A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative

Flamant, Cyrille ; Chazette, Patrick ; Caumont, Olivier ; Di Girolamo, Paolo ; Behrendt, Andreas ; Sicard, Michaël ; Totems, Julien ; Lange, Diego ; Fourrié, Nadia ; Brousseau, Pierre ; Augros, Clotilde ; Baron, Alexandre ; Cacciani, Marco ; Comerón, Adolfo ; De Rosa, Benedetto ; Ducrocq, Véronique ; Genau, Pascal ; Labatut, Laurent ; Muñoz-Porcar, Constantino ; Rodríguez-Gómez, Alejandro ; Summa, Donato ; Thundathil, Rohith ; Wulfmeyer, Volker

Année de publication
2021
Résumé
<p align=justify>Extreme heavy precipitation events (HPEs) pose a threat to human life but remain difficult to predict because of the lack of adequate high frequency and high-resolution water vapor (WV) observations in the low troposphere (below 3 km). To fill this observational gap, we aim at implementing an integrated prediction tool, coupling network measurements of WV profiles, and a numerical weather prediction model to precisely estimate the amount, timing, and location of rainfall associated with HPEs in southern France (struck by ~ 7 HPEs per year on average during the fall). The Water vapor Lidar Network Assimilation (WaLiNeAs) project will deploy a network of 6 autonomous Raman WV lidars around the Western Mediterranean to provide measurements with high vertical resolution and accuracy to be assimilated in the French Application of Research to Operations at Mesoscale (AROME-France) model, using a four-dimensional ensemble-variational approach with 15-min updates. This integrated prediction tool is expected to enhance the model capability for kilometer-scale prediction of HPEs over southern France up to 48 h in advance. The field campaign is scheduled to start early September 2022, to cover the period most propitious to heavy precipitation events in southern France. The Raman WV lidar network will be operated by a consortium of French, German, Italian, and Spanish research groups. This project will lead to recommendations on the lidar data processing for future operational exploitation in numerical weather prediction (NWP) systems.</p>
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