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To this date, estimates of the climate response to mineral dust remain largely uncertain because of our limited capability to quantify dust distribution in the atmosphere. Focusing on the Central and East Asian dust source regions, this thesis aims to develop a coupled regional dust modeling system to provide an improved modeling capability of atmospheric dust as well as to aid the integration of ground-based and satellite observations. The objectives of this study are as follows: 1) evaluate the capabilities of the available data to detect and quantify mineral dust in the atmosphere; 2) develop and test a coupled regional dust modeling system able to simulate size resolved dust concentrations accounting for the regional specifics of Central and East Asia; and 3) outline a methodology for data and modeling integration. The capabilities of ground-based and satellite data to characterize dust in the atmosphere are examined in great details. Based on analysis of MODIS data reflectance and radiances, we found evidence for regional signature of dust in near-IR and proposed a new probabilistic dust-cloud mask that explicitly takes into account the spatial variability characteristics of dust aerosols. We developed a coupled regional dust modeling system (WRF-DuMo) by incorporating a dust emission module (DuMo) into the NCAR WRF model. The WRF-DuMo unique capabilities include explicit treatment of land surface properties in Central and East Asia, a suite of dust emission schemes with different levels of complexity, multiple options for dust injection in the atmosphere and flexible parameters of the initial size distribution of emitted dust. Two representative dust events that originated in East Asia in the springs of 2001 and 2007 have been modeled with WRF-DuMo. Simulations with different initial size distribution of dust, injection and emission parameterizations have been performed to investigate their relative role on the modeled dust fields. We performed an integrated analysis of modeled dust fields and satellite observations by introducing an ensemble model dust index, which used in conjunction with satellite dust retrievals improves the capability to characterize dust fields. Finally, we provide recommendations for the development of an integrated observational and modeling dust framework.

När en helikopter befinner sig i brownout saknar besättningen referenser till omvärlden vilket kan försämra det spatiala medvetandet. Om besättningen inte har koll på helikopterns position i luftrummet kan det leda till skador på både materiel och besättning i samband med landning. Denna rapport undersökte, på ett övergripande plan, vilka möjligheter sensorer inom det elektromagnetiska spektret hade att bidra med information till det spatiala medvetandet. Rapporten fann att sensorerna behöver vara anpassningsbara till följd av stoftmolnets vida varierande karaktäristik. Sensorerna hade möjlighet att generera en bild av landningsplatsen före brownout inträffar. Radar lämpade sig bäst till att uppdatera bilden när brownout inträffat då de elektrooptiska sensorerna kraftigt begränsas av stoftmolnet. Däremot genererade de elektrooptiska sensorerna en bild av omgivningen som motsvarade ögats tolkning av samma omgivning, något som en radar har svårare att göra. / When a helicopter is in brownout the crew looses references to the world outside which could decrease the spatial awareness. If the crew does not know the accurate position of the helicopter it can lead to damage on both equipment and crew when landing. This report examined, on a general level, how sensors within the electromagnetic spectrum could contribute with information to the spatial awareness. The report found that the sensors need to be adaptable due to the widely varying characteristics of the dustcloud. The sensors were able to generate an image of the landingsite prior to brownout. Radar was best suited to updating the image when brownout had occurred, whereas the electro-optical sensors are highly attenuated by the dust cloud. However, the electro-optical sensors generated an image of the environment corresponding to the eye's interpretation of the same surrounding, something that a radar has greater difficulty with accomplishing.