Estimating and diagnosing model error variances in the Météo‐France global NWP model

Abstract

AbstractA methodology for estimating model error statistics is proposed. Its application to the global operational model ARPEGE of Météo‐France provides valuable insights into the spatio‐temporal dynamics of model error variances. In particular larger model errors are found in the midlatitude storm tracks (high cyclonic activity) for dynamical variables such as 500 hPa geopotential height and the 850 hPa wind speed.The average model errors over both hemispheres show a linear growth until they reach saturation. Model errors are also shown to grow more rapidly than predictability errors; this leads to a crossover time beyond which model error contribution to forecast error starts to play the dominant role. Moreover, model errors saturate more rapidly than predictability errors. On the other hand, spectral analysis shows an upscale energy transfer and a faster growth at synoptic scales for both model errors and predictability errors. This indicates that, for dynamical variables, the growth of both errors is most likely driven by baroclinic instability.The results found in this study could provide valuable information for a future implementation of a stochastic physics approach to account for model errors in the operational ensemble prediction system.