Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7705-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7705-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Non-Hydrostatic RegCM4 (RegCM4-NH): model description and case studies over multiple domains
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Paolo Stocchi
Institute of Atmospheric Sciences and Climate, National Research Council of Italy, CNR-ISAC, Bologna, Italy
Emanuela Pichelli
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Jose Abraham Torres Alavez
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Russell Glazer
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Graziano Giuliani
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Fabio Di Sante
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Rita Nogherotto
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
Filippo Giorgi
Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
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Cited articles
Anthes, R. A., Hsie, E.-Y., and Kuo, Y. -H.: Description of the Penn
State/NCAR Mesoscale Model: Version 4 (MM4),
National Center for Atmospheric Research, Boulder, CO, USA, NCAR Techn. Note, 4, 79 pp., NCAR/TN-282+STR, https://doi.org/10.5065/D64B2Z90, 1987.
Anyah, R., Semazzi, F. H. M., and Xie, L.: Simulated Physical Mechanisms
Associated with Climate Variability over Lake Victoria Basin in East Africa,
Mon. Weather Rev., 134, 3588–3609, 2006.
Anyah, R. O. and Semazzi, F.: Idealized simulation of hydrodynamic
characteristics of Lake Victoria that potentially modulate regional climate,
Int. J. Climatol., 29, 971–981, https://doi.org/10.1002/joc.1795, 2009.
Ashouri, H., Hsu, K., Sorooshian, S., Braithwaite, D. K., Knapp, K. R.,
Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily
Precipitation Climate Data Record from Multisatellite Observations for
Hydrological and Climate Studies, Bull. Am. Meteorol. Soc., 96, 69–83, https://doi.org/10.1175/BAMS-D-13-00068.1, 2015.
Ban, N., Schmidli, J., and Schär, C.: Evaluation of the
convection-resolving regional climate modeling approach in decade-long
simulations, J. Geophys. Res.-Atmos., 119, 7889–7907, https://doi.org/10.1002/2014JD021478, 2014.
Ban, N., Schmidli, J., and Schär ,C.: Heavy precipitation
in a changing climate: does short-term summer precipitation increase
faster?, Geophys. Res.-Lett., 42, 1165–1172, https://doi.org/10.1002/2014GL062588, 2015.
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belušić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Bøssing Christensen, O., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J. E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Reca Remedio, A., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tölle, M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V., and Zander, M. J.: The first multi-model ensemble of
regional climate simulations at kilometer-scale resolution, part I:
evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021.
Beheng, K.: A parameterization of warm cloud microphysical conversion
processes, Atmos. Res., 33, 193–206, 1994.
Bennington, V., Notaro, M., and Holman, K. D.: Improving Climate Sensitivity
of Deep Lakes within a Regional Climate Model and Its Impact on Simulated
Climate, J. Climl., 27, 2886–2911, 2014.
Bretherton, C. S., McCaa, J. R., and Grenier, H.: A new parameterization for
shallow cumulus convection and its application to marine subtropical
cloud-topped boundary lay-ers. I. Description and 1D results, Mon. Weather
Rev., 132, 864–882, 2004.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Higgins, R. W., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132, 2008.
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Convection-permitting models: A step-change in rainfall forecasting, Meteor.
Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016.
Coppola, E., Giorgi, F., Mariotti, L., and Bi, X.: RegT-Band: a tropical band
version of RegCM4, Clim. Res., 52, 115–133, 2012.
Coppola, E., Sobolowski, S., Pichelli, E., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernandez, J., Fita, L., Fumiere, Q., Giorgi, F., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E, Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T., Rockel, B., Schär, C., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhetz, H., Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
Coppola, E., Stocchi, P., Pichelli, E., Torres, A., Glazer, R., Graziano, G., Di Sante, F., Nogherotto, R., and Giorgi, F.: RegCM-NH namelists for test cases presented in the paper “Non-Hydrostatic RegCM4 (RegCM4-NH): Model description and case studies over multiple domains”, Zenodo [code], https://doi.org/10.5281/zenodo.5106399, 2021.
Dacre, H. F., Clark, P. A., Martinez-Alvarado, O., Stringer, M. A., and
Lavers, D. A.: How do atmospheric rivers form?, Bull. Amer. Meteorol. Soc.,
96, 1243–1255, https://doi.org/10.1175/BAMS-D-14-00031.1, 2015.
Dee, D. P., Källén, E., Simmons, A. J., and Haimberger, L.: Comments on “Reanalyses suitable for characterizing long-term trends”, B. Am. Meteorol. Soc., 92, 65–70, https://doi.org/10.1175/2010BAMS3070.1, 2011.
Diallo, I., Giorgi, F., and Stordal, F.: Influence of Lake Malawi on regional
climate from a double nested regional climate model experiment, Clim. Dynam., 50, 3397–3411, https://doi.org/10.1007/s00382-017-3811-x, 2018.
Dickinson, R. E., Errico, R. M., Giorgi, F., and Bates, G. T.: A regional climate model
for the western United States, Climatic Change, 15, 383–422,
https://doi.org/10.1007/BF00240465, 1989.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P.: Biosphere–atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model, TechRep, National Center for Atmospheric Research, Boulder, CO, USA, 80 pp., NCAR.TN-387+STR, 1993.
Done, J., Davis, C. A., and Weisman M. L.: The next generation of NWP:
Explicit forecasts of convection using the Weather Research and Forecasting
(WRF) model, Atmos. Sci. Lett., 5, 110–117, https://doi.org/10.1002/asl.72,
2004.
Dudhia, J.: Numerical study of convection observed during the winter monsoon
experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46,
3077–3107, 1989.
Durran, D. R. and Klemp, J. B.: A compressible model for the simulation of
moist mountain waves, Mon. Weather Rev., 111, 2341–2361, 1983.
Elguindi, N., Bi, X., Giorgi, F., Nagarajan, B., Pal, J., Solmon, F.,
Rauscher, S., Zakey, S., O'Brien, T., Nogherotto, R., and Giuliani, G.:
Regional Climate Model, RegCM Reference Manual Version 4.7, 49 pp., https://zenodo.org/record/4603616, 2017.
Emanuel, K. A.: A scheme for representing cumulus convection in large-scale
models, J. Atmos. Sci, 48, 2313–2335, 1991.
Fairall, C. W., Bradley, E. F., Godfrey, J. S., Wick, G. A., Edson, J. B., and Young, G. S.: The cool skin and the warm layer in bulk flux calculations, J. Geophys. Res., 101, 1295–1308, 1996a.
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G. S.: Bulk parameterization of air-sea fluxes for TOGA COARE, J. Geophys. Res.,
101, 3747–3764, 1996b.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared
precipitation with stations–a new environmental record for monitoring
extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015.
Gimeno, L., Nieto, R., Vaìsquez, M., and Lavers, D. A.: Atmospheric rivers: A mini-review, Front. Earth Sci., 2, 1–6, https://doi.org/10.3389/feart.2014.00002, 2014.
Giorgi, F.: Thirty years of regional climate modeling: where are we and where
are we going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, 2019.
Giorgi, F. and Bates, G. T.: The Climatological Skill of a Regional Model
over Complex Terrain, Mon. Weather Rev., 117, 2325–2347,
https://doi.org/10.1175/1520-0493(1989)117<2325:TCSOAR>2.0.CO;2, 1989.
Giorgi, F. and Mearns, L. O.: Introduction to special section: regional
climate modeling revisited, J. Geophys. Res., 104, 6335–6352, 1999.
Giorgi, F., Marinucci, M. R., and Bates, G.: Development of a second
generation regional climate model (RegCM2). I. Boundary layer and radiative
transfer processes, Mon. Weather Rev., 121, 2794–2813, 1993a.
Giorgi, F., Marinucci, M. R., Bates, G., and De Canio, G.: Development of a
second generation regional climate model (RegCM2), part II: convective
processes and assimilation of lateral boundary conditions, Mon. Weather
Rev., 121, 2814–2832, 1993b.
Giorgi, F., Francisco, R., and Pal, J. S.: Effects of a sub-gridscale
topography and landuse scheme on surface climateand hydrology. I. Effects of
temperature and water vapor disaggregation, J. Hydrometeorol., 4, 317–333,
2003.
Giorgi, F., Jones, C., and Asrar, G.: Addressing climate information needs at
the regional level: the CORDEX framework, WMO Bull., 58, 175–183, 2009.
Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M. B., Bi, X., Elguindi, N., Diro, G. T., Nair, V., Giuliani, G., Turuncoglu, U. U., Cozzini, S., Güttler, I., O'Brien, T. A., Tawfik, A. B., Shalaby, A., Zakey, A. S., Steiner, A. L., Stordal, F., Sloan, L. C., and Brankovic, C.: RegCM4: model description and preliminary tests over multiple CORDEX domains, Clim. Res., 52, 7–29, https://doi.org/10.3354/cr01018, 2012.
Giorgi, F., Solmon, F., Xunjang, B., Coppola, E., Giuliani, G., Turunçoğlu, U., Güttler, I., Mariotti, L., Nogherotto, R., O'Brien, T. A., Tawfik, A., Elguindi, N., Piani, S., Pal, J., Tefera Diro, G., and Shalaby, A.: ictp-esp/RegCM: Paper Release, Zenodo [code], https://doi.org/10.5281/zenodo.4603556, 2021.
Grell, G. A.: Prognostic evaluation of assumptions used by cumulus
parameterizations, Mon. Weather Rev., 121, 764–787, 1993.
Grell, G. A., Dudhia J., and Stauffer, D. R.: A Description of the Fifth
Generation Penn State/NCAR Mesoscale Model (MM5),
National Center for Atmospheric Research, Boulder, CO, USA, NCAR Tech. Note, 122, NCAR/TN-398+STR 1994.
Gunn, K. L. S. and Marshall, J. S.: The distribution with size of
aggregate snowflakes, J. Meteor., 15, 452–461,
https://doi.org/10.1175/1520-0469(1958)015<0452:TDWSOA>2.0.CO;2, 1958.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
Hewitt, C. D. and Lowe, J. A.: Toward a European climate prediction system,
Bull. Amer. Meteor. Soc., 99, 1997–2001,
https://doi.org/10.1175/BAMS-D-18-0022.1, 2018.
Higgins, R. W., Kousky, V. E., and Xie, P.: Extreme Precipitation Events in the South-Central United States during May and June 2010: Historical Perspective, Role of ENSO, and Trends, J. Hydrometeorol., 12, 1056–1070, https://doi.org/10.1175/JHM-D-10-05039.1, 2011.
Holtslag, A., de Bruijn, E., and Pan, H. L.: A high resolution air mass
transformation model for short-range weather fore-casting, Mon. Weather Rev.,
118, 1561–1575, 1990.
Hong, S.-Y., Juang, H.-M. H., and Zhao, Q.: Implementation of prognostic
cloud scheme for a regional spectral model, Mon. Weather Rev., 126, 2621–2639,
1998.
Hong, S.-Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics
Scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006.
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, 2004.
Hostetler, S. W., Bates, G. T., and Giorgi, F.: Interactive nesting of a lake
thermal model within a regional climate model for climate change studies, J.
Geophys. Res., 98, 5045–5057, 1993.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeor., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.
International Federation of Red Cross and Red Crescent Societies (IFRC): World Disasters Report 2014: focus on culture and risk. Technical Report, International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland, 276 pp., 2014.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method
that Produces Global Precipitation Estimates from Passive Microwave and
Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeor, 5,
487–503, 2004.
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J.
Appl. Meteor., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
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.
Kendon, E. J., Roberts, N. M., Senior, C. A., and Roberts, M. J.: Realism of
rainfall in a very high-resolution regional climate model, J. Climate, 25,
5791–5806, https://doi.org/10.1175/JCLI-D-11-00562.1, 2012.
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan,
S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.: Do
convection-permitting regional climate models improve projections of future
precipitation change?, Bull. Amer. Meteor. Soc., 98, 79–93,
https://doi.org/10.1175/BAMS-D-15-0004.1, 2017.
Kessler, E.: On the Distribution and Continuity of Water Substance in
Atmospheric Circulations, in: Meteorological Monographs, Amer. Meteor. Soc., Boston, MA, 10, 84 pp., https://doi.org/10.1007/978-1-935704-36-2_1, 1969.
Khairoutdinov, M. and Kogan, Y.: A new cloud physics parameterization in a
large-eddy simulation model of marine stratocumulus, Bull. Amer. Meteorol.
Soc., 128, 229–243, 2000.
Kiehl, J., Hack, J., Bonan, G., Boville, B., Breigleb, B., Williamson, D.,
and Rasch, P.: Description of the NCAR Community Climate Model (CCM3),
National Center for Atmospheric Research, Boulder, CO, USA, NCAR Tech. Note, NCAR, 159 pp., NCAR/TN-420+STR, 1996.
Klemp, J. B. and Lilly, D. K.: Numerical simulation of hydrostatic mountain
waves, J. Atmos. Sci., 35, 78–107, 1978.
Klemp, J. B. and Dudhia, J.: An Upper Gravity-Wave Absorbing Layer for NWP
Applications, Mon. Weather Rev., 176, 3987–4004, 2008.
Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A., Forbes, R.,
and Halliwell, C.: Characteristics of high-resolution versions of the Met
Office Unified Model for forecasting convection over the United Kingdom,
Mon. Weather Rev., 136, 3408–3424, https://doi.org/10.1175/2008MWR2332.1,
2008.
LeVeque, R. J.: Finite Difference Methods for Ordinary
and Partial Differential Equations, SIAM, Philadelphia, USA, https://doi.org/10.1137/1.9780898717839, 2007.
Lin, Y., Farley, R., and Orville, H.: Bulk parameterization of the snow field
in a cloud model, J. Appl. Meteor. Clim., 22, 1065–1092, 1983.
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with
size, J. Meteor., 5, 165–166, 1948.
Matte, D., Laprise, R., Thériault, J. M., and Lucas-Picher, P.: Spatial
spin-up of fine scales in a regional climate model simulation driven by
low-resolution boundary conditions, Clim. Dynam., 49, 563–574, https://doi.org/10.1007/s00382-016-3358-2, 2017.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682,
1997.
Nogherotto, R., Tompkins, A. M., Giuliani, G., Coppola, E., and Giorgi, F.: Numerical framework and performance of the new multiple-phase cloud microphysics scheme in RegCM4.5: precipitation, cloud microphysics, and cloud radiative effects, Geosci. Model Dev., 9, 2533–2547, https://doi.org/10.5194/gmd-9-2533-2016, 2016.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M.,
Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F.,
Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M.,
Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L: Technical Description of
version 4.5 of the Community Land Model (CLM), National Center for Atmospheric Research, Boulder, CO, USA, NCAR Techn. Note, 422 pp., NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M, 2013.
Pal, J. S., Small, E., and Eltahir, E.: Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM, J. Geophys. Res., 105, 29579–29594, 2000.
Pal, J. S., Giorgi, F., Bi, X., Elguindi, N., Solmon, F., Gao, X., Rauscher,
S. A., Francisco, R., Zakey, A., Winter, J., Ashfaq, M., Syed, F. S., Bell,
J. L., Diffenbaugh, N. S., Karmacharya, J., Konaré, A., Martinez, D., da
Rocha, R. P., Sloan, L. C., and Steiner, A. L.: The ICTP RegCM3 and RegCNET:
regional climate modeling for the developing world., Bull. Amer. Meteorol.
Soc., 88, 1395–1409, 2007.
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P., Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso, R. M., Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J., Schär, C, Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The first multi-model
ensemble of regional climate simulations at kilometer-scale resolution part
2: historical and future simulations of precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021.
Prein, A. F. and Gobiet, A.: Impacts of uncertainties in European gridded
precipitation observations on regional climate analysis, Int. J. Climatol., 37, 305–327, https://doi.org/10.1002/joc.4706, 2017.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on
regional convection-permitting climate modeling: demonstrations, prospects,
and challenges, Rev. Geophys., 53, 323–361, 2015.
Ralph, F. M., Neiman, P. J., Wick, G. A., Gutman, S. I., Dettinger, M. D.,
Cayan, D. R., and White, A. B.: Flooding on California's Russian River: Role
of atmospheric rivers, Geophys. Res. Lett., 33, L13801,
https://doi.org/10.1029/2006GL026689, 2006.
Ralph, F. M., Dettinger, M. D., Cairns, M. M., Galarneau, T. J., and
Eylander, J.: Defining “atmospheric river”: How the Glossary of
Meteorology helped resolve a debate, Bull. Amer. Meteor. Soc., 99, 837–839,
https://doi.org/10.1175/BAMS-D-17-0157.1, 2018.
Rutledge, S. A. and Hobbs, P. V.: The mesoscale and microscale structure and
organization of clouds and precipitation in midlatitude cyclones. Part VIII:
A model for the “seeder-feeder” process in warm-frontal rainbands, J.
Atmos. Sci., 40, 1185–1206, 1983.
Schwartz, C. S.: Reproducing the September 2013 record-breaking rainfall
over the Colorado Front Range with high-resolution WRF forecasts, Weather
Forecast., 29, 393–402, https://doi.org/10.1175/WAF-D-13-00136.1, 2014.
Sitz, L. E., Sante, F., Farneti, R., Fuentes-Franco, R., Coppola, E., Mariotti, L., Reale, M., Sannino, G., Barreiro, M., Nogherotto, R., Giuliani, G., Graffino, G., Solidoro, C., Cossarini, G., and Giorgi, F.: Description and Evaluation of the Earth
System Regional Climate Model (RegCM–ES), J. Adv. Model. Earth Sy., 9, 1863–1886, https://doi.org/10.1002/2017MS000933, 2017.
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,
National Center for Atmospheric Research, NCAR, Boulder, CO, USA, NCAR Techn. Note, 125 pp., NCAR/TN-475+STR, 2008.
Song, Y., Semazzi, H. M. F., Xie, L., and Ogallo, L. J.: A coupled regional
climate model for the Lake Victoria Basin of East Africa, Int. J. Climatol., 24, 57–75, 2004.
Sun, X., Xie, L., Semazzi, F., and Liu, B.: Effect of Lake Surface
Temperature on the Spatial Distribution and Intensity of the Precipitation
over the Lake Victoria Basin, Mon. Weather Rev. 143, 1179–1192, 2015.
Sundqvist, H., Berge, E., and Kristjansson, J.: Condensation and cloud
parameterization studies with a mesoscale numerical weather prediction
model, Mon. Weather Rev., 117, 1641–1657, 1989.
Talling, J. F.: The incidence of vertical mixing, and some biological and
chemical consequences, in: Tropical African lakes, Verh. Int. Ver. Limnol.,
17, 998–1012, https://doi.org/10.1080/03680770.1968.11895946, 1969.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parametrization in
large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989.
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather Rev.,
121, 3040–3061, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2, 1993.
Tiedtke, M.: An extension of cloud-radiation parameterization in the ECMWF
model: The representation of subgrid-scale variations of optical depth, Mon.
Weather Rev., 124, 745–750, 1996.
Tompkins, A.: Ice supersaturation in the ECMWF integrated forecast system,
Q. J. Roy. Meteor. Soc., 133, 53–63, 2007.
Tripoli, G. J. and Cotton, W. R.: A numerical investigation of several
factors contributing to the observed variable intensity of deep convection
over south Florida, J. Appl. Meteor., 19, 1037–1063, 1980.
Weisman, M. L., Davis, C., Wang, W., Manning, K. W., and Klemp, J. B.:
Experiences with 0–36-h explicit convective forecasts with the WRF-ARW
model, Weather Forecast., 23, 407–437,
https://doi.org/10.1175/2007WAF2007005.1, 2008.
Weusthoff, T., Ament, F., Arpagaus, M., and Rotach, M. W.: Assessing the
benefits of convection-permitting models by neighborhood verification:
Examples from MAP D-PHASE, Mon. Weather Rev., 138, 3418–3433, https://doi.org/10.1175/2010MWR3380.1, 2010.
Williams, P. D.: A proposed modification to the Robert–Asselin time filter,
Mon. Weather Rev., 137, 2538–2546, 2009.
Zeng, X., Zhao, M., and Dickinson, R. E.: Intercomparison of bulk aerodynamic
algorithms for the computation of sea surface fluxes using TOGA COARE and
TAO data, J. Clim., 11, 2628–2644, 1998.
Zhu, Y. and Newell, R. E.: A proposed algorithm for moisture fluxes from
atmospheric rivers, Mon. Weather Rev., 126, 725–735,
https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2, 1998.
Short summary
In this work we describe the development of a non-hydrostatic version of the regional climate model RegCM4-NH, implemented to allow simulations at convection-permitting scales of <4 km for climate applications. The new core is described, and three case studies of intense convection are carried out to illustrate the model performances. Comparison with observations is much improved with respect to with coarse grid runs. RegCM4-NH offers a promising tool for climate investigations at a local scale.
In this work we describe the development of a non-hydrostatic version of the regional climate...