Articles | Volume 7, issue 3
Geosci. Model Dev., 7, 1247–1250, 2014
https://doi.org/10.5194/gmd-7-1247-2014
Geosci. Model Dev., 7, 1247–1250, 2014
https://doi.org/10.5194/gmd-7-1247-2014

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, https://doi.org/10.5194/gmd-6-1831-2013, 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.