NCEP Reanalysis Tutorial

1. Description

1.1. General Info

  • The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present.
  • A large subset of this data is available from PSD in its original 4 times daily format and as daily averages. 
  • However, the data from 1948-1957 is a little different, in the regular (non-Gaussian) gridded data. That data was done at 8 times daily in the model, because the inputs available in that era were available at 3Z, 9Z, 15Z, and 21Z, whereas the 4x daily data has been available at 0Z, 6Z, 12Z, and 18Z. These latter times were forecasted and the combined result for this early era is 8x daily.
  • The local ingestion process took only the 0Z, 6Z, 12Z, and 18Z forecasted values, and thus only those were used to make the daily time series and monthly means here.

1.2. Terms of Data Use

1.2.1. Acknowledgement

  • For dataset source, please cite:Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.
  • Please note: If you acquire NCEP Reanalysis data products from PSD, we ask that you acknowledge us in your use of the data. This may be done by including text such asNCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/ in any documents or publications using these data. We would also appreciate receiving a copy of the relevant publications. This will help PSD to justify keeping the NCEP Reanalysis data set freely available online in the future.

1.3. Data Contributors


1.4. Related Resource


2. Data Details

2.1. Pressure Level Data

Spatial resolution 2.5 X 2.5 (degree)
Temporal resolution Daily, Monthly
Levels 17 Pressure levels(1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10) (mb)
Some variables have less: omega (to 100mb) and Humidities (to 300mb)
Parameters
- Air temperature(air)
- Geopotential height(hgt)
- Relative humidity(rhum)
- Specific humidity(shum)
- Omega(omega)
- U-wind(uwnd)
- V-Wind (vwnd)

2.2. Surface Data

Spatial resolution 2.5 X 2.5 (degree)
Levels Surface, Sea
Temporal resolution Daily, Monthly
Parameters
- Sea level pressure(slp)
- Pressure(pres.sfc)
- Land-sea mask(land)

2.3. Surface Flux Data

Spatial resolution T62 Gaussian grid with 192x94 points(88.542N-88.542S, 0E-358.125E)
Levels Surface, 2m, 10m
Temporal resolution Daily, Monthly
Parameters
- Air Temperature 2m (air.2m)
- Maximum temperature at 2m(tmax.2m)
- Minimum temperature at 2m (tmin.2m)
- Specific humidity at 2 meter(shum.2m)
- U-wind at 10 m(uwnd.10m)
- V-wind at 10 m(vwnd.10m)
- Downward longwave radiation flux(dlwrf.sfc)
- Downward solar radiation flux(dswrf.sfc)
- Latent heat net flux(lhtfl.sfc)
- Precipitation rate(prate.sfc)
- Sensible heat net flux(shtfl.sfc)
- Upward longwave radiation flux(ulwrf.sfc)
- Upward solar radiation flux(uswrf.sfc)
- Ground Heat Flux(gflux.sfc) (monthly only)
- Net Longwave Radiation Flux(nlwrs.sfc) (monthly only)
- Net Shortwave Radiation Flux(nswrs.sfc) (monthly only) Sea level pressure(slp)

2.4. Other flux

Spatial resolution T62 Gaussian grid with 192x94 points(88.542N-88.542S, 0E-358.125E)
Levels Nominal top of atmosphere
Temporal resolution Daily, Monthly
Parameters
- Downward solar radiation flux(dswrf.ntat)
- Upward longwave radiation flux(ulwrf.ntat)
- Upward solar radiation flux(uswrf.ntat)

3. Reference

1. Description

1.1. General Info

  • The NCEP-DOE Reanalysis 2 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1979 through the previous year. A large subset of this data is available from PSD in its original 4 times daily format and as daily averages.
  • Bad MSLP: The MSLP data in the NetCDF files representing 2004 values that were added to our archives in early April, 2004, were done incorrectly. The source data from NCEP were fine; it was a processing error here. This was corrected late on 2005/06/23. If you have downloaded 2004 MSLP data from us at any of the three temporal frequencies, you are strongly urged to reacquire the data.
  • Bad skin temperatures in 1982: The skin temperature (variable name skt) for 1982 had bad data. These data were updated late on 2003/05/14 based on new GRIB file from NCEP. All three temporal frequencies were affected and thus updated.

1.2. Terms of Data Use

1.2.1. Acknowledgement

  • Please note: If you acquire NCEP_Reanalysis 2 data products from PSD, we ask that you acknowledge us in your use of the data. This may be done by including text such asNCEP_Reanalysis 2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/ in any documents or publications using these data. We would also appreciate receiving a copy of the relevant publications. This will help PSD to justify keeping the NCEP_Reanalysis 2 data set freely available online in the future. Thank you!

1.3. Data Contributors


1.4. Related Resource


2. Data Details

2.1. Pressure Level Data

Spatial resolution 2.5 X 2.5 (degree)
Temporal resolution Daily, Monthly
Levels 17 Pressure levels(1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10) (mb)
Parameters
- Air temperature(air)
- Geopotential height(hgt)
- Relative humidity(rhum)
- Specific humidity(shum)
- Omega(omega)
- U-wind(uwnd)
- V-Wind (vwnd)

2.2. Surface Data

Spatial resolution 2.5 X 2.5 (degree)
Levels Surface, Sea
Temporal resolution Daily, Monthly
Parameters
- Mean sea level pressure(mslp)
- Pressure(pres.sfc)
- Land-sea mask(land)

2.3. Gaussian Grid Data

Spatial resolution T62 Gaussian grid with 192x94 points(88.542N-88.542S, 0E-358.125E)
Levels Surface, 2m, 10, Nominal top of atmosphere
Temporal resolution Daily, Monthly
Parameters
- Air Temperature 2m (air.2m)
- Maximum temperature at 2m(tmax.2m)
- Minimum temperature at 2m (tmin.2m)
- Specific humidity(shum.2m)
- U-wind(uwnd.10m)
- V-wind(vwnd.10m)
- Pressure(pres.sfc)
- Downward longwave radiation flux(dlwrf.sfc)
- Downward solar radiation flux(dswrf.sfc, dswrf.ntat)
- Latent heat net flux(lhtfl.sfc)
- Precipitation rate(prate.sfc)
- Sensible heat net flux(shtfl.sfc)
- Upward longwave radiation flux(ulwrf.sfc, ulwrf.ntat)
- Upward solar radiation flux(uswrf.sfc, uswrf.ntat)
- Ground Heat Flux(gflux.sfc) (monthly only)
- Land-sea mask(land.sfc)

3. Reference

How to download NCEP data Tutorial

[NCEP Reanalysis 1] DAILY

url: https://download.apcc21.org/NCEP1/[timestep]/[level]/[variable name]/[file name]
             timestep: DAILY
             level: other_gauss, pressure, surface, surface_gauss
             variable name: other_gauss   -> dswrf.ntat, ulwrf.ntat, uswrf.ntat
                            pressure      -> air, hgt, omega, rhum, shum, uwnd, vwnd
                            surface       -> pres.sfc, slp
                            surface_gauss -> air.2m, dlwrf.sfc, dswrf.sfc, lhtfl.sfc, prate.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.sfc. uswrf.sfc, uwnd.10m, vwnd.10m
             file name: [variable name].gauss.YYYY.nc (other_gauss, surface_gauss),
                        [variable name].YYYY.nc   (pressure, surface)

Sample:

wget https://download.apcc21.org/NCEP1/DAILY/other_gauss/dswrf.ntat/dswrf.ntat.gauss.2022.nc
wget https://download.apcc21.org/NCEP1/DAILY/pressure/air/air.2022.nc
wget https://download.apcc21.org/NCEP1/DAILY/surface/pres.sfc/pres.sfc.2022.nc
wget https://download.apcc21.org/NCEP1/DAILY/surface_gauss/air.2m/air.2m.gauss.2022.nc

[NCEP Reanalysis 1] MONTHLY

url: https://download.apcc21.org/NCEP1/[timestep]/[level]/[file name]
             timestep: MONTHLY
             level: other_gauss, pressure, surface, surface_gauss
			 variable name: other_gauss   -> dswrf.ntat, ulwrf.ntat, uswrf.ntat
					pressure      -> air, hgt, omega, rhum, shum, uwnd, vwnd
					surface       -> pres.sfc, slp
					surface_gauss -> air.2m, dlwrf.sfc, dswrf.sfc, lhtfl.sfc, prate.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.sfc. uswrf.sfc, uwnd.10m, vwnd.10m
             file name: [variable name].mon.mean.nc
Sample:

wget https://download.apcc21.org/NCEP1/MONTHLY/other_gauss/dswrf.ntat.mon.mean.nc
wget https://download.apcc21.org/NCEP1/MONTHLY/pressure/air.mon.mean.nc
wget https://download.apcc21.org/NCEP1/MONTHLY/surface/pres.sfc.mon.mean.nc
wget https://download.apcc21.org/NCEP1/MONTHLY/surface_gauss/air.2m.mon.mean.nc

[NCEP Reanalysis 2] DAILY

url: https://download.apcc21.org/NCEP2/[timestep]/[level]/[variable name]/[file name]
             timestep: DAILY
             level: gaussian_grid, pressure, surface
             variable name: gaussian_grid -> air.2m, dlwrf.sfc, dswrf.ntat, dswrf.sfc, lhtfl.sfc, prate.sfc, pres.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.ntat, ulwrf.sfc, uswrf.ntat, uswrf.sfc, uwnd.10m, vwnd.10m
                            pressure      -> air, hgt, omega, rhum, uwnd, vwnd
                            surface       -> mslp, pres.sfc
             file name: [variable name].gauss.YYYY.nc (gaussian_grid),
                        [variable name].YYYY.nc   (pressure, surface)

Sample:

wget https://download.apcc21.org/NCEP2/DAILY/gaussian_grid/air.2m/air.2m.gauss.2022.nc
wget https://download.apcc21.org/NCEP2/DAILY/pressure/air/air.2022.nc
wget https://download.apcc21.org/NCEP2/DAILY/surface/pres.sfc/pres.sfc.2022.nc

[NCEP Reanalysis 2] MONTHLY

url: https://download.apcc21.org/NCEP2/[timestep]/[level]/[file name]
             timestep: MONTHLY
             level: gaussian_grid, pressure, surface
             variable name: gaussian_grid -> air.2m, dlwrf.sfc, dswrf.ntat, dswrf.sfc, lhtfl.sfc, prate.sfc, pres.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.ntat, ulwrf.sfc, uswrf.ntat, uswrf.sfc, uwnd.10m, vwnd.10m
                            pressure      -> air, hgt, omega, rhum, uwnd, vwnd
                            surface       -> mslp, pres.sfc
             file name: [variable name].mon.mean.nc
Sample:

wget https://download.apcc21.org/NCEP2/MONTHLY/gaussian_grid/air.2m.mon.mean.nc
wget https://download.apcc21.org/NCEP2/MONTHLY/pressure/air.mon.mean.nc
wget https://download.apcc21.org/NCEP2/MONTHLY/surface/pres.sfc.mon.mean.nc
How to use CLIK API
[NCEP Reanalysis 1] DAILY 
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP1',
                 'dataset': 'NCEP1',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'year': '[YYYY]',
                           'variable': '[variable name]',
          },
          '[file name to save]'
)

timestep: DAILY
level: other_gauss, pressure, surface, surface_gauss
variable name: other_gauss   -> dswrf.ntat, ulwrf.ntat, uswrf.ntat
               pressure      -> air, hgt, omega, rhum, shum, uwnd, vwnd
               surface       -> pres.sfc, slp
               surface_gauss -> air.2m, dlwrf.sfc, dswrf.sfc, lhtfl.sfc, prate.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.sfc. uswrf.sfc, uwnd.10m, vwnd.10m

[NCEP Reanalysis 1] MONTHLY 
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP1',
                 'dataset': 'NCEP1',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'variable': '[variable name]',
          },
          '[file name to save]'
)

timestep: MONTHLY
level: other_gauss, pressure, surface, surface_gauss
variable name: other_gauss   -> dswrf.ntat, ulwrf.ntat, uswrf.ntat
               pressure      -> air, hgt, omega, rhum, shum, uwnd, vwnd
               surface       -> pres.sfc, slp
               surface_gauss -> air.2m, dlwrf.sfc, dswrf.sfc, lhtfl.sfc, prate.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.sfc. uswrf.sfc, uwnd.10m, vwnd.10m

[NCEP Reanalysis 2] DAILY 
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP2',
                 'dataset': 'NCEP2',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'year': '[YYYY]',
                           'variable': '[variable name]',
          },
          '[file name to save]'
)

timestep: DAILY
level: gaussian_grid, pressure, surface
variable name: gaussian_grid -> air.2m, dlwrf.sfc, dswrf.ntat, dswrf.sfc, lhtfl.sfc, prate.sfc, pres.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.ntat, ulwrf.sfc, uswrf.ntat, uswrf.sfc, uwnd.10m, vwnd.10
               pressure      -> air, hgt, omega, rhum, uwnd, vwnd
               surface       -> mslp, pres.sfc

[NCEP Reanalysis 2] MONTHLY 
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP2',
                 'dataset': 'NCEP2',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'variable': '[variable name]',
          },
          '[file name to save]'
)

timestep: DAILY
level: gaussian_grid, pressure, surface
variable name: gaussian_grid -> air.2m, dlwrf.sfc, dswrf.ntat, dswrf.sfc, lhtfl.sfc, prate.sfc, pres.sfc, shtfl.sfc, shum.2m, tmax.2m, tmin.2m, ulwrf.ntat, ulwrf.sfc, uswrf.ntat, uswrf.sfc, uwnd.10m, vwnd.10
               pressure      -> air, hgt, omega, rhum, uwnd, vwnd
               surface       -> mslp, pres.sfc
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP1',
                 'dataset': 'NCEP1',
                 'timestep': 'DAILY',
                           'level': 'pressure',
                           'year': '2022',
                           'variable': 'air',
          },
          'air.2022.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP1',
                 'dataset': 'NCEP1',
                 'timestep': 'MONTHLY',
                           'level': 'pressure',
                           'variable': 'air',
          },
          'air.mon.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP2',
                 'dataset': 'NCEP2',
                 'timestep': 'DAILY',
                           'level': 'pressure',
                           'year': '2022',
                           'variable': 'air',
          },
          'air.2022.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'NCEP2',
                 'dataset': 'NCEP2',
                 'timestep': 'MONTHLY',
                           'level': 'pressure',
                           'variable': 'air',
          },
          'air.mon.nc'
)