Brooks, H. E., Lee, J. W., and Craven, J. P.: The spatial distribution of
severe thunderstorm and tornado environments from global reanalysis data,
Atmos. Res., 67–68, 73–94,
https://doi.org/10.1016/S0169-8095(03)00045-0, 2003.
a
Calbó, J. and Sanchez-Lorenzo, A.: Cloudiness climatology in the Iberian
Peninsula from three global gridded datasets (ISCCP, CRU TS 2.1, ERA-40),
Theor. Appl. Climatol., 96, 105–115,
https://doi.org/10.1007/s00704-008-0039-z, 2009.
a
Correoso, J. F., Hernández, E., García-Herrera, R., Barriopedro, D.,
and Paredes, D.: A 3-year study of cloud-to-ground lightning flash
characteristics of Mesoscale convective systems over the Western
Mediterranean Sea, Atmos. Res., 79, 89–107,
https://doi.org/10.1016/j.atmosres.2005.05.002, 2006.
a
Craven, J. P., Jewell, R. E., and Brooks, H. E.: Comparison between Observed
Convective Cloud-Base Heights and Lifting Condensation Level for Two
Different Lifted Parcels, Weather Forecast., 17, 885–890,
https://doi.org/10.1175/1520-0434(2002)017<0885:CBOCCB>2.0.CO;2, 2002.
a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
a
Diffenbaugh, N. S., Scherer, M., and Trapp, R. J.: Robust increases in severe
thunderstorm environments in response to greenhouse forcing, P.
Natl. Acad. Sci. USA, 110, 16361–16366,
https://doi.org/10.1073/pnas.1307758110, 2013.
a
Doswell, C. A., Ramis, C., Romero, R., and Alonso, S.: A Diagnostic Study of
Three Heavy Precipitation Episodes in the Western Mediterranean Region,
Weather Forecast., 13, 102–124,
https://doi.org/10.1175/1520-0434(1998)013<0102:ADSOTH>2.0.CO;2, 1998.
a
Enno, S.-E., Sugier, J., Alber, R., and Seltzer, M.: Lightning flash density in
Europe based on 10 years of ATDnet data, Atmos. Res., 235,
104769,
https://doi.org/10.1016/j.atmosres.2019.104769, 2020.
a,
b,
c
Esteban-Parra, M. J., Rodrigo, F. S., and Castro-Diez, Y.: Spatial and temporal
patterns of precipitation in Spain for the period 1880–1992, International
J. Climatol., 18, 1557–1574,
https://doi.org/10.1002/(SICI)1097-0088(19981130)18:14<1557::AID-JOC328>3.0.CO;2-J,
1998.
a,
b
Fernández, J., Montávez, J. P., Sáenz, J., González-Rouco,
J. F., and Zorita, E.: Sensitivity of the MM5 mesoscale model to physical
parameterizations for regional climate studies: Annual cycle, J.
Geophys. Res.-Atmos., 112, D04101,
https://doi.org/10.1029/2005JD006649, 2007.
a
Galway, J. G.: The lifted index as a predictor of latent instability, B. Am. Meteorol. Soc., 37, 528–529, 1956. a
Gascón, E., Merino, A., Sánchez, J., Fernández-González, S.,
García-Ortega, E., López, L., and Hermida, L.: Spatial distribution
of thermodynamic conditions of severe storms in southwestern Europe,
Atmos. Res., 164–165, 194–209,
https://doi.org/10.1016/j.atmosres.2015.05.012, 2015.
a
Gayà, M., Homar, V., Romero, R., and Ramis, C.: Tornadoes and waterspouts
in the Balearic Islands: phenomena and environment characterization,
Atmos. Res., 56, 253–267,
https://doi.org/10.1016/S0169-8095(00)00076-4, 2001.
a
George, J. J.: Weather forecasting for aeronautics, Academic Press, San Diego,
p. 411, 1960. a
González-Rojí, S. J., Sáenz, J., Ibarra-Berastegi, G., and
Díaz de Argandoña, J.: Moisture balance over the Iberian Peninsula
according to a regional climate model: The impact of 3DVAR data
assimilation, J. Geophys. Res.-Atmos., 123, 708–729,
https://doi.org/10.1002/2017JD027511, 2018.
a,
b,
c,
d,
e
González-Rojí, S. J., Wilby, R. L., Sáenz, J., and
Ibarra-Berastegi, G.: Harmonized evaluation of daily precipitation downscaled
using SDSM and WRF+WRFDA models over the Iberian Peninsula, Clim. Dynam.,
53, 1413–1433,
https://doi.org/10.1007/s00382-019-04673-9, 2019.
a
González-Rojí, S. J., Sáenz, J., Díaz de Argandoña, J.,
and Ibarra-Berastegi, G.: Moisture Recycling over the Iberian Peninsula:
The Impact of 3DVAR Data Assimilation, Atmosphere, 11, 19,
https://doi.org/10.3390/atmos11010019, 2020a.
a
González-Rojí, S. J., Carreno-Madinabeitia, S., Sáenz, J., and Ibarra-Berastegi, G.: Pseudo-Soundings for Publication “Changes in the simulation of instability indices over the Iberian Peninsula due to the use of 3DVAR data assimilation” [data set], Zenodo,
https://doi.org/10.5281/zenodo.3611343, 2020b.
a
Graf, M. A., Sprenger, M., and Moore, R. W.: Central European tornado
environments as viewed from a potential vorticity and Lagrangian
perspective, Atmos. Res., 101, 31–45,
https://doi.org/10.1016/j.atmosres.2011.01.007, 2011.
a
Holley, D. M., Dorling, S. R., Steele, C. J., and Earl, N.: A climatology of
convective available potential energy in Great Britain, Int.
J. Climatol., 34, 3811–3824,
https://doi.org/10.1002/joc.3976, 2014.
a,
b,
c,
d,
e,
f
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A Revised Approach to Ice
Microphysical Processes for the Bulk Parameterization of Clouds and
Precipitation, Mon. Weather Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004.
a
Huntrieser, H., Schiesser, H. H., Schmid, W., and Waldvogel, A.: Comparison of
Traditional and Newly Developed Thunderstorm Indices for Switzerland,
Weather Forecast., 12, 108–125,
https://doi.org/10.1175/1520-0434(1997)012<0108:COTAND>2.0.CO;2, 1997.
a
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.
a
Iturrioz, I., Hernández, E., Ribera, P., and Queralt, S.: Instability and its relation to precipitation over the Eastern Iberian Peninsula, Adv. Geosci., 10, 45–50,
https://doi.org/10.5194/adgeo-10-45-2007, 2007.
a,
b,
c
Jerez, S., Montavez, J. P., Gomez-Navarro, J. J., Jimenez, P. A.,
Jimenez-Guerrero, P., Lorente, R., and Gonzalez-Rouco, J. F.: The role of the
land-surface model for climate change projections over the Iberian
Peninsula, J. Geophys. Res.-Atmos., 117, D01109,
https://doi.org/10.1029/2011JD016576, 2012.
a
Jones, R. G., Murphy, J. M., and Noguer, M.: Simulation of climate change over
europe using a nested regional-climate model. I: Assessment of control
climate, including sensitivity to location of lateral boundaries, Q. J. Roy. Meteor. Soc., 121, 1413–1449,
https://doi.org/10.1002/qj.49712152610, 1995.
a
Kunz, M.: The skill of convective parameters and indices to predict isolated
and severe thunderstorms, Natural Hazards and Earth System Sciences, 7,
327–342,
https://doi.org/10.5194/nhess-7-327-2007, 2007.
a,
b
Lee, J.: Tornado Proximity Soundings from the NCEP/NCAR Reanalysis Data,
PhD thesis, University of Oklahoma, Grand Forks, North Dakota, 2002. a
Letkewicz, C. E. and Parker, M. D.: Forecasting the Maintenance of Mesoscale
Convective Systems Crossing the Appalachian Mountains, Weather
Forecast., 25, 1179–1195,
https://doi.org/10.1175/2010WAF2222379.1, 2010.
a
López, L., Marcos, J. L., Sánchez, J. L., Castro, A., and Fraile, R.:
CAPE values and hailstorms on northwestern Spain, Atmos. Res.,
56, 147–160,
https://doi.org/10.1016/S0169-8095(00)00095-8, 2001.
a,
b,
c
Lucas, C., Zipser, E. J., and LeMone, M. A.: Convective Available Potential
Energy in the Environment of Oceanic and Continental Clouds: Correction and
Comments, J. Atmos. Sci., 51, 3829–3830,
https://doi.org/10.1175/1520-0469(1994)051<3829:CAPEIT>2.0.CO;2, 1994.
a
Marsh, P. T., Brooks, H. E., and Karoly, D. J.: Preliminary investigation into
the severe thunderstorm environment of Europe simulated by the Community
Climate System Model 3, Atmos. Res., 93, 607–618,
https://doi.org/10.1016/j.atmosres.2008.09.014, 2009.
a,
b
Miller, R. C.: Notes on analysis and severe-storm forecasting procedures of the
Air Force Global Weather Central, vol. 200, AWS Technical Report, Air Weather Service (MAC), United States Air Force, 1972.
a,
b
Mohr, S., Kunz, M., and Geyer, B.: Hail potential in Europe based on a
regional climate model hindcast, Geophys. Res. Lett., 42,
10904–10912,
https://doi.org/10.1002/2015GL067118, 2015.
a,
b
Molina, D. S., Fernández-González, S., González, J. C. S., and
Oliver, A.: Analysis of sounding derived parameters and application to severe
weather events in the Canary Islands, Atmos. Res., 237, 104865,
https://doi.org/10.1016/j.atmosres.2020.104865, 2020.
a
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,
https://doi.org/10.1007/s10546-005-9030-8, 2006.
a
Piper, D. and Kunz, M.: Spatiotemporal variability of lightning activity in Europe
and the relation to the North Atlantic Oscillation teleconnection pattern, Nat. Hazards Earth Syst. Sci., 17, 1319–1336,
https://doi.org/10.5194/nhess-17-1319-2017, 2017.
a
Púčik, T., Groenemeijer, P., Rýva, D., and Kolář, M.:
Proximity Soundings of Severe and Nonsevere Thunderstorms in Central
Europe, Mon. Weather Rev., 143, 4805–4821,
https://doi.org/10.1175/MWR-D-15-0104.1, 2015.
a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at:
https://www.R-project.org/ (last access: 5 May 2021), 2020. a
Rädler, A. T., Groenemeijer, P., Faust, E., and Sausen, R.: Detecting
Severe Weather Trends Using an Additive Regressive Convective Hazard Model
(AR-CHaMo), J. Appl. Meteor. Clim., 57, 569–587,
https://doi.org/10.1175/JAMC-D-17-0132.1, 2018.
a
Rädler, A. T., Groenemeijer, P. H., Faust, E., Sausen, R., and Púčik, T.: Frequency of severe thunderstorms across Europe expected to
increase in the 21st century due to rising instability, Npj Climate and
Atmospheric Science, 2, 30,
https://doi.org/10.1038/s41612-019-0083-7, 2019.
a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496,
https://doi.org/10.1175/2007JCLI1824.1, 2007.
a
Riemann-Campe, K., Fraedrich, K., and Lunkeit, F.: Global climatology of
Convective Available Potential Energy (CAPE) and Convective Inhibition
(CIN) in ERA-40 reanalysis, Atmos. Res., 93, 534–545,
https://doi.org/10.1016/j.atmosres.2008.09.037, 2009.
a,
b
Rodríguez-Puebla, C., Encinas, A. H., Nieto, S., and Garmendia, J.:
Spatial and temporal patterns of annual precipitation variability over the
Iberian Peninsula, Int. J. Climatol., 18, 299–316,
https://doi.org/10.1002/(SICI)1097-0088(19980315)18:3<299::AID-JOC247>3.0.CO;2-L, 1998.
a,
b
Romero, R., Ramis, C., and Guijarro, J.: Daily rainfall patterns in the
Spanish Mediterranean area: an objective classification, Int.
J. Climatol., 19, 95–112,
https://doi.org/10.1002/(SICI)1097-0088(199901)19:1<95::AID-JOC344>3.0.CO;2-S, 1999.
a,
b
Romero, R., Gayà, M., and Doswell, C. A.: European climatology of severe
convective storm environmental parameters: A test for significant tornado
events, Atmos. Res., 83, 389–404,
https://doi.org/10.1016/j.atmosres.2005.06.011, 2007.
a,
b,
c,
d
Rummukainen, M.: State-of-the-art with regional climate models, Wiley
Interdisciplinary Reviews: Climate Change, 1, 82–96,
https://doi.org/10.1002/wcc.8,
2010.
a
Sáenz, J., González-Rojí, S. J., Carreno-Madinabeitia, S., and
Ibarra-Berastegi, G.: Analysis of atmospheric thermodynamics using the R
package aiRthermo, Comput. Geosci., 122, 113–119,
https://doi.org/10.1016/j.cageo.2018.10.007, 2019.
a,
b
Showalter, A. K.: A Stability Index for Thunderstorm Forecasting, B.
Am. Meteorol. Soc., 34, 250–252, 1953. a
Siedlecki, M.: Selected instability indices in Europe, Theor.
Appl. Climatol., 96, 85–94,
https://doi.org/10.1007/s00704-008-0034-4, 2009.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.:
Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model
evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733,
https://doi.org/10.1002/jgrd.50203, 2013.
a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, NCAR Technical Note NCAR/TN-475+STR,
https://doi.org/10.5065/D68S4MVH, 2008.
a
Taszarek, M., Brooks, H. E., and Czernecki, B.: Sounding-Derived Parameters
Associated with Convective Hazards in Europe, Mon. Weather Rev., 145,
1511–1528,
https://doi.org/10.1175/MWR-D-16-0384.1, 2017.
a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek, M.,
Gayno, G., Wegiel, J., and Cuenca, R.: Implementation and verification of the
unified NOAH land surface model in the WRF model, in: 20th conference on
weather analysis and forecasting/16th conference on numerical weather
prediction, vol. 1115, available at:
https://ams.confex.com/ams/84Annual/webprogram/Paper69061.html (last access: 15 June 2021), 2004. a
Tullot, I. F.: Climatología de España y Portugal, vol. 76,
Universidad de Salamanca, Salamanca, Spain, 2000. a
Ulazia, A., Sáenz, J., Ibarra-Berastegui, G., González-Rojí,
S. J., and Carreno-Madinabeitia, S.: Using 3DVAR data assimilation to
measure offshore wind energy potential at different turbine heights in the
West Mediterranean, Appl. Energ., 208, 1232–1245,
https://doi.org/10.1016/j.apenergy.2017.09.030, 2017.
a,
b
Ulazia, A., Ibarra-Berastegi, G., Sáenz, J., Carreno-Madinabeitia, S., and
González-Rojí, S. J.: Seasonal Correction of Offshore Wind Energy
Potential due to Air Density: Case of the Iberian Peninsula, Sustainability,
11, 3648,
https://doi.org/10.3390/su11133648, 2019.
a
Viceto, C., Marta-Almeida, M., and Rocha, A.: Future climate change of
stability indices for the Iberian Peninsula, Int. J.
Climatol., 37, 4390–4408,
https://doi.org/10.1002/joc.5094, 2017.
a,
b,
c,
d,
e,
f,
g,
h
Virts, K. S., Wallace, J. M., Hutchins, M. L., and Holzworth, R. H.:
Highlights of a New Ground-Based, Hourly Global Lightning Climatology,
B. Am. Meteorol. Soc., 94, 1381–1391,
https://doi.org/10.1175/BAMS-D-12-00082.1, 2013.
a,
b
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, vol. 100,
Academic Press, 2011. a
Xu, G., Xi, B., Zhang, W., Cui, C., Dong, X., Liu, Y., and Yan, G.: Comparison
of atmospheric profiles between microwave radiometer retrievals and
radiosonde soundings, J. Geophys. Res.-Atmos., 120,
10313–10323,
https://doi.org/10.1002/2015JD023438, 2015.
a
Ye, B., Del Genio, A. D., and Lo, K. K.-W.: CAPE Variations in the Current
Climate and in a Climate Change, J. Climate, 11, 1997–2015,
https://doi.org/10.1175/1520-0442-11.8.1997, 1998.
a
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary
Layer Clouds
over the Southeast Pacific in ARW-WRF Using a Modified
Tiedtke Cumulus Parameterization Scheme, Mon. Weather Rev., 139,
3489–3513,
https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
a
Zheng, D., Van Der Velde, R., Su, Z., Wen, J., and Wang, X.: Assessment of
Noah land surface model with various runoff parameterizations over a
Tibetan river, J. Geophys. Res.-Atmos, 122,
1488–1504,
https://doi.org/10.1002/2016JD025572, 2017.
a
