Atmospheric Reanalyses Comparison Table

Created by on - Updated on 12/20/2023 02:26

Atmospheric Reanalyses Comparison Table

Name Source Time Range Assimilation
Model Resolution Model Output Resolution Model Areal Coverage Publicly Available Dataset Resolution Dataset Output Times and Time Averaging
Arctic System Reanalysis (ASR)

Byrd Polar Research Center

Polar Meteorology Group

2000-2010 (30km)

2000-2011 (10km)


10 and 30km

71 sigma levels

10 and 30km Arctic 10 and 30km 3-hourly WRF outputs; selected variables for surface and upper-air fields. Monthly averages of selected fields.

1995-present (6km)


2007-2013 (2km)

Continuous nudging

Continuous and Latent heat nudging 

6km, 40 eta levels


2km, 50 eta levels

6km and 2km   6km and 2km 15 minute output for 2D variables, hourly output for 3D output, daily and monthly aggregations; selected variables available online, further variables on request
ECMWF Interim Reanalysis (ERA Interim) ECMWF 1979-present 4D-VAR TL255L60 and N128 reduced Gaussian  TL255L60 and N128 reduced Gaussian (~79km globally) Global User defined, down to 0.75x0.75 3-hourly for most surface fields; 6-hourly for upper-air fields
Monthly averages of daily means, and of 6-hourly fields
ECMWF 40 year Reanalysis (ERA-40) ECMWF 1958-2001 3D-VAR TL159L60 and N80 reduced Gaussian TL159L60 and N80 reduced Gaussian (~125km globally) Global 2.5x2.5 / 1.125x1.125 3-hourly for most surface fields; 6-hourly for upper-air fields
Monthly averages of daily means, and of 6-hourly fields
ERA5 ECMWF 1950-present 4D-VAR ? T639 (HRES) and T319 (EDA) or on a reduced Gaussian grid with a resolution of N320 (HRES) and N160 (EDA) Global User defined, down to 30km (0.28x0.28) hourly estimates of a large number of atmospheric, land and oceanic climate variables.
ERA-20C ECMWF 1900-2010 4D-Var TL159L91 and N80 reduced Gaussian TL159L91 and N80 reduced Gaussian (~125km globally) Global User defined, down to 0.25x0.25 3-hourly for most fields, except for some surface fields (6-hourly

Japanese 25-year Reanalysis (JRA-25)



JMA Climate Data Assimilation System (JCDAS)

Japan Meteorological Agency (JMA) and Central Research Institute for Electric Power Industry (CRIEPI)






3D-VAR T106L40 T106L40  Gaussian Global

1.25 x 1.25

2.5 x 2.5




Japanese 55-year Reanalysis (JRA-55) Japan Meteolorogical Agency


(extended to Jan.2024)

4D-VAR TL319L60 TL319L60 reduced Gaussian Global 1.25 x 1.25


partly 3-hourly



Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)
Japan Meteolorogical Agency Sep.1947 - present 4D-VAR TL479L100 TL479L100 reduced Gaussian Global 1.25 x 1.25


partly 3-hourly or 1-hourly



NASA MERRA NASA GMAO 1/1979-2/2016 3D-VAR, with incremental update 2/3 lon x1/2 lat deg; 72 sigma levels 2/3 lon x1/2 lat deg 3d Analysis and 2d variables;  1.25 deg 3d Diagnostics; 72 model levels and 42 pressure levels Global 2/3 lon x1/2 lat deg 3d Analysis and 2d variables;  1.25 deg 3d Diagnostics; 72 model levels and 42 pressure levels 2d Diagnostics - 1 hourly avg, centered at half hour; 3d Diagnostics - 3 hourly avg, centered at 0130, 0430 ... 2230; 3d Analysis - Instantaneous 6 hourly; 2d Diagnostics, Monthly mean diurnal average; Monthly means for all collections; daily averages processed at servers on-the-fly
NASA MERRA-2 NASA GMAO 1/1980-present 3D-VAR, with incremental update; Includes aerosol data assimilation, observation corrected precipitation forcing for land surface and aerosol wet deposition Native cube sphere grid output is interpolated to 5/8 lon x1/2 lat deg; 72 sigma levels 5/8 lon x1/2 lat deg 3d Analysis and 2d variables;  3d Diagnostics; 72 model levels and 42 pressure levels Global 5/8 lon x1/2 lat deg 3d Analysis and 2d variables;  3d Diagnostics; 72 model levels and 42 pressure levels 2d Diagnostics - 1 hourly avg, centered at half hour; 3d Diagnostics - 3 hourly avg, centered at 0130, 0430 ... 2230; 3d Analysis - Instantaneous 6 hourly; 2d Diagnostics, Monthly mean diurnal average; Monthly means for all collections; Daily min/max T2m; Glacier/Sea Ice; Aerosols; daily averages processed at servers on-the-fly
NCEP Climate Forecast System Reanalysis (CFSR) NCEP 1979-present 3D-VAR T382 L64 .5x.5 and 2.5x2.5 Global .5x.5 and 2.5x2.5 Hourly, 4 times daily
NCEP/DOE Reanalysis AMIP-II (R2) NCEP/DOE 1979-present 3D-VAR T62 L28 2.5x2.5 Global 2.5x2.5 4 times daily/daily/monthly, also LTMs
NCEP/NCAR Reanalysis I (R1) NCEP/NCAR 1948-present 3D-VAR T62 L28 2.5x2.5 and 2x2 gaussian Global 2.5x2.5 and 2x2 gaussian 4 times daily/daily/monthly also LTMs
NCEP North American Regional Reanalysis (NARR) NCEP 1979-present RDAS 32km 32km N America 32km 4/8 times daily/daily/monthly also LTMs. 
NOAA-CIRES 20th Century Reanalysis (20CR) NOAA PSL 1871-2012 Ensemble Kalman Filter T62 L28 2x2 Global 2x2 4/8 times daily, daily,monthly, also LTMs
NOAA-CIRES 20th Century Reanalysis (20CRV2c) NOAA/ESRL PSD 1851-2014 Ensemble Kalman Filter T62 L28 2x2 Global 2x2 4/8 times daily, daily,monthly, also LTMs
NOAA-CIRES 20th Century Reanalysis (20CRV3) NOAA/ESRL PSD 1836-2015 Ensemble Kalman Filter T254 L64 ~0.7x0.7 Global 1x1 L28 8 times daily, daily,monthly, also LTMs
NOAA Last Millennium Reanalysis (LMR) NOAA 1-2000 Ensemble Kalman Filter T62 2x2 Global 2x2 annual

* To insert a new row, select 'edit'. Then select an element in the row you want to add a new row after. Then click your right mouse button. Select 'row' from the list. Then select 'insert row after'.

The satellite errors started around 1979 and hence many of the reanalyses started then (as there many more observations of the atmosphere and particularly the ocean available from the satellites than had been available before. Starting in 1979 results in a more consistent 'climate' in a reanalyses. The downside is there are are observations from before satellites and it is useful to study the atm/ocean before then, of course)

For your information, there are several comparison tables for all available global atmospheric reanalyses as of 2016 in the following paper:

 Fujiwara, M., J. S. Wright, G. L. Manney, L. J. Gray, J. Anstey, T. Birner, S. Davis, E. P. Gerber, V. L. Harvey, M. I. Hegglin, C. R. Homeyer, J. A. Knox, K. Krüger, A. Lambert, C. S. Long, P. Martineau, A. Molod, B. M. Monge-Sanz, M. L. Santee, S. Tegtmeier, S. Chabrillat, D. G. H. Tan, D. R. Jackson, S. Polavarapu, G. P. Compo, R. Dragani, W. Ebisuzaki, Y. Harada, C. Kobayashi, W. McCarty, K. Onogi, S. Pawson, A. Simmons, K. Wargan, J. S. Whitaker, and C.-Z. Zou: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems, Atmos. Chem. Phys., 17, 1417-1452, doi:10.5194/acp-17-1417-2017, 2017.

Please also visit the project page at:


Thanks, Masatomo!

I have added the link to the paper to the list of Comparison Table links at

If the S-RIP project page has a direct link to the comparison tables, we could add those instead, or in addition. They could also go on the Notes page at


best wishes,



Thu, 04/17/2014 - 09:49

Dear sirs, In addition to your comment here, may I suggest that you obtain an account and make a post of your findings, with a key figure or two? You can also include a link to any publication. Examples are and We are happy to help with any questions. After logging in, you will see additional areas, such as Help, that provide guidance on making a contribution. I encourage you do so. best wishes, gil compo for

Anonymous (not verified)

Wed, 04/16/2014 - 21:53

We based on four radiosonde datasets and three reanalysis datasets, analyzed the long-term tropospheric temperature changes over China for the period 1958-2001 and the uncertainties. The results from all datasets except for HadAT2 and 20th Century Reanalysis data (20CR) show a significant warming trend in the lower-troposphere temperature averaged over China during 1958-2001, but this trend decreases with height and is replaced by a cooling tendency at 500hPa, reaching maximum cooling at 300hPa. The year-by-year changes of temperature over China are largely in agreement among the radiosonde and the reanalysis datasets. The uncertainties of upper-troposphere temperature changes are larger compared with those of the lower troposphere. The uncertainty was relatively small during 1970-1990 and large during 1958-1970 and from 1990 to the present. The trend uncertainty is large in the Northwest for the lower troposphere and in South China for the upper troposphere, with the largest trend uncertainty over Northwest China at 850hPa and east to 100ºE at 300hPa. For the average temperature trend over China, the largest uncertainty peaks at 300hPa for 1958-2001. The tropospheric temperature of China at 300hPa, derived from HadAT2, warms up; this differs greatly from the other datasets. The warming trend is produced mainly by the stations over South China and is due to the choice of neighbor stations during construction. The temperature trend at 300hPa in NCEP-NCAR is cooler than the other datasets due to its abrupt cooling around the early 1990s. 20CR can partly capture the cooling tendency at 300hPa over North China but with weaker magnitude.

Anonymous (not verified)

Sat, 08/17/2013 - 00:59

Dear all, I need wind (U and V) data at 50 meter height using Era-Interim data. Please any one can suggest me how can i get it. otherwise is there any conversion from converting wind at any pressure level data to wind at 50m. Kindly help me regarding this. yours sincerely, M.Malleswararao

Anonymous (not verified)

Wed, 02/13/2013 - 22:08

which one of the new generation (ERA-Interim, MERRA, CFSR) reanalysis are more reliable for tropospheric water vapour studies ?

There is no easy answer to this question. In part, independent validation is thin (most large sources of water vapor are assimilated already), water vapor is one of the most sensitive prognostic variables to assimilate (see work by Bengtsson), and also low specificity of the question. I would expect each reanalysis to have strengths and weaknesses depending on region, season or time period. There is a fair bit work on these in the literature. So whatever the specific metrics are that answer your question, if you find it through a literature search or evaluating the data itself, there are pages at this www site where you can contribute what you find!

Felipe Neris (not verified)

Mon, 11/12/2012 - 17:08

I could not pass without congratulating the author of this table. It os essential for researches all over the world to know, in such an easy way, the differences between the reanalysis.

Michael Bosilovich (not verified)

Wed, 11/07/2012 - 09:11

Well, the answer to your question isn't so straightforward. To start, Pressure Temperature and water vapor (and winds) are assimilated state variables. The analysis should provide fields that replicate the observations fairly well. Indeed, there tends to be low variations from one reanalysis to the next in the state fields. This is true for upper troposphere extratropics, but the uncertainty increases closer to the surface and closer to the tropics. Also, water vapor tends to be more uncertain than temperature. One of your statements troubles me: "reanalysis values are as good as observations". Reanalyses are not observations, even for the state variables, it is not valid. Only observations are observations, and reanalyses are a processing of the data. For example, you say "as good as observations", to which I would ask, as good as *which* observations? Reanalyses assimilate sondes, aircraft and radiances in the upper troposphere for temperature, all have a role in the analyzed temperature, and can have differences from any one of the assimilated observations. To assess the uncertainty of a reanalysis, there are some things that can be done. Intercomparisons among reanalyses can define a variance and range of results. Reanalyses may provide the analysis increments, the amount that the state variables are affected by observations (over a long period, quantifies the model bias) or some may provide the innovations (sometimes called feedback files) which provide the forecast and analysis error compared to each observation assimilated. Of course, validation against independent observations is always a good exercise for any reanalysis. I hope that helps.


Sun, 11/04/2012 - 23:37

Thank you for such an helpful comparison table. I would like to know the accuracy of pressure, temperature and rH values given by reanalysis data. As far my knowledge goes, P & T is a 1st order variable and reanalysis values are as good as observations. Is that true? Is the accuracy of P, T & rH is going to affected by soil moisture, radiations, clouds cover, snow melting etc. arround tropics? Thanks

Warren Wiscombe (not verified)

Mon, 02/20/2012 - 12:53

It would be helpful to have a table column for time resolution. I understand it is mostly either 3- or 6-hr but outsiders may not know this. Also, units are not specified for some resolution numbers. The unitless ones are obviously degrees but this should be stated.

It's a good point since they do vary in available time resolution; MERRA as an example comes with has hourly averages.

Shaun (not verified)

Mon, 08/08/2011 - 12:03

One suggestion for this table--add a "date updated" for the table. This would be helpful to know how recent the table was updated--I've found various tables like this, but it's difficult to know which is current. By the way, the ERA-Interim now goes back to 1979--this could be updated! Thank you!

David (not verified)

Thu, 05/05/2011 - 11:36

Hello, first I would like to congratulate all that contribute to this excellent website, which is very helpful and full of valuable information! I believe that the "state of the art" in reanalysis are, nowadays, the ERA-Interim, NASA-MERR and NCEP-CFSR reanalysis. I consulted your Comparison Table for the Atmospheric reanalysis, but the "Publicly Available Dataset Resolution" presents datasets that are not freely available to public, like the ERA-Interim of 0.7º. On the other hand, I consulted the UCAR website and they seem to have available datasets for the NCEP-CFSR reanalysis with 0.313º of resolution! Is this true?? I have been spending huge time consulting what are the available reanalysis datasets, and what are the best to use, but I found several websites and articles about this subject with a lot of different reanalysis products and i must confess that i am a bit confused... I would like to ask if someone has an opinion of which reanalysis data set is better to use in my case: Near surface wind fields in Iberian Peninsula (inland and offshore) for the year of 2008 using WRF model, considering only freely public available data sets and preferable in GRIB format. Thank you all!!

Admittedly the table entry for availability of ERA-Interim data is confusing.. Our public data server currently provides data for public download only at 1.5º resolution and on pressure levels. NCAR provides the full-resolution data (0.7º), but only to North-American users. And, of course, ECMWF member state users have access to all ECMWF data. The REALLY GOOD NEWS is that within the next few months we will open an expanded public data server, which will provide unrestricted access to all ERA-Interim data at full horizontal and vertical resolution, i.e. on all 60 model levels in addition to the 37 pressure levels currently provided. This is a major step for us. We are just fine-tuning the system to make sure that we can provide good quality of service - stay tuned... See our main web page at for up-to-date information.

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