High-Resolution Data Assimilation for Two Maritime Extreme Weather Events: A comparison between 3DVar and EnKF

Abstract. Populated coastal regions in the Mediterranean are known to be severely affected by extreme weather events. Generally, they are initiated over maritime regions, where a lack of in-situ observations is present, hampering the initial conditions estimations and hence, the forecast accuracy. To face this problem, Data Assimilation (DA) is used to improve the estimation of the initial conditions and their respective forecasts. Although comparisons between different DA methods have been performed at global scales, few studies are performed at high-resolution, focusing on extreme weather events triggered over the sea and enhanced by complex topographic regions. In this study, we investigate the role of assimilating different types of conventional and remote-sensing observations using the variational 3DVar and the ensemble-based EnKF, which are of the most common DA schemes used globally at National Weather Centers. To this aim, two different events are chosen because of both the different areas of occurrence and the triggering mechanisms. Both the 3DVar and the EnKF are used at convection permitting scales to improve the predictability of these two high-impact coastal extreme weather episodes, which were poorly predicted by numerical weather prediction models: (a) the heavy precipitation event IOP13 and (b) the intense Mediterranean Tropical-like cyclone Qendresa. Results show that the EnKF and 3DVar perform similarly for the IOP13 event for most of the verification metrics, although looking at the ROC and AUC scores, the EnKF clearly outperforms the 3DVar. However, the ensemble mean of the EnKF is in general worse than the 3DVar for Qendresa, although some of the ensemble members of the EnKF individually outperforms the 3DVar allowing for gaining information on the physics of the event and hence the benefits of using an ensemble-based DA scheme.