Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-2999-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2999-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Sep 2025
Research article |
| 05 Sep 2025
| 05 Sep 2025
High-resolution data assimilation for two maritime extreme weather events: a comparison between 3D-Var and EnKF
Diego S. Carrió
CORRESPONDING AUTHOR
Meteorology Group, Department of Physics, University of the Balearic Islands, Palma, Spain
Vincenzo Mazzarella
CETEMPS, Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila 67100, Italy
Rossella Ferretti
CETEMPS, Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila 67100, Italy
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Cited articles
Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., and Calvet, J.-C.: Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces, Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, 2020.
Amengual, A., Carrió, D. S., Ravazzani, G., and Homar, V.: A comparison of ensemble strategies for flash flood forecasting: The 12 October 2007 case study in Valencia, Spain, J. Hydrometeorol., 18, 1143–1166, https://doi.org/10.1175/JHM-D-16-0281.1, 2017.
Amengual, A., Hermoso, A., Carrió, D. S., and Homar, V.: The Sequence of Heavy Precipitation and Flash Flooding of 12 and 13 September 2019 in Eastern Spain. Part II: A Hydrometeorological Predictability Analysis Based on Convection-Permitting Ensemble Strategies, J. Hydrometeorol., 22, 2153–2177, https://doi.org/10.1175/JHM-D-20-0182.1, 2021.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999.
Anderson, J. L. and Collins, N.: Scalable implementations of ensemble filter algorithms for data assimilation, J. Atmos. Ocean. Tech., 24, 1452–1463, https://doi.org/10.1175/JTECH2049.1, 2007.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Ballard, S. P., Li, Z., David, S., and Caron, J.: Performance of 4D-Var NWP-based nowcasting of precipitation at the Met Office for summer 2012, Q. J. Roy. Meteor. Soc., 142, 472–487, 2016.
Barker, D. M., Huang, W., Guo, Y.-R., Bourgeois, A., and Xiao, X. N.: A three-dimensional variational data assimilation system for MM5: implementation and initial results, Mon. Weather Rev., 132, 897–914, 2004.
Bowman, A. W. and Azzalini, A.: Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations, Oxford University Press, Oxford, Vol. 18, ISBN 0 19 852396 3, 1997.
Bryan, G. H. and Rotunno, R.: Statistical convergence in simulated moist absolutely unstable layers, in: Preprints, 11th Conf. on Mesoscale Processes, Albuquerque, NM, Amer. Meteor. Soc. M, Vol. 1, https://ams.confex.com/ams/32Rad11Meso/techprogram/paper_96719.htm (last access: 12 March 2025), 2005.
Capecchi, V., Antonini, A., Benedetti, R., Fibbi, L., Melani, S., Rovai, L., Ricchi, A., and Cerrai, D.: Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy, Water, 13, 1727, https://doi.org/10.3390/w13131727, 2021.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Climate Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018.
Carrió, D. S. and Homar, V.: Potential of sequential EnKF for the short-range prediction of a maritime severe weather event, Atmos. Res., 178, 426–444, https://doi.org/10.1016/j.atmosres.2016.04.011, 2016.
Carrió, D. S., Homar, V., Jansa, A., Romero, R., and Picornell, M. A.: Tropicalization process of the 7 November 2014 Mediterranean cyclone: Numerical sensitivity study, Atmos. Res., 197, 300–312, https://doi.org/10.1016/j.atmosres.2017.07.018, 2017.
Carrió, D. S., Homar, V., and Wheatley, D. M.: Potential of an EnKF storm-scale data assimilation system over sparse observation regions with complex orography, Atmos. Res., 216, 186–206, https://doi.org/10.1016/j.atmosres.2018.10.004, 2019.
Carrió, D. S., Bishop, C. H., and Kotsuki, S.: Empirical determination of the covariance of forecast errors: An empirical justification and reformulation of hybrid covariance models, Q. J. Roy. Meteor. Soc., 147, 2033–2052, https://doi.org/10.1002/qj.4008, 2021.
Carrió, D. S., Jansà, A., Homar, V., Romero, R., Rigo, T., Ramis, C., Hermoso, A., and Maimó, A.: Exploring the benefits of a Hi-EnKF system to forecast an extreme weather event. The 9th October 2018 catastrophic flash flood in Mallorca, Atmos. Res., 265, 105917, https://doi.org/10.1016/j.atmosres.2021.105917, 2022.
Carrió Carrió, D. S.: Replicate data for ”High-resolution data assimilation for two maritime extreme weather events: a comparison between 3D-Var and EnKF”, V1, CORA.Repositori de Dades de Recerca [data set], https://doi.org/10.34810/data2515, 2025.
Cioni, G., Cerrai, D., and Klocke, D.: Investigating the predictability of a Mediterranean tropical-like cyclone using a storm-resolving model, Q. J. Roy. Meteor. Soc., 144, 1598–1610, https://doi.org/10.1002/qj.3322, 2018.
Clayton, A. M., Lorenc, A. C., and Barker, D. M.: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office, Q. J. Roy. Meteor. Soc., 139, 1445–1461, https://doi.org/10.1002/qj.2054, 2013.
Corrales, P. B., Galligani, V., Ruiz, J., Sapucci, L., Dillon, M. E., Skabar, Y. G., Sacco, M., Schwartz, C. S. and Nesbitt, S. W.: Hourly assimilation of different sources of observations including satellite radiances in a mesoscale convective system case during RELAMPAGO campaign, Atmos. Res., 281, 106456, https://doi.org/10.1016/j.atmosres.2022.106456, 2023.
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, https://doi.org/10.1002/qj.49712051912, 1994.
Cressman, G. P.: An operational objective analysis system, Monthly Weather Review, 87, 367–374, https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2, 1959.
Di Muzio, E., Riemer, M., Fink, A. H., and Maier-Gerber, M.: Assessing the predictability of Medicanes in ECMWF ensemble forecasts using an object-based approach, Q. J. Roy. Meteor. Soc., 145, 1202–1217, https://doi.org/10.1002/qj.3489, 2019.
Dowell, D. C., Wicker, L. J., and Snyder, C.: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses, Mon. Weather Rev., 139, 272–294, https://doi.org/10.1175/2010MWR3438.1, 2011.
Drobinski, P., Ducrocq, V., Alpert, P., Anagnostou, E., Béranger, K., Borga, M., Braud, I., Chanzy, A., Davolio, S., Delrieu, G., Estournel, C., Boubrahmi, N. F., Font, J., Grubišić, V., Gualdi, S., Homar, V., Ivančan-Picek, B., Kottmeier, C., Kotroni, V., Lagouvardos, K., Lionello, P., Llasat, M. C., Ludwig, W., Lutoff, C., Mariotti, A., Richard, E., Romero, R., Rotunno, R., Roussot, O., Ruin, I., Somot, S., Taupier-Letage, I., Tintore, J., Uijlenhoet, R., and Wernli, H.: HyMeX: A 10-year multidisciplinary program on the Mediterranean water cycle, B. Am. Meteorol. Soc., 95, 1063–1082, https://doi.org/10.1175/BAMS-D-12-00242.1, 2014.
Dudhia, J.: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.
Emanuel, K.: Genesis and maintenance of “Mediterranean hurricanes”, Adv. Geosci., 2, 217–220, https://doi.org/10.5194/adgeo-2-217-2005, 2005.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Federico, S., Torcasio, R. C., Puca, S., Vulpiani, G., Comellas Prat, A., Dietrich, S., and Avolio, E.: Impact of radar reflectivity and lightning data assimilation on the rainfall forecast and predictability of a summer convective thunderstorm in Southern Italy, Atmosphere, 12, 958, https://doi.org/10.3390/atmos12080958, 2021.
Ferrer Hernández, A. L., González Jardines, P. M., Sierra Lorenzo, M., and de la Caridad Aguiar Figueroa, D.: Impact of the Assimilation of Non-Precipitating Echoes Reflectivity Data on the Short-Term Numerical Forecast of SisPI, Environmental Sciences Proceedings, 19, 13, https://doi.org/10.3390/ecas2022-12845, 2022.
Ferretti, R., Pichelli, E., Gentile, S., Maiello, I., Cimini, D., Davolio, S., Miglietta, M. M., Panegrossi, G., Baldini, L., Pasi, F., Marzano, F. S., Zinzi, A., Mariani, S., Casaioli, M., Bartolini, G., Loglisci, N., Montani, A., Marsigli, C., Manzato, A., Pucillo, A., Ferrario, M. E., Colaiuda, V., and Rotunno, R.: Overview of the first HyMeX Special Observation Period over Italy: observations and model results, Hydrol. Earth Syst. Sci., 18, 1953–1977, https://doi.org/10.5194/hess-18-1953-2014, 2014.
Fitzpatrick, P. J., Li, Y., Hill, C., Karan, H., Lim, E., and Xiao, Q.: The impact of radar data assimilation on a squall line in Mississippi, in: 22nd Conference on Weather Analysis and Forecasting/18th Conference on Numerical Weather Prediction, 25 June 2007, https://ams.confex.com/ams/88Annual/techprogram/paper_130773.htm (last access: 2 September 2025), 2007.
Flaounas, E., Lagouvardos, K., Kotroni, V., Claud, C., Delanoë, J., Flamant, C., Madonna, E., and Wernli, H.: Processes leading to heavy precipitation associated with two Mediterranean cyclones observed during the HyMeX SOP1, Q. J. Roy. Meteor. Soc., 142, 275–286, https://doi.org/10.1002/qj.2618, 2016.
Fujita, T., Stensrud, D. J., and Dowell, D. C.: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties, Mon. Weather Rev., 135, 1846–1868, 2007.
Gao, J., Fu, C., Stensrud, D. J., and Kain, J. S.: OSSEs for an ensemble 3DVAR data assimilation system with radar observations of convective storms, J. Atmos. Sci., 73, 2403–2426, 2016.
Gao, X., Gao, S., and Yang, Y.: A comparison between 3DVAR and EnKF for data assimilation effects on the Yellow Sea fog forecast, Atmosphere, 9(9), 346, https://doi.org/10.3390/atmos9090346, 2018.
Garcies, L. and Homar, V.: Ensemble sensitivities of the real atmosphere: application to Mediterranean intense cyclones, Tellus A, 61, 394–406, 2009.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, 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.
Hacker, J. P., Anderson, J. L., and Pagowski, M.: Improved vertical covariance estimates for ensemble-filter assimilation of near-surface observations, Mon. Weather Rev., 135, 1021–1036, 2007.
Hamill, T. M. and Snyder, C.: A hybrid ensemble Kalman filter–3D variational analysis scheme, Mon. Weather Rev., 128, 2905–2919, 2000.
Honda, T., Miyoshi, T., Lien, G. Y., Nishizawa, S., Yoshida, R., Adachi, S. A., Terasaki, K., Okamoto, K., Tomita, H., and Bessho, K.: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015), Mon. Weather Rev., 146, 213–229, 2018.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, 2006.
Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998.
Huang, X. Y., Xiao, Q., Barker, D. M., Zhang, X., Michalakes, J., Huang, W., Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X., Won, D. J., Lin, H. C., and Kuo, Y. H.: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results, Mon. Weather Rev., 137, 299–314, 2009.
Hung, M. K., Tien, D. D., Quan, D. D., Duc, T. A., Dung, P. T. P., Hole, L. R., and Nam, H. G.: Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam's Coast, Atmosphere, 14, 1201, https://doi.org/10.3390/atmos14081201, 2023.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Janjić, Z. I.: The step-mountain coordinate: Physical package, Mon. Weather Rev., 118, 1429–1443, 1990.
Jansa, A., Alpert, P., Arbogast, P., Buzzi, A., Ivancan-Picek, B., Kotroni, V., Llasat, M. C., Ramis, C., Richard, E., Romero, R., and Speranza, A.: MEDEX: a general overview, Nat. Hazards Earth Syst. Sci., 14, 1965–1984, https://doi.org/10.5194/nhess-14-1965-2014, 2014.
Jones, T. A., Knopfmeier, K., Wheatley, D., Creager, G., Minnis, P., and Palikonda, R.: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part II: Combined radar and satellite data experiments, Weather Forecast., 31, 297-327, 2016.
Jones, T. A., Otkin, J. A., Stensrud, D. J., and Knopfmeier, K.: Assimilation of satellite infrared radiances and Doppler radar observations during a cool season observing system simulation experiment, Mon. Weather Rev., 141, 3273–3299, 2013.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802, 1990.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170–181, 2004.
Kalnay, E.: Atmospheric modeling, data assimilation and predictability, Cambridge University Press, ISBN 0-521-79179-0, 2003.
Lagasio, M., Silvestro, F., Campo, L., and Parodi, A.: Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum, J. Hydrometeorol., 20, 1307–1337, https://doi.org/10.1175/JHM-D-18-0219, 2019.
Le Dimet, F. X. and Talagrand, O.: Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus A, 38, 97–110, 1986.
Lee, J. H., Lee, H. H., Choi, Y., Kim, H. W., and Lee, D. K.: Radar data assimilation for the simulation of mesoscale convective systems, Adv. Atmos. Sci., 27, 1025–1042, 2010.
Li, X., Ming, J., Xue, M., Wang, Y., and Zhao, K.: Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble-3DVar data assimilation system and test with radar T-TREC wind assimilation for tropical Cyclone Chanthu (2010), J. Geophys. Res.-Atmos., 120, 4017–4039, https://doi.org/10.1002/2014JD022706, 2015.
Li, X., Zeng, M., Wang, Y., Wang, W., Wu, H., and Mei, H.: Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line, Adv. Atmos. Sci., 33, 1143–1157, 2016.
Lorenc, A. C.: A global three-dimensional multivariate statistical interpolation scheme, Mon. Weather Rev., 109, 701–721, 1981.
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q. J. Roy. Meteor. Soc., 112, 1177–1194, 1986.
Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, 2003.
Llasat, M. C., Llasat-Botija, M., Prat, M. A., Porcú, F., Price, C., Mugnai, A., Lagouvardos, K., Kotroni, V., Katsanos, D., Michaelides, S., Yair, Y., Savvidou, K., and Nicolaides, K.: High-impact floods and flash floods in Mediterranean countries: the FLASH preliminary database, Adv. Geosci., 23, 47–55, https://doi.org/10.5194/adgeo-23-47-2010, 2010.
Mason, I.: A model for assessment of weather forecasts, Aust. Met. Mag., 30, 291–303, 1982.
Mass, C. F., Ovens, D., Westrick, K., and Colle, B. A.: Does increasing horizontal resolution produce more skillful forecasts?: The Results of Two Years of real-Time Numerical Weather Prediction over the Pacific Northwest, B. Am. Meteorol. Soc., 83, 407–430, 2002.
Mazzarella, V., Maiello, I., Ferretti, R., Capozzi, V., Picciotti, E., Alberoni, P., Marzano, F., and Budillon, G.: Reflectivity and velocity radar data assimilation for two flash flood events in central Italy: A comparison between 3D and 4D variational methods, Q. J. Roy. Meteor. Soc., 146, 348–366, 2020.
Mazzarella, V., Ferretti, R., Picciotti, E., and Marzano, F. S.: Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy, Nat. Hazards Earth Syst. Sci., 21, 2849–2865, https://doi.org/10.5194/nhess-21-2849-2021, 2021.
Mittermaier, M. and Roberts, N.: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score, Weather Forecast., 25, 343–354, 2010.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, 2006.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, 2009.
Pakalidou, N. and Karacosta, P.: Study of very long-period extreme precipitation records in Thessaloniki, Greece, Atmos. Res., 208, 106–115, 2018.
Park, S. K. and Županski, D.: Four-dimensional variational data assimilation for mesoscale and storm-scale applications, Meteorol. Atmos. Phys., 82, 173–208, 2003.
Parrish, D. and Derber, J.: The National Meteorological Center's spectral statistical-interpolation analysis system, Mon. Weather Rev., 120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2, 1992.
Petterssen, S.: Weather analysis and forecasting: motion and motion systems, McGraw-Hill, ISBN 9780070496859, 0070496854, 1956.
Pichelli, E., Rotunno, R., and Ferretti, R.: Effects of the Alps and Apennines on forecasts for Po Valley convection in two HyMeX cases, Q. J. Roy. Meteor. Soc., 143, 2420–2435, https://doi.org/10.1002/qj.3096, 2017.
Poterjoy, J.: A localized particle filter for high-dimensional nonlinear systems, Mon. Weather Rev., 144, 59–76, 2016.
Pu, Z., Li, X., Velden, C. S., Aberson, S. D., and Liu, W. T.: The impact of aircraft dropsonde and satellite wind data on numerical simulations of two landfalling tropical storms during the tropical cloud systems and processes experiment, Weather Forecast., 23, 62–79, 2008.
Pytharoulis, I., Matsangouras, I. T., Tegoulias, I., Kotsopoulos, S., Karacostas, T. S., and Nastos, P. T.: Numerical study of the medicane of November 2014, in: Perspectives on Atmospheric Sciences, Springer International Publishing, 115–121, https://doi.org/10.1007/978-3-319-35095-0_17, 2017.
Pytharoulis, I.: Analysis of a Mediterranean tropical-like cyclone and its sensitivity to the sea surface temperatures, Atmos. Res., 208, 167–179, 2018.
Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J. F., and Simmons, A.: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics, Q. J. Roy. Meteor. Soc., 126, 1143–1170, 2000.
Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D., Inverarity, G. W., Lorenc, A. C., and Payne, T. J.: The Met Office global four-dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 133, 347–362, 2007.
Roberts, N. M.: The impact of a change to the use of the convection scheme to high-resolution simulations of convective events, Met Office Forecasting Research Technical Report no. 407, 2003.
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78–97, 2008.
Romero, R., Guijarro, J. A., Ramis, C., and Alonso, S.: A 30-year (1964–1993) daily rainfall data base for the Spanish Mediterranean regions: First exploratory study, Int. J. Climatol., 18, 541–560, 1998.
Romine, G. S., Schwartz, C. S., Snyder, C., Anderson, J. L., and Weisman, M. L.: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts, Mon. Weather Rev., 141, 1263–1284, 2013.
Schwartz, C. S., Kain, J. S., Weiss, S. J., Xue, M., Bright, D. R., Kong, F., Thomas, K. W., Levit, J. J., Coniglio, M. C., and Wandishin, M. S.: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership, Weather Forecast., 25, 263–280, 2010.
Scott, D. W.: Multivariate density estimation: theory, practice, and visualization, John Wiley & Sons, ISBN 978-0-471-69755-8, 2015.
Shen, F., Song, L., Li, H., He, Z., and Xu, D.: Effects of different momentum control variables in radar data assimilation on the analysis and forecast of strong convective systems under the background of northeast cold vortex, Atmos. Res., 280, 106415, https://doi.org/10.1016/j.atmosres.2022.106415, 2022.
Silverman, B. W.: Density estimation for statistics and data analysis, Routledge, https://doi.org/10.1201/9781315140919, 2018.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3, National Center For Atmospheric Research Boulder CO Mesoscale and Microscale Meteorology Div., https://doi.org/10.5065/D68S4MVH, 2008.
Stanski, H. R., Wilson, L. J., and Burrows, W. R.: Survey of common verification methods in meteorology, World Weather Watch Tech. Report No. 8, WMO/TD No. 358, WMO, Geneva, 114 pp., https://doi.org/10.13140/2.1.2377.6642, 1989.
Stensrud, D. J., Bao, J. W., and Warner, T. T.: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems, Mon. Weather Rev., 128, 2077–2107, 2000.
Stensrud, D. J., Xue, M., Wicker, L. J., Kelleher, K. E., Foster, M. P., Schaefer, J. T., Schneider, R. S., Benjamin, S. G., Weygandt, S. S., Ferree, J. T., and Tuell, J. P.: Convective-scale warn-on-forecast system: A vision for 2020, B. Am. Meteorol. Soc., 90, 1487–1500, 2009.
Swets, J. A.: The Relative Operating Characteristic in Psychology: A technique for isolating effects of response bias finds wide use in the study of perception and cognition, Science, 182, 990–1000, 1973.
Tewari, M., Chen, M., Wang, F., Dudhia, W., LeMone, J., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification of the unified NOAH land surface model in the WRF model (Formerly Paper Number 17.5), in: Proceedings of the 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, WA, USA, 12–16 January 2004, Vol. 14, 2004.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, 2008.
Tong, M. and Xue, M.: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments, Mon. Weather Rev., 133, 1789–1807, 2005.
Torcasio, R. C., Federico, S., Comellas Prat, A., Panegrossi, G., D'Adderio, L. P., and Dietrich, S.: Impact of lightning data assimilation on the short-term precipitation forecast over the Central Mediterranean Sea, Remote Sensing, 13, 682, https://doi.org/10.3390/rs13040682, 2021.
Tukey, J. W.: Exploratory data analysis, Addison-Wesley Publishing Company, Reading, Massachussets, 2, 131–160, 1977.
Van Leeuwen, P. J.: Particle filtering in geophysical systems, Mon. Weather Rev., 137, 4089–4114, 2009.
Velden, C., Lewis, W. E., Bresky, W., Stettner, D., Daniels, J., and Wanzong, S.: Assimilation of high-resolution satellite-derived atmospheric motion vectors: Impact on HWRF forecasts of tropical cyclone track and intensity, Mon. Weather Rev., 145, 1107–1125, 2017.
Wang, H., Huang, X. Y., Sun, J., Xu, D., Fan, S., Zhong, J., and Zhang, M.: A comparison between the 3/4DVAR and hybrid ensemble-VAR techniques for radar data assimilation, 36th AMS Conference on Radar Meteorology, 16–20 September, Breckenridge, Colorado, 2013.
Wang, Y., Yussouf, N., Kerr, C. A., Stratman, D. R., and Matilla, B. C.: An experimental 1-km Warn-on-Forecast System for hazardous weather events, Mon. Weather Rev., 150, 3081–3102, 2022.
Wheatley, D. M., Knopfmeier, K. H., Jones, T. A., and Creager, G. J.: Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar data experiments, Weather Forecast., 30, 1795–1817, 2015.
Wheatley, D. M., Stensrud, D. J., Dowell, D. C., and Yussouf, N.: Application of a WRF mesoscale data assimilation system to springtime severe weather events 2007–09, Mon. Weather Rev., 140, 1539–1557, 2012.
Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y., and Toth, Z.: Ensemble data assimilation with the NCEP global forecast system, Mon. Weather Rev., 136, 463–482, 2008.
Wu, X., Zhang, S., Liu, Z., Rosati, A., and Delworth, T. L.: A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model, Clim. Dynam., 40, 1789–1798, 2013.
Xiao, Q. and Sun, J.: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002, Mon. Weather Rev., 135, 3381–3404, 2007.
Yang, S. C., Corazza, M., Carrassi, A., Kalnay, E., and Miyoshi, T.: Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model, Mon. Weather Rev., 137, 693–709, https://doi.org/10.1175/2008MWR2396.1, 2009.
Yano, J. 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.
Yussouf, N., Dowell, D. C., Wicker, L. J., Knopfmeier, K. H., and Wheatley, D. M.: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama, Mon. Weather Rev., 143, 3044–3066, 2015.
Yussouf, N., Jones, T. A., and Skinner, P. S.: Probabilistic high‐impact rainfall forecasts from landfalling tropical cyclones using Warn‐on‐Forecast system, Quarterly Journal of the Royal Meteorological Society, 146, 2050–2065, 2020.
Short summary
Populated coastal regions in the Mediterranean are known to be severely affected by extreme weather events that are initiated over maritime regions. These weather events are known to pose a serious problem in terms of numerical predictability. Different data assimilation techniques are used in this study with the main aim of enhancing short-range forecasts of two challenging severe weather events.
Populated coastal regions in the Mediterranean are known to be severely affected by extreme...
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