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

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

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