Assimilation of land-surface temperature observations in a 2DEnVar surface data assimilation system within AROME-France

Année de publication

2025

The assimilation of land-surface temperature (LST) observations from satellite instruments is of major interest to represent the land-surface initial state more accurately in numerical weather prediction (NWP) systems. Current activities at Météo-France in the limited-area Application of Research to Operations at Mesoscale (AROME-France) model are dedicated to the assimilation of LST observations, thanks to an ensemble-based method recently developed for surface data assimilation (DA). The developments consist of assimilating the LST observations into the two-dimensional ensemble-based variational (2DEnVar) screen-level scheme (2DEnVar-LST configuration), as has already been performed for 2-m temperature (T2m) and relative humidity (Rh2m). This leads tothe extension of the control vector and the representation of cross-correlations between these three variables in the background-error covariance ?B matrix of the day. The LST and 2-m temperature analysis increments produced by this system are then used with equal weights as "pseudo-observations" in the soil DA system to initialise soil temperature. The evaluation of 2DEnVar-LST against 2DEnVar (with no assimilation of LST observations) shows a positive impact on forecasts near the surface and in the lower atmosphere up to 700 hPa. This result is due mainly to the representation of relevant cross-covariances between T2m, Rh2m, and LST in the flow-dependent ensemble ?B matrix. The ensemble-based 2DEnVar system appears to be robust and reliable for the assimilation of new types of observations from satellites.</div>

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