Downloads

• Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, 2011. • Bannister, R.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. • Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, 2008a. • Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteor. Soc., 134, 1971–1996, 2008b. • Bannister, R. N.: ABC-DA model software, GitHub available at: https://github.com/rossbannister/ABC-DA_1.4da (last access: 24 August 2020), 2019. • Bannister, R. N.: Sample of initial condition data for the ABC simplified atmospheric model and data assimilation system (vn1.4da) (Version vn1.4da), Zenodo, https://doi.org/10.5281/zenodo.3946359, 2020. • Bannister, R. N., Migliorini, S., and Dixon, M.: Ensemble prediction for nowcasting with a convection-permitting model–II: Forecast error statistics, Tellus A, 63, 497–512, 2011. • Bannister, R. N., Chipilski, H., and Martinez-Alvarado, O.: Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales, Q. J. Roy. Meteor. Soc., 146, 1–48, https://doi.org/10.1002/qj.3652, 2020. • Bartello, P. and Mitchell, H. L.: A continuous three-dimensional model of short-range forecast error covariances, Tellus A, 44, 217–235, 1992. • Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, 2000. • Berre, L., Ecaterina ¸Stefanescu, S., and Belo Pereira, M.: The representation of the analysis effect in three error simulation techniques, Tellus A, 58, 196–209, 2006. • Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteor. Soc., 142, 2231–2243, 2016. • Buehner, M.: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting, Q. J. Roy. Meteor. Soc., 131, 1013–1043, 2005. • Caron, J.-F. and Fillion, L.: An examination of background error correlations between mass and rotational wind over precipitation regions, Mon. WeatherRev., 138, 563–578, 2010. • Chen, Y., Rizvi, S. R., Huang, X.-Y., Min, J., and Zhang, X.: Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions, Meteorol. Atmos. Phys., 121, 79–98, 2013. • Chen, Y., Xia, X., Min, J., Huang, X.-Y., and Rizvi, S. R.: Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events, Meteorol. Atmos. Phys., 128, 579–600, 2016. • Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.: Convection-permitting models: a step-change in rainfall forecasting, Meteorol. Appl., 23, 165–181, 2016. • Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteor. Soc., 120, 1367–1387, 1994. • Cullen, M.: Four-dimensional variational data assimilation: A new formulation of the background-error covariance matrix based on a potential-vorticity representation, Q. J. Roy. Meteor. Soc., 129, 2777–2796, 2003. • Cullen, M. and Davies, T.: A conservative split-explicit integration scheme with fourth-order horizontal advection, Q. J. Roy. Meteor. Soc., 117, 993–1002, 1991. • Cullen, M. J. P.: A mathematical theory of large-scale atmosphere/ocean flow, Imperial College Press, London, UK, 2006. • Deckmyn, A. and Berre, L.: A wavelet approach to representing background error covariances in a limited-area model, Mon. Weather Rev., 133, 1279–1294, 2005. • Derber, J. and Bouttier, F.: A reformulation of the background error covariance in the ECMWF global data assimilation system, Tellus A, 51, 195–221, 1999. • Dimet, F.-X. L. and Talagrand, O.: Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus A, 38, 97–110, 1986. • Fabry, F. and Sun, J.: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation, Mon. Weather Rev., 138, 242–255, 2010. • Gauthier, P., Buehner, M., and Fillion, L.: Background-error statistics modelling in a 3D variational data assimilation scheme: Estimation and impact on the analyses, in: Proc. ECMWF Workshop on Diagnosis of Data Assimilation Systems, ECMWF (the European Centre for Medium Range Weather Forecasts), 131–145, 1999. • Golub, G. H. and Van Loan, C. F.: Matrix computations. 1996, Johns Hopkins University, Press, Baltimore, MD, USA, 374–426, 1996. • Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M., Navascues, B., Mogensen, K., and Thorsteinsson, S.: Three-dimensional variational data assimilation for a limited area model, Tellus A, 53, 425–446, 2001. • Houtekamer, P. and Zhang, F.: Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532, 2016. • Ingleby, N. B.: The statistical structure of forecast errors and its representation in the Met. Office global 3-D variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 127, 209–231, 2001. • Kalnay, E.: Atmospheric modeling, data assimilation and predictability, Cambridge University Press, Cambridge, UK, 2002. • Katz, D., Lawless, A. S., Nichols, N. K., Cullen, M., and Bannister, R. N.: Correlations of control variables in variational data assimilation, Q. J. Roy. Meteor. Soc., 137, 620–630, 2011. • Lawless, A.: A note on the analysis error associated with 3D-FGAT, Q. J. Roy. Meteor. Soc., 136, 1094–1098, 2010. • Lee, M.-S., Barker, D., Huang, W., and Kuo, Y.-H.: First guess at appropriate time (FGAT) with WRF 3DVAR, in: WRF/MM5 Users Workshop, 22–25, available at: https://www.researchgate.net/publication/242428553_First_ Guess_at_Appropriate_Time_FGAT_with_WRF_3DVAR (last accees: 24 August 2020), 2004. • Lewis, J. M., Lakshmivarahan, S., and Dhall, S.: Dynamic data assimilation: a least squares approach, vol. 13, Cambridge University Press, 2006. • Liu, C., Xiao, Q., and Wang, B.: An ensemble-based fourdimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test, Mon. Weather Rev., 136, 3363–3373, 2008. • Lorenc, A., Ballard, S., Bell, R., Ingleby, N., Andrews, P., Barker, D., Bray, J., Clayton, A., Dalby, T., Li, D., Payne, T., and Saunders, F.: The Met. Office global three-dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 126, 2991–3012, 2000. • Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP: comparison with 4D-Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, 2003. • Parrish, D. F. and Derber, J. C.: The National Meteorological Center’s spectral statistical-interpolation analysis system, Mon. Weather Rev., 120, 1747–1763, 1992. • Penny, S. G.: The hybrid local ensemble transform Kalman filter, Mon. Weather Rev., 142, 2139–2149, 2014. • Pereira, M. B. and Berre, L.: The use of an ensemble approach to study the background error covariances in a global NWP model, Mon. Weather Rev., 134, 2466–2489, 2006. • Petrie, R. E., Bannister, R. N., and Cullen, M. J. P.: The “ABC model”: a non-hydrostatic toy model for use in convective-scale data assimilation investigations, Geosci. Model Dev., 10, 4419–4441, https://doi.org/10.5194/gmd-10-4419-2017, 2017. • Pielke, R.: Mesoscale Meteorological Modeling, Academic Press, San Diego, CA, USA, 2002. • Polavarapu, S., Ren, S., Rochon, Y., Sankey, D., Ek, N., Koshyk, J., and Tarasick, D.: Data assimilation with the Canadian middle atmosphere model, Atmos.-Ocean, 43, 77–100, 2005. • Schwartz, C. S. and Liu, Z.: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVar, ensemble Kalman filter, and “hybrid” variational–ensemble data assimilation systems, Mon. Weather Rev., 142, 716–738, 2014. • Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P., OnvleeHooimeyer, J., Joe, P., Barker, D. M., Li, P.-W., Golding, B., Xu, M., and Pinto, J.: Use of NWP for nowcasting convective precipitation: Recent progress and challenges, B. Am. Meteorol. Soc., 95, 409–426, 2014. • Sun, J., Wang, H., Tong, W., Zhang, Y., Lin, C.-Y., and Xu, D.: Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting, Mon. Weather Rev., 144, 149–169, 2016. • Tang, Y., Lean, H. W., and Bornemann, J.: The benefits of the Met Office variable resolution NWP model for forecasting convection, Meteorol. Appl., 20, 417–426, 2013. • Wang, X., Snyder, C., and Hamill, T. M.: On the theoretical equivalence of differently proposed ensemble-3D-Var hybrid analysis schemes, Mon. Weather Rev., 135, 222–227, 2007. • Wlasak, M. and Cullen, M.: Modelling static 3-D spatial background error covariances–the effect of vertical and horizontal transform order, Adv. Sci. Res., 11, 63–67, 2014. • Yano, J.-I., Ziemianski, M. Z., Cullen, M., Termonia, P., Onvlee, J., Bengtsson, L., Carrassi, A., Davy, R., Deluca, A., Gray, S. L., Homar, V., Köhler, M., Krichak, S., Michaelides, S., Phillips, V. T. J., Soares, P. M. M., and Wyszogrodzki, A. A.: Scientific challenges of convective-scale numerical weather prediction, B. Am. Meteorol. Soc., 99, 699–710, 2018.