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Two Methods for Data Assimilation of Wind Direction Cover

Two Methods for Data Assimilation of Wind Direction

By:   
Open Access
|Feb 2023

References

  1. Anderson, J, Hoar, T, Raeder, K, Liu, H, Collins, N, Torn, R and Avellano, A. 2009. The data assimilation research testbed: A community facility. Bulletin of the American Meteorological Society, 90: 12831296. DOI: 10.1175/2009BAMS2618.1
  2. Anderson, JL. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Weather Rev., 129: 28842903. DOI: 10.1175/1520-0493(2001)129<;2884:AEAKFF>2.0.CO;2
  3. Anderson, JL. 2003. A local least squares framework for ensemble filtering. Mon. Weather Rev., 131: 634642. DOI: 10.1175/1520-0493(2003)131<;0634:ALLSFF>2.0.CO;2
  4. Anderson, JL. 2010. A non-Gaussian ensemble filter update for data assimilation. Mon. Weather Rev., 138: 41864198. DOI: 10.1175/2010MWR3253.1
  5. Anderson, JL. 2019. A nonlinear rank regression method for ensemble Kalman filter data assimilation. Mon. Weather Rev., 147: 28472860. DOI: 10.1175/MWR-D-18-0448.1
  6. Anderson, JL. 2022. A quantile-conserving ensemble filter framework. Part I: Updating an observed variable. Mon. Weather Rev., 150: 10611074. DOI: 10.1175/MWR-D-21-0229.1
  7. Bishop, CH. 2016. The GIGG-EnKF: ensemble Kalman filtering for highly skewed non-negative uncertainty distributions. Q. J. Roy. Meteor. Soc., 142: 13951412. DOI: 10.1002/qj.2742
  8. Brohan, P, Allan, R, Freeman, E, Wheeler, D, Wilkinson, C and Williamson, F. 2012. Constraining the temperature history of the past millennium using early instrumental observations. Climate of the Past, 8: 15511563. DOI: 10.5194/cp-8-1551-2012
  9. Compo, GP, Whitaker, JS, Sardeshmukh, PD, Matsui, N, Allan, RJ, Yin, X, Gleason, BE, Vose, RS, Rutledge, G, Bessemoulin, P, et al. 2011. The twentieth century reanalysis project. Q. J. Roy. Meteor. Soc., 137: 128. DOI: 10.1002/qj.776
  10. de Paula Gomez-Delgado, F, Gallego, D, Peña-Ortiz, C, Vega, I, Ribera, P and Garcia-Herrera, R. 2019. Long term variability of the northerly winds over the eastern mediterranean as seen from historical wind observations. Global and Planetary Change, 172: 355364. DOI: 10.1016/j.gloplacha.2018.10.008
  11. Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter. Springer. DOI: 10.1007/978-3-642-03711-5
  12. Freeman, E, Woodruff, SD, Worley, SJ, Lubker, SJ, Kent, EC, Angel, WE, Berry, DI, Brohan, P, Eastman, R, Gates, L, et al. 2017. ICOADS Release 3.0: a major update to the historical marine climate record. Int. J. Climatology, 37: 22112232. DOI: 10.1002/joc.4775
  13. Giese, BS, Seidel, HF, Compo, GP and Sardeshmukh, PD. 2016. An ensemble of ocean reanalyses for 1815–2013 with sparse observational input. J. Geophys. Res.-Oceans, 121: 68916910. DOI: 10.1002/2016JC012079
  14. Grooms, I. 2022. A comparison of nonlinear extensions to the ensemble Kalman filter. Computational Geosciences, 118. DOI: 10.1007/s10596-022-10141-x
  15. Grooms, I. 2023. iangrooms/Wind_Direction_DA: Two methods for data assimilation of wind direction. DOI: 10.5281/zenodo.7534894
  16. Kennedy, C and Carpenter, M. 2003. Additive Runge-Kutta schemes for convection-diffusion-reaction equations. Appl. Numer. Math., 44: 139181. DOI: 10.1016/S0168-9274(02)00138-1
  17. Laloyaux, P, de Boisseson, E, Balmaseda, M, Bidlot, J-R, Broennimann, S, Buizza, R, Dalhgren, P, Dee, D, Haimberger, L, Hersbach, H, et al. 2018. CERA-20C: A coupled reanalysis of the twentieth century. J. Adv. Model. Earth Syst., 10: 11721195. DOI: 10.1029/2018MS001273
  18. Mardia, KV. 1975. Statistics of directional data. Journal of the Royal Statistical Society: Series B (Methodological), 37: 349371. DOI: 10.1111/j.2517-6161.1975.tb01550.x
  19. Murphy, E, Huang, W, Bessac, J, Wang, J and Kotamarthi, R. 2022. Jointmodeling ofwind speed andwind direction through a conditional approach. URL: https://arxiv.org/abs/2211.13612.
  20. Penny, SG and Miyoshi, T. 2016. A local particle filter for high-dimensional geophysical systems. Nonlinear Proc. Geoph., 23: 391405. DOI: 10.5194/npg-23-391-2016
  21. Poli, P, Hersbach, H, Dee, DP, Berrisford, P, Simmons, AJ, Vitart, F, Laloyaux, P, Tan, DG, Peubey, C, Thépaut, J-N, et al. 2016. ERA-20C: An atmospheric reanalysis of the twentieth century. J. Climate, 29: 40834097. DOI: 10.1175/JCLI-D-15-0556.1
  22. Poterjoy, J. 2016. A localized particle filter for high-dimensional nonlinear systems. Mon. Weather Rev., 144: 5976. DOI: 10.1175/MWR-D-15-0163.1
  23. Prieto, M, Gallego, D, García-Herrera, R and Calvo, N. 2005. Deriving wind force terms from nautical reports through content analysis. the spanish and french cases. Climatic Change, 73: 3755. DOI: 10.1007/s10584-005-6956-2
  24. Silverman, B. 1998. Density estimation for statistics and data analysis. CRC Press.
  25. Slivinski, LC, Compo, GP, Whitaker, JS, Sardeshmukh, PD, Giese, BS, McColl, C, Allan, R, Yin, X, Vose, R, Titchner, H, et al. 2019. Towards amore reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. Roy. Meteor. Soc., 145: 28762908. DOI: 10.1002/qj.3598
  26. Soderlind, G. 2002. Automatic control and adaptive time-stepping. Numer. Algorithms, 31: 281310. DOI: 10.1023/A:1021160023092
  27. Tenreiro, C. 2022. Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection. Journal of Nonparametric Statistics, 34: 377406. DOI: 10.1080/10485252.2022.2057974
  28. Whitaker, JS and Hamill, TM. 2002. Ensemble data assimilation without perturbed observations. Mon. Weather Rev., 130: 19131924. DOI: 10.1175/1520-0493(2002)130<;1913:EDAWPO>2.0.CO;2
  29. Whitaker, JS and Hamill, TM. 2012. Evaluating methods to account for system errors in ensemble data assimilation. Mon. Weather Rev., 140: 30783089. DOI: 10.1175/MWR-D-11-00276.1
Language: English
Page range: 145 - 158
Submitted on: Oct 3, 2022
Accepted on: Feb 8, 2023
Published on: Feb 27, 2023
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2023 Ian Grooms, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.