data assimilation

Palatella, L., & Grasso, F. (2018). The EKF-AUS-NL algorithm implemented without the linear tangent model and in presence of parametric model error. Softwarex. http://doi.org/10.1016/j.softx.2018.01.001
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Marra, A. C., Federico, S., Montopoli, M., Avolio, E., Baldini, L., Casella, D., et al. (2019). The precipitation structure of the Mediterranean tropical-like cyclone numa: Analysis of GPM observations and numerical weather prediction model simulations. Remote Sensing. http://doi.org/10.3390/rs11141690
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Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., et al. (2019). Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Quarterly Journal Of The Royal Meteorological Society. http://doi.org/10.1002/qj.3598
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Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., et al. (2019). Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Quarterly Journal Of The Royal Meteorological Society. http://doi.org/10.1002/qj.3598
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Yang, C., Storto, A., & Masina, S. (2019). Quantifying the effects of observational constraints and uncertainty in atmospheric forcing on historical ocean reanalyses. Climate Dynamics. http://doi.org/10.1007/s00382-018-4331-z
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Poletti, L., Silvestro, F., Davolio, S., Pignone, F., & Rebora, N. (2019). Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts. Hydrology And Earth System Sciences. http://doi.org/10.5194/hess-23-3823-2019
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Chianese, E., Galletti, A., Giunta, G., Landi, T. C., Marcellino, L., Montella, R., & Riccio, A. (2018). Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations. Ecological Modelling. http://doi.org/10.1016/j.ecolmodel.2018.07.019
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Chianese, E., Galletti, A., Giunta, G., Landi, T. C., Marcellino, L., Montella, R., & Riccio, A. (2018). Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations. Ecological Modelling. http://doi.org/10.1016/j.ecolmodel.2018.07.019
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Iermano, I., Moore, A. M., & Zambianchi, E. (2016). Impacts of a 4-dimensional variational data assimilation in a coastal ocean model of southern Tyrrhenian Sea. Journal Of Marine Systems. http://doi.org/10.1016/j.jmarsys.2015.09.006
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Iermano, I., Moore, A. M., & Zambianchi, E. (2016). Impacts of a 4-dimensional variational data assimilation in a coastal ocean model of southern Tyrrhenian Sea. Journal Of Marine Systems. http://doi.org/10.1016/j.jmarsys.2015.09.006
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