A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model

Hydrology and Earth System Sciences - Tập 25 Số 9 - Trang 4917-4945
Nicolas Gasset1, Vincent Fortin1, Milena Dimitrijevic1, Marco L. Carrera1, Bernard Bilodeau1, Ryan Muncaster1, Étienne Gaborit1, Guy Roy2, Nedka Pentcheva2, Maxim Bulat2, Xihong Wang2, Radenko Pavlovic2, Franck Lespinas2, Dikra Khedhaouiria1, Juliane Mai3
1Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
2Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
3Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada

Tóm tắt

Abstract. Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis. ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution. Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS). Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses. All systems used are close to operational model versions and configurations. In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated. Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system. The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS. Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer. Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals. The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations.

Từ khóa


Tài liệu tham khảo

Abaza, M., Fortin, V., Gaborit, É., Bélair, S., and Garnaud, C.: Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain – Richelieu River Watershed, J. Hydrol. Eng., 25, 04020045, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001983, 2020. a

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.3390/atmos9040138, 2003. a, b, c

Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b

Alavi, N., Bélair, S., Fortin, V., Zhang, S., Husain, S. Z., Carrera, M. L., and Abrahamowicz, M.: Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme, J. Hydrometeorol., 17, 2315–2332, https://doi.org/10.1175/JHM-D-15-0189.1, 2016. a

Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing, J. Hydrometeorol., 14, 1259–1277, https://doi.org/10.1175/JHM-D-12-0161.1, 2013. a, b

Awoye, O. H. R., Bajracharya, A. R., Stadnyk, T., and Asadzadeh, M.: Is the physical hydrologic model HYPE well suited for the simulation of water quantity in North-American watersheds? – A modelling experiment with the newly developed RDRS meteorological reanalysis data, in: vol. 2019, AGU Fall Meeting Abstracts, 9–13 December 2019, San Francisco, H33M–2162, 2019. a

Balsamo, G., Mahfouf, J.-F., Bélair, S., and Deblonde, G.: A Land Data Assimilation System for Soil Moisture and Temperature: An Information Content Study, J. Hydrometeorol., 8, 1225–1242, https://doi.org/10.1175/2007JHM819.1, 2007. a, b, c

Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: A Global Land Surface Reanalysis Data Set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015. a

Bélair, S., Mailhot, J., Strapp, J., and MacPherson, J.: An Examination of Local versus Nonlocal Aspects of a TKE-Based Boundary Layer Scheme in Clear Convective Conditions, J. Appl. Meteorol., 38, 1499–1518, https://doi.org/10.1175/1520-0450(1999)038<1499:AEOLVN>2.0.CO;2, 1999. a

Bélair, S., Brown, R., Mailhot, J., Bilodeau, B., and Crevier, L.-P.: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part II: Cold Season Results, J. Hydrometeorol., 4, 371–386, https://doi.org/10.1175/1525-7541(2003)4<371:OIOTIL>2.0.CO;2, 2003a. a, b, c, d, e

Bélair, S., Crevier, L.-P., Mailhot, J., Bilodeau, B., and Delage, Y.: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part I: Warm Season Results, J. Hydrometeorol., 4, 352–370, https://doi.org/10.1175/1525-7541(2003)4<352:OIOTIL>2.0.CO;2, 2003b. a, b

Bélair, S., Mailhot, J., Girard, C., and Vaillancourt, P.: Boundary Layer and Shallow Cumulus Clouds in a Medium-Range Forecast of a Large-Scale Weather System, Mon. Weather Rev., 133, 1938–1960, https://doi.org/10.1175/MWR2958.1, 2005. a, b, c, d, e

Bélair, S., Roch, M., Leduc, A., Vaillancourt, P., Laroche, S., and Mailhot, J.: Medium-Range Quantitative Precipitation Forecasts from Canada's New 33-km Deterministic Global Operational System, Weather Forecast., 24, 690–708, https://doi.org/10.1175/2008WAF2222175.1, 2009. a

Benedict, I., Van Heerwaarden, C., Weerts, A., and Hazeleger, W.: The benefits of spatial resolution increase in global simulations of the hydrological cycle evaluated for the Rhine and Mississippi basins, Hydrol. Earth Syst. Sci., 23, 1779–1800, https://doi.org/10.5194/hess-23-1779-2019, 2019. a

Benoit, R., Côté, J., and Mailhot, J.: Inclusion of a TKE Boundary Layer Parameterization in the Canadian Regional Finite-Element Model, Mon. Weather Rev., 117, 1726–1750, https://doi.org/10.1175/1520-0493(1989)117<1726:IOATBL>2.0.CO;2, 1989. a

Bernier, N. B. and Bélair, S.: High Horizontal and Vertical Resolution Limited-Area Model: Near-Surface and Wind Energy Forecast Applications, J. Appl. Meteorol. Clim., 51, 1061–1078, https://doi.org/10.1175/JAMC-D-11-0197.1, 2012. a, b

Boluwade, A., Stadnyk, T., Fortin, V., and Roy, G.: Assimilation of Precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, Early Run) in the Canadian Precipitation Analysis (CaPA), J. Hydrol.: Reg. Stud., 14, 10–22, https://doi.org/10.1016/j.ejrh.2017.10.005, 2017. a

Bosilovich, M. G., Chen, J., Robertson, F. R., and Adler, R. F.: Evaluation of Global Precipitation in Reanalyses, J. Appl. Meteorol. Clim., 47, 2279–2299, https://doi.org/10.1175/2008JAMC1921.1, 2008. a

Bosilovich, M. G., Robertson, F. R., and Chen, J.: Global Energy and Water Budgets in MERRA, J, Climate, 24, 5721–5739, https://doi.org/10.1175/2011JCLI4175.1, 2011. a

Brasnett, B.: A Global Analysis of Snow Depth for Numerical Weather Prediction, J. Appl. Meteorol., 38, 726–740, 1999. a, b, c, d

Brown, R., Fang, B., and Mudryk, L.: Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016, Atmos.-Ocean, 57, 149–156, https://doi.org/10.1080/07055900.2019.1598843, 2019. a, b

Caron, J.-F., Milewski, T., Buehner, M., Fillion, L., Reszka, M., Macpherson, S., and St-James, J.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System, Mon. Weather Rev., 143, 2560–2580, https://doi.org/10.1175/MWR-D-14-00353.1, 2015. a

Caron, J.-F., Zadra, A., Anselmo, D., Milewski, T., and Patoine, A.: Regional Deterministic Prediction System (RDPS) – Update from version 4.2.0 to version 5.0.0, Technical note, Canadian Meteorological Center, Environment and Climate Change Canada, available at: https://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/lib/technote_rdps-500_20160907_e.pdf (last access: 27 August 2021), 2016. a, b

Carrera, M. L., Bélair, S., Fortin, V., Bilodeau, B., Charpentier, D., and Doré, I.: Evaluation of Snowpack Simulations over the Canadian Rockies with an Experimental Hydrometeorological Modeling System, J. Hydrometeorol., 11, 1123–1140, https://doi.org/10.1175/2010JHM1274.1, 2010. a, b

Carrera, M. L., Bélair, S., and Bilodeau, B.: The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study, J. Hydrometeorol., 16, 1293–1314, https://doi.org/10.1175/JHM-D-14-0089.1, 2015. a, b, c, d, e, f

CaSPAr: Canadian Surface Prediction Archive, https://caspar-data.ca, last access: 3 September 2021. a

Chikhar, K. and Gauthier, P.: Impact of Analyses on the Dynamical Balance of Global and Limited-Area Atmospheric Models: Impact of Analyses on the Dynamical Balance of Atmospheric Models, Q. J. Roy. Meteorol. Soc., 140, 2535–2545, https://doi.org/10.1002/qj.2319, 2014. a

Côté, J., Desmarais, J.-G., Gravel, S., Méthot, A., Patoine, A., Roch, M., and Staniforth, A.: The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part II: Results, Mon. Weather Rev., 126, 1397–1418, https://doi.org/10.1175/1520-0493(1998)126<1397:TOCMGE>2.0.CO;2, 1998a. a, b, c

Côté, J., Gravel, S., Méthot, A., Patoine, A., Roch, M., and Staniforth, A.: The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation, Mon. Weather Rev., 126, 1373–1395, https://doi.org/10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2, 1998b. a, b, c

Deacu, D., Fortin, V., Klyszejko, E., Spence, C., and Blanken, P.: Predicting the net basin supply to the Great Lakes with a hydrometeorological model, J. Hydrometeorol., 13, 1739–1759, https://doi.org/10.1175/JHM-D-11-0151.1, 2012. a, b, c

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. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b, c

Fairbairn, D., Barbu, A. L., Napoly, A., Albergel, C., Mahfouf, J.-F., and Calvet, J.-C.: The Effect of Satellite-Derived Surface Soil Moisture and Leaf Area Index Land Data Assimilation on Streamflow Simulations over France, Hydrol. Earth Syst. Sci., 21, 2015–2033, https://doi.org/10.5194/hess-21-2015-2017, 2017. a

Fillion, L., Mitchell, H. L., <span id="page4943"/>Ritchie, H., and Staniforth, A.: The Impact of a Digital Filter Finalization Technique in a Global Data Assimilation System, Tellus A, 47, 304–323, https://doi.org/10.3402/tellusa.v47i3.11518, 1995. a, b

Fletcher, S.: Data Assimilation for the Geosciences, 1st Edn., Elsevier, Colorado State University, Fort Collins, CO, USA, 2017. a

Fortin, V. and Gronewold, A. D.: Water Balance of the Laurentian Great Lakes, in: Encyclopedia of Lakes and Reservoirs, Springer, Dordrecht, 864–869, https://doi.org/10.1007/978-1-4020-4410-6_268, 2012. a

Fortin, V., Roy, G., Donaldson, N., and Mahidjiba, A.: Assimilation of Radar Quantitative Precipitation Estimations in the Canadian Precipitation Analysis (CaPA), J. Hydrol., 531, 296–307, https://doi.org/10.1016/j.jhydrol.2015.08.003, 2015. a, b, c

Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., and Mahidjiba, A.: Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA System Overview and Literature Review, Atmos.-Ocean, 56, 178–196, https://doi.org/10.1080/07055900.2018.1474728, 2018. a, b, c

Fry, L. M., Hunter, T. S., Phanikumar, M. S., Fortin, V., and Gronewold, A. D.: Identifying Streamgage Networks for Maximizing the Effectiveness of Regional Water Balance Modeling, Water Resour. Res., 49, 2689–2700, https://doi.org/10.1002/wrcr.20233, 2013. a

Fujiwara, M., Wright, J. S., Manney, G. L., Gray, L. J., Anstey, J., Birner, T., Davis, S., Gerber, E. P., Harvey, V. L., Hegglin, M. I., Homeyer, C. R., Knox, J. A., Krüger, K., Lambert, A., Long, C. S., Martineau, P., Molod, A., Monge-Sanz, B. M., Santee, M. L., Tegtmeier, S., Chabrillat, S., Tan, D. G. H., Jackson, D. R., Polavarapu, S., Compo, G. P., Dragani, R., Ebisuzaki, W., Harada, Y., Kobayashi, C., McCarty, W., Onogi, K., Pawson, S., Simmons, A., Wargan, K., Whitaker, J. S., and Zou, C.-Z.: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and Overview of the Reanalysis Systems, Atmos. Chem. Phys., 17, 1417–1452, https://doi.org/10.5194/acp-17-1417-2017, 2017. a

Gaborit, É., Fortin, V., Xu, X., Seglenieks, F., Tolson, B., Fry, L. M., Hunter, T., Anctil, F., and Gronewold, A. D.: A Hydrological Prediction System Based on the SVS Land-Surface Scheme: Efficient Calibration of GEM-Hydro for Streamflow Simulation over the Lake Ontario Basin, Hydrol. Earth Syst. Sci., 21, 4825–4839, https://doi.org/10.5194/hess-21-4825-2017, 2017. a

Gagnon, N., Deng, X., Houtekamer, P., Erfani, A., Charron, M., Beauregard, S., Frenette, R., Racette, D., and Lahlou, R.: Global Ensemble Prediction System (GEPS) – Update from Version 4.0.1 to Version 4.1.1, Technical note, Canadian Meteorological Center, Environment and Climate Change Canada, available at: https://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/lib/technote_geps-411_20151215_e.pdf (last access: 27 August 2021), 2015. a, b

Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018JD030094, 2019. a

Girard, C., Plante, A., Desgagné, M., McTaggart-Cowan, R., Côté, J., Charron, M., Gravel, S., Lee, V., Patoine, A., Qaddouri, A., Roch, M., Spacek, L., Tanguay, M., Vaillancourt, P. A., and Zadra, A.: Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type, Mon. Weather Rev., 142, 1183–1196, https://doi.org/10.1175/MWR-D-13-00255.1, 2013. a

Girard, C., Plante, A., Desgagné, M., Mctaggart-Cowan, R., Côté, J., Charron, M., Gravel, S., Lee, V., Patoine, A., Qaddouri, A., Roch, M., Spacek, L., Tanguay, M., Vaillancourt, P., and Zadra, A.: Staggered vertical discretization of the canadian environmental multiscale (GEM) model using a coordinate of the log-hydrostatic-pressure type, Mon. Weather Rev., 142, 1183–1196, https://doi.org/10.1175/MWR-D-13-00255.1, 2014. a, b

Gronewold, A. D. and Rood, R. B.: Recent water level changes across Earth's largest lake system and implications for future variability, J. Great Lakes Res., 45, 1–3, https://doi.org/10.1016/j.jglr.2018.10.012, 2019. a

Gronewold, A. D., Fortin, V., Caldwell, R., and Noel, J.: Resolving Hydrometeorological Data Discontinuities along an International Border, B. Am. Meteorol. Soc., 99, 899–910, https://doi.org/10.1175/BAMS-D-16-0060.1, 2017. a

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Royal Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b

Hines, C. O.: Doppler-spread parameterization of gravity-wave momentum deposition in the middle atmosphere. Part 1: Basic formulation, J. Atmos. Sol.-Ter. Phys., 59, 371–386, https://doi.org/10.1016/S1364-6826(96)00079-X, 1997a. a

Hines, C. O.: Doppler-spread parameterization of gravity-wave momentum deposition in the middle atmosphere. Part 2: Broad and quasi monochromatic spectra, and implementation, J. Atmos. Sol.-Ter. Phys., 59, 387–400, https://doi.org/10.1016/S1364-6826(96)00080-6, 1997b. a

Houtekamer, P. L., Deng, X., Mitchell, H. L., Baek, S.-J., and Gagnon, N.: Higher Resolution in an Operational Ensemble Kalman Filter, Mon. Weather Rev., 142, 1143–1162, https://doi.org/10.1175/MWR-D-13-00138.1, 2013. a

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation, Technical documentation, NASA Goddard Space Flight Center, available at: https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf (last access: 27 August 2021), 2020. a, b

Kain, J. and Fritsh, J.: A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization, J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990. a

Kain, J. S. and Fritsch, J. M.: Convective Parameterization for Mesoscale Models: The Kain-Fritsch Scheme, American Meteorological Society, Boston, MA, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16, 1993. a

Lavaysse, C., Carrera, M., Bélair, S., Gagnon, N., Frenette, R., Charron, M., and Yau, M. K.: Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System, Mon. Weather Rev., 141, 1506–1526, https://doi.org/10.1175/MWR-D-11-00354.1, 2012. a

Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., and Stadnyk, T.: Performance Evaluation of the Canadian Precipitation Analysis (CaPA), J. Hydrometeorol., 16, 2045–2064, https://doi.org/10.1175/JHM-D-14-0191.1, 2015. a, b, c, d, e, f, g, h

Li, J. and Barker, H.: A Radiation Algorithm with Correlated-k Distribution. Part I: Local Thermal Equilibrium, J. Atmos. Sci., 62, 286–309, https://doi.org/10.1175/JAS-3396.1, 2005. a

Li, X., Charron, M., Spacek, L., and Candille, G.: A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations, Mon. Weather Rev., 136, 443–462, https://doi.org/10.1175/2007MWR2109.1, 2008. a

Lin, H., Gagnon, N., Beauregard, S., Muncaster, R., Markovic, M., Denis, B., and Charron, M.: GEPS-Based Monthly Prediction at the Canadian Meteorological Centre, Mon. Weather Rev., 144, 4867–4883, https://doi.org/10.1175/MWR-D-16-0138.1, 2016. a

Lin, Y. and Mitchell, K.: The NCEP Stage II/IV hourly precipitation analyses: development and applications, in: Preprints of the 19th Conference on Hydrology, American Meteorological Society, 9–13 January 2005, San Diego, CA, Paper 1.2, available at: https://www.emc.ncep.noaa.gov/mmb/SREF/pcpanl/refs/stage2-4.19hydro.pdf (last access: 1 September 2021), 2005. a, b

Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014. a

Lott, F. and Miller, M. J.: A new subgrid-scale orographic drag parametrization: Its formulation and testing, Q. J. Roy. Meteorol. Soc., 123, 101–127, https://doi.org/10.1002/qj.49712353704, 1997. a

Lott, N., Baldwin, R., and Jones, P.: The FCC Integrated Surface Hourly Database, A New Resource of Global Climate Data, Tech. Rep. 2001-01, US National Climate Data Center, available at: https://rda.ucar.edu/datasets/ds463.3/docs/ish-tech-report.pdf (last access: 30 August 2021), 2001. a

Lucas-Picher, P., Boberg, F., Christensen, J. H., and Berg, P.: Dynamical Downscaling with Reinitializations: A Method to Generate Finescale Climate Datasets Suitable for Impact Studies, J. Hydrometeorol., 14, 1159–1174, https://doi.org/10.1175/JHM-D-12-063.1, 2013. a

Mahfouf, J.-F., Brasnett, B., and Gagnon, S.: A Canadian Precipitation Analysis (CaPA) Project: Description and Preliminary Results, Atmos.-Ocean, 45, 1–17, https://doi.org/10.3137/ao.v450101, 2007. a, b, c

Mai, J.: CaSPAr, GitHub [data set], available at: https://github.com/julemai/CaSPAr/wiki/How-to-get-started-and-download-your-first-data, last access: 3 September 2021. a, b

Mai, J., Kornelsen, K. C., Tolson, B. A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F., and Coulibaly, P.: The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally, B. Am. Meteorol. Soc., 101, E341–E356, https://doi.org/10.1175/BAMS-D-19-0143.1, 2020. a

Mai, J., Tolson, B. A., Shen, H., Gaborit, É., Fortin, V., Gasset, N., Awoye, H., Stadnyk, T. A., Fry, L. M., Bradley, E. A., Seglenieks, F., Temgoua, A. G. T., Princz, D. G., Gharari, S., Haghnegahdar, A., Elshamy, M. E., Razavi, S., Gauch, M., Lin, J., Ni, X., Yuan, Y., McLeod, M., Basu, N. B., Kumar, R., Rakovec, O., Samaniego, L., Attinger, S., Shrestha, N. K., Daggupati, P., Roy, T., Wi, S., Hunter, T., Craig, J. R., and Pietroniro, A.: Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E), J. Hydrol. Eng., 26, 05021020, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002097, 2021. a, b, c, d, e

Mailhot, J., Bélair, S., Benoit, R., Bilodeau, B., Delage, Y., Fillion, L., Garand, L., Girard, C., and Tremblay, A.: Scientific Description of RPN Physics Library – Version 3.6, Technical documentation, Environment and Climate Change Canada, available at: https://collaboration.cmc.ec.gc.ca/science/rpn/physics/physic98.pdf (last access: 28 August 2021), 1998. a

Mailhot, J., Bélair, S., Lefaivre, L., Bilodeau, B., Desgagné, M., Girard, C., Glazer, A., Leduc, A., Méthot, A., Patoine, A., Plante, A., Rahill, A., Robinson, T., Talbot, D., Tremblay, A., Vaillancourt, P., Zadra, A., and Qaddouri, A.: The 15‐km version of the Canadian regional forecast system, Atmos.-Ocean, 44, 133–149, https://doi.org/10.3137/ao.440202, 2006. a

Marke, T., Mauser, W., Pfeiffer, A., and Zängl, G.: A Pragmatic Approach for the Downscaling and Bias Correction of Regional Climate Simulations: Evaluation in Hydrological Modeling, Geosci. Model Dev., 4, 759–770, https://doi.org/10.5194/gmd-4-759-2011, 2011. a

McFarlane, N.: The Effect of Orographically Excited Gravity Wave Drag on the General Circulation of the Lower Stratosphere and Troposphere, J. Atmos. Sci., 44, 1775–1800, https://doi.org/10.1175/1520-0469(1987)044<1775:TEOOEG>2.0.CO;2, 1987. a

McFarlane, N., Girard, C., and Shantz, D.: Reduction of Systematic Errors In NWP and General Circulation Models by Parameterized Gravity Wave Drag, J. Meteorol. Soc. Jpn. Ser. II, 64A, 713–728, https://doi.org/10.2151/jmsj1965.64A.0_713, 1986. a

McTaggart-Cowan, R. and Zadra, A.: Representing Richardson Number Hysteresis in the NWP Boundary Layer, Mon. Weather Rev., 143, 1232–1258, https://doi.org/10.1175/MWR-D-14-00179.1, 2014. a

McTaggart-Cowan, R., Girard, C., Plante, A., and Desgagné, M.: The Utility of Upper-Boundary Nesting in NWP, Mon. Weather Rev., 139, 2117–2144, https://doi.org/10.1175/2010MWR3633.1, 2011. a

Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006. a

Muñoz Sabater, J., Dutra, E., Schepers, D., Albergel, C., Boussetta, S., Agusti-Panareda, A., Zsoter, E., and Hersbach, H.: ERA5-Land: An improved version of the ERA5 reanalysis land component, in: Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop, IPMA, Lisbon, Portugal, p. 20, 2018. a, b

Noilhan, J. and Mahfouf, J. F.: The ISBA Land Surface Parameterisation Scheme, Global Planet. Change, 13, 145–159, https://doi.org/10.1016/0921-8181(95)00043-7, 1996. a

Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface Processes for Meteorological Models, Mon. Weather Rev., 117, 536–549, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a

Pudykiewicz, J., Benoit, R., and Mailhot, J.: Inclusion and verification of a predictive Cloud-Water Scheme in a Regional Numerical Weather Prediction Model, Mon. Weather Rev., 120, 612–626, https://doi.org/10.1175/1520-0493(1992)120<0612:IAVOAP>2.0.CO;2, 1992. a

Qaddouri, A. and Lee, V.: The Canadian Global Environmental Multiscale Model on the Yin–Yang Grid System, Q. J. Roy. Meteorol. Soc., 137, 1913–1926, https://doi.org/10.1002/qj.873, 2011. a

Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q., Mahanama, S. P. P., and Touré, A.: Assessment and Enhancement of MERRA Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011. a

Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011. a

Shrestha, R., Tachikawa, Y., and Takara, K.: Effects of forcing data resolution in river discharge simulation, Annu. J. Hydraul. Eng., 46, 139–144, https://doi.org/10.2208/prohe.46.139, 2002. a

Shrestha, R., Tachikawa, Y., and Takara, K.: Input data resolution analysis for distributed hydrological modeling, J. Hydrol., 319, 36–50, https://doi.org/10.1016/j.jhydrol.2005.04.025, 2006. a

Smith, G. C., Roy, F., Mann, P., Dupont, F., Brasnett, B., Lemieux, J.-F., Laroche, S., and Bélair, S.: A New Atmospheric Dataset for Forcing Ice-Ocean Models: Evaluation of Reforecasts Using the Canadian Global Deterministic Prediction System: CGRF Dataset for Forcing Ice-Ocean Models, Q. J. Roy. Meteorol. Soc., 140, 881–894, https://doi.org/10.1002/qj.2194, 2014.  a

Soci, C., Bazile, E., Besson, F., and Landelius, T.: High-resolution precipitation re-analysis system for climatological purposes, Tellus A, 68, 29879, https://doi.org/10.3402/tellusa.v68.29879, 2016. a

Sundqvist, H., Berge, E., and Kristjánsson, J. E.: Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model, Mon. Weather Rev., 117, 1641, https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2, 1989. a

Takacs, L. L., Suárez, M. J., and Todling, R.: Maintaining Atmospheric Mass and Water Balance in Reanalyses, Q. J. Roy. Meteorol. Soc., 142, 1565–1573, 2016. a

Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrol. Earth Syst. Sci., 24, 2527–2544, https://doi.org/10.5194/hess-24-2527-2020, 2020. a

Wang, X. L., Xu, H., Qian, B., Feng, Y., and Mekis, E.: Adjusted Daily Rainfall and Snowfall Data for Canada, Atmos.-Ocean, 55, 155–168, https://doi.org/10.1080/07055900.2017.1342163, 2017. a

Yoshimura, K. and Kanamitsu, M.: Dynamical Global Downscaling of Global Reanalysis, Mon. Weather Rev., 136, 2983–2998, https://doi.org/10.1175/2008MWR2281.1, 2008. a

Zadra, A., Roch, M., Laroche, S., and Charron, M.: The subgrid‐scale orographic blocking parametrization of the GEM Model, Atmos.-Ocean, 41, 155–170, https://doi.org/10.3137/ao.410204, 2003. a

Zadra, A., Gauthier, J.-P., and Leroux, A.: GenPhysX: A user's guide to input/output and methods, Tech. rep., Canadian Meteorological Centre, Environment Canada, Dorval, 2008. a