References

This language version doesn't exist

 

Addor, N., S. Jaun, F. Fundel, and M. Zappa, 2011: An operational hydrological ensemble  prediction system for the city of Zurich (Switzerland): Skill, case studies and scenarios. Hydrol. Earth Syst. Sci., 15, 2327–2347, doi:10.5194/hess-15-2327-2011.

Alpert, P., T. Ben-Gai, A. Baharad, Y. Benjamini, D. Yekutieli, M. Colacino, L. Diodato, C. Ramis, V. Homar, R. Romero, S. Michaelides, and A. Manes, 2002:  The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values. Geophys. Res. Letters, 29, 11, 31-1 - 31-4.

Amengual, A., T. Diomede, C. Marsigli, A. Martín, A. Morgillo, P. Papetti, R. Romero, and S. Alonso, 2008: A hydrometeorological model intercomparison as a tool to quantify the forecast uncertainty in a medium size basin. Nat. Haz. and Earth. Syst. Sci., 8, 819-838.

Amengual, A., R. Romero, M. Vich, and S. Alonso, 2009: Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: Application to a medium size basin of Mediterranean Spain. Hydrol. and Earth. Syst. Sci., 13, 793-811.

Amengual A., V. Homar, and O. Jaume, 2015: Potential of a probabilistic hydrometeorological forecasting approach for the 28 September 2012 extreme flash flood in Murcia, Spain. Atmos. Res., 166, 10–23.

Amengual, A., D. S. Carrió, G. Ravazzani, and V. Homar, 2017: A comparison of ensemble strategies for flash flood forecasting: The 12 october 2007 case study in Valencia, Spain. J. Hydrometeor., 18(4), 1143-1166.

Amengual, A., A. Hermoso, D. S. Carrió, and V. Homar, 2020: The sequence of heavy precipitation and flash flooding of 12 and 13 September 2019 in eastern Spain. Part II: A hydrometeorological andpredictability analysis based on radar and distinct ensemble strategies. J. Hydrometeor. (submitted).

Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129(12), 2884-2903.

Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The data assimilation research testbed: A community facility. BAMS, 90(9), 1283-1296.

Arakawa, A., and C. S.  Konor, 2009: Unification of the anelastic and quasi-hydrostatic systems of equations. Mon. Wea. Rev., 137, 710-726.

Argüeso, D., R. Romero, and V. Homar, 2020: Precipitation features of the Maritime Continent in parameterized and explicit convection models. J. Climate, 33, 2449-2466.

Beljaars, A., G. Balsamo, P. Bechtold, A. Bozzo, R. Forbes, R. J. Hogan, M. Köhler, J. J. Morcrette, A. M. Tompkins, P. Viterbo, and N. Wedi, 2018: The numerics of physical parametrization in the ECMWF model. Front. Earth. Sci., https://doi.org/10.3389/feart.2018.00137.

Borga, M., P. Boscolo, F. Zanon, and M. Sangati, 2007: Hydrometeorological analysis of the 29 August 2003 flash flood in the Eastern Italian Alps. J. Hydrometeor., 8(5): 1049-1067.

Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 2917–2928.

Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. R. Meteorol. Soc., 125(560), 2887–2908.

Buizza, R., 2003: Weather prediction: Ensemble prediction. Encyclopedia of Atmospheric Sciences, Academic Press, 2546–255.

Camarasa-Belmonte, A. M., and F. Beltrán Segura, 2001: Flood events in Mediterranean ephemeral streams (ramblas) in Valencia region, Spain. Catena, 45, 229–249.

Carrió, D. S., and V. Homar, 2016: Potential of sequential EnKF for the short-range prediction of a maritime severe weather event. Atm. Res., 178, 426-444.

Carrió, D. S., Homar, V., and Wheatley, D. M., 2019: Potential of an EnKF Storm-Scale Data Assimilation System Over Sparse Observation Regions with Complex Orography. Atm. Res., 216, 186-206.

CEDEX, GEAMA, FLUMEN, and CIMNE, 2010: IBER: Modelización bidimensional del flujo en lámina libre en aguas poco profundas. Manual básico de usuario.

Christensen, H. M., S. J. Lock, I. M. Moroz, and T. N. Palmer, 2017: Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme. Quart. J. R. Meteorol. Soc., 143(706), 2168–2181.

Clark, A. J., W. A. Gallus, M. Xue, and F. Kong, 2010: Growth of spread in convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 25(2), 594–612.

Delis, A. I., and Th. Katsaounis, 2005: Numerical solution of the two-dimensional shallow water equations by the application of relaxation methods. Appl. Math. Modelling, 29, 754-783.

Doswell III, C. A., H. E. Brooks, and R. A. Maddox, 1996: Flash Flood Forecasting: An Ingredients-Based Methodology. Wea. Forecasting, 11, 560–581.

Doswell III, C. A., C. Ramis, R. Romero, and S. Alonso, 1998: A diagnostic study of three heavy precipitation episodes in the western Mediterranean region. Wea. Forecasting, 13, 102-124.

Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982–2005.

Doyle, J. D., D. R. Durran, C. Chen, B. A. Colle, M. Georgelin, V. Grubisic, W. R. Hsu, C. Y. Huang, D. Landau, Y. L. Lin, G. S. Poulos, W. Y. Sun, D. B. Weberr, M. G. Wurtele, and M. Xue, 2000: An intercomparison of model-predicted wave breaking for the 11 January 1972 Boulder windstorm. Mon. Wea. Rev., 128, 901-914.

Doyle, J. D., S. Gaberšek, Q. Jiang, L. Bernardet, J. M. Brown, A. Dörnbrack, E. Filaus, V. Grubišić, D. J. Kirshbaum, O. Knoth, S. Koch, J. Schmidli, I. Stiperski, S. Vosper, and S. Zhong, 2011: An intercomparison of T-REX mountain-wave simulations and implications for mesoscale predictability. Mon. Wea. Rev., 139, 2811-2831.

Dupré, A., P. Drobinski, B. Alonzo, J. Badosa, C. Briard, and R. Plougonven, 2020: Sub-hourly forecasting of wind speed and wind energy. Renew. Energy, 145, 2373-2379.

Durran, D. R., 1999: Numerical methods for wave equations in geophysical fluid dynamics. Springer-Verlag.

Durran, D. L., 2010: Numerical Methods for Fluid Dynamics (With Applications to Geophysics), Second Edition, Springer, 516 pp.

EEA – European Environmental Agency, 2019: Economic losses from climate-related extremes in Europe. https://www.eea.europa.eu/data-and-maps/indicators/direct-losses-from-weather-disasters 3/assessment-2.

Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans, 99(C5), 10143-10162.

Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367.

Fiori, E., A. Comellas, L. Molini, N. Rebora, F. Siccardi, D. J. Gochis, S. Tanelli, and A. Parodi, 2014: Analysis and hindcast simulations of an extreme rainfall event in the Mediterranean area: the Genoa 2011 case. Atmos. Res., 138, 13–29.

Foley, A. M., P. G. Leahy, A. Marvuglia, and E. J. McKeogh, 2012: Current methods and advances in forecasting of wind power generation. Renew. Energy, 37(1), 1-8.

Fujita, T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135 (5).

Furnari, L., G. Mendicino, and A. Senatore, 2020: Hydrometeorological ensemble forecast of a highly localized convective event in the Mediterranean. Water, 12(6), 1545.

Gallus, W. A, and J. B. Klemp, 2000: Behavior of flow over step orography. Mon. Wea. Rev., 128, 1153-1164.

Gaume, E., M. Livet, M. Desbordes, and J. P. Villeneuve, 2004: Hydrological analysis of the river Aude, France, flash flood on 12 and 13 November 1999. J. Hydrol., 286(1-4), 135-154.

Giorgi, F., 2006: Climate change hot-spots. Geophys. Res. Lett., 33, L08707.

Giraldo, F. X., and M. Restelly, 2008: A study of spectral element and discontinuous Galerkin methods for the Navier-Stokes equations in nonhydrostatic mesoscale atmospheric modeling: Equation sets and test cases. J. Compt. Phys., 227, 3849-3877.

Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread–error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203–221.

Guillijns, S., O. B. Mendoza, J. Chandrasekar, B. L. R. DeMoor, D. S. Bernstein, and A. Ridley, 2006: What is the ensemble Kalman filter and how well does it work? 2006 American Control Conf., Minneapolis, MN, IEEE, 6 pp.

Hacker, J. P., S. Y. Ha, C. Snyder, J. Berner, F. A. Eckel, E. Kuchera, M. Pocernich, S. Rugg, J. Schramm, and X. Wang, 2011: The U.S. Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus, Series A: Dyn. Meteor. Ocean., 63(3), 625–641.

Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128(8), 2905-2919.

Hart, R., 2003: A cyclone phase space derived from thermal wind and thermal asymmetry. Mon. Wea. Rev., 131, 585-616.

Hayhoe, K., J. Edmonds, R. E. Kopp, A. N. LeGrande, B. M. Sanderson, M. F. Wehner, and D. J. Wuebbles, 2017: Climate models, scenarios, and projections. In Climate Science Special Report: Fourth National Climate Assessment, Volume I. D.J. Wuebbles, D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock, Eds. U.S. Global Change Research Program, pp. 133-160, doi:10.7930/J0WH2N54.

Hooke, J. M., 2016: Geomorphological impacts of an extreme flood in SE Spain. Geomorphology, 263: 19-38.

Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230(1-2), 112-126.

IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp.

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.

Jankov, I., J. Berner, J. Beck, H. Jiang, J. B. Olson, G. Grell, T. Smirnova, S. G. Benjamin, and J. M. Brown, 2017: A Performance Comparison between Multiphysics and Stochastic Approaches within a North American RAP Ensemble. Mon. Wea. Rev., 145(4), 1161–1179.

Jung, J., and R. P. Broadwater, 2014: Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev., 31, 762-777.

King, A. D. et al., 2015: The timing of anthropogenic emergence in simulated climate extremes. Environ. Res. Lett., 10, 094015–10.

Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247–267.

Klemp, J. B., J. Dudhia, and A. D. Hassiotis, 2008: An upper gravity-wave absorbing layer for NWP applications. Mon Wea. Rev., 136, 3987-4004.

Kostylev, V., and A. Pavlovski, 2011: Solar power forecasting performance–towards industry standards. In 1st international workshop on the integration of solar power into power systems, Aarhus, Denmark.

Kuster, C., Y. Rezgui, and M. Mourshed, 2017: Electrical load forecasting models: A critical systematic review. Sustain. Cities and Soc., 35, 257-270.

Lauritzen, P. H., C. Jablonowski, M. A. Taylor, and R. D. Nair (eds.), 2011: Numerical techniques for global atmospheric models. Springer.

Lei, M., L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, 2009: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev., 13(4), 915-920.

Leoncini, G., R. S. Plant, S. L. Gray, and P. A. Clark, 2013: Ensemble forecasts of a flood producing storm: comparison of the influence of model-state perturbations and parameter modifications. Quart. J. Roy. Meteorol. Soc., 139 (670), 198–211.

Leveque, R. J., 2002: Finite volume methods for hyperbolic problems. Ed. Cambridge University Press, 558 pp.

Lionello, P. et al., 2012: The Climate of the Mediterranean Region: from the past to the future. Elsevier, 592pp.

Liu, L., C. Gao, W. Xuan, and Y. P. Xu, 2017: Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China. J. Hydrol., 554, 233-250.

Lorenzo-Lacruz, J., A. Amengual, C. García, E. Morán-Tejeda, V. Homar, A. Maimó-Far, A. Hermoso, C. Ramis, and R. Romero, 2019: Hydro-meteorological reconstruction and geomorphological impact assessment of the October 2018 catastrophic flash flood at Sant Llorenç, Mallorca (Spain). Nat. Hazards Earth Syst. Sci., 19(11), 2597-2617.

Maimó-Far, A., A. Tantet, V. Homar, and P. Drobinski, 2020: Predictable and unpredictable climate variability impacts on optimal renewable energy mixes: the example of Spain. Energies, 13, 5132; doi:10.3390/en13195132.

Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67(1), 171–194.

Maraun, D., 2013: When will trends in European mean and heavy daily precipitation emerge? Environ. Res. Lett, 8, 14004–14008.

Marquis, J., Y. Richardson, P. Markowski, D. Dowell, J. Wurman, K. Kosiba, P.Robinson, and G. Romine, 2014: An investigation of the Goshen County, Wyoming, tornadic supercell of 5 June 2009 using EnKF assimilation of mobile mesonet and radar observations collected during VORTEX2. Part I: Experiment design and verification of the EnKF analyses. Mon. Wea. Rev., 142, 530–554.

McCabe, A., R. Swinbank, W. Tennant, and A. Lock, 2016: Representing model uncertainty in the Met Office convection-permitting ensemble prediction system and its impact on fog forecasting. Quart. J. R. Meteorol. Soc., 142(700), 2897–2910.

Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada Level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound. Layer Meteor., 119(2), 397–407.

Nuissier, O., B. Joly, B. Vié, and V. Ducrocq, 2012: Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events. Nat. Hazards Earth Syst. Sci., 12(10), 2993.

Okumus, I., and A. Dinler, 2016: Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conv. Manag., 123, 362-371.

Paeth, H. et al., 2017: Quantifying the evidence of climate change in the light of uncertainty exemplified by the Mediterranean hot spot region. Global and Planetary Change, 151, 144–151.

Picornell, M. A., A. Jansà, A. Genovés, and J. Campins, 2001: Automated Database of Mesocyclones from the HIRLAM-0.5º Analyses in the Western Mediterranean. Int. J. Climatol., 21, 335-354.

Poterjoy, J., R. A. Sobash, and J. L. Anderson, 2017: Convective-scale data assimilation for the weather research and forecasting model using the local particle filter. Mon. Wea. Rev., 145(5), 1897-1918.

Potvin, C. K., and M. L. Flora, 2015: Sensitivity of idealized supercell simulations to horizontal grid spacing: implications for warn-on-forecast. Mon. Wea. Rev., 143, 2998-3024.

Ramis, C., and R. Romero, 1995: A first numerical simulation of the development and structure of the sea breeze in the island of Mallorca. Ann. Geophy., 13, 981-994.

Ravazzani, G., A. Amengual, A. Ceppi, V. Homar, R. Romero, G. Lombardia, and M. Mancini, 2016: Potentialities of ensemble strategies for flood forecasting over the Milano urban area. J. Hydrol., 539, 237–253.

Robert, A., 1993: Bubble convection with a semi-implicit formulation of the Euler equations. J. Atmos. Sci., 50, 1865-1873.

Romero, R., 2008: A method for quantifying the impacts and interactions of potential vorticity anomalies in extratropical cyclones. Quart. J. R. Meteorol. Soc., 134, 385-402.

Romero, R., C. Ramis, and V. Homar, 2014: On the severe convective storm of 29th October 2013 in the Balearic Islands: observational and numerical study. Quart. J. R. Meteorol. Soc., 141, 1208-1222.

Romero, R., M. Vich, and C. Ramis, 2019: A pragmatic approach for the numerical prediction of meteotsunamis in Ciutadella harbour (Balearic Islands). Ocean Modelling, DOI 10.1016/j.ocemod.2019.101441.

Roux, H., A. Amengual, R. Romero, E. Bladé, and M. Sanz-Ramos, 2020: Evaluation of two hydro-meteorological ensemble strategies for flash flood forecasting over a catchment of the eastern Pyrenees. Nat. Hazards Earth Syst. Sci., 20(2), 425-450.

Sanz-Ramos, M., B. Martí-Cardona, E. Bladé, I. Seco, A. Amengual, H. Roux, and R. Romero, 2020: NRCS-CN estimation from onsite and remote sensing data for management of a reservoir in the Eastern Pyrenees. J. Hydrol. Eng., 25(9): 05020022.

Schaake, J. C., T. M. Hamill, R. Buizza, and M. Clark, 2007: HEPEX: the hydrological ensemble prediction experiment. BAMS, 88(10), 1541-1548.

Schär, C., and D. R. Durran, 1997: Vortex formation and vortex shedding in continuously stratified flows past isolated topography. J. Amos. Sci., 54, 534-554.

Schär, C., D. Leuenberger, O. Fuhrer, D. Lüthi, and C. Girard, 2002: A new terrain-following vertical coordinate formulation for atmospheric prediction models. Mon. Wea. Rev., 130, 2459-2480.

Skamarock, W.C. J. B. Klemp, M. G. Duda, L. D. Fowler, and S. H. Park, 2012: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tessellations and C-grid staggering. Mon. Wea. Rev., 140, 3090-3105.

Schaake, J. C., T. M. Hamill, R. Buizza, and M. Clark, 2007: HEPEX: the hydrological ensemble prediction experiment. BAMS, 88(10), 1541-1548.

Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. R. Meteorol. Soc., 131(612), 3079–3102.

Sippel, J. A., S. A. Braun, F. Zhang, and Y. Weng, 2013: Ensemble Kalman filter assimilation of simulated HIWRAP Doppler velocity data in a hurricane. Mon. Wea. Rev., 141, 2683–2704.

Smith, J. A., M. L. Baeck, J. E. Morrison, and P. Sturdevant-Rees, 2000: Catastrophic rainfall and flooding in Texas. J. Hydrometeor., 1(1), 5-25.

Snook, N., M. Xue, and Y. Jung, 2011: Analysis of a tornadic mesoscale convective vortex based on ensemble kalman filter assimilation of casa x-band and WSR-88D radar data. Mon. Wea. Rev., 139 (11), 3446–3468.

Stensrud, D. J., J. W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128(7 I), 2077–2107.

Stensrud, D. J., 2009: Parameterization schemes: Keys to understanding numerical weather prediction models. Cambridge University Press.

Straka, J. M., R. B. Wilhelmson, L. J. Wicker, J. R. Anderson, and K. K. Droegemeier, 1993: Numerical solutions of a non-linear density current: A benchmark solution and comparisons. Int. J. Num. Meth. Fluids, 17, 1-22.

Tanamachi, R. L., L. J. Wicker, D. C. Dowell, H. B. Bluestein, D. T. Dawson, and M. Xue, 2013: EnKF assimilation of high-resolution, mobile Doppler radar data of the 4 May 2007 Greensburg, Kansas, supercell into a numerical cloud model. Mon. Wea. Rev., 141, 625–648.

Tapiador, F. J., W. K. Tao, J. J. Shi, C. F. Angelis, M. A. Martinez, C. Marcos, A. Rodríguez, and A. Hou, 2012: A comparison of perturbed initial conditions and multiphysics ensembles in a severe weather episode in Spain. J. Appl. Meteor. Climatol., 51, 489–504.

Taylor, J. W., and R. Buizza, 2003: Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecasting, 19(1), 57-70.

Todini, E., 2017: Flood forecasting and decision making in the new millennium. Where are we? Water Resour. Manag., 31, 3111–3129.

Uppala, S. M., and Coauthors, 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012.

Van Leer, B., 1977: Towards the ultimate conservative difference scheme IV. A new approach to numerical convection. J. Comput. Phys., 23, 276-299.

Vich, M., and R. Romero, 2010: Multiphysics superensemble forecast applied to Mediterranean heavy precipitation situations. Nat. Haz. and Earth. Syst. Sci., 10, 2371-2377.

Vich, M., R. Romero, and H. E. Brooks, 2011a: Ensemble prediction of Mediterranean high-impact events using potential vorticity perturbations. Part I: Comparison against the multiphysics approach. Atmos. Res., 102, 227-241.

Vich, M., R. Romero, and V. Homar, 2011b: Ensemble prediction of Mediterranean high-impact events using potential vorticity perturbations. Part II: Adjoint-derived sensitivity zones. Atmos. Res., 102, 311-319.

Vincendon, B., V. Ducrocq, O. Nuissier, and B. Vié, 2011: Perturbation of convection-permitting NWP forecasts for flash flood ensemble forecasting. Nat. Hazards Earth Syst. Sci., 11, 1529–1544.

Wastl, C., Y. Wang, A. Atencia, and C. Wittmann, 2019: Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT). Geosc. Model Develop., 12(1), 261–273.

Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504–520.

Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527–548.

Wheatley, D. M., D. J. Stensrud, D. C. Dowell, and N. Yussouf, 2012. Application of a WRF mesoscale data assimilation system to springtime severe weather events 2007–09. Mon. Wea. Rev., 140 (5), 1539–1557.

Wicker, L. J., and W. C. Skamarock, 1998: A time-splitting scheme for the elastic equations incorporating second-order Runge-Kutta time differencing. Mon Wea. Rev., 126, 1992-1999.

Wilber, A. C., G. L. Smith, S. K. Gupta, and P. W. Stackhouse, 2006: Annual cycles of surface shortwave radiative fluxes. J. Climate, 19(4), 535-547.

WMO - World Meteorological Organization, 2015: Atlas of mortality and economic losses from weather, climate and water extremes (1970-2012). WMO- No. 1123.

Yang, C., A. A. Thatte, and L. Xie, 2014: Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation. IEEE Trans. Sustain. Energy, 6(1), 104-112.

Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma city tor- nadic supercell storm using single- and double-moment microphysics schemes. Mon. Wea. Rev., 141 (10).

Zamo, M., O. Mestre, P. Arbogast, and O. Pannekoucke, 2014: A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, Part I: Deterministic forecast of hourly production. Solar Energy, 105, 792-803.

Zappa, M., et al., 2008: MAP D‐PHASE: Real‐time demonstration of hydrological ensemble prediction systems. Atm. Sci. Let., 9.2: 80-87.

Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble kalman filter. Mon. Wea. Rev., 132 (5).