Examensarbeten för masterexamen // Master Theses
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- PostA Bayesian machine learning approach to geostationary infrared precipitation retrievals(2020) Tellwe, Gustav; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Eriksson, Patrick; Pfreundschuh, SimonThis project uses geostationary satellite data to retrieve precipitation rates at surface level. It is achieved through the use of quantile regression neural networks (QRNN) calibrated against rain rates from the Global Precipitation Measurement (GPM) Core Observatory satellite. The area of exploration is located over the Amazon rainforest. The main difficulty of this problem is that geostationary data is not directly related to rain as it only perceives the cloud top temperatures. It does, however, have a high temporal and spatial resolution which makes it interesting for applications in remote areas of the Earth where groundbased radar equipment is unavailable. The result of the project is mainly a comparison between different neural network architectures such as multi-layer perceptron (MLP) and convolutional neural networks (CNN), but there is also a minor comparison to an adapted version of a Hydroestimator (HE) that is currently in use by the National Institute for Space Research (INPE) in Brazil. The best performing configuration, with regards to the loss function, in this study was a CNN. It performed significantly better than the adapted HE for a test conducted over two days in March. An unsuccessful attempt to improve the results using time-series was also conducted. Furthermore, a U-net architecture was also tested on rain rate data that has been resolution-enhanced through interpolation.
- PostDecomposing global warming using Bayesian statistics(2015) Ljungqvist, Gustav; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentIn this thesis an energy balance model and regression with internal climate variability indices are employed to model ocean heat content and global mean surface temperatures. The energy balance model takes radiative forcing as input. The nature of the contribution of anthropogenic aerosol emissions to the radiative forcing is not very well-known, and in previous research its path is usually scaled by some factor. Here the path is allowed to vary, which reflects the historical uncertainty. Parameters are estimated using Markov chain Monte Carlo methods. The results show that the aerosol path flexibility substantially increases the probability of very high values of the equilibrium climate sensitivity (ECS), but marginally decreases the most probable value. The inclusion of long-term internal climate variability in the form of the Atlantic Multidecadal Oscillation (AMO) in the regression does not reduce the average error of the estimated temperature. This indicates that observed historical multidecadal temperature oscillations might be better explained by changes in external forcing than by the AMO. It is also shown that including AMO only affects the estimated ECS to a small extent in most scenarios.
- PostDeployment of air defense(2019) Karlsson, Sofia; Johansson, Erik; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Andersson, Claes; Andersson, Claes
- PostGeostationary passive retrieval of ice water path with quantile regression neural networks(2021) Amell Tosas, Adrià; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Eriksson, Patrick; Eriksson, Patrick; Pfreundschuh, SimonAccurate characterisation of clouds can help determine their influence on weather hazards, climate effects, and the hydrological cycle. The retrieval of atmospheric ice mass can contribute to this characterisation and one way to quantify it is with the ice water path (IWP). The polar orbiting CloudSat satellite provides profiles of the ice in clouds, but it suffers from a long revisit period and its cross-track footprint is less than 2km. This poses a challenge in the study of atmospheric ice variability, both in time and space. Imagers aboard geostationary satellites can provide pictures of one side of the Earth in short time intervals, which can help overcome this limitation. Current algorithms to retrieve IWP from geostationary passive remote sensors are based on physical approaches or target a specific type of cloud. Quantile regression neural networks (QRNNs) can capture complex relationships for different conditional quantiles of a dependent variable. This can be used to predict a retrieval value, as well as the case-specific uncertainty. This work employs QRNNs to retrieve the IWP distribution using data from the SEVIRI instrument aboard the geostationary Meteosat-9 satellite calibrated against DARDAR, a product derived from combining CloudSat and CALIPSO measurements. The QRNNs are trained and evaluated on a large African region, covering both land and ocean areas. A multilayer perceptron and a convolutional neural network are compared, and it is seen that the use of spatial information improves both the retrieval and the associated uncertainty. Models trained using all SEVIRI channels show better overall performance in several metrics, although models that use only infrared channels show a relatively similar performance. Moreover, the retrieval with infrared channels shows to satisfactorily retrieve the IWP throughout the diurnal cycle. Models that use visible and infrared channels likely suffer an artefact in the diurnal cycle, but it cannot be completely assessed due to the coverage of the reference data. Monthly means and diurnal variations are compared with a physical-based IWP retrieval for two tropical areas, and it is found correlations that favour the QRNN models. Finally, it is suggested that the models trained on Meteosat-9 observations may also be used on observations from other Meteosat satellites, expanding the usage of the models beyond the lifetime of Meteosat-9.
- PostModelling the transition from animal to human culture(2022) Cvetkovic Destouni, Sofia; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Andersson, Claes; Andersson, Claes
- PostRetrieving precipitation over Brazil. A quantile regression neural networks approach(2021) Ingemarsson, Ingrid; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Eriksson, Patrick; Eriksson, Patrick; Pfreundschuh, SimonClose and accurate monitoring of precipitation on a global scale is key to understanding our future climate as well as our current weather. Geostationary weather satellites, as opposed to other measuring methods, provide high-resolution information covering large regions. The sensors carried can not, however, measure precipitation directly but are restricted to capturing cloud top temperatures (in IR radiation). Earlier work presents a range of models that aim to relate these geostationary observations to precipitation, including simple regression as well as more elaborate machine learning techniques. In this thesis we aim at predicting a posterior distribution instead of a single precipitation value for each set of cloud top temperatures. To achieve this, we make use of Quantile Regression Neural Networks (QRNNs), a supervised machine learning approach. The two main questions asked are as follows: Can this deep learning method be used to improve upon algorithms currently in operation? and Can spatial information be used to improve the retrieval? The models are trained on GOES-16 IR data over Brazil with a precipitation product from the GPM Core Observatory. Our results on held-out test data show that it is possible to model the precipitation distribution using a QRNN. Additionally, a 20% decrease in mean squared error and a 25% decrease in mean absolute error is observed on the test data when using the spatially aware model, which illustrates the general performance improvement by utilizing the spatial information. The QRNN models also show promising results on an independent rain gauge dataset where they are compared against the currently-in-operation Hydro-Estimator. Here our most promising QRNN shows a 30% decrease in mean squared error compared to the present model.
- PostStrategies to avoid persistent contrail conditions in aviation(2023) Gönczi, Johan; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Johansson, Daniel; Johansson, Daniel