Bannister, R. N.: Balance conditions in variational data assimilation for a high-resolution forecast model, Q. J. Roy. Meteor. Soc., 147, 2917–2934,
https://doi.org/10.1002/qj.4106, 2021.
a
Bannister, R. N., Migliorini, S., Rudd, A. C., and Baker, L. H.: Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble, Geosci. Model Dev. Discuss. [preprint],
https://doi.org/10.5194/gmd-2017-260, in review, 2017.
a
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.
a,
b,
c
Banos, I. H., Mayfield, W. D., Ge, G., Sapucci, L. F., Carley, J. R., and Nance, L.: Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study, Geosci. Model Dev., 15, 6891–6917,
https://doi.org/10.5194/gmd-15-6891-2022, 2022.
a
Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, 2000. a
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. a
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. a
Durran, D. R.: Numerical methods for wave equations in geophysical fluid dynamics, Springer, New York, ISBN 0387983767. 1999. a
Ehrendorfer, M.: A review of issues in ensemble-based Kalman filtering, Meteorol. Z., 16, 795–818, 2007. a
Fabry, F. and Meunier, V.: Why are radar data so difficult to assimilate skillfully?, Mon. Weather Rev., 148, 2819–2836, 2020. a
Gill, A. E.: Atmosphere-Ocean Dynamics, Academic Press, ISBN 0122835220, 1982. a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bucanek, 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.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Roy. Meteor. Soc., 144, 1218–1256, 2018. a
Holton, J. and Hakim, G.: An Introduction to Dynamic Meteorology, 5th Edn., Academic Press, Waltham, MA, ISBN 9780123848666, 2013.
a,
b
Houtekamer, P. and Zhang, F.: Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532, 2016. a
Kent, T., Bokhove, O., and Tobias, S.: Dynamics of an idealized fluid model for investigating convective-scale data assimilation, Tellus A, 69, 1369332,
https://doi.org/10.1080/16000870.2017.1369332, 2017.
a
Lee, J. C. K., Amezcua, J., and Bannister, R. N.: Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework, Geosci. Model Dev., 15, 6197–6219,
https://doi.org/10.5194/gmd-15-6197-2022, 2022.
a
Leung, T. Y., Leutbecher, M., Reich, S., and Shepherd, T. G.: Atmospheric predictability: revisiting the inherent finite-time barrier, J. Atmos. Sci., 76, 3883–3892,
https://doi.org/10.1175/JAS-D-19-0057.1, 2019.
a
Lorenc, A.: A study of ob monitoring statistics from radiosondes, composited for low-level cloud layers, Met Office NWP Forecasting Research Technical Report, 504, 1–32, 2007. a
Lorenz, E. N.: The predictability of a flow which possesses many scales of motion, Tellus, 21, 289–307, 1969. a
Ménétrier, B. and Montmerle, T.: Heterogeneous background-error covariances for the analysis and forecast of fog events, Q. J. Roy. Meteor. Soc., 137, 2004–2013, 2011. a
Michel, Y., Auligné, T., and Montmerle, T.: Heterogeneous convective-scale background error covariances with the inclusion of hydrometeor variables, Mon. Weather Rev., 139, 2994–3015, 2011.
a,
b
Montmerle, T.: Optimization of the assimilation of radar data at the convective scale using specific background error covariances in precipitation, Mon. Weather Rev., 140, 3495–3506, 2012. a
Montmerle, T. and Berre, L.: Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale, Q. J. Roy. Meteor. Soc., 136, 1408–1420, 2010.
a,
b,
c,
d
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.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l,
m,
n,
o,
p,
q,
r
Pielke, R.: Mesoscale Meteorological Modeling, Academic Press, San Diego, California, ISBN 9780123852380, 2002. a
Salby, M. L.: Fundamentals of atmospheric physics, vol. 61, Academic press, San Diego, California, ISBN 9786611049874, 1996.
a,
b,
c
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4.3, NCAR/TN-556+STR, National Center for Atmospheric Research,
https://doi.org/10.5065/1dfh-6p97, 2021.
a
Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P., Onvlee-Hooimeyer, 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. a
van Leeuwen, P. J.: Comment on “Data assimilation using an ensemble Kalman filter technique”, Mon. Weather Rev., 127, 1374–1377, 1999. a
Vetra-Carvalho, S., Dixon, M., Migliorini, S., Nichols, N. K., and Ballard, S. P.: Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model, Q. J. Roy. Meteor. Soc., 138, 1709–1720, 2012. a
Würsch, M. and Craig, G. C.: A simple dynamical model of cumulus convection for data assimilation research, Meteorol. Z., 23, 483–490, 2014.
a,
b
Yang, Y., Gao, S., Wang, Y., and Shi, H.: Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea, Remote Sens., 14, 4537,
https://doi.org/10.3390/rs14184537, 2022.
a
Yano, K.-I., Ziemiański, 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. a
Zhang, M. and Zhang, F.: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model, Mon. Weather Rev., 140, 587–600, 2012. a
