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Ancienne version 31

changes.mady.by.user Roberge, François

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Nouvelle version Actuel

changes.mady.by.user Winger, Katja

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The Alliance / Compute Canada resources

GEM (CRCM)

Citation

When using model results from GEM or CRCM4/5/6, cite the following two papers:

  • Girard, C. et al. (2014). Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type. Mon. Wea. Rev., 142, 1183–1196, https://doi.org/10.1175/MWR-D-13-00255.1
  • McTaggart‐Cowan, R. et al. (2019). Modernization of Atmospheric Physics Parameterization in Canadian NWP. JAMES, Vol. 11, Issue 11, p. 3593-3635. https://doi.org/10.1029/2019MS001781

Articles describing the dynamical core of GEM

  • Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch and A. Staniforth, 1998 a: The operational CMC-MRB global environmental multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 1373-1395.
  • Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch and A. Staniforth, 1998 b: The operational CMC-MRB global environmental multiscale (GEM) model. Part II: Results. Mon. Wea. Rev., 126, 1397-1418.

Articles describing physics schemes of GEM & CRCM

PDF: Descriptions of GEM physics version 3.6 from 1998

Physics of GEM5:

Li and Barker correlated-k radiation:

  • Li, J. and H.W. Barker, 2005: A radiation algorithm with correlated-k distribution. Part I: local thermal equilibrium. J. Atmos. Sci., 62, 286-309.

Kain and Fritsch convection:

  • Kain, J.S. and J.M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.

Shallow convection: "ktrsnt":

  • Bélair S, Mailhot J, Girard C, Vaillancourt P, 2005: Boundary-layer and shallow cumulus clouds in a medium-range forecast of a large-scale weather system. Mon Weather Rev., 133, 1938-1960

Sundquist condensation:

  • Sundqvist, H., E. Berge and J.E. Kristjansson, 1989: Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model. Mon. Wea. Rev., 117, 1641-1657.

ISBA, land surface scheme:

  • Bélair, S., L-P. Crevier, J. Mailhot, B. Bilodeau, and Y. Delage, 2003: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part I: Warm Season Results. J. Hydromet., 4, Issue 2, 352-370
  • Bélair, S., R. Brown, J. Mailhot, B. Bilodeau, and L.-P. Crevier, 2003: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part II: Cold Season Results. J. Hydromet., 4, Issue 2, 371-386

CLASS, land surface scheme:

  • D. L. Verseghy, 1991: CLASS--A Canadian Land Surface Scheme for GCMS: I. Soil Model. International Journal of Climatology IJCLEU, vol. p 111-133, p. 44
  • D. L. Verseghy, N. A. McFarlane, and M. Lazare, 1993: CLASS-A Canadian land surface scheme for GCMS, II. Vegetation model and coupled runs. Int. J. Climatol., vol. 13, no. 4, pp. 347-370
  • D. L. Verseghy, 2000: The Canadian Land Surface Scheme (CLASS): its history and future. Atmosphere-Ocean, vol. 38, no. 1, pp. 1-1

McFarlane gravity-wave drag:

  • McFarlane, N.A., 1987: The effect of orographically excited gravity-wave drag on the circulation of the lower stratosphere and troposphere. J. Atmos. Sci., 44, 1175-1800

Orographic blocking:

  • Zadra, A., M. Roch, S. Laroche and M. Charron, 2003: The subgrid scale orographic blocking parametrization of the GEM model. Atmos.-Ocean, 41, 155-170.

Geophysical fields (USGS aka GLC2000):

  • E. Bartholomé and A. S. Belward, 2005: GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26:9, 1959-1977, DOI: 10.1080/01431160412331291297

CRCM5 evolving towards GEM4:

  • Girard, C., A. Plante et al., 2014: Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type. Monthly Weather Review, DOI: 10.1175/MWR-D-13-00255.1
    Click here to see the paper.

FLake:

  • Mironov, D., Heise, E., Kourzeneva, E., Ritter, B., Schneider, N. and co-authors., 2010: Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Environ. Res.15, 218-230.

Prediction Particles Properties (P3):

For the usage of geophysical fields cite (depending of which dataset was used):

References

Click on the following link for GEM References.

Storm tracking (group of Philippe Gachon)

  • Poan, E.D., Gachon, P., Laprise, R., Aider, R. et Dueymes, G. (2018). Investigating added value of regional climate modeling in North American winter storm tracks simulations. Climate Dynamics, 50(5-6), 1799–1818. http://dx.doi.org/10.1007/s00382-017-3723-9
  • Radojevic, M, 2006: Activité des cyclones Extra-tropicaux simulés par le modèle canadien de circulation générale. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec, Canada, 119p.
  • Rosu, C., 2005 : Les caractéristiques des cyclones et l'apport d'eau dans les bassins versants du Québec. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec,Canada, 118p.Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology,Weather and Forecasting, 12, 595–612.
  • Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology, Weather and Forecasting, 12, 595–612.

Storm tracking (code by K. Winger)

Hurricane tracking (code by K. Winger)

  • Caron, L.-P., C. G. Jones and K. Winger, 2009: Simulating high-resolution Atlantic tropical cyclones using GEM: Part II. Blue Book of the Working Group on Numerical Experimentation (WGNE) of the World Weather Research Program (WWRP).
  • Caron L-P, Jones CG, Winger K (2011) Impact of resolution and downscaling technique in simulating recent Atlantic tropical cyclone activity. Clim Dyn. 37(5–6):869–892 doi:10.1007/s00382-010-0846-7 
  • Dandoy, S., Pausata, F. S. R., Camargo, S., Laprise, R., Winger, K., and Emanuel, K. A. (2021) Atlantic Hurricane response to Sahara greening and reduced dust emissions during the mid-Holocene. DOI: 10.5194/cp-2020-112

Datasets 

ANUSPLIN

  • Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E., & Papadopol, P. (2009). Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology, 48(4), 725–741. https://doi.org/10.1175/2008JAMC1979.1
  • Hopkinson, R. F., Mckenney, D. W., Milewska, E. J., Hutchinson, M. F., Papadopol, P., & Vincent, A. L. A. (2011). Impact of aligning climatological day on gridding daily maximum-minimum temperature and precipitation over Canada. Journal of Applied Meteorology and Climatology, 50(8), 1654–1665. https://doi.org/10.1175/2011JAMC2684.1
  • McKenney, D. W., Hutchiinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R. F., Price, D., & Owen, T. (2011). Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92(12), 1611–1622. https://doi.org/10.1175/2011BAMS3132.1

CaLDAS

  • Carrera, M. L., Bélair, S., & Bilodeau, B. (2015). The Canadian Land Data Assimilation System (CaLDAS): Description and synthetic evaluation study. Journal of Hydrometeorology, 16(3), 1293–1314. C. Jouan, J. A. Milbrandt, P. A. Vaillancourt, F. Chosson, and H. Morrison, 2020: Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction. Weather and Forecasting, p. 2541�2565, DOI:https://doi.org/10.1175/WAFJHM-D-2014-01110089.1

Geophysical fields

...

CaSPAr (products downloaded trough such as CaPA-fine)

  • Mai, J., Kornelsen, K., Tolson, B., Fortin, V., Gasset, N., & Bouhemhem, D. et al. (2020). The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin Of The American Meteorological Society. doi: 10.1175/bams-d-19-0143.1

CMIP5 (CanESM, MPI-ESM) 

  • When using data from CMIP5 simulations "you are obligated to acknowledge CMIP5 and the participating modeling groups". Check under the following link for citation: http://cmip5.whoi.edu/?page_id=339

CMORPH

  • Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F., & Lin, R. (2017). Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. Journal of Hydrometeorology, 18(6), 1617–1641. https://doi.org/10.1175/JHM-D-16-0168.1

CRU V4

  • Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0453-3

Daymet 

  • Thornton, P., Running, S., & White, M. (1997). Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal Of Hydrology, 190(3-4), 214-251. https://doi.org/10.1016/S0022-1694(96)03128-9
  • Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328
  • Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840
  • Thornton, P.E., R. Shrestha, M.M. Thornton, S.C. Kao, Y. Wei, and B.E. Wilson. 2020. Gridded daily weather data for North America with comprehensive uncertainty quantification – Daymet version 4. https://daymet.ornl.gov/files/Thornton_Daymet_V4_submitted_2021-01-20.pdf

CERA-20C

  • Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. and Janssen, P. (2016), A coupled data assimilation system for climate reanalysis. Q.J.R. Meteorol. Soc., 142: 65-78. doi:10.1002/qj.2629
  • Laloyaux, P., de Boisséson, E., Dahlgren, P. (2016), CERA-20C: An Earth system approach to climate reanalysis. ECMWF newsletter, 150: 25-30. doi:10.21957/ffs36birj2

ECCChourly wind-bias-adjusted precipitation

  • Smith, C. D., Mekis, E., Hartwell, M., and Ross, A. (2022): The hourly wind-bias adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019), Earth Syst. Sci. Data Discuss. [preprint],

...

Storm tracking (group of Philippe Gachon)

  • Poan, E.D., Gachon, P., Laprise, R., Aider, R. et Dueymes, G. (2018). Investigating added value of regional climate modeling in North American winter storm tracks simulations. Climate Dynamics, 50(5-6), 1799–1818. http://dx.doi.org/10.1007/s00382-017-3723-9
  • Radojevic, M, 2006: Activité des cyclones Extra-tropicaux simulés par le modèle canadien de circulation générale. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec, Canada, 119p.
  • Rosu, C., 2005 : Les caractéristiques des cyclones et l'apport d'eau dans les bassins versants du Québec. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec,Canada, 118p.Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology,Weather and Forecasting, 12, 595–612.
  • Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology, Weather and Forecasting, 12, 595–612.

Storm tracking (code by K. Winger)

Hurricane tracking (code by K. Winger)

  • Caron, L.-P., C. G. Jones and K. Winger, 2009: Simulating high-resolution Atlantic tropical cyclones using GEM: Part II. Blue Book of the Working Group on Numerical Experimentation (WGNE) of the World Weather Research Program (WWRP).
  • Caron L-P, Jones CG, Winger K (2011) Impact of resolution and downscaling technique in simulating recent Atlantic tropical cyclone activity. Clim Dyn. 37(5–6):869–892 doi:10.1007/s00382-010-0846-7 
  • Dandoy, S., Pausata, F. S. R., Camargo, S., Laprise, R., Winger, K., and Emanuel, K. A. (2021) Atlantic Hurricane response to Sahara greening and reduced dust emissions during the mid-Holocene. DOI: 10.5194/cp-2020-112

Datasets 

ANUSPLIN

  • Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E., & Papadopol, P. (2009). Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology, 48(4), 725–741. https://doi.org/10.1175/2008JAMC1979.1
  • Hopkinson, R. F., Mckenney, D. W., Milewska, E. J., Hutchinson, M. F., Papadopol, P., & Vincent, A. L. A. (2011). Impact of aligning climatological day on gridding daily maximum-minimum temperature and precipitation over Canada. Journal of Applied Meteorology and Climatology, 50(8), 1654–1665. https://doi.org/10.1175/2011JAMC2684.1
  • McKenney, D. W., Hutchiinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R. F., Price, D., & Owen, T. (2011). Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92(12), 1611–1622. https://doi.org/10.1175/2011BAMS3132.1

CaLDAS

  • Carrera, M. L., Bélair, S., & Bilodeau, B. (2015). The Canadian Land Data Assimilation System (CaLDAS): Description and synthetic evaluation study. Journal of Hydrometeorology, 16(3), 1293–1314. https://doi.org/10.1175/JHM-D-14-0089.1

CaSPAr (products downloaded trough such as CaPA-fine)

  • Mai, J., Kornelsen, K., Tolson, B., Fortin, V., Gasset, N., & Bouhemhem, D. et al. (2020). The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin Of The American Meteorological Society. doi: 10.1175/bams-d-19-0143.1

CMIP5 (CanESM, MPI-ESM) 

  • When using data from CMIP5 simulations "you are obligated to acknowledge CMIP5 and the participating modeling groups". Check under the following link for citation: http://cmip5.whoi.edu/?page_id=339

CMORPH

  • Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F., & Lin, R. (2017). Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. Journal of Hydrometeorology, 18(6), 1617–1641. https://doi.org/10.1175/JHM-D-16-0168.1

CRU V4

  • Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0453-3

Daymet 

  • Thornton, P., Running, S., & White, M. (1997). Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal Of Hydrology, 190(3-4), 214-251. https://doi.org/10.1016/S0022-1694(96)03128-9
  • Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328
  • Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840
  • Thornton, P.E., R. Shrestha, M.M. Thornton, S.C. Kao, Y. Wei, and B.E. Wilson. 2020. Gridded daily weather data for North America with comprehensive uncertainty quantification – Daymet version 4. https://daymet.ornl.gov/files/Thornton_Daymet_V4_submitted_2021-01-20.pdf

CERA-20C

  • Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. and Janssen, P. (2016), A coupled data assimilation system for climate reanalysis. Q.J.R. Meteorol. Soc., 142: 65-78. doi:10.1002/qj.2629
  • Laloyaux, P., de Boisséson, E., Dahlgren, P. (2016), CERA-20C: An Earth system approach to climate reanalysis. ECMWF newsletter, 150: 25-30. doi:10.21957/ffs36birj2

ERA-Interim

To cite the source of the data, you may use the following data citation (as part of the bibliography):

European Centre for Medium-range Weather Forecast (ECMWF) (2011): The ERA-Interim reanalysis dataset, Copernicus Climate Change Service (C3S) (accessed <insert date of access here>), available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim 

If no specific advice is given by the journals, it is usually recommended that the above data citation is put in the acknowledgements section.
More information can be found under:
      https://confluence.ecmwf.int/display/CKB/ERA-Interim%3A+documentation#ERAInterim:documentation-HowtoacknowledgeandciteERA-Interim

ERA5

  • Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home
  • Hersbach, H, Bell, W, Berrisford, P, Horányi, A, J., M-S, Nicolas, J, Radu, R, Schepers, D, Simmons, A, Soci, C, Dee, D. (2019) Global reanalysis: goodbye ERA-Interim, hello ERA5; Newsletter Feature Article. ECMWF Newsletter, https://www.ecmwf.int/node/19027, 10.21957/vf291hehd7.

ERA5-Land

GLDAS

  • Li, B., H. Beaudoing, and M. Rodell, NASA/GSFC/HSL (2018), GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/LYHA9088MFWQ
  • Li, B., Rodell, M., Sheffield, J., Wood, E. and Sutanudjaja, E. (2019). Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models, Scientific Reports, 9, 10746(2019), doi:10.1038/s41598-019-47219-z.

GLEAM v3

  • Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., & Verhoest, N. E. C. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017

Globsnow

ERA-Interim

To cite the source of the data, you may use the following data citation (as part of the bibliography):

European Centre for Medium-range Weather Forecast (ECMWF) (2011): The ERA-Interim reanalysis dataset, Copernicus Climate Change Service (C3S) (accessed <insert date of access here>), available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim 

If no specific advice is given by the journals, it is usually recommended that the above data citation is put in the acknowledgements section.
More information can be found under:
      https://confluence.ecmwf.int/display/CKB/ERA-Interim%3A+documentation#ERAInterim:documentation-HowtoacknowledgeandciteERA-Interim

ERA5

  • Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home
  • Hersbach, H, Bell, W, Berrisford, P, Horányi, A, J., M-S, Nicolas, J, Radu, R, Schepers, D, Simmons, A, Soci, C, Dee, D. (2019) Global reanalysis: goodbye ERA-Interim, hello ERA5; Newsletter Feature Article. ECMWF Newsletter, https://www.ecmwf.int/node/19027, 10.21957/vf291hehd7.

ERA5-Land

GLDAS

  • Li, B., H. Beaudoing, and M. Rodell, NASA/GSFC/HSL (2018), GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/LYHA9088MFWQ
  • Li, B., Rodell, M., Sheffield, J., Wood, E. and Sutanudjaja, E. (2019). Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models, Scientific Reports, 9, 10746(2019), doi:10.1038/s41598-019-47219-z.

GLEAM v3

  • Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., & Verhoest, N. E. C. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017

Globsnow

MODIS - MCD43C3.061

  • Schaaf, C. B., Liu, J., Gao, F., & Strahler, A. H. (2011). Aqua and terra MODIS albedo and reflectance anisotropy products. In Remote Sensing and Digital Image Processing (Vol. 11, pp. 549–561). Springer International Publishing. https://doi.org/10.1007/978-1-4419-6749-7_24

  • Luojus, Kari; Pulliainen, Jouni; Takala, Matias; Lemmetyinen, Juha; Moisander, Mikko (2020): GlobSnow v3.0 snow water equivalent (SWE). PANGAEA, 

    Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d’Entremont, R. P., Hu, B., … Roy, D. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1–2), 135–148.

    https://doi.org/10.

    1594/PANGAEA.911944

    1016/S0034-4257(02)00091-3

MSWEP

  • Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., … Wood, E. F. (2017). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12), 6201–6217. https://doi.org/10.5194/hess-21-6201-2017
  • Beck, H., Wood, E., Pan, M., Fisher, C., Miralles, D., & van Dijk, A. et al. (2019). MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment. Bulletin Of The American Meteorological Society, 100(3), 473-500. doi: 10.1175/bams-d-17-0138.1

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