Overview
This document provides an overview of precipitation products of ERA5, in the context of the larger ERA5 dataset. As background, ERA5 is the 5th generation of the global atmospheric reanalysis (the latest – it replaces the ERA-Interim reanalysis) produced by the Copernicus Climate Change Service at ECMWF, covering the period from January 1950 to present. It provides hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty.
Provider's contact information
ERA5 is produced by the Copernicus Climate Change Service (C3S) at ECMWF.
Copernicus User support (copernicus-support@ecmwf.int (external to C3S)).
Licensing
Licence: Copernicus (Licence agreement information can be found here or here).
Dataset citable as: 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
Variable name and units:
Precipitation of ERA5 is the main focus of this document. The available parameters are provided in the table below.
| Name | Units | Description |
|---|---|---|
| Total precipitation | m | Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. |
| Snowfall | m of water equivalent | Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. |
| Convective precipitation | m | This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Convective rain rate | kg m-2 s-1 | This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Convective snowfall | m of water equivalent | This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Convective snowfall rate water equivalent | kg m-2 s-1 | This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Instantaneous large-scale surface precipitation fraction | Dimensionless | This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. |
| Large-scale precipitation fraction | 0/1 | This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. |
| Large scale rain rate | kg m-2 s-1 | This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Large-scale snowfall water equivalent | m of water equivalent | This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Large scale snowfall rate | kg m-2 s-1 | This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Large-scale precipitation | m | This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Maximum total precipitation rate since previous post-processing | kg m-2 s-1 | The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. |
| Minimum total precipitation rate since previous post-processing | kg m-2 s-1 | The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. |
| Precipitation type | Dimensionless | This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. |
| Mean convective precipitation rate | kg m-2 s-1 | This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Mean convective snowfall rate | kg m-2 s-1 | This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Mean large-scale precipitation fraction | Dimensionless | This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. |
| Mean large-scale precipitation rate | kg m-2 s-1 | This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Mean large-scale snowfall rate | kg m-2 s-1 | This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
| Mean total precipitation rate | kg m-2 s-1 | This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
Hourly and monthly subsets are available at the links below:
- hourly data on single levels from 1950 to 1978,
- hourly data on single levels from 1979 to present,
- monthly averaged data on single levels from 1950 to 1978,
- monthly averaged data on single levels from 1979 to present,
Note: ECMWF provides a conversion table for accumulated variables (total precipitation/fluxes) for ERA5-Land and ERA5 (the convention for accumulations used in ERA5-Landand in ERA5). The table shows how accumulated variables from a number of C3S and ECMWF datasets should be processed to derive values for an hour, a day, a month and a year. In the documentation, 'total precipitation' and 'solar radiation' are used for illustration, but the same processing should be applied to all precipitation and radiative flux variables.
Spatial coverage and resolution:
ERA5 precipitation, like all ERA5 data, is a global dataset. The atmospheric data is available on a regular latitude-longitude grid at 0.25o x 0.25o resolution (converted from native reduced‐Gaussian grid resolution of approximately 31 km x 31 km), and on 37 pressure levels.
Temporal coverage and resolution:
ERA5 precipitation data, like all ERA5 data, is available from 1950 to present (split into two entries: primary from 1979 onwards and a back extension from 1950-1978). The back extension is a preliminary version that has been released in 2020, and an updated version (that corrects some issues in the tropics) will appear around the end of 2021.
The data is available at hourly and monthly sampling (see above).
ERA5 precipitation, like all ERA5 data, is updated daily with a latency of about 5 days in an early product and with a final release 2 to 3 months later.
Information about observations (number, homogeneity)
ERA5’s data assimilation uses observations for all geophysical quantities from about 0.75 million observations per day in 1979 and about 24 Million in 2018. The 2D-OI uses surface observations at 'screen level'. The online technical documentation provides tables with the satellite and in-situ observations used as input into ERA5.
The satellite measurements used in ERA5 are: temperature, humidity, ozone, column water vapour, cloud liquid water, precipitation, ocean surface wind speed, wind vector, soil moisture, wave height.
The in-situ data is provided by WMO WIS and consists in measurements for: surface pressure, temperature, humidity, wind, wind profiles and snow depth. Figure 4 from Hersbach et al. (2020) presents the conventional observations assimilated per day in ERA5 during the period 1979–2018.
ERA5 assimilates rain rates from ground-based radar–gauge composite observations from 2009, and snow cover (NH only) from NOAA/NESDIS IMS.
The time evolving nature of the assimilated observations means that caution should be employed when using ERA5 to evaluate long-term variability and trends.
Methodology
Like any other climate variable from a reanalysis product, ERA5 precipitation is strongly influenced by the data assimilation methodology. ERA5 is produced using 4D-Var data assimilation with the ECMWF’s Integrated Forecast System (IFS) model (CY41R2). The forecast model has 137 hybrid sigma/pressure (model) levels in the vertical, with the top level at 0.01 hPa. The IFS is coupled to a land-surface model and an ocean wave model. The model uses as boundary conditions the sea surface temperature, the sea ice cover, the greenhouse gases, the aerosols, and the total solar irradiance. Climate variables are offered from the atmospheric model, the surface model and the wave model.
The ERA5 dataset contains one (31 km) high resolution realization (HRES) and a reduced resolution 10-member ensemble (EDA). The model time step is 12 minutes for the HRES and 20 minutes for the Ensemble Data Assimilation (EDA), though occasionally these numbers are adjusted to cope with instabilities. Climate variables result from analyses and short (18 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most of climate variables from the analyses are also available from the forecasts. However, there are several climate variables from forecast, e.g. mean rates and accumulations, that are not available from the analyses. More information on the differences between analysis, forecast, instantaneous, accumulated and mean rate parameters are provided on https://confluence.ecmwf.int/pages/viewpage.action?pageId=85402030.
The ERA5 atmospheric analysis is based on a hybrid incremental 4-dimensional variational data assimilation (4D-Var) system including variational bias correction (VarBias). The method finds the best estimate of the state of the atmosphere/land/surface ocean within an assimilation time window, given a background forecast valid at the start of the window and observations falling within that window. The 4D-Var data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).
Uncertainty estimate: An uncertainty estimate is sampled by a 10-member lower-resolution Ensemble Data Assimilation (EDA) which provides background-error estimates for the deterministic HRES 4D-Var Data Assimilation system. The analysis method is the same for each EDA member and follows that of the HRES. Each member (except the control) is run with different random perturbations added to the observations. Likewise, the model physical tendencies are perturbed in the short forecasts that link subsequent analysis windows. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.
A strength of reanalysis (including ERA5) is the use of a consistent assimilation/forecast methodology throughout the analysis cycle. Thus, even though the observations assimilated are evolving in time (see above), the data assimilation approach can be considered fixed throughout the products analysis period, which adds to the homogeneity of the dataset.
Information about the technical and scientific quality
ERA5 precipitation represents one of the products of the latest global atmospheric reanalysis produced by Copernicus Climate Change Service at ECMWF. It is archived at a shorter (hourly) time step, has a finer spatial resolution, uses a more advanced assimilation system and includes more sources of data than previous versions. It is accompanied by extensive technical documentation and two principal scientific documentation papers. A list of ‘known issues’ is maintained at the online documentation (https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Knownissues).
A prerelease quality control revealed some problems affecting the performance in the tropics (tropical cyclones are too intense) and that the deep soil moisture tends to be too dry for the 1950-1978 dataset. A new version of the data should gradually become available by late 2021. This issue will be of little direct relevance to ERA5 precipitation in Canada’s north, but the user should be aware of the reason for this update.
Information on model improvement: The forecast model of the ERA5 is the IFS Cycle 41r2. In the ten-year period between ERA-Interim (Cy31r2) and ERA5 (Cy41r2), many significant improvements have been made to the representation of atmospheric physical processes (see Section 4 of Hersbach et al. (2020)). There are several changes in ERA5’s land-surface model and parameterization schemes. ERA5’s HTESSEL land surface scheme (Balsamo et al., 2015) accounts for seasonally varying monthly vegetation maps specified from a MODIS-based satellite dataset. In addition, an enhanced snowpack parameterization allows a more realistic timing of runoff and terrestrial water storage variations and a better match of the albedo to satellite products. The chosen parameterization for lakes (FLake), allows consideration of both subgrid and resolved water bodies, which is potentially relevant for the lake-enriched Canadian sub-Arctic. This series of changes contributes to significant improvements in the soil moisture and land surface fluxes consistency, which allowed for the usage of satellite data in ERA5 to analyze soil moisture. This will influence the surface energy budget. Some important improvements in the wave model include: an updated model bathymetry with a more recent version of ETOPO2 and a revised unresolved bathymetry scheme. Some of these changes will also affect coastal regions as well as better accounting for wave propagation along coastlines and better modeling of the impact of previously unresolved features like islands and narrow embayments (e.g. Moore et al. in prep).
Limitations and strengths for application in North Canada
ERA5 is a new atmospheric reanalysis and there are not available scientific evaluations of the dataset dedicated specifically to northern Canada. However, it should be noted that in northern Canada, there are currently no sub-daily records over a long historical period for many weather stations. Reanalyses data with hourly output cover this gap and, with suitable investigation and calibration (downscaling), could be valuable resources.
As for all gridded data, care should be taken when comparing ERA5 precipitation with observations, because in reanalyses, precipitation is representing averages over a model grid box and model time step.
Also, as mentioned above, changes in the amounts and types of observational data that are assimilated may produce artificial trends or variability in reanlaysis variables. For ERA5 this has been observed for wind in the boundary layer (Hersbach et al. 2020).
References to documents describing the methodology or/and the dataset
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 29-Apr-2019 >), https://doi.org/10.24381/cds.adbb2d47
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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S. and Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Online technical documentation: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation
Link to download the data and format of data:
Data Access: Copernicus | NCAR | ECMWF
ERA5 is available in GRIB1 and NetCDF formats
Link to download hourly and monthly data on Copernicus:
- hourly data on single levels from 1950 to 1978,
- hourly data on single levels from 1979 to present,
- hourly data on pressure levels from 1950 to 1978,
- hourly data on pressure levels from 1979 to present,
- monthly averaged data on single levels from 1950 to 1978,
- monthly averaged data on single levels from 1979 to present,
- monthly averaged data on pressure levels from 1950 to 1978,
- monthly averaged data on pressure levels from 1979 to present.
Publications including dataset evaluation or comparison with other data in Canada
Tarek, Mostafa, François P. Brissette, and Richard Arsenault. "Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America." Hydrology and Earth System Sciences 24.5 (2020): 2527-2544. (It compares ERA5 and ERA-Interim with stations in US and Canada south of 60ᵒ latitude).
Sheridan, Scott C., Cameron C. Lee, and Erik T. Smith. "A comparison between station observations and reanalysis data in the identification of extreme temperature events." Geophysical Research Letters 47.15 (2020): e2020GL088120. (It compares observations, ERA5, ERA5-LAND, and NARR, in the United States and Canada- only a very small number of stations are in North Canada).
Betts, Alan K., Darren Z. Chan, and Raymond L. Desjardins. "Near-surface biases in ERA5 over the Canadian Prairies." Frontiers in Environmental Science 7 (2019): 129. (ERA5 is compared with hourly data for 4 stations in Saskatchewan, Canada).
Cao B., Quan X., Brown N., Stewart-Jone E., and Gruber S., 2019, GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses, Geosci. Model Dev., 12, 4661–4679, 2019 https://doi.org/10.5194/gmd-12-4661-2019 (2m temperature from ERA5 is compared with ERA-Interim, JRA-55 and MERRA-2 at a site located near the north shore of Lac de Gras in the Northwest Territories, Canada)