Atmospheric Reanalysis: Overview & Comparison Tables

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Atmospheric Reanalysis: Overview & Comparison Tables
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Reanalysis a systematic approach to produce data sets for climate monitoring and research. Reanalyses are created via an unchanging ("frozen") data assimilation scheme and model(s) which ingest all available observations every 6-12 hours over the period being analyzed. This unchanging framework provides a dynamically consistent estimate of the climate state at each time step. The one component of this framework which does vary are the sources of the raw input data. This is unavoidable due to the ever changing observational network which includes, but is not limited to, radiosonde, satellite, buoy, aircraft and ship reports. Currently, approximately 7-9 million observations are ingested at each time step. Over the duration of each reanalysis product, the changing observation mix can produce artificial variability and spurious trends. Still, the various reanalysis products have proven to be quite useful when used with appropriate care.

Key Strengths

Key Strengths

Global data sets, consistent spatial and temporal resolution over 3 or more decades, hundreds of variables available; model resolution and biases have steadily improved

Reanalyses incorporate millions of observations into a stable data assimilation system that would be nearly impossible for an individual to collect and analyze separately, enabling a number of climate processes to be studied

Reanalysis data sets are relatively straightforward to handle from a processing standpoint (although file sizes can be very large)

Key Limitations

Key Limitations

Observational constraints, and therefore reanalysis reliability, can considerably vary depending on the location, time period, and variable considered

The changing mix of observations, and biases in observations and models, can introduce spurious variability and trends into reanalysis output

Diagnostic variables relating to the hydrological cycle, such as precipitation and evaporation, should be used with extreme caution

Please cite data sources, following the data providers' instructions
Dataset DOIs
Hosted Climate Index Files
Data Access
Usage Restrictions

Varies with the source. Please remember to cite your sources.

Expert User Guidance

Expert User Guidance

The following was contributed by John Fasullo, NCAR, NOV, 2011:

#The use of 'n-th generation reanalysis' to classify different reanalyses [which you will see on the pages linked to in the table below] is based on the incremental advancement of reanalysis techniques. While uniform distinctions between groups of reanalyses are somewhat artificial, the introduction of major advancements in the reanalysis methodologies over time and the opportunity to learn from earlier reanalysis efforts are clear. In this context, our description of reanalysis efforts as 1st-generation (such as the initial reanalysis efforts of NCEP-NCAR and NCEP-DOE) and 2nd-generation (ERA40,JRA25) correspond, in part, to their evolving abilities to assimilate the full suite of available satellite radiances. For example, the NCEP-NCAR and NCEP-DOE reanalyses do not assimilate satellite water vapor channels over ocean while follow-on efforts do. The 3rd-generation efforts, such as ERAIMERRACFSR, used more sophisticted data assimilation approaches (e.g. analysis increments and 4D-Var) and models and, again, addressed issues discovered in the 2nd-generation reanalysis efforts. Nonetheless, the artificial nature of these distinctions is evident in CFSR, which in many ways can be viewed as a 1st generation effort as it is the first reanalysis to include an interactive ocean in its reanalysis forecasts. Other reanalyses, such as the 20th Century reanalysis, further defy this simple classification as in attempting to extend its record back in time to the late 19th century, it intentionally excludes much of the modern observational record.##


The following was contributed by Dennis Shea, NCAR, NOV, 2011:

#Generally, users are advised to use third generation reanalysis products (ERA-Interim, MERRA, CFSR). To illustrate "use with appropriate care", the included figure of evaporation minus precipitation (Trenberth et al., 2011 J Clim v24 p4907), shows agreement over much of the globe. However, over Africa the derived estimates are of different sign!

Intercomparisons between similar generation reanalysis products may yield estimates of the uncertainty of some variables and derived quantities. Additionally, reanalyses can serve as focal point for model intercomparisons.##


The following was contributed by Dick Dee, ECMWF, March, 2012 (full contribution on the ERA-Interim page):

Reanalysis data sets in general

Key strengths:

  • The data are multivariate, spatially and temporally complete, and gridded
  • The data combine information from many sources (observations and models)
  • The data set is physically and dynamically coherent, according to the models used

Key weaknesses:

  • Changes in the observing system can cause changes in mean errors
  • Mixing observations with models tends to violate conservation properties
  • Uncertainties in the reanalysis data are difficult to understand and quantify 


Reanalysis data are often used to represent the "true state of the atmosphere according to observations." In actual fact, reanalysis combines inaccurate and incomplete observations with imperfect models, using methods and procedures that are technically and scientifically complex. Limitations and caveats of reanalysis data mainly result from:

  • Lack of observations. The atmosphere is not now, nor ever has been, fully observed.
  • Errors in the observations, and lack of information about those errors.
  • Shortcomings in the assimilating model, and lack of information about model errors.
  • Shortcomings in data assimilation methodology.
  • Technical errors and mistakes.
  • Computational limitations (e.g. limitations in spatial and temporal resolution)

Several of these items have to do with a lack of information. They represent fundamental limitations that are not restricted to reanalysis but play a role in any observational data set. (Note: replacing a skillfull forecast model by straightforward spatial interpolation does not solve anything - it is tantamount to removing, not adding, information).

To assess uncertainties in specific variables produced by reanalysis requires answering the following questions:

  • How strongly is the variable constrained by observations? Is it directly or indirectly observed?
  • What is the spatial and temporal distribution of the observations? How does this change in time?
  • How accurately can the model represent the variable? Does the model have skill in extrapolating and/or predicting it?

Users interested in the quality of low-frequency variability and/or trend estimates need to consider these aspects throughout the time period in question. Temporal variation in the observational constraint can produce artificial shifts in the reanalysis time series, especially if the assimilating model has systematic errors. See Section 8 in Dee and Uppala (2008) for a stratospheric example of this problem.

Given the continuous changes in the observing system, and the fact that all models have some systematic errors, users should be cautious when using reanalysis data for climate studies. It is necessary (but not always possible) to verify trend estimates by comparing with independent data sets, e.g. as in Simmons et al (2010).

Most users do not have access to the information needed to answer the difficult questions listed above. On the other hand, producers of reanalysis data do not have the resources (nor the application-specific knowledge) to answer them either. The challenge is to provide better tools and information to support users in making their own uncertainty assessments. In particular, it should be made much easier for a user to get detailed information about the observations used in reanalysis, including the quality assessment and bias adjustments produced by the reanalysis process itself.##

Key Figures

Climate Data Guide Image

What are the trends in monsoon rainfall? This figure illustrates the evolution of the global monsoon rainfall over land as estimated from various reanalyses and GPCP. Several identifiably spurious features exist including contradictory trends prior to (A) and during the satellite era (B), and inconsistencies with moisture convergence estimated from the mass-corrected atmospheric moisture budget (C). Figure by John Fasullo based on Fasullo 2012 (see Key Publication #5 in the References section).

Climate Data Guide Image

Evaporation minus Precipitation in MERRA (top) and ERA-Interim (bottom) based on Trenberth et al. (2011). (Contributed by J Fasullo)

Climate Data Guide Images

Taylor diagram illustrating pattern similarity and standard deviations for ERA-Interim annual mean precipitable water (1989-2001) compared with other reanalyses. [Climate Data Guide; D. Shea]

Time series (1979-2013) of ERA-Interim global 2-meter temperatures. Source:

Time series (1870-2013) of 20th-Century Reanalysis global 2-meter temperatures. Source:

Difference (ERAI-CFSR) between annual mean temperatures for 2013. Source:

Other Information

Earth system components and main variables
Type of data product

Years of record
Data time period extended
Input Data
Vertical Levels:
Missing Data Flag
Ocean or Land
Spatial Resolution
Model Resolution (reanalysis)
Data Assimilation Method
Model Vintage (reanalysis)

Key Publications
Not available...

Summary of atmospheric reanalysis products

Name Source Domain Years of record Timestep Spatial Resolution Formats Model Resolution Scheme & Model Vintage
Arctic System Reanalysis (ASR) Byrd Polar Research Center, The Ohio State University/ David Bromwich , NCAR , CIRES , U Illinois Arctic to Sub-daily, Monthly ASR v1; 30 km; 71 levels; 10hPA top , ASR v2; 15 km; 71 levels; 10hPA top netCDF

30 km and 15 km

CERA-20C: ECMWF's Coupled Ocean-Atmosphere Reanalysis of the 20th Century ECMWF Global to Sub-daily, Daily, Monthly ~ 125km; 160 x 320; 91 model levels/ 37 pressure levels / 16 potential temperature levels, and the 2 PVU potential vorticity level netCDF, GRIB 4DVAR


Climate Forecast System Reanalysis (CFSR) NCEP Global to Sub-daily, Monthly .5°x.5° & 2.5°x2.5°, 0.266 hPA top GRIB

T382 x 64 levels



ERA-20C: ECMWF's atmospheric reanalysis of the 20th century (and comparisons with NOAA's 20CR) ECMWF Global to Sub-daily, Daily, Monthly ~ 125km; 160 x 320; 91 model levels/ 37 pressure levels / 16 potential temperature levels, and the 2 PVU potential vorticity level netCDF, GRIB 4DVAR


ERA-Interim ECMWF Global to Sub-daily, Daily, Monthly 0.75°x0.75°x60 lev 0.1 hPA top GRIB, netCDF

T255, 60 levels



ERA5 atmospheric reanalysis ECMWF Global to Sub-daily, Daily, Monthly ~31 km, 137 levels to 1 Pa GRIB, netCDF 4DVAR

2016; IFS release 41r2

JRA-55 Japanese Meteorological Agency Global to Sub-daily, Monthly T319 x 60 levels, 0.1 hPA top GRIB

T319 x 60 levels



NASA MERRA NASA Global to Sub-daily, Monthly 0.5° x 0.667° x 72 , 0.01 hPA top HDF, netCDF

0.5° x 0.667° x 72



NASA's MERRA2 reanalysis NASA Global Modeling and Assimilation Office Global to Sub-daily, Daily, Monthly ½° latitude by ⅝° longitude by 72 model levels (also interpolated to 42 pressure levels) netCDF

Cubed sphere grid, stored at ½° latitude by ⅝° longitude by 72 model levels (also interpolated to 42 pressure levels)



NCEP NARR NCEP North America to Climatology, Sub-daily, Monthly 32km GRIB

32km x 45 eta



NCEP-NCAR (R1): An Overview NCEP , NCAR Global to Sub-daily, Daily, Monthly 2.5°x2.5°; 3 hPA top GRIB, netCDF

T62 - 28 levels



NOAA 20th-Century Reanalysis, Version 2 and 2c NOAA ESRL , CIRES CDC / Gil Compo Global to Sub-daily, Daily, Monthly 2°x2°, 28 levels 10 hPA top netCDF, GRIB

T62 28 levels

Ensemble Kalman Filter