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Information on weather extremes is essential for risk awareness in planning of infrastructure and agriculture, and it may also playa key role in our ability to adapt to recurrent or more or less unique extreme events. This thesis reports new statistical methodologies that can aid climate risk assessment under conditions of climate change. This increasing access to high temporal resolution of data is a central factor when developing novel techniques for this purpose. In particular, a procedure is introduced for analysis of long-term changes in daily and sub-daily records of observed or modelled weather extremes. Extreme value theory is employed to enhance the power of the proposed statistical procedure, and inter-site dependence is taken into account to enable regional analyses. Furthermore, new methods are presented to summarize and visualize spatial patterns in the temporal synchrony and dependence of weather events such as heavy precipitation at a network of meteorological stations. The work also demonstrates the significance of accounting for temporal synchrony in the diagnostics of inter-site asymptotic dependence., Information om extrema väderhändelser är väsentligt för riskmedveten planering av infrastruktur och jordbruk. Sådan information kan också spela en avgörande roll för vår förmåga att anpassa oss till extremhändelser som är regelbundet återkommande eller mer eller mindre unika. Denna avhandling beskriver nya statistiska metoder som kan bidra till att bedöma klimatrisker när klimatet förändras. Den ökande tillgången till data med hög tidsupplösning är en central faktor när vi utvecklar nya tekniker för detta ändamål. Speciellt introducerar vi metodik för att analysera långsiktiga förändringar i dagliga eller ännu mera högupplösta data avseende observerade eller modellerade väderextremer. Extremvärdesteori utnyttias för att öka styrkan av statistiska tester, och hänsyn tas till beroendet mellan data från olika platser så att regionala bedömningar blir möjliga. Vidare presenteras nya metoder för att sammanfatta och visualisera rumsliga mönster i samstämmigheten av och beroendet mellan extrema väderhändelser såsom hög nederbördsintensitet i ett nätverk av meteorologiska stationer. Arbetet visar också betydelsen av ta hänsyn till den tidsmässiga överensstämmelsen när man undersöker det asymptotiska beroendet mellan extrema händelser vid olika stationer.

In diabetes mellitus pancreas fails to produce insulin enough to maintain tissue utilisation of blood glucose. To maintain a normalised metabolism many diabetic patients have to provide insulin from injections or an infusion pump. The disease affects some percent of western population. The insulin provided by the patient has to be adjusted to food and physical exercise. This may be achieved by following rules from diabetes professionals, but also by self learning how food, insulin and physical exercise interact and influence the blood glucose. It seems that the patients who have this intuitive knowledge of the interactions are the ones who manages their diabetes treatment best and have the highest quality of life. The learning have, however, been performed by trial and error - to the price of severe inconvenience and bad metabolic control. A combination of computer science and diabetes physiology has resulted in the computer program Särimner. It is created to give the user a possibility to experiment with diabetes treatment. Food, insulin and physical exercise may be varied and the blood glucose is calculated. Sarimner provides a short-cut to obtaining knowledge since it allows experiments, stimulates discussions and let the user by himself formulate and test hypotheses regarding experienced problems. Since Särimner may be adjusted to look more alike an individual, the experiments are driven by the user's own curiosity. He may experiment with situations of importance to himself and finally make himself the expert of his own treatment situation. The way Särimner is designed, allows interested users to get "under the skin" of the model and study details in the physiological processes. This transparency makes it possible to search for explanations to treatment phenomena. One drawback with the model is that it is quite complex and requires some knowledge from a user with the ambition to understand all the processes. To measure the impact of Särimner training, 11 diabeti

In diabetes mellitus pancreas fails to produce insulin enough to maintain tissue utilisation of blood glucose. To maintain a normalised metabolism many diabetic patients have to provide insulin from injections or an infusion pump. The disease affects some percent of western population. The insulin provided by the patient has to be adjusted to food and physical exercise. This may be achieved by following rules from diabetes professionals, but also by self learning how food, insulin and physical exercise interact and influence the blood glucose. It seems that the patients who have this intuitive knowledge of the interactions are the ones who manages their diabetes treatment best and have the highest quality of life. The learning have, however, been performed by trial and error - to the price of severe inconvenience and bad metabolic control. A combination of computer science and diabetes physiology has resulted in the computer program Särimner. It is created to give the user a possibility to experiment with diabetes treatment. Food, insulin and physical exercise may be varied and the blood glucose is calculated. Sarimner provides a short-cut to obtaining knowledge since it allows experiments, stimulates discussions and let the user by himself formulate and test hypotheses regarding experienced problems. Since Särimner may be adjusted to look more alike an individual, the experiments are driven by the user's own curiosity. He may experiment with situations of importance to himself and finally make himself the expert of his own treatment situation. The way Särimner is designed, allows interested users to get "under the skin" of the model and study details in the physiological processes. This transparency makes it possible to search for explanations to treatment phenomena. One drawback with the model is that it is quite complex and requires some knowledge from a user with the ambition to understand all the processes. To measure the impact of Särimner training, 11 diabeti

In diabetes mellitus pancreas fails to produce insulin enough to maintain tissue utilisation of blood glucose. To maintain a normalised metabolism many diabetic patients have to provide insulin from injections or an infusion pump. The disease affects some percent of western population. The insulin provided by the patient has to be adjusted to food and physical exercise. This may be achieved by following rules from diabetes professionals, but also by self learning how food, insulin and physical exercise interact and influence the blood glucose. It seems that the patients who have this intuitive knowledge of the interactions are the ones who manages their diabetes treatment best and have the highest quality of life. The learning have, however, been performed by trial and error - to the price of severe inconvenience and bad metabolic control. A combination of computer science and diabetes physiology has resulted in the computer program Särimner. It is created to give the user a possibility to experiment with diabetes treatment. Food, insulin and physical exercise may be varied and the blood glucose is calculated. Sarimner provides a short-cut to obtaining knowledge since it allows experiments, stimulates discussions and let the user by himself formulate and test hypotheses regarding experienced problems. Since Särimner may be adjusted to look more alike an individual, the experiments are driven by the user's own curiosity. He may experiment with situations of importance to himself and finally make himself the expert of his own treatment situation. The way Särimner is designed, allows interested users to get "under the skin" of the model and study details in the physiological processes. This transparency makes it possible to search for explanations to treatment phenomena. One drawback with the model is that it is quite complex and requires some knowledge from a user with the ambition to understand all the processes. To measure the impact of Särimner training, 11 diabeti

In diabetes mellitus pancreas fails to produce insulin enough to maintain tissue utilisation of blood glucose. To maintain a normalised metabolism many diabetic patients have to provide insulin from injections or an infusion pump. The disease affects some percent of western population. The insulin provided by the patient has to be adjusted to food and physical exercise. This may be achieved by following rules from diabetes professionals, but also by self learning how food, insulin and physical exercise interact and influence the blood glucose. It seems that the patients who have this intuitive knowledge of the interactions are the ones who manages their diabetes treatment best and have the highest quality of life. The learning have, however, been performed by trial and error - to the price of severe inconvenience and bad metabolic control. A combination of computer science and diabetes physiology has resulted in the computer program Särimner. It is created to give the user a possibility to experiment with diabetes treatment. Food, insulin and physical exercise may be varied and the blood glucose is calculated. Sarimner provides a short-cut to obtaining knowledge since it allows experiments, stimulates discussions and let the user by himself formulate and test hypotheses regarding experienced problems. Since Särimner may be adjusted to look more alike an individual, the experiments are driven by the user's own curiosity. He may experiment with situations of importance to himself and finally make himself the expert of his own treatment situation. The way Särimner is designed, allows interested users to get "under the skin" of the model and study details in the physiological processes. This transparency makes it possible to search for explanations to treatment phenomena. One drawback with the model is that it is quite complex and requires some knowledge from a user with the ambition to understand all the processes. To measure the impact of Särimner training, 11 diabeti

In diabetes mellitus pancreas fails to produce insulin enough to maintain tissue utilisation of blood glucose. To maintain a normalised metabolism many diabetic patients have to provide insulin from injections or an infusion pump. The disease affects some percent of western population. The insulin provided by the patient has to be adjusted to food and physical exercise. This may be achieved by following rules from diabetes professionals, but also by self learning how food, insulin and physical exercise interact and influence the blood glucose. It seems that the patients who have this intuitive knowledge of the interactions are the ones who manages their diabetes treatment best and have the highest quality of life. The learning have, however, been performed by trial and error - to the price of severe inconvenience and bad metabolic control. A combination of computer science and diabetes physiology has resulted in the computer program Särimner. It is created to give the user a possibility to experiment with diabetes treatment. Food, insulin and physical exercise may be varied and the blood glucose is calculated. Sarimner provides a short-cut to obtaining knowledge since it allows experiments, stimulates discussions and let the user by himself formulate and test hypotheses regarding experienced problems. Since Särimner may be adjusted to look more alike an individual, the experiments are driven by the user's own curiosity. He may experiment with situations of importance to himself and finally make himself the expert of his own treatment situation. The way Särimner is designed, allows interested users to get "under the skin" of the model and study details in the physiological processes. This transparency makes it possible to search for explanations to treatment phenomena. One drawback with the model is that it is quite complex and requires some knowledge from a user with the ambition to understand all the processes. To measure the impact of Särimner training, 11 diabeti