Using a much improved atmospheric model and assimilation system from those used in ERA-40, ERA-Interim represents a third generation reanalysis. Several of the inaccuracies exhibited by ERA-40 such as too-strong precipitation over oceans from the early 1990's onwards and a too-strong Brewer-Dobson circulation in the stratosphere, were eliminated or significantly reduced. ERA-Interim now extends back to 1979 and the analysis continues to be extended forward in near-real-time.
The following was contributed by Dick Dee (ECMWF), March, 2012 (some of this has been excerpted and placed on the reanalysis overview page):
#Reanalysis data sets in general
Progress (relative to ERA-40) was made in the following areas:
Specific problems in ERA-Interim
A list of known quality issues with ERA-Interim is maintained by the producers at http://www.ecmwf.int/research/era/do/get/index/QualityIssues. These include the following spurious shifts in ERA-Interim time series related to changes in the observing system:
In addition, the ERA-Interim snow analyses from 1 July 2003 to 23 February 2010 are affected by a geo-location error introduced during the processing of NESDIS snow cover data. Data locations were shifted by about 100km toward the South-East, causing incorrect removal of snow in some coastal areas in the Northern Hemisphere during winter.
Assessing the quality of reanalysis data
Please visit http://www.ecmwf.int/research/era for up-to-date information about ERA-Interim production, data availability, quality issues, documentation, etc.
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:
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:
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.#
NCAR Research Data Archive (6-hrly)
NCAR Research Data Archive (Surface Analysis and Forecasts)
NCAR Research Data Archive (Monthly Means)
ERA Interim Data Server
NCAR CAS Data Catalog derived mass, moisture, energy budgets
Click the thumbnails to view larger sizes
|A timeline of the complete observing system used in ERA-Interim. Figure produced by Paul Poli. (Contributed by D. Dee)|
|Globally averaged departures of ERA-Interim tropospheric temperatures relative to radiosonde observations (top) and radiance data from MSU channel 2 (center). The bottom panel shows globally averaged bias adjustments for the MSU channel 2 radiances, computed during the reanalysis using a variational bias correction scheme. DIfferent colors correspond to different satellites. (contributed by D. Dee)|
|Evolution of skill in ECMWF operational forecasts (top panel) and in re-forecasts produced by the ERA-Interim system (bottom panel). Colored plumes show anomaly correlations for 3-day, 5-day,7-day and 10-day forecasts of 500hPa geopotential height, averaged for the northern and southern hemispheres. Comparing the top and the bottom panels helps separate the effect of changes in the observing system. Figure produced by Adrian Simmons. (Contributed by D. Dee)|
Dee, Dick & National Center for Atmospheric Research Staff (Eds). Last modified 13 Nov 2013. "The Climate Data Guide: ERA-Interim." Retrieved from https://climatedataguide.ucar.edu/climate-data/era-interim.