Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4917-2021
© 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/hess-25-4917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Sep 2021
Research article |
| 07 Sep 2021
| 07 Sep 2021
A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model
Nicolas Gasset
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Milena Dimitrijevic
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Marco Carrera
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Bernard Bilodeau
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Ryan Muncaster
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Étienne Gaborit
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Guy Roy
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Nedka Pentcheva
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Maxim Bulat
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Xihong Wang
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Radenko Pavlovic
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Franck Lespinas
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Dikra Khedhaouiria
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Juliane Mai
Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
Related authors
No articles found.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2025-1076, https://doi.org/10.5194/egusphere-2025-1076, 2025
Short summary
Short summary
Four process-based and four data-driven hydrological models are compared using different training data. We found process-based models to perform better with small data sets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
Short summary
Short summary
The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-69, https://doi.org/10.5194/hess-2023-69, 2023
Revised manuscript not accepted
Short summary
Short summary
A blended model allows multiple hydrologic processes to be represented in a single model, which allows for a model to achieve high performance without the need to modify its structure for different catchments. Here, we improve upon the initial blended version by testing more than 30 blended models in twelve catchments to improve the overall model performance. We validate our proposed, updated blended model version with independent catchments, and make this version available for open use.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, https://doi.org/10.5194/npg-29-329-2022, 2022
Short summary
Short summary
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
Short summary
Short summary
This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Michelle Viswanathan, Tobias K. D. Weber, Sebastian Gayler, Juliane Mai, and Thilo Streck
Biogeosciences, 19, 2187–2209, https://doi.org/10.5194/bg-19-2187-2022, https://doi.org/10.5194/bg-19-2187-2022, 2022
Short summary
Short summary
We analysed the evolution of model parameter uncertainty and prediction error as we updated parameters of a maize phenology model based on yearly observations, by sequentially applying Bayesian calibration. Although parameter uncertainty was reduced, prediction quality deteriorated when calibration and prediction data were from different maize ripening groups or temperature conditions. The study highlights that Bayesian methods should account for model limitations and inherent data structures.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, https://doi.org/10.5194/hess-24-5835-2020, 2020
Yilong Wang, Grégoire Broquet, François-Marie Bréon, Franck Lespinas, Michael Buchwitz, Maximilian Reuter, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, and Philippe Ciais
Geosci. Model Dev., 13, 5813–5831, https://doi.org/10.5194/gmd-13-5813-2020, https://doi.org/10.5194/gmd-13-5813-2020, 2020
Cited articles
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., 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
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
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., 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
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., 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
Short summary
In this paper, we highlight the importance of including land-data assimilation as well as offline precipitation analysis components in a regional reanalysis system. We also document the performance of the first multidecadal 10 km reanalysis performed with the GEM atmospheric model that can be used for seamless land-surface and hydrological modelling in North America. It is of particular interest for transboundary basins, as existing datasets often show discontinuities at the border.
In this paper, we highlight the importance of including land-data assimilation as well as...
