ECMWF ERA5

Description

General Info

  • ERA5 is the fifth generation of ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently, data is available from 1979. The ERA5 reanalysis will be completed by 2020, by when the dataset will cover the period from 1950 to present. ERA5 replaces the ERA-Interim reanalysis.
  • ERA5 was produced using 4D-Var data assimilation in CY41R2 of ECMWF’s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure levels in the vertical, with the top level at 0.01 hPa. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolution.
  • Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). Vertical resolution is 37 pressure levels from surface to 1 hPa.

Data Contributors


Related Resource


Data Details

1. Daily

  • Pressure level
Spatial resolution 0.25 X 0.25 (degree)
Temporal resolution Daily
Levels 37 vertical levels from the surface up to 1 hPa
Parameters
- Temperature(t)
- Geopotential(z)
- U component of wind(u)
- V component of wind(v)
- Relative humidity(r)
- Specific humidity(q)
- Vertical velocity(w)

  • Single level
Spatial resolution 0.25 X 0.25 (degree)
Temporal resolution Daily
Parameters
- 2 metre temperature(t2m)
- 10 metre U wind component(u10)
- 10 metre V wind component(v10)
- Mean sea level pressure(msl)
- Surface pressure(sp)

2. Monthly

  • Pressure level
Spatial resolution 0.25 X 0.25 (degree)
Temporal resolution Monthly
Levels 37 vertical levels from the surface up to 1 hPa
Parameters
- Temperature(t)
- Geopotential(z)
- U component of wind(u)
- V component of wind(v)
- Relative humidity(r)
- Specific humidity(q)
- Vertical velocity(w)

  • Single level
Spatial resolution 0.25 X 0.25 (degree)
Temporal resolution Monthly
Parameters
- 2 metre temperature(t2m)
- 10 metre U wind component(u10)
- 10 metre V wind component(v10)
- Mean sea level pressure(msl)
- Surface pressure(sp)
- Sea surface temperature(sst)
- Land-sea mask(lsm)

3. Hourly

  • Single level
Spatial resolution 0.25 X 0.25 (degree)
Temporal resolution Hourly
Parameters
- 2 metre temperature(t2m)
- Total precipitation(tp)
- Maximum temperature at 2 metres since previous post-processing (mx2t)
- Minimum temperature at 2 metres since previous post-processing (mn2t)
- Sea surface temperature(sst)
- Land-sea mask(lsm)

Reference

How to download ECMWF ERA5 data

url: https://download.apcc21.org/ERA5/[timestep]/[level]/[variable name]/[file name]
             timestep: DAILY, MONTHLY, HOURLY
             level: pressure, single
             file name: [variable name]_YYYYMM.nc (DAILY, HOURLY),
                        [variable name]_YYYY.nc   (MONTHLY)
										

Sample:

wget https://download.apcc21.org/ERA5/DAILY/pressure/r/r_202012.nc
wget https://download.apcc21.org/ERA5/DAILY/single/t2m/t2m_202012.nc
wget https://download.apcc21.org/ERA5/HOURLY/single/tp/tp_202012.nc
wget https://download.apcc21.org/ERA5/MONTHLY/pressure/u/u_2021.nc
wget https://download.apcc21.org/ERA5/MONTHLY/single/sp/sp_2021.nc
										
How to use CLIK API
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'variable': '[variable name]',
                           'year': '[YYYY]',
                           'month': '[MM]',
          },
          '[file name to save]'
)

timestep: DAILY, MONTHLY, HOURLY
level: pressure level, single level
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'DAILY',
                           'level': 'single level',
                           'variable': 't2m',
                           'year': '2020',
                           'month': '12',
          },
          't2m_202012.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'HOURLY',
                           'level': 'single level',
                           'variable': 'tp',
                           'year': '2020',
                           'month': '12',
          },
          'tp_202012.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'MONTHLY',
                           'level': 'single level',
                           'variable': 't2m',
                           'year': '2020',
          },
          't2m_2020.nc'
)