Articles | Volume 23, issue 1
Nonlin. Processes Geophys., 23, 1–12, 2016
https://doi.org/10.5194/npg-23-1-2016
Nonlin. Processes Geophys., 23, 1–12, 2016
https://doi.org/10.5194/npg-23-1-2016

Research article 26 Jan 2016

Research article | 26 Jan 2016

Diagnosing non-Gaussianity of forecast and analysis errors in a convective-scale model

R. Legrand et al.

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Cited articles

Anderson, E. and Järvinen, H.: Variational quality control, Q. J. Roy. Meteorol. Soc., 125, 697–722, https://doi.org/10.1002/qj.49712555416, 1999.
Anderson, T. W. and Darling, D. A.: A test of goodness of fit, J. Am. Stat. Assoc., 49, 765–769, 1954.
Anscombe, Francis J. and Glynn, William J.: Distribution of the kurtosis statistic b2 for normal samples, Biometrika, 70, 227–234, 1983.
Auligné, T., Lorenc, A., Michel, Y., Montmerle, T., Jones, A., Hu, M., and Dudhia, J.: Toward a new cloud analysis and prediction system, B. Am. Meteorol. Soc., 92, 207–210, 2011.
Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, 2000.