Land surface initialization strategy for a global reforecast dataset

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.