Articles | Volume 7, issue 3
Methods for assessment of models
30 Jun 2014
Methods for assessment of models |  | 30 Jun 2014

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

T. Chai and R. R. Draxler

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Cited articles

Chai, T., Carmichael, G. R., Tang, Y., Sandu, A., Heckel, A., Richter, A., and Burrows, J. P.: Regional NOx emission inversion through a four-dimensional variational approach using SCIAMACHY tropospheric NO2 column observations, Atmos. Environ., 43, 5046–5055, 2009.
Chai, T., Kim, H.-C., Lee, P., Tong, D., Pan, L., Tang, Y., Huang, J., McQueen, J., Tsidulko, M., and Stajner, I.: Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements, Geosci. Model Dev., 6, 1831–1850,, 2013.
Chatterjee, A., Engelen, R. J., Kawa, S. R., Sweeney, C., and Michalak, A. M.: Background error covariance estimation for atmospheric CO2 data assimilation, J. Geophys. Res., 118, 10140–10154, 2013.
Horn, R. A. and Johnson, C. R.: Matrix Analysis, Cambridge University Press, 1990.
Huber, P. and Ronchetti, E.: Robust statistics, Wiley New York, 2009.