Skip to main content
Log in

Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations

  • Review
  • Published:
Asia-Pacific Journal of Atmospheric Sciences Aims and scope Submit manuscript

Abstract

Dynamical downscaling has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical downscaling for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in downscaling due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical downscaling were also described.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alexandru, A., R. de Elia, R. Laprise, L. Separovic, and S. Biner, 2009: Sensitivity study of regional climate model simulations to large-scale nudging parameters. Mon. Wea. Rev., 137, 1666–1686.

    Article  Google Scholar 

  • Annamalai, H., J. Sling, K. R. Sperber, and K. Hodges, 1999: The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP-NCAR reanalysis. Mon. Wea. Rev., 127, 1157–1186.

    Article  Google Scholar 

  • Anthes, R. A., 1983: Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev., 111, 1306–1335.

    Article  Google Scholar 

  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493–2525.

    Article  Google Scholar 

  • —, and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992, doi: 10.1175/JAS-D-12-0330.1.

    Article  Google Scholar 

  • Bennett, A. F., 1976: Open boundary conditions for dispersive waves. J. Atmos. Sci., 33, 176–182.

    Article  Google Scholar 

  • Brunet, G., and Coauthors, 2010: Collaboration of the weather and climate communities to advance sub-seasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 91, 1397–1406, doi: 10.1175/2010BAMS3013.1.

    Article  Google Scholar 

  • Castro, C. L., R. A. Pielke Sr., and G. Leoncini, 2005: Dynamical downs-caling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res., 110, D05108, doi:10.1029/2004JD004721.

    Google Scholar 

  • Davies, H. C., 1976: A lateral boundary formulation for multi-level pre-diction models. Quart. J. Roy. Meteor. Soc., 102, 405–418, doi:10.1002/ qj.49710243210.

    Google Scholar 

  • Di Luca, A., R. Elía, and R. Laprise, 2013: Potential for small scale added value of RCM’s downscaled climate change signal. Clim. Dynam., 40, 601–618.

    Article  Google Scholar 

  • Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in a RCM and sensitivity tests to the resolution of the lateral boundary conditions during summer. Clim. Dynam., 25, 555–580.

    Article  Google Scholar 

  • de Ela, R., R. Laprise, and B. Denis, 2002: Forecasting skill limits of nested limited-area models: a perfect-model approach. Mon. Wea. Rev., 130, 2006–2023.

    Article  Google Scholar 

  • Denis, B., R. Laprise, D. Caya, and J. Côté, 2002: Downscaling ability of one-way nested regional climate model: the Big-Brother Experiment. Clim. Dynam., 18, 627–646.

    Google Scholar 

  • Decker, M., M. A. Brunke, Z. Wang, K. Sakaguchi, X. Zeng, and M. G. Bosilovich, 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944.

    Article  Google Scholar 

  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State / NCAR mesoscale model: Validation tests and simulations of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513.

    Article  Google Scholar 

  • Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value to global model data: A review and selected examples. Bull. Amer. Meteor. Soc., 92, 1181–1192.

    Article  Google Scholar 

  • Fox-Rabinovitz, M., J. Côté, B. Dugas, M. Déqué, and J. L. McGregor, 2006: Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP). J. Geophys. Res., 111, D16104, doi:10.1029/2005JD006520.

    Article  Google Scholar 

  • Fu, C., and Coauthors, 2005: Regional climate model intercomparison project for Asia. Bull. Amer. Meteor. Soc., 86, 257–266.

    Article  Google Scholar 

  • Giorgi, F., 1990: Simulation of regional climate using a limited area model nested in a general circulation model.J. Climate, 3, 941–963.

    Article  Google Scholar 

  • —, and G. T. Bates, 1989: The climatological skill of a regional model over complex terrain. Mon. Wea. Rev., 117, 2325–2347.

    Article  Google Scholar 

  • —, and X. Bi, 2000: A study of internal variability of a regional climate model. J. Geophys. Res., 105, 29503–29521.

    Google Scholar 

  • —, M. R. Marinucci, G. Bates, and G. DeCanio, 1993: Development of a second generation regional climate model (RegCM2). II. Con-vective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814–2832.

    Google Scholar 

  • —, and Coauthors, 2001: Regional climate information-evaluation and projections. Chapter 10 of: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (J. T. Houghton et al., eds), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 583–638.

    Google Scholar 

  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, doi:10.1029/ 2007JD008972.

    Google Scholar 

  • Grell, G. A., and S. R. Freitas, 2013: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys. Discuss., 13, 23845–23893, doi:10.5194/acpd-13-23845-2013.

    Article  Google Scholar 

  • Gutmann, E. D., R. M. Rasmussen, C. Liu, K. Ikeda, D. J. Gochis, M. P. Clark, J. Dudhia, and G. Thompson, 2012: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Climate, 25, 262–281.

    Article  Google Scholar 

  • Ham, S., and S.-Y. Hong, 2013: Sensitivity of simulated intraseasonal oscillation to four convective parameterization schemes in a coupled climate model. Asia-Pac. J. Atmos. Sci., 49, 483–496.

    Article  Google Scholar 

  • Hong, S.-Y., and E.-C. Chang, 2012: Spectral nudging sensitivity experi-ments in a regional climate model. Asia-Pac. J. Atmos. Sci., 48, 345–355.

    Article  Google Scholar 

  • —, and J. Dudhia, 2012: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies. Bull. Amer. Meteor. Soc., 93, ES6–ES9, http://dx.doi.org/10.1175/2011BAMS3224.1.

    Google Scholar 

  • —, and E. Kalnay, 2000: Role of sea surface temperature and soil-moisture feedback in the 1998 Oklahoma-Texas drought. Nature, 408, 842–844.

    Google Scholar 

  • —, and H.-L. Pan, 1998: Convective trigger function for a mass flux cumulus parameterization scheme. Mon. Wea. Rev., 126, 2599–2620.

    Google Scholar 

  • —, and H.-L. Pan, 2000: Impact of soil moisture anomalies on seasonal, summertime circulation over North America in a regional climate model. J. Geophys. Res., 105, D24, 29625–29634.

    Google Scholar 

  • —, H. M. Kim, J.-E. Kim, S.-O. Hwang, and H. Park, 2011: The impact of model uncertainties on the analyzed data in a global data assimilation system. Terr. Atmos. Oceanic. Sci., 22, 41–47.

    Google Scholar 

  • —, and Coauthors, 2013a: The Global/Regional Integrated Model System (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219–243, doi:10.1007/s13143-013-0023-0.

  • —, M.-S. Koo, J. Jang, J.-E. E. Kim, H. Park, M.-S. Joh, J.-H. Kang, and T.-J. Oh, 2013b: An evaluation of the software system dependency of a global atmospheric model. Mon. Wea. Rev., 141, 4165–4172, doi: 10.1175/mwr-d-12-00352.1.

  • Hu, H., and W. T. Liu, 2002: QuikSCAT reveals the surface circulation of the Catalina Eddy. Geophys. Res. Lett., 29, 1821, doi:10.1029/2001GL-014203, 2002.

    Article  Google Scholar 

  • Huffman, G.J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydro-meteor., 2, 36–50.

    Google Scholar 

  • Hwang, S.-O., and S.-Y. Hong, 2012: The impact of observation systems on medium-range weather forecasting in a global forecast system. Asia-Pac. J. Atmos. Sci., 48, 159–170.

    Article  Google Scholar 

  • —, and —, 2010: Investigation of moisture field assimilation in the NCEP/DOE reanalysis system. J. Atmos. Sol. -Terr. Phys., 72, 556–564.

    Article  Google Scholar 

  • —, —, and M. Kanamitsu, 2010: Impacts of assimilated data on reanalysis climatology. Asia-Pac. J. Atmos. Sci., 46, 185–197.

    Google Scholar 

  • Im, E.-S., J.-B. Ahn, and D.-W. Kim, 2012a: An assessment of future dryness over Korea based on the ECHAM5-RegCM3 model chain underA1B emission scenario. Asia-Pac. J. Atmos. Sci., 48, 325–337, doi: 10.1007/s13143-012-0031-5.

    Article  Google Scholar 

  • —, B.-J. Lee, J.-H. Kwon, S.-R. In, and S.-O. Han, 2012b: Potential increase of flood hazards in Korea due to global warming from a high-resolution regional climate simulation. Asia-Pac. J. Atmos. Sci., 48, 107–113, doi:10.1007/s13143-012-0010-x. Intergovernmental Panel on Climate Change (IPCC), 1995: Climate Change 1995. The science of climate change. J. T. Houghton, L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg and K. Maskell, Eds, Cambridge University Press, 572 pp.

    Article  Google Scholar 

  • —, 2007: Climate Change 2007: Synthesis Report.IPCC, 104 pp.

  • Jo, S., Y. Lim, J. Lee, H.-S. Kang, and H.-S. Oh, 2012: Bayesian regression model for seasonal forecast of precipitation over Korea. Asia-Pac. J. Atmos. Sci., 48, 205–212, doi:10.1007/s13143-012-0021-7.

    Article  Google Scholar 

  • Jolliffe, I. T., and D B. Stephenson, 2011: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd Edition, 292 pp.

    Book  Google Scholar 

  • Juang, H.-M. H., and M. Kanamitsu, 1994: The NMC nested regional spectral model. Mon. Wea. Rev., 122, 3–26.

    Article  Google Scholar 

  • —, and S.-Y. Hong, 2001: Sensitivity of the NCEP regional spectral model to domain size and nesting strategy. Mon. Wea. Rev., 129, 2904–2922.

    Article  Google Scholar 

  • —, —, and M. Kanamitsu, 1997: The NCEP regional spectral model: An update. Bull. Amer. Meteor. Soc., 78, 2125–2143.

    Google Scholar 

  • Kanamitsu, M., and H. Kanamaru, 2007: Fifty-Seven-year California Reanalysis Downscaling at 10 km (CaRD10). Part I: System detail and validation with observations. J. Climate, 20, 5553–5571.

    Article  Google Scholar 

  • —, and L. DeHaan, 2011: The Added Value Index: A new metric to quantify the added value of regional models. J. Geophys. Res., 116, D11106, doi:10.1029/2011JD015597.

    Article  Google Scholar 

  • —, E. Yulaeva, H. Li, and S.-Y. Hong, 2013: Catalina Eddy as revealed by the historical downscaling of reanalysis. Asia-Pac. J. Atmos. Sci., 49, 467–481, doi:10.1007/s13143-013-0041-y.

  • —, K. Yoshimura, Y.-B. Yhang, and S.-Y. Hong, 2010: Errors of interannual variability and trend in dynamical downscaling of reanalysis. J. Geophys. Res., 115, D17115, doi:10.1029/2009JD013511.

  • Kanamaru, H., and M. Kanamitsu, 2006: Scale selective bias correction in a downscaling of global analysis using a regional model. Mon. Wea. Rev., 135, 334–350, doi:10.1175/MWR3294.1.

    Article  Google Scholar 

  • Kida, H., T. Koide, H. Sasaki, and M. Chiba, 1991: A new approach for coupling a limited area model to a GCM for regional climate simulations. J. Meteor. Soc. Japan, 69, 723–728.

    Google Scholar 

  • Kim, J.-W., J.-T. Chang, N. L. Baker, D. S. Wilks, and W. L. Gates, 1984: The statistical problem of climate inversion: Determination of the relation- ship between local and large-scale climate. Mon. Wea. Rev., 112, 2069–2077.

    Article  Google Scholar 

  • Kim, J., and Coauthors, 2013: Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim. Dynam., doi:10.1007/s00382-013-1751-7.

    Google Scholar 

  • Koo, M.-S., and S.-Y. Hong, 2010: Diurnal Variations of Simulated Precipitation over East Asia in Two Regional Climate Models. J. Geophys. Res., 115, D05105, doi:10.1029/2009JD012574.

    Google Scholar 

  • —, —, and J. Kim, 2009: An evaluation of the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) data over South Korea.Asia-Pac. J. Atmos. Sci., 45, 265–282.

    Google Scholar 

  • Laprise, R., 2008: Regional climate modelling. J. Comput. Phys., 227, 3641–3666.

    Article  Google Scholar 

  • —, and Coauthors, 2012: Considerations of domain size and large-scale driving for nested regional climate models: Impact on internal variability and ability at developing small-scale details. Climate Change, 181–199.

    Chapter  Google Scholar 

  • Lee, D.-K., D.-H. Cha, C.-S. Jin, and S.-J. Choi, 2013: A regional climate change simulation over East Asia. Asia-Pac. J. Atmos. Sci., 49, 655–664, doi:10.1007/s13143-013-0058-2.

    Article  Google Scholar 

  • Lee, J.-W., and S.-Y. Hong, 2013: Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model. Theor. Appl. Climatol., doi: 10.1007/s00704-013-1034-6.

    Google Scholar 

  • —, —, E.-C. Chang, M.-S. Suh, and H.-S. Kang, 2013: Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Clim. Dynam., doi:10.1007/s00382-013-1841-6

    Google Scholar 

  • Leung, R., L. O. Mearns, F. Giorgi, and R. L. Wilby, 2003: Regional climate research: Needs and opportunities. Bull. Amer. Meteor. Soc., 84, 89–95.

    Article  Google Scholar 

  • Li, H., M. Kanamitsu, and S.-Y. Hong, 2012: California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system. J. Geophys. Res., 117, D12118, doi:10.1029/2011-JD017372.

    Google Scholar 

  • —, —, —, K. Yoshimura, D. R. Cayan, and V. Misra, 2013: A high-resolution ocean-atmosphere coupled downscaling of the present climate over California. Clim. Dynam., doi:10.1007/s00382-013-1670-7.

    Google Scholar 

  • Lord, S. J., 1982: Interaction of a cumulus cloud ensemble with the large-scale environment. Part III: Semi-prognostic test of the Arakawa-Schubert cumulus parameterization. J. Atmos. Sci., 39, 88–103.

    Article  Google Scholar 

  • Lorenz, E. N., 1993: The essence of chaos. Seattle, University of Washington Press, 240 pp.

    Book  Google Scholar 

  • Lucas-Picher, P., D. Caya, S. Biner, and R. Laprise, 2008: Quantification of the lateral boundary forcing of a regional climate model using an aging tracer. Mon. Wea. Rev., 136, 4980–4996.

    Article  Google Scholar 

  • —, F. Boberg, J. H. Christensen, P. B., and P. Berg, 2013: Dynamical downscaling with reinitializations: A method to generate finescale climate datasets suitable for impact studies. J. Hydrometeor., 14, 1159–1174.

    Article  Google Scholar 

  • Ma, W., Y. Ma, M. Li, Z. Hu, L. Zhong, Z. Su, H. Ishikawa, and J. Wang, 2009: Estimating surface fluxes over the north Tibetan Plateau area with ASTER imagery. Hydrol. Earth Syst. Sci., 13, 57–67, doi:10.5194/hess-13-57-2009.

    Article  Google Scholar 

  • Marbaix, P., H. Gallée, O. Brasseur, and J.-P. van Ypersele, 2003: Lateral boundary conditions in regional climate models: A detailed study of the relaxation procedure. Mon. Wea. Rev., 131, 461–479.

    Article  Google Scholar 

  • McGregor, J. L., 1997: Regional climate modeling. Meteor. Atmos. Phys., 63, 105–117.

    Article  Google Scholar 

  • —, 2013: Recent developments in variable-resolution global climate modelling. Climatic Change, 119, doi:10.1007/s10584-013-0866-5.

  • —, and Dix, M. R., 2008. An updated description of the conformal-cubic atmospheric model. High Resolution Simulation of the Atmosphere and Ocean, Hamilton, K. and Ohfuchi, W., Eds., Springer, 51–76.

    Google Scholar 

  • Mesinger, F., and K. Veljovic, 2013: Limited area NWP and regional climate modeling: A test of the relaxation vs Eta lateral boundary conditions. Meteor. Atmos. Phys., 119, 1–16, doi:10.1007/s00703-012-0217-5.

    Article  Google Scholar 

  • Mearns, L. O., and Coauthors, 2012: The north American regional climate change assessment program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 1337–1362.

    Article  Google Scholar 

  • Misra, V., 2007: Addressing the issue of systematic errors in a regional climate model. J. Climate, 20, 801–818.

    Article  Google Scholar 

  • Murphy, J., 1999: An evaluation of statistical and dynamical techniques for downscaling local climate. J. Climate, 12, 2256–2284.

    Article  Google Scholar 

  • Oh, S.-G., M.-S. Suh, and D.-H. Cha, 2013: Impact of lateral boundary conditions on precipitation and temperature extremes over South Korea in the CORDEX regional climate simulation using RegCM4. Asia-Pac. J. Atmos. Sci., 49, 497–509, doi:10.1007/s13143-013-0044-8.

    Article  Google Scholar 

  • Omrani, H., P. Drobinski, and T. Dubos, 2012: Spectral nudging in regional climate modelling: how strongly should we nudge? Quart. J. Roy. Meteor. Soc., 138, 1808–1813, doi: 10.1002/qj.1894.

    Article  Google Scholar 

  • Pan, Z., E. Takle, W. Gutowski, and R. Turner, 1999: Long simulation of regional climate as a sequence of short segments. Mon. Wea. Rev., 127, 308–321.

    Article  Google Scholar 

  • Park, B.-K., and S.-Y. Hong, 2013: Effects of physics packages on medium-range forecasts in a global forecasting system. J. Atmos. Sol. -Terr. Phys., 100-101, 50–58.

    Article  Google Scholar 

  • Peña-Arancibia, J. L., A. I. J. M. van Dijk, L. J. Renzullo, and M. Mulligan, 2013: Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an snsemble method for regions in Australia and South and East Asia. J. Hydrometeor., 14, 1323–1333.

    Article  Google Scholar 

  • Pfeifroth, U., R. Mueller, and B. Ahrens, 2013: Evaluation of satellite-based and reanalysis precipitation data in the tropical Pacific. J. Appl. Meteor. Climatol, 52, 634–644.

    Article  Google Scholar 

  • Phelps, M. W., A. Kumar, and J. J. O’Brien, 2004: Potential predictability in the NCEP CPC dynamical seasonal forecast system. J. Climate, 17, 3775–3785.

    Article  Google Scholar 

  • Pielke, R. A., Sr, 2013: Comments on “The North American Regional Climate Change Assessment Program: Overview of Phase I Results”. Bull. Amer. Meteor. Soc., 1075–1077.

    Google Scholar 

  • —, and R. L. Wilby, 2012: Regional climate downscaling: What’s the point? Eos. Amer. Geophys. Union, 93, 52–53, doi:10.1029/2012-EO050008.

    Article  Google Scholar 

  • Randall, D., M. Khairoutdinov, A. Arakawa, W. Grabowski, 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547–1564.

    Article  Google Scholar 

  • 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 tesselations and C-grid staggering. Mon. Wea. Rev., 140, 3090–3105.

    Article  Google Scholar 

  • —, and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf]..

    Google Scholar 

  • Schmidli, J., C. M. Goodess, C. Frei, M. R. Haylock, Y. Hundecha, J. Ribalaygua, and T. Schmith, 2007: Statistical and dynamical down-scaling of precipitation: An evaluation and comparison of scenarios for the European Alps.J. Geophys. Res., 112, D04105, doi:10.1029/2005 JD007026.

    Article  Google Scholar 

  • Seth, A., and F. Giorgi, 1998: The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model. J. Climate, 11, 2698–2712.

    Article  Google Scholar 

  • Shige, S., Y. N. Takayabu, W.-K. Tao, W., and C.-L. Shie, 2007: Spectral retrieval of latent heating profiles from TRMM PR data. Part II: Algorithm improvement and heating estimates over tropical ocean regions. J. Appl. Meteor. Climatol., 46, 7, 1098–1124.

    Article  Google Scholar 

  • Shin, H. H., S.-Y. Hong, 2013: Analysis of resolved and parameterized vertical transports in convective boundary layers at gray-zone resolutions. J. Atmos. Sci., 70, 3248–3261, doi: 10.1175/JAS-D-12-0290.1.

    Article  Google Scholar 

  • Shrestha, D. L., D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi, 2013: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrol. Earth Syst. Sci., 17, 1913–1931, doi:10.5194/hess-17-1913-2013.

    Article  Google Scholar 

  • Stern, W., and K. Miyakoda, 1995: Feasibility of seasonal forecasts inferred from multiple GCM simulations. J. Climate, 8, 1071–1085.

    Article  Google Scholar 

  • Stern, W. F., and J. J. Ploshay, 1992: A scheme for continuous data assimilation. Mon. Wea. Rev., 120, 1417–1432.

    Article  Google Scholar 

  • Sugi, M., R. Kawamura, and N. Sato, 1997: A study of SST-forced variability and potential predictability of seasonal mean fields using the JMA global model. J. Meteor. Soc. Japan, 75, 717–736.

    Google Scholar 

  • Sun, L., H. Li, S. E. Zebiak, D. F. Moncunill, F. D. A. D. S. Filho, and A. D. Moura, 2006: An operational dynamical downscaling prediction system for Nordeste Brazil and the 2002-04 real-time forecast evaluation. J. Climate, 19, 1990–2007.

    Article  Google Scholar 

  • Takle, E. S., and Coauthors, 1999: Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results. J. Geo-phys. Res., 104(D16), 19443–19461, doi:10.1029/1999JD900352.

    Article  Google Scholar 

  • Thatcher M., and J. L. McGregor, 2009: Using a scale-selective filter for dynamical downscaling with the conformal cubic atmospheric model. Mon. Wea. Rev., 137, 1742–1752.

    Article  Google Scholar 

  • Tian, Y., and C. D. Peters-Lidard, 2010: A global map of uncertainties in satellite-based precipitation measurements. Geophys. Res. Lett., 37, L24407, doi:10.1029/2010GL046008.

    Article  Google Scholar 

  • Trenberth, K. E., and L. Smith, 2008: Atmospheric energy budgets in the Japanese Reanalysis: Evaluation and variability. J. Meteor. Soc. Japan, 86, 579–592.

    Article  Google Scholar 

  • von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664–3673.

    Article  Google Scholar 

  • Wang, X., Z. Zhong, Y. Hu, and H. Yuan, 2010: Effect of lateral boundary scheme on the simulation of tropical cyclone track in regional climate model RegCM3. Asia-Pac. J. Atmos. Sci., 46, 221–230, doi:10.1007/s13143-010-0019-y.

    Article  Google Scholar 

  • Wang Y., L. R. Leung, J. L. McGregor, D.-K. Lee, W.-C. Wang, Y.-H. Ding, and F. Kimura, 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteor. Soc. Japan, 82, 1599–1628.

    Article  Google Scholar 

  • Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611–627.

    Article  Google Scholar 

  • Yang, Z., and R.W. Arritt, 2002: Tests of a perturbed physics ensemble approach for regional climate modeling. J. Climate, 15, 2881–2896.

    Article  Google Scholar 

  • Yhang, Y.-B., and S.-Y. Hong, 2011: A study on large-scale nudging effects in regional climate model simulation. Asia-Pac. J. Atmos. Sci., 47, 235–243.

    Article  Google Scholar 

  • Yoon, J.-H., L. R. Leung, and J. Correia Jr., 2012: Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J. Geophys. Res., 117, D21109, doi:10.1029/2012JD017650.

    Google Scholar 

  • Yoshimura, K., and M. Kanamitsu, 2008: Dynamical global downscaling of global reanalysis.Mon. Wea. Rev., 136, 2983–2998.

    Article  Google Scholar 

  • —, and —, 2013: Incremental correction for the dynamical downscaling of ensemble mean atmospheric fields. Mon. Wea. Rev., 141, 3087–3101, doi:10.1175/MWR-D-12-00271.1.

    Article  Google Scholar 

  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simu-lations to the parameterization of cumulus convection in the Canadian climate centre general circulation model. Atmos.-Ocean, 33, 407–446.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Song-You Hong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hong, SY., Kanamitsu, M. Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations. Asia-Pacific J Atmos Sci 50, 83–104 (2014). https://doi.org/10.1007/s13143-014-0029-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13143-014-0029-2

Keywords

Navigation