada, S. and Wada, A. (2016) Sensitivity to horizontal resolution of the simulated intensifying rate and inner-core structure of typhoon ida, an extremely intense typhoon. J. Meteor. Soc. Japan, 94A: 181190. DOI: 10.2151/jmsj.2015-037
  23. Keresturi, E., Wang, Y., Meier, F., Weidle, F., Wittmann, C. and Atencia, A. (2019) Improving initial condition perturbations in a convection-permitting ensemble prediction system. Quart. J. Roy. Meteor. Soc., 145(720): 9931012. DOI: 10.1002/qj.3473
  24. Kretschmer, M., Hunt, B.R., Ott, E., Bishop, C.H., Rainwater, S. and Szunyogh, I. (2015) A composite state method for ensemble data assimilation with multiple limited-area models. Tellus A, 67(1): 26495. DOI: 10.3402/tellusa.v67.26495
  25. Kunii, M. and Miyoshi, T. (2012) Including uncertainties of sea surface temperature in an ensemble Kalman filter: A case study of Typhoon Sinlaku (2008). Wea. Forecasting, 27(6): 15861597. DOI: 10.1175/WAF-D-11-00136.1
  26. Lorenz, E.N. (1995) Predictability: A problem partly solved. In: Seminar on Predictability, Shinfield Park, ECMWF on 4–9 September 1995, pp. 118. https://www.ecmwf.int/en/elibrary/75462-predictability-problem-partly-solved.
  27. Lorenz, E.N. (2005) Designing chaotic models. J. Atmos. Sci., 62(5): 15741587. DOI: 10.1175/JAS3430.1
  28. Lynch, P. and Huang, X.-Y. (1992) Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120(6): 10191034. DOI: 10.1175/1520-0493(1992)120<;1019:IOTHMU>2.0.CO;2
  29. Milan, M., Clayton, A., Lorenc, A., Macpherson, B., Tubbs, R. and Dow, G. (2023) Large-scale blending in an hourly 4D-Var framework for a numerical weather prediction model. Quart. J. Roy. Meteor. Soc., 149(755): 20672090. DOI: 10.1002/qj.4495
  30. Parrish, D.F. and Derber, J. C. (1992) The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120(8): 17471763. DOI: 10.1175/1520-0493(1992)120<;1747:TNMCSS>2.0.CO;2
  31. Raymond, W.H. (1988) High-order low-pass implicit tangent filters for use in finite area calculations. Mon. Wea. Rev., 116(11): 21322141. DOI: 10.1175/1520-0493(1988)116<;2132:HOLPIT>2.0.CO;2
  32. Saito, K., Seko, H., Kunii, M. and Miyoshi, T. (2012) Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus A, 64(1): 11594. DOI: 10.3402/tellusa.v64i0.11594
  33. Simmons, A.J., Mureau, R. and Petroliagis, T. (1995) Error growth and estimates of predictablity from the ECMWF forecasting system. Quart. J. Roy. Meteor. Soc., 121(527): 17391771. DOI: 10.1002/qj.49712152711
  34. Vendrasco, E.P., Sun, J., Herdies, D.L., and de Angelis, C.F. (2016) Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecasts. J. Appl. Meteor. Clim., 55(3): 673690. DOI: 10.1175/JAMC-D-15-0010.1
  35. von Storch, H., Langenberg, H. and Feser, F. (2000) A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128(10): 36643673. DOI: 10.1175/1520-0493(2000)128<;3664:ASNTFD>2.0.CO;2
  36. Wang, H., Huang, X.-Y., Xu, D. and Liu, J. (2014a) A scale-dependent blending scheme for WRFDA: Impact on regional weather forecasting. Geosci. Model Dev., 7(4): 18191828. DOI: 10.5194/gmd-7-1819-2014
  37. Wang, Y., Bellus, M., Geleyn, J.-F., Ma, X., Tian, W. and Weidle, F. (2014b) A new method for generating initial condition perturbations in a regional ensemble prediction system: Blending. Mon. Wea. Rev., 142(5): 20432059. DOI: 10.1175/MWR-D-12-00354.1
  38. Wilks, D.S. (2011). Statistical methods in the atmospheric sciences. 3rd ed. Amsterdam: Elsevier. DOI: 10.1016/B978-0-12-385022-5.00001-4
  39. Yang, X. (2005). Analysis blending using spatial filter in grid-point model coupling. HIRLAM Newsletter, 10: 4955.
  40. Yoon, Y., Ott, E. and Szunyogh, I. (2010) On the propagation of information and the use of localization in ensemble Kalman filtering. J. Atmos. Sci., 67(12): 38233834. DOI: 10.1175/2010JAS3452.1
  41. Zhang, H., Chen, J., Zhi, X., Wang, Y. and Wang, Y. (2015) Study on multi-scale blending initial condition perturbations for a regional ensemble prediction system. Adv. Atmos. Sci., 32(8): 11431155. DOI: 10.1007/s00376-015-4232-6
  42. Zupanski, M. (2005) Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133(6): 17101726. DOI: 10.1175/MWR2946.1
  43. Zupanski, M. (2021) The maximum likelihood ensemble filter with state space localization. Mon. Wea. Rev., 149(10): 35053524. DOI: 10.1175/MWR-D-20-0187.1
Language: English
Page range: 1 - 19
Submitted on: Sep 18, 2024
Accepted on: Feb 1, 2025
Published on: Feb 28, 2025
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2025 Saori Nakashita, Takeshi Enomoto, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.