- Dee, D. P., and 35 co-authors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. R. Meteorol. Soc., 137, 553-597. DOI: 10.1002/qj.828. Free and Open access!
- Simmons, A. J. et al., 2014: Estimating low-frequency variability and trends in atmospheric temperature using ERA-Interim. Quart. J. R. Meteorol. Soc., DOI: 10.1002/qj.2317. Free and Open access!
- Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low-frequency variations in surface atmospheric humidity, temperature and precipitation: Inferences from reanalyses and monthly gridded observational datasets. J. Geophys. Res., 115, D01110, doi:10.1029/2009JD012442.
- Kobayashi, S., M. Matricardi, D. P. Dee, and S. Uppala, 2009: Toward a consistent reanalysis of the upper stratosphere based on radiance measurements from SSU and AMSU-A. Quart. J. R. Meteorol. Soc., 135, 2086-2099, doi: 10.1002/qj.514.
- Dee, D. P., and S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. R. Meteorol. Soc., 135, 1830-1841, doi:10.1002/qj.493.
- Uppala, S., et al., 2005: The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc., 131, 2961-3012, doi:10.1256/qj.04.176.
Re: ERA-Interim resolutions
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Time format SST data of ERA-Interim dataset
Re: Time format SST data of ERA-Interim dataset
How is the SST calculated in ERA-Interim ?
Re: How is the SST calculated in ERA-Interim ?
height extent of ERA-Int temperature bias from Solar issue
Re: Accuracy of humidty and temperature
Re: Accuracy of humidty and temperature
Quantifying with definitive assurance the accuracy of a data point as defined by ISO 5725-1 (trueness and precision) requires an absolute reference. However, the atmosphere is not covered at all times and all locations with such a network of absolute measurement points. Please read the ERA-Interim page on the NCAR Climate Data Guide for a detailed qualitative discussion of all the difficulties in estimating accuracy in reanalyses ( https://climatedataguide.ucar.edu/reanalysis/era-interim ). However, with these limitations in mind, we can estimate quantitatively the relative accuracy (trueness and precision) of atmospheric reanalyses, with respect to unevenly distributed and imperfect observations. Of course, these metrics are only valid at the observation locations and times, but they may serve as guidance. First, look for example at Figure 16 by Poli et al. 2010 for regional vertical profile estimates of global relative accuracy (trueness estimated by bias, and precision estimated by standard deviation) of ERA-Interim temperatures, with respect to radiosondes. Bear in mind that these numbers are not an upper bound as errors could be correlated or present similar biases, as radiosondes are assimilated in reanalyses, so the errors in either radiosondes and in ERA-Interim could be larger than the difference between the two. These numbers are also not a lower bound for the relative accuracy of radiosondes and ERA-Interim, as in some locations and times the errors could be smaller than shown by these estimates covering large areas of the globe. The same Figure also shows comparison with aircraft measurements, which gives you a hint to appreciate the relative accuracy estimates using another observation reference. Second, depending on your application, the variation in time of the accuracy (trueness and precision) may also be of importance. Look at time-series, e.g. Figure 18 by Dee et al. 2011 (see Figure 19 for humidity), as well as Figures 6, 7, and 9 by Poli et al. 2010 to appreciate the magnitude of changes in relative accuracy (trueness and precision). Last, for a hint at how these relative accuracy estimates vary locally in the spatial domain, consider also that remote regions may present larger errors due to paucity of observational information in the reanalyses, such as shown in Figure 1 by Dee and Uppala 2009 for locations at latitudes greater than 70 degrees North.
Dee, D. P. and Uppala, S. (2009), Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Q.J.R. Meteorol. Soc., 135: 1830–1841. doi: 10.1002/qj.493
Poli, P., Healy, S. B. and Dee, D. P. (2010), Assimilation of Global Positioning System radio occultation data in the ECMWF ERA–Interim reanalysis. Q.J.R. Meteorol. Soc., 136: 1972–1990. doi: 10.1002/qj.722
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828
How is the 0.5, 0.25 and other degree resoltution from N128 reduced Gaussian grid in ERa-Interim. Where