Simulations with a very high resolution (∼25km) global climate model indicate that severe Autumn storms will impact Europe more often in a warmer future climate. The increase is mainly attributed to storms with a tropical origin, especially in the later part of the 21st century. As their genesis region expands and warms, tropical cyclones become more intense and have a higher chance of reaching Europe. This study focuses on the properties and evolution of such storms to clarify the occurring changes.The studied tropical cyclones show a typical evolution of tropical development, extratropical transition and a final re-intensification. A reduction of the transit area separating the regions of baroclinic and tropical development allows more storms to cross and redevelop into powerful extratropical cyclones. Many of these modelled storms feature hybrid properties during a considerable part of their life cycle, exhibiting the hazards of both tropical and extratropical systems. In addition to tropical cyclones, cold core extratropical storms and tropical disturbances mainly originating over the Gulf Stream region also increasingly impact Western Europe. Despite of their different history, all of the studied storms have a striking similarity: the formation of a warm seclusion. Although their occurrence is rare in the studied region, observations confirm that the strongest Autumn storms in the present climate are indeed warm seclusion cyclones. Damaging winds associated with the occurrence of a sting jet are observed in a large portion of the storms. Baroclinic instability is of great importance during the re-intensification as is the presence of an atmospheric river. The latter provides the core with warm and most air that aids the intensification through latent heat release. Atmospheric rivers will considerably increase in strength towards the future, as will the associated flooding risks. 1 AcknowledgementsBefore moving on, I would like to thank the KNMI for providing me with the opportunity to spend 8 months among them as part of my master's degree in meteorology. Their high quality data and skilled people enabled me to obtain some fascinating results but also to challenge myself and become a better scientist. Research is a shared effort and several people in particular should be accredited for their help. Primarily I want to mention Dr. Reindert J. Haarsma of the KNMI who has been my daily supervisor. Not only did he come up with the topic of this study, he also helped me a great deal getting to know the data and software and spent countless hours revising preliminary results and paving the way forward. Secondly, I should thank Dr. Aarnout J. van Delden of IMAU, Utrecht University of being my supervisor and helping me with a lot of practical matters. His knowledge and enthusiasm were a great addition and led to many good ideas which proved to be vital at times. Finally, Dr. Hylke de Vries also spent a lot of time helping me to succeed, especially with programming and visualising the results for which he has...
Crowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in meteorology, especially for urban areas where routine weather observations are scarce. Previous studies showed that smartphone battery temperature readings can be used to estimate the daily and citywide air temperature via a direct heat transfer model. This work extends model estimates by studying smaller temporal and spatial scales. The study finds the number of battery readings influences the accuracy of temperature retrievals. Optimal results are achieved for 700 or more retrievals. An extensive dataset of over 10 million battery temperature readings for estimating hourly and daily air temperatures is available for São Paulo, Brazil. The air temperature estimates are validated with measurements from a WMO station, an Urban Flux Network site, and data from seven citizen weather stations. Daily temperature estimates are good (coefficient of determination ρ2 of 86%), and the study shows they improve by optimizing model parameters for neighborhood scales (<1 km2) as categorized in local climate zones (LCZs). Temperature differences between LCZs can be distinguished from smartphone battery temperatures. When validating the model for hourly temperature estimates, the model requires a diurnally varying parameter function in the heat transfer model rather than one fixed value for the entire day. The results show the potential of large crowdsourced datasets in meteorological studies, and the value of smartphones as a measuring platform when routine observations are lacking.
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