A Bias-Corrected 3-hourly 0.125 Gridded Meteorological Forcing Data Set (1979 - 2016) for Land Surface Modeling in North America

https://doi.org/10.20383/101.0111

GeoNetwork record:www.gwfnet.net/geonetwork/srv/eng/catalog.search#/metadata/13124_iso.xml

Cold regions hydrology is very sensitive to the impacts of climate warming. More physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrologic responses to climate change. However, hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly temperature and precipitation. Cold regions often have sparse surface observations, particularly at high elevations that generate the major amount of runoff. The effects of mountain topography and high latitudes are not well reflected in the observational record. The best available gridded data in these regions is from the high resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and the Canadian Precipitation Analysis (CaPA) reanalysis but this dataset has a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record, but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long record product (WFDEI-GEM-CaPA). First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3h x 0.125ᵒ resolution during the 2005-2016 period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The variables (units) available are specific humidity, precipitation (kg m-2 s-1), surface_air_pressure (Pa), Surface Downwelling Longwave Radiation (W m-2), surface_downwelling_shortwave_flux_in_air (W m-2), Surface Downwelling Shortwave Radiation (W m-2), wind speed (m/s) and Air Temperature (K). Note that this dataset was created using a 365-day calendar, hence leap years have a missing day. These data are in NetCDF format and can be downloaded via the Cuizinart Platform (http://cuizinart.io) by selecting dataset labelled wfdei-gem-capa.

This dataset provides an improved set of forcing data that can be used in hydrological modelling over most of North America. The aim of this study is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long record product (WFDEI-GEM-CaPA). This data set will be used to support the objectives under the Global Water Futures Program funded by Canada First Research Excellence Fund.

Provides a bias-corrected 3-hourly 0.125 gridded meteorological forcing data set (1979 ? 2016) for land surface modelling in North America.

Asong, Z. E., Wheater, H., Pomeroy, J., Pietroniro, A., & Elshamy, M. (2018). A Bias-Corrected 3-hourly 0.125 Gridded Meteorological Forcing Data Set (1979 ? 2016) for Land Surface Modeling in North America. Waterloo, Canada: Canadian Cryospheric Information Network (CCIN). (https://dx.doi.org/10.20383/101.0111).

https://doi.org/10.20383/101.0111
FRDR
https://www.frdr-dfdr.ca/repo/dataset/0eb7926c-6e9b-8a97-57d4-9b06c988d5d3
Download Link
https://auth.globus.org/p/login?client_id=8fb65971-8d89-4f6d-a1ae-8577c62673bc&redirect_uri=%2Fv2%2Foauth2%2Fauthorize%3Fclient_id%3D8fb65971-8d89-4f6d-a1ae-8577c62673bc%26scope%3Durn%253Aglobus%253Aauth%253Ascope%253Aauth.globus.org%253Aview_identities%2520urn%253Aglobus%253Aauth%253Ascope%253Anexus.api.globus.org%253Agroups%2520urn%253Aglobus%253Aauth%253Ascope%253Atransfer.api.globus.org%253Aall%26response_type%3Dtoken%26redirect_uri%3Dhttps%253A%252F%252Fglobus.frdr.ca%252Flogin%26redirect_name%3DFederated%2520Research%2520Data%2520Repository%2520Globus%2520Web%2520App%26state%3Dfqwuzlmfhwi%26viewlocale%3Den_US&scope=urn%3Aglobus%3Aauth%3Ascope%3Aauth.globus.org%3Aview_identities+urn%3Aglobus%3Aauth%3Ascope%3Anexus.api.globus.org%3Agroups+urn%3Aglobus%3Aauth%3Ascope%3Atransfer.api.globus.org%3Aall&redirect_name=Federated+Research+Data+Repository+Globus+Web+App&viewlocale=en_US&response_type=token