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dc.contributor.advisorGraversen, Rune
dc.contributor.authorFritzner, Sindre Markus
dc.date.accessioned2020-04-28T08:31:54Z
dc.date.available2020-04-28T08:31:54Z
dc.date.issued2020-05-15
dc.description.abstractAccurate sea-ice prediction is essential for safe operations in the Arctic and potentially also for weather forecast at high-latitudes. The increasing number of sea-ice related satellite observations in the Arctic can be used to improve the model predictions through data assimilation. For sea ice, sea-ice concentration (SIC) observations have been available for many years. Observational information of SIC can be used to constrain the sea-ice extent in models. In addition to SIC, other sea-ice related observations such as sea-ice thickness (SIT) and snow depth have recently become available. The assimilation of these observations is expected to have a substantial impact on the sea-ice forecast. In this thesis, the main goal is to enhance the sea-ice model forecast accuracy by improving the initial model state on which the forecast is based. Primarily, the assimilation of sea-ice-related observations that are previously little used in sea-ice data assimilation is investigated. This includes the assimilation of SIT, snow depth and high-resolution SIC observations. A secondary objective of this thesis is to reduce the computational cost of both sea-ice assimilation and modelling. A new direct and computationally cheap method for data assimilation, the Multi-variate nudging (MVN) method, is proposed as an alternative to more complex assimilation methods for sea-ice. In addition, to reduce the computational cost of the sea-ice prediction, two machine-learning methods were applied for sea-ice forecasting, a fully convolutional network and a k nearest neighbours. It is found that the assimilation of observations other than SIC has the potential to enhance the accuracy of sea-ice models and improve predictions. The proposed new assimilation method, the MVN, proves to be a valid assimilation alternative to the Ensemble Kalman Filter when few observation types are available, and the computational resources are limited. The machine-learning forecasts are found to improve upon persistence and show comparable skills to the dynamical model. Hence there is a potential for machine-learning methods for sea-ice predictions which should be developed further.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractRecent global climate change has led to an increased focus on Arctic sea ice. For the prediction of future climate scenarios, accurate prediction models are important. In addition, accurate sea-ice predictions are important for safe operations in the Arctic and potentially also for weather forecast at high latitudes. In this thesis, we aim to improve numerical sea-ice predictions by utilising satellite observations. With satellites, a vast number of Arctic sea-ice observations are produced every day, and these observations can be combined with numerical models for improved prediction. By utilising observations of sea-ice extent, sea-ice thickness and snow thickness, we improve the sea-ice prediction accuracy. In addition, as an alternative to traditional physical-based prediction models, we investigate the use of statistical prediction models based on machine learning. With these models, a similar prediction accuracy as the dynamical model is found, proving that this can potentially be a simple prediction alternative.en_US
dc.description.sponsorshipNorwegian Research Council grant no. 237906, Norwegian Metacenter for Computational Science (NOTUR) project NN9348Ken_US
dc.identifier.isbn978-82-8236-395-2
dc.identifier.urihttps://hdl.handle.net/10037/18141
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.relation.haspart<p>Paper I: Fritzner, S.M., Graversen, R.G., Wang, K. & Christensen, K.H. (2018). Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation. <i>Journal of Glaciology, 64</i>(245), 387–396. Also available in Munin at <a href=https://hdl.handle.net/10037/13969>https://hdl.handle.net/10037/13969</a>. <p>Paper II: Fritzner, S., Graversen, R., Christensen, K.H., Rostosky, P. & Wang, K. (2019). Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system. <i>The Cryosphere, 13</i>, 491–509. Also available in Munin at <a href=https://hdl.handle.net/10037/16412>https://hdl.handle.net/10037/16412</a>. <p> Paper III: Fritzner, S., Graversen, R. & Christensen, K.H. Assessment of high-resolution dynamical and machine learning models for prediction of sea-ice concentration in a regional application. (Submitted manuscript).en_US
dc.relation.isbasedonFritzner, S. (2019). Assessment of high-resolution dynamical and statistical models for prediction of sea-ice concentration [Data set]. Norstore. <a href=https://doi.org/10.11582/2019.00038>https://doi.org/10.11582/2019.00038</a>.en_US
dc.relation.isbasedonFritzner, S. (2019). Model output, Article: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modeling system [Data set]. Norstore. <a href=https://doi.org/10.11582/2019.00005>https://doi.org/10.11582/2019.00005</a>.en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)
dc.subject.courseIDDOKTOR-004
dc.subjectSjøisen_US
dc.subjectData assimilasjonen_US
dc.titleOn sea-ice forecastingen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


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