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A 32-year global ensemble reforecast dataset has recently been developed at Meteo-France that is approximatively consistent with the operational global ensemble forecast system (called PEARP). Unlike at ECMWF or NCEP, Meteo-France does not possess a reanalysis of its own operational forecast system. Then, the initial atmospheric state and boundary conditions of the reforecasts are from the ECMWF ERA-interim reanalysis. This article presents a sensitivity study of the reforecasts to the method of land-surface initialization. To this end, two sets of short-range hindcasts using different land-surface initialization approaches are compared. The first set is initiliazed from interpolated ERA-interim land-surface fields based on a transfer function. The second set is initialized from offline simulations of the Meteo-France land-surface model (SURFEX) driven by the 3-hourly near-surface atmospheric fields of the ERA-interim reanalysis. Each set is run from 18UTC initial conditions and up to +108h. Because better results are overall found using offline SURFEX simulations, this latter approach was chosen to perform an ensemble reforecast dataset. Then, this ensemble reforecast database will be used to build a climatology of the operational ensemble prediction system of Meteo-France , which will, in turn, help better estimate forecast systematic errors and, more importantly, improve the forecast of rare extreme weather events.

This study investigates the impact of a calibration technique using estimated global model error variances on the performance of short-range ensemble forecasts. The calibration technique is rather simple as it consists of adding a random white noise of zero mean and variance equal to estimated model error variances. Two calibrated ensemble forecasts are generated, one using a homogeneous calibration factor and another one using a heterogeneous calibration factor. Based on probabilistic scores, the qualities of these two ensemble forecasts are evaluated against a non-calibrated ensemble and the operational ensemble prediction system of Meteo-France. The results show that this calibration technique significantly improves the reliability and, to a less extent, the resolution of ensemble forecasts. Better reliability results are found using a spatially heterogeneous calibration factor instead of using the homogeneous calibration factor at forecast ranges beyond 48 and 72 h.

A methodology for estimating model error statistics is proposed. Its application to the global operational model ARPEGE of Meteo-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.

A new C1 finite element complete in 7th degree polynomial basis is described and compared with other C1 elements in an eigen-elements analysis.

Many of the known finite elements are based on a variational procedure in a functional basis of monomials. The present paper attempts to formulate completely a finite element procedure which is non-variational on a functional basis which is left to the choice of the user.