Climate Dynamics

, Volume 39, Issue 9–10, pp 2497–2522 | Cite as

WRF high resolution dynamical downscaling of ERA-Interim for Portugal

  • Pedro M. M. Soares
  • Rita M. Cardoso
  • Pedro M. A. Miranda
  • Joana de Medeiros
  • Margarida Belo-Pereira
  • Fátima Espirito-Santo


This study proposes a dynamically downscaled climatology of Portugal, produced by a high resolution (9 km) WRF simulation, forced by 20 years of ERA-Interim reanalysis (1989–2008), nested in an intermediate domain with 27 km of resolution. The Portuguese mainland is characterized by large precipitation gradients, with observed mean annual precipitation ranging from about 400 to over 2,200 mm, with a very wet northwest and rather dry southeast, largely explained by orographic processes. Model results are compared with all available stations with continuous records, comprising daily information in 32 stations for temperature and 308 for precipitation, through the computation of mean climatologies, standard statistical errors on daily to seasonally timescales, and distributions of extreme events. Results show that WRF at 9 km outperforms ERA-Interim in all analyzed variables, with good results in the representation of the annual cycles in each region. The biases of minimum and maximum temperature are reduced, with improvement of the description of temperature variability at the extreme range of its distribution. The largest gain of the high resolution simulations is visible in the rainiest regions of Portugal, where orographic enhancement is crucial. These improvements are striking in the high ranking percentiles in all seasons, describing extreme precipitation events. WRF results at 9 km compare favorably with published results supporting its use as a high-resolution regional climate model. This higher resolution allows a better representation of extreme events that are of major importance to develop mitigation/adaptation strategies by policy makers and downstream users of regional climate models in applications such as flash floods or heat waves.


Regional climate modeling WRF model High resolution Climatology ERA-Interim Portugal 



The authors thank the two anonymous reviewers of this manuscript for their comments and suggestions. This work was funded by the Portuguese Science Foundation (FCT) under project REWRITE- PTDC/CLI/73814/2006, and PEST-OE/CTE/LA0019/2011.


  1. Alexandru A, de Elia R, Laprise R, Separovic L, Biner S (2008) Sensitivity study of regional climate model simulations to large scale nudging parameters. Mon Weather Rev 137:1666–1686. doi: 10.1175/2008MWR2620.1 CrossRefGoogle Scholar
  2. Argüeso D, Hidalgo-Muñoz JM, Gámiz-Fortis SR, Esteban-Parra MJ, Dudhia J, Castro-Diez Y (2011) Evaluation of WRF parameterizations for climate studies over Southern Spain using a multistep regionalization. J Clim 24:5633–5651. doi: 10.1175/JCLI-D-11-00073.1 CrossRefGoogle Scholar
  3. Barstad I, Sorteberg A, Flatøy F, Déqué M (2009) Precipitation, temperature and wind in Norway: dynamical downscaling of ERA40. Clim Dyn 33:769–776. doi: 10.1007/s00382-008-0476-5 CrossRefGoogle Scholar
  4. Belo-Pereira M, Dutra E, Viterbo P (2011) Evaluation of global precipitation datasets over the Iberian Peninsula. J Geophys Res. doi: 10.1029/2010JD015481 (in press)
  5. Berrisford P, Dee D, Fielding K, Fuentes M, Kallberg P, Kobayashi S, Uppala S (2009) The ERA-Interim Archive. ERA report series. 1. Technical report. European Centre for Medium-Range Weather Forecasts, Shinfield Park, ReadingGoogle Scholar
  6. Betts AK (1986) A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart J Roy Meteor Soc 112:677–691Google Scholar
  7. Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, and arctic air-mass data sets. Quart J Roy Meteor Soc 112:693–709Google Scholar
  8. Brankovic C, Gregory D (2001) Impact of horizontal resolution on seasonal integrations. Clim Dyn 18:123–143CrossRefGoogle Scholar
  9. Bukovsky MS, Karoly DJ (2009) Precipitation simulations using WRF as a nested regional climate model. J Appl Meteor Climatol 48:2152–2159. doi: 10.1175/2009JAMC2186.1 CrossRefGoogle Scholar
  10. Caldwell PM, Chin H-NS, Bader DC, Bala G (2009) Evaluation of a WRF based dynamical downscaling simulation over California. Climatic Change 95:499–521CrossRefGoogle Scholar
  11. Castro CL, Pielke RA Sr, Leoncini G (2005) Dynamical downscaling: an assessment of value added using a regional climate model. J Geophys Res (Atmospheres) 110:D05108. doi: 10.1029/2004JD004721 CrossRefGoogle Scholar
  12. Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  13. Christensen JH, Carter TR, Giorgi F (2002) PRUDENCE employs new methods to assess European climate change. EOS 83:147CrossRefGoogle Scholar
  14. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X et al (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, et al. (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge and New York: Cambridge University PressGoogle Scholar
  15. Collins WD et al (2004) Description of the NCAR community atmospheric model (CAM 3.0). NCAR tech. note, NCAR/TN-4641STR, 226 ppGoogle Scholar
  16. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan BJ, Yin X, Gleason BE, Vose RS, Rutledge G et al (2011) The twentieth century reanalysis project. Quarterly J Roy Meteorol Soc 137:1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  17. Derbyshire SH, Beau I, Bechtold P, Grandpeix J-Y, Piriou J-M, Redelsperger J-L, Soares PMM (2004) Sensitivity of moist convection to environmental humidity. Quart J Roy Meteor Soc 130:3055–3080CrossRefGoogle Scholar
  18. Dickinson RE, Errico RM, Giorgi F, Bates GT (1989) A regional climate model for western United States. Clim Change 15:383–422Google Scholar
  19. Fischer E, Lawrence D, Sanderson B (2011) Quantifying uncertainties in projections of extremes—a perturbed land surface parameter experiment. Clim Dyn 37:1381–1398. doi: 10.1007/s00382-010-0915-y
  20. Flaounas E, Bastin S, Janicot S (2011) Regional climate modelling of the 2006 West African monsoon: sensitivity to convection and planetary boundary layer parameterisation using WRF. Clim Dyn 36:1083–1105. doi: 10.1007/s00382-010-0785-3 CrossRefGoogle Scholar
  21. Fowler HJ, Blenkinshop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27:1547–1578CrossRefGoogle Scholar
  22. Giorgi F (2002) Variability and trends of sub-continental scale surface climate in the twentieth century. Obs Clim Dyn, Part I. doi: 10.1007/s00382-001-0204-x Google Scholar
  23. Giorgi F, Bates GT (1989) The climatological skill of a regional model over complex terrain. Mon Weather Rev 117:2325–2347CrossRefGoogle Scholar
  24. Giorgi F, Mearns LO (1991) Approaches to regional climate change simulation: a review. Rev of Geophys 29:191–216CrossRefGoogle Scholar
  25. Giorgi F, Mearns L (1999) Introduction to special section: regional climate modeling revisited. J Geophys Res 104(D6):6335–6352CrossRefGoogle Scholar
  26. Giorgi F et al (2001) Regional climate information: Evaluation and projections. In climate change 2001: the scientific basis: contribution of working group I to the third assessment report of the intergovernmental panel on climate change. In: Houghton JT et al. (ed).Cambridge and New York: Cambridge University Press, pp 583–638Google Scholar
  27. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113:D20119. doi: 10.1029/2008JD010201 CrossRefGoogle Scholar
  28. Heikkilä U, Sandvik A, Sorterberg A (2010) Dynamical downscaling or ERA-40 in complex terrain using WRF regional Climate model. Clim Dyn. doi: 10.1007/s00382-010-0928-6 Google Scholar
  29. Herrera S, Fita L, Fernández J, Gutiérrez JM (2010) Evaluation of the mean and extreme precipitation regimes from the ENSEMBLES regional climate multimodel simulations over Spain. J Geophys Res 115:D21117. doi: 10.1029/2010JD013936 CrossRefGoogle Scholar
  30. Hofstra N, HaylockM NewM, Jones PD (2009a) Testing EOBS European high-resolution gridded dataset of daily precipitation and surface temperature. J Geophys Res. doi: 10.1029/2009JD011799 Google Scholar
  31. Hofstra N, New M, McSweeney C (2009b) The influence of interpolation and station network density on the distribution and extreme trends of climate variables in gridded data. Clim Dyn (in press)Google Scholar
  32. Hong S-Y, Lim J-OJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  33. Jacob D, Bärring L, Christensen OB, Christensen JH et al (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Change 81:31–52CrossRefGoogle Scholar
  34. Janjic ZI (1990) The step-mountain coordinate: physical package. Mon Weather Rev 118:1429–1443CrossRefGoogle Scholar
  35. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon Weather Rev 122:927–945CrossRefGoogle Scholar
  36. Janjic ZI (2000) Comments on “development and evaluation of a convection scheme for use in climate models”. J Atmos Sci 57:3686CrossRefGoogle Scholar
  37. Janjic ZI (2001) Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NCEP office note 437, 61 ppGoogle Scholar
  38. Jiao Y, Caya D (2006) An investigation of summer precipitation simulated by the Canadian regional climate model. Mon Weather Rev 134:919–932. doi: 10.1175/MWR3103.1 CrossRefGoogle Scholar
  39. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Quart J Roy Meteor Soc 121:1413–1449Google Scholar
  40. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77:437–471CrossRefGoogle Scholar
  41. Kanamaru H, Kanamitsu M (2007) Fifty-seven-year California reanalysis downscaling at 10 km (CaRD10). part II: comparison with North American regional reanalysis. J Clim 20:5572–5592. doi: 10.1175/2007JCLI1482.1 CrossRefGoogle Scholar
  42. Kanamitsu M, Kanamaru H (2007) 57-year California reanalysis downscaling at 10 km (CaRD10) Part I. System detail and validation with observations. J Clim 20:5527–5552. doi: 10.1175/2007JCLI1482.1 CrossRefGoogle Scholar
  43. Klein Tank AMG et al (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int J of Climatol 22:1441–1453CrossRefGoogle Scholar
  44. Klok EJ, Klein Tank AMG (2009) Updated and extended European dataset of daily climate observations. Int J Climatol 29:1182–1191. doi: 10.1002/joc.1779 CrossRefGoogle Scholar
  45. Laprise R (2008) Regional climate modeling. J Comput Phys 227:3641–3666. doi: 10.1016/ CrossRefGoogle Scholar
  46. Leduc M, Laprise R (2009) Regional climate model sensitivity to domain size. Clim Dyn 32:833–854. doi: 10.1007/s00382-008-0400-z CrossRefGoogle Scholar
  47. Leung LR, Qian Y (2009) Atmospheric rivers induced heavy precipitation and flooding in the western U.S. simulated by the WRF regional climate model. Geophys Res Lett 36:L03820. doi: 10.1029/2008GL036445 CrossRefGoogle Scholar
  48. Leung LR, Mearns LO, Giorgi F, Wilby RL (2003) Regional climate research: needs and opportunities. Bull Am Meteorol Soc 84:89–95. doi: 10.1175/BAMS-84-1-89 CrossRefGoogle Scholar
  49. Liang XZ, Choi HI, Kunkel KE, Dai Y, Joseph E, Wang JXL (2005) Surface boundary conditions for mesoscale regional climate models. Earth Interactions 9Google Scholar
  50. Lo JCF, Yang ZL, Pielke RA Sr (2008) Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. J Geophys Res 113:D09112. doi: 10.1029/2007JD009216 CrossRefGoogle Scholar
  51. McGregor JL (1997) Regional climate modelling. Meteorol Atmos Phys 63:105–117CrossRefGoogle Scholar
  52. Meehl GA, Stocker TF, Collins WD, Friedlingstein P et al (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the 4th assessment report of the IPCC. Cambridge University Press, Cambridge, pp 747–846Google Scholar
  53. Miguez-Macho G, Stenchikov GL, Robock A (2004) Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J Geophys Res 109:D13104. doi: 10.1029/2003JD004495 CrossRefGoogle Scholar
  54. Miranda PMA, Coelho F, Tomé AR, Valente MA, Carvalho A, Pires C, Pires HO, Cabrinha VC, Ramalho C (2002) 20th century Portuguese climate and climate scenarios. In: Santos FD, Forbes K, Moita R (eds) Climate Change in Portugal: Scenarios, Impacts and Adptation Measures 2–83 GradivaGoogle Scholar
  55. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  56. Mölders N, Kramm G (2010) A case study on wintertime inversions in Interior Alaska with WRF. Atmos Res 95(2–3):314–332CrossRefGoogle Scholar
  57. Paredes D, Trigo RM, García-Herrera R, Trigo IF (2006) Understanding precipitation changes in Iberia in early spring: Weather typing and storm-tracking approaches. J Hydrometeorol 7:101–113CrossRefGoogle Scholar
  58. Prömmel K, Geyer B, Jones JM, Widmann M (2010) Evaluation of the skill and added value of a reanalysis-driven regional simulation for Alpine temperature. Int J Climatol 30:760–773. doi: 10.1002/joc.1916 Google Scholar
  59. Randall DA, Wood RA et al (2007) Climate models and their evaluation. In: climate change 2007: The physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.) Cambridge University Press, Cambridge and New York, NYGoogle Scholar
  60. Rauscher SA, Coppola E, Piani C, Giorgi F (2010) Resolution effects on regional climate model simulations of seasonal precipitation over Europe. Part I: Seasonal. Clim Dynamics 35:685–711CrossRefGoogle Scholar
  61. Sistema Nacional de Informação de Recursos Hídricos (2010). Available at
  62. Skamarock WC et al (2008) A description of the advanced research WRF version 3. NCAR tech. note TN-475_STR, 113 ppGoogle Scholar
  63. Smith RB, Barstad I (2004) A linear theory of orographic precipitation. J Atmos Sci 61:1377–1391CrossRefGoogle Scholar
  64. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  65. Stauffer DR, Seaman NL (1990) Use of four-dimensional data assimilation in a limited area mesoscale model. Part I: experiments with synoptic-scale data. Mon Weather Rev 118:1250–1277CrossRefGoogle Scholar
  66. Sylla MB, Coppola E, Mariotti L, Giorgi F, Ruti PM, Dell’Aquila A, Bi X (2009) Multiyear simulation of the African climate using a regional climate model (RegCM3) with the high resolution ERA-interim reanalysis. Clim Dyn. doi: 10.1007/s00382-009-0613-9 Google Scholar
  67. Teixeira J et al (2011) Tropical and sub-tropical cloud transitions in weather and climate prediction models: the GCSS/WGNE Pacific crosssection intercomparison (GPCI). J Clim (in press)Google Scholar
  68. Uppala SM et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  69. van der Linden P, Mitchell JFB (eds) (2009) E ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3 PB, UK, 160 ppGoogle Scholar
  70. Vannitsem SF, Chomé F (2005) One-way nested regional climate simulations and domain size. J Clim 18:229–233CrossRefGoogle Scholar
  71. von Storch H, Langenberg H, Feser F (2000) A spectral nudging technique for dynamical downscaling purposes. Mon Weather Rev 128:3664–3673CrossRefGoogle Scholar
  72. Waldron KM, Peagle J, Horel JD (1996) Sensitivity of a spectrally filtered and nudged limited area model to outer model options. Mon Weather Rev 124:529–547CrossRefGoogle Scholar
  73. Wang Y, Leung LR, McGregor JL, Lee D-K, Wang W-C et al (2004) Regional climate modelling: progress, challenges, and prospects. J Meteorol Soc Japan 82:1599–1628CrossRefGoogle Scholar
  74. Warner T, Peterson RA, Treadon RE (1997) A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull Am Meteor Soc 78:2599–2617CrossRefGoogle Scholar
  75. Wilks DS (2006) Statistical methods in the atmospheric sciences. Elsevier, AmsterdamGoogle Scholar
  76. Zhang Y, Dulière V, Mote P, Salathé EP Jr (2009) Evaluation of WRF and HadRM mesoscale climate simulations over the United States Pacific Northwest. J Clim 22:5511–5526CrossRefGoogle Scholar
  77. Zwiers FW, Kharin VV (1998) Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J Clim 11:2200–2222CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Pedro M. M. Soares
    • 1
    • 3
  • Rita M. Cardoso
    • 1
  • Pedro M. A. Miranda
    • 1
  • Joana de Medeiros
    • 1
  • Margarida Belo-Pereira
    • 2
  • Fátima Espirito-Santo
    • 2
  1. 1.Instituto Dom LuizUniversity of LisbonLisbonPortugal
  2. 2.Instituto de MeteorologiaLisbonPortugal
  3. 3.Faculdade de Ciências da Universidade de LisboaLisbonPortugal

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