Heterogeneous background‐error covariances for the analysis and forecast of fog events

An ensemble assimilation, which is based on the operational cloud-resolving model Applications de la Recherche a l’Operationnel a Meso-Echelle (AROME) and its 3D-Var assimilation system, is used to diagnose background-error covariances separately in areas with and without fog. The fog and haze analysis system Cartographies des Analyses du RIsque de BrOUillard (CARIBOU) is used as reference to calibrate the best fog predictor from model fields, which was found to be a low-level nebulosity. It appears that the physical processes in fog layers lead to very specific balances between control variables as well as much shorter vertical correlation length-scales at low levels in background-error covariances. In order to spread the information from surface and satellite observations with adequate structures in fog areas, a binary heterogeneity based on the use of geographical masks is added to the background-error covariances. After the elimination of discontinuities at the mask borders, the positive impact of this formalism on the analysis-increment structure is discussed. Impact studies based on long-term real cases indicate that the global impact is closely related to the quality of the fog mask, for which future improvements are awaited. Copyright © 2011 Royal Meteorological Society

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