[ca] Els desastres naturals d'origen hidro-meteorològic constitueixen el major risc a nivell mundial. A Catalunya, cada any es succeeixen diferents episodis de temps advers i inundacions, provocant també danys importants en béns materials, pèrdues en l'agricultura, o pèrdua de vides humanes. Aquestes dades poden augmentar en les condicions cada cop més acusades d'escalfament global. Per reduir l'impacte d'aquest fenòmens és necessari millorar els sistemes d'alerta primerenca a molt curt termini, així com la monitorització dels sistemes meteorològics causants d'aquests fenòmens. En aquest context l'objectiu principal d'aquesta tesi doctoral es millorar el coneixement profund de la dinàmica de les tempestes severes, la seva identificació, predicció a molt curt termini, i monitoratge a temps real. Assolir aquest objectiu implica millorar la prevenció dels seus efectes en superfície. La tesis aborda una problemàtica encara no resolta sobre el moviment anòmal d'aquestes tempestes, que esdevé un gran repte a l'hora de pronosticar-ne la seva evolució en les properes hores, i per tant, el seu impacte. A més, es centra a Catalunya, degut a la seva proximitat al Mar Mediterrani i la complexa topografia, factors claus resultants en una meteorologia variada quasi a nivell de municipi, on hi ha l'avantatge de disposar d'una bona cobertura radar, eina essencial per la caracterització de les tempestes. Primer, es proposa una metodologia que permet identificar les situacions potencialment convectives a partir de camps de precipitació acumulada diària, seleccionant aquestes per cercar les tempestes i determinar si el seu moviment és anòmal (del Moral et al., 2017). Definida la mostra d'estudi, es desenvolupa un algoritme que permet millorar la identificació i seguiment d'aquestes tempestes, sobretot quan es tracta d'aquelles amb moviment anòmal (del Moral et al., 2018a). El funcionament de l'algorisme es verifica en dos règims de convecció diferent: casos severs d'interior (del Moral et al., 2018b), i pluges intenses a la costa (del Moral et al., 2020a). Finalment, s'introdueix per primer cop en un país sud-Europeu la tècnica dual-Doppler: obtenció de variables dinàmiques dins de les pròpies tempestes a partir d'observacions radar, per a l'estudi de les interaccions de més petita escala (del Moral et al., 2020b).

[en] Natural disasters of hydro-meteorological origin are the biggest risk worldwide. In Catalonia (NE of the Iberian Peninsula), severe weather and flash floods occur each year, resulting in major damage to property, losses in agriculture, and also of human lives. To reduce its impact, we need to improve the early warning systems and storm short-term forecasting. There's a need to gain in-depth knowledge of severe thunderstorm dynamics, since the current accused conditions of global warming can impact in factors triggering these storms. The main objective of the present thesis is to enhance the knowledge of severe storms dynamics and to improve their identification and monitoring in real time, in order to help prevent their surface effects on the citizens. The project addresses the unresolved problem of storm anomalous motion, as it becomes a great challenge to predict their evolution and impact in the next few hours. For this purpose, the area of Catalonia has been chosen as the study region of this project, due to the proximity to the sea and complex topography, which are often key factors in varying the weather at a local scale. There is also the advantage of having good radar coverage, which will be the essential tool for characterizing storms. We first propose a methodology that identifies potentially convective days from daily cumulative rainfall fields, selects them to search for storms, and determines if their motion is anomalous. We have found that the area with the highest convective activity between 2008-2015 in Catalonia was located in the eastern Pre-Pyrenees, due to the possible creation of a convergence line. It has also been identified that there are more convective structures with possible anomalous propagation in summer and spring, with the main patterns being related to splitting, merging, stationarity and elongated storms. Once the study sample is defined, we have developed an algorithm to improve the identification and tracking of these thunderstorms, especially those with anomalous propagations. The keys of improvement have been based on proposing new techniques in the three main modules; 2D, 3D identification and tracking. In addition, it incorporates alerts before possible cell splitting or merging. These changes have shown that the algorithm is able to faithfully reproduce storm life cycle, correctly identify in advanced anomalous motion, and correctly distinguish storms in highly dense convective situations. The algorithm has been verified first over 30 severe cases, proving that it can identify anomalous movements with a mean 30-min lead-time, being the splitting, the easiest one to do. It has also been demonstrated a good ability at not only identifying these movements but also separating cases with and without anomalous motion. On the other hand, the algorithm has demonstrated a good performance in cases of heavy rainfall on a Catalan flood-prone coastal area of touristic interest. It is identified that storms are usually organized in convergence lines, and that topography and the sea play a very important role, whether affecting the movement, the time of exposure, or the amount of precipitable water causing flash floods. Finally, the dual-Doppler technique is applied in Catalonia for the first time. This allows getting complete information of the internal dynamics of a thunderstorm, without the need of running idealized models, and then, getting to know the local topographic influence on the evolution and organization. It is demonstrated that the complex local topography changes and/or amplifies the wind flow inside and near thunderstorms, modifying completely their life cycle and their possible interactions with their neighbor cells. It is also shown that this qualitative improvement into storm-scale dynamic knowledge can improve the nowcasting techniques and the early warning systems in the future.