References
- 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: 1283–1296. DOI: 10.1175/2009BAMS2618.1
- Anderson, JL. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Weather Rev., 129: 2884–2903. DOI: 10.1175/1520-0493(2001)129<;2884:AEAKFF>2.0.CO;2
- Anderson, JL. 2003. A local least squares framework for ensemble filtering. Mon. Weather Rev., 131: 634–642. DOI: 10.1175/1520-0493(2003)131<;0634:ALLSFF>2.0.CO;2
- Anderson, JL. 2010. A non-Gaussian ensemble filter update for data assimilation. Mon. Weather Rev., 138: 4186–4198. DOI: 10.1175/2010MWR3253.1
- Anderson, JL. 2019. A nonlinear rank regression method for ensemble Kalman filter data assimilation. Mon. Weather Rev., 147: 2847–2860. DOI: 10.1175/MWR-D-18-0448.1
- Anderson, JL. 2022. A quantile-conserving ensemble filter framework. Part I: Updating an observed variable. Mon. Weather Rev., 150: 1061–1074. DOI: 10.1175/MWR-D-21-0229.1
- Bishop, CH. 2016. The GIGG-EnKF: ensemble Kalman filtering for highly skewed non-negative uncertainty distributions. Q. J. Roy. Meteor. Soc., 142: 1395–1412. DOI: 10.1002/qj.2742
- 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: 1551–1563. DOI: 10.5194/cp-8-1551-2012
- 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: 1–28. DOI: 10.1002/qj.776
- 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: 355–364. DOI: 10.1016/j.gloplacha.2018.10.008
- Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter. Springer. DOI: 10.1007/978-3-642-03711-5
- 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: 2211–2232. DOI: 10.1002/joc.4775
- 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: 6891–6910. DOI: 10.1002/2016JC012079
- Grooms, I. 2022. A comparison of nonlinear extensions to the ensemble Kalman filter. Computational Geosciences, 1–18. DOI: 10.1007/s10596-022-10141-x
- Grooms, I. 2023. iangrooms/Wind_Direction_DA: Two methods for data assimilation of wind direction. DOI: 10.5281/zenodo.7534894
- Kennedy, C and Carpenter, M. 2003. Additive Runge-Kutta schemes for convection-diffusion-reaction equations. Appl. Numer. Math., 44: 139–181. DOI: 10.1016/S0168-9274(02)00138-1
- 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: 1172–1195. DOI: 10.1029/2018MS001273
- Mardia, KV. 1975. Statistics of directional data. Journal of the Royal Statistical Society: Series B (Methodological), 37: 349–371. DOI: 10.1111/j.2517-6161.1975.tb01550.x
- 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 . - Penny, SG and Miyoshi, T. 2016. A local particle filter for high-dimensional geophysical systems. Nonlinear Proc. Geoph., 23: 391–405. DOI: 10.5194/npg-23-391-2016
- 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: 4083–4097. DOI: 10.1175/JCLI-D-15-0556.1
- Poterjoy, J. 2016. A localized particle filter for high-dimensional nonlinear systems. Mon. Weather Rev., 144: 59–76. DOI: 10.1175/MWR-D-15-0163.1
- 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: 37–55. DOI: 10.1007/s10584-005-6956-2
- Silverman, B. 1998. Density estimation for statistics and data analysis. CRC Press.
- 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: 2876–2908. DOI: 10.1002/qj.3598
- Soderlind, G. 2002. Automatic control and adaptive time-stepping. Numer. Algorithms, 31: 281–310. DOI: 10.1023/A:1021160023092
- Tenreiro, C. 2022. Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection. Journal of Nonparametric Statistics, 34: 377–406. DOI: 10.1080/10485252.2022.2057974
- Whitaker, JS and Hamill, TM. 2002. Ensemble data assimilation without perturbed observations. Mon. Weather Rev., 130: 1913–1924. DOI: 10.1175/1520-0493(2002)130<;1913:EDAWPO>2.0.CO;2
- Whitaker, JS and Hamill, TM. 2012. Evaluating methods to account for system errors in ensemble data assimilation. Mon. Weather Rev., 140: 3078–3089. DOI: 10.1175/MWR-D-11-00276.1
DOI: https://doi.org/10.16993/tellusa.2005 | Journal eISSN: 3035-9554
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.
