Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data

This thesis discusses the retrieval of sea ice parameters using the combination of remote sensing data and a sea ice model for the region of the Baffin Bay, Hudson Bay, Labrador Sea and the Gulf of St. Lawrence. The Los Alamos sea ice model, CICE, which is used as a module for coupled global ice-oce...

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Main Author: Prasad, Siva
Format: Thesis
Language:English
Published: Memorial University of Newfoundland 2018
Subjects:
Online Access:https://research.library.mun.ca/13355/
https://research.library.mun.ca/13355/1/thesis.pdf
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spelling ftmemorialuniv:oai:research.library.mun.ca:13355 2023-10-01T03:54:51+02:00 Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data Prasad, Siva 2018-06 application/pdf https://research.library.mun.ca/13355/ https://research.library.mun.ca/13355/1/thesis.pdf en eng Memorial University of Newfoundland https://research.library.mun.ca/13355/1/thesis.pdf Prasad, Siva <https://research.library.mun.ca/view/creator_az/Prasad=3ASiva=3A=3A.html> (2018) Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data. Doctoral (PhD) thesis, Memorial University of Newfoundland. thesis_license Thesis NonPeerReviewed 2018 ftmemorialuniv 2023-09-03T06:49:14Z This thesis discusses the retrieval of sea ice parameters using the combination of remote sensing data and a sea ice model for the region of the Baffin Bay, Hudson Bay, Labrador Sea and the Gulf of St. Lawrence. The Los Alamos sea ice model, CICE, which is used as a module for coupled global ice-ocean models, was used for this work. The model was implemented with a 7-category thickness distribution, open boundaries and a variable coefficient for ice-ocean heat flux. A slab ocean mixed-layer model based on density criteria was used for the standalone regional implementation of the model. The model estimates of ice concentration were validated using seasonal means, and anomalies. A combined optimal interpolation and nudging scheme was implemented to assimilate Sea Surface Temperature (SST) and ice concentration from Advanced very-high-resolution radiometer (AVHRR) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) respectively. The inclusion of the variable drag coefficient required updates of ice volume and dependent tracers corresponding to the updates in the ice concentration estimates. The sea ice variables of thickness, freeboard, level ice draft and keel depth were compared with the estimates derived from Soil Moisture and Ocean Salinity (SMOS), CryoSat2, and a ULS instrument respectively. The assimilated model provided better estimates of ice concentration, thickness, freeboard and level ice draft. The model estimated ice thickness compared well with the thin ice thickness estimated from the SMOS data, except during March, when there is significant ice extent. The reason for this discrepancy could be attributed to the absence of mixed layer heat flux forcing in the model and also the effect of snow and the onset of melt that alters the observation. Field measurements were also used for the comparison of model estimates. The measurements from the Upward Looking Sonar (ULS) instrument located at Makkovick Bank were used to estimate the level ice draft and keel depth. The observations from ULS along ... Thesis Baffin Bay Baffin Bay Baffin Hudson Bay Labrador Sea Sea ice Memorial University of Newfoundland: Research Repository Baffin Bay Hudson Hudson Bay
institution Open Polar
collection Memorial University of Newfoundland: Research Repository
op_collection_id ftmemorialuniv
language English
description This thesis discusses the retrieval of sea ice parameters using the combination of remote sensing data and a sea ice model for the region of the Baffin Bay, Hudson Bay, Labrador Sea and the Gulf of St. Lawrence. The Los Alamos sea ice model, CICE, which is used as a module for coupled global ice-ocean models, was used for this work. The model was implemented with a 7-category thickness distribution, open boundaries and a variable coefficient for ice-ocean heat flux. A slab ocean mixed-layer model based on density criteria was used for the standalone regional implementation of the model. The model estimates of ice concentration were validated using seasonal means, and anomalies. A combined optimal interpolation and nudging scheme was implemented to assimilate Sea Surface Temperature (SST) and ice concentration from Advanced very-high-resolution radiometer (AVHRR) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) respectively. The inclusion of the variable drag coefficient required updates of ice volume and dependent tracers corresponding to the updates in the ice concentration estimates. The sea ice variables of thickness, freeboard, level ice draft and keel depth were compared with the estimates derived from Soil Moisture and Ocean Salinity (SMOS), CryoSat2, and a ULS instrument respectively. The assimilated model provided better estimates of ice concentration, thickness, freeboard and level ice draft. The model estimated ice thickness compared well with the thin ice thickness estimated from the SMOS data, except during March, when there is significant ice extent. The reason for this discrepancy could be attributed to the absence of mixed layer heat flux forcing in the model and also the effect of snow and the onset of melt that alters the observation. Field measurements were also used for the comparison of model estimates. The measurements from the Upward Looking Sonar (ULS) instrument located at Makkovick Bank were used to estimate the level ice draft and keel depth. The observations from ULS along ...
format Thesis
author Prasad, Siva
spellingShingle Prasad, Siva
Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
author_facet Prasad, Siva
author_sort Prasad, Siva
title Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
title_short Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
title_full Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
title_fullStr Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
title_full_unstemmed Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
title_sort retrieval of sea ice parameters using fusion of high resolution model and remote sensing data
publisher Memorial University of Newfoundland
publishDate 2018
url https://research.library.mun.ca/13355/
https://research.library.mun.ca/13355/1/thesis.pdf
geographic Baffin Bay
Hudson
Hudson Bay
geographic_facet Baffin Bay
Hudson
Hudson Bay
genre Baffin Bay
Baffin Bay
Baffin
Hudson Bay
Labrador Sea
Sea ice
genre_facet Baffin Bay
Baffin Bay
Baffin
Hudson Bay
Labrador Sea
Sea ice
op_relation https://research.library.mun.ca/13355/1/thesis.pdf
Prasad, Siva <https://research.library.mun.ca/view/creator_az/Prasad=3ASiva=3A=3A.html> (2018) Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data. Doctoral (PhD) thesis, Memorial University of Newfoundland.
op_rights thesis_license
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