On sea-ice forecasting
Accurate 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, s...
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UiT Norges arktiske universitet
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ftunivtroemsoe:oai:munin.uit.no:10037/18141 2023-05-15T15:03:45+02:00 On sea-ice forecasting Fritzner, Sindre Markus 2020-05-15 https://hdl.handle.net/10037/18141 eng eng UiT Norges arktiske universitet UiT The Arctic University of Norway 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. Journal of Glaciology, 64 (245), 387–396. Also available in Munin at https://hdl.handle.net/10037/13969 . 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. The Cryosphere, 13 , 491–509. Also available in Munin at https://hdl.handle.net/10037/16412 . 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). Fritzner, S. (2019). Assessment of high-resolution dynamical and statistical models for prediction of sea-ice concentration [Data set]. Norstore. https://doi.org/10.11582/2019.00038 . Fritzner, 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. https://doi.org/10.11582/2019.00005 . 978-82-8236-395-2 https://hdl.handle.net/10037/18141 openAccess Copyright 2020 The Author(s) Sjøis Data assimilasjon DOKTOR-004 Doctoral thesis Doktorgradsavhandling 2020 ftunivtroemsoe https://doi.org/10.11582/2019.00038 https://doi.org/10.11582/2019.00005 2021-06-25T17:57:24Z Accurate 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. Doctoral or Postdoctoral Thesis Arctic Journal of Glaciology Sea ice The Cryosphere University of Tromsø: Munin Open Research Archive Arctic |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
Sjøis Data assimilasjon DOKTOR-004 |
spellingShingle |
Sjøis Data assimilasjon DOKTOR-004 Fritzner, Sindre Markus On sea-ice forecasting |
topic_facet |
Sjøis Data assimilasjon DOKTOR-004 |
description |
Accurate 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. |
format |
Doctoral or Postdoctoral Thesis |
author |
Fritzner, Sindre Markus |
author_facet |
Fritzner, Sindre Markus |
author_sort |
Fritzner, Sindre Markus |
title |
On sea-ice forecasting |
title_short |
On sea-ice forecasting |
title_full |
On sea-ice forecasting |
title_fullStr |
On sea-ice forecasting |
title_full_unstemmed |
On sea-ice forecasting |
title_sort |
on sea-ice forecasting |
publisher |
UiT Norges arktiske universitet |
publishDate |
2020 |
url |
https://hdl.handle.net/10037/18141 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Journal of Glaciology Sea ice The Cryosphere |
genre_facet |
Arctic Journal of Glaciology Sea ice The Cryosphere |
op_relation |
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. Journal of Glaciology, 64 (245), 387–396. Also available in Munin at https://hdl.handle.net/10037/13969 . 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. The Cryosphere, 13 , 491–509. Also available in Munin at https://hdl.handle.net/10037/16412 . 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). Fritzner, S. (2019). Assessment of high-resolution dynamical and statistical models for prediction of sea-ice concentration [Data set]. Norstore. https://doi.org/10.11582/2019.00038 . Fritzner, 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. https://doi.org/10.11582/2019.00005 . 978-82-8236-395-2 https://hdl.handle.net/10037/18141 |
op_rights |
openAccess Copyright 2020 The Author(s) |
op_doi |
https://doi.org/10.11582/2019.00038 https://doi.org/10.11582/2019.00005 |
_version_ |
1766335604591689728 |