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

Prasad, Siva (2018) Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data. Doctoral (PhD) thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (78MB)

Abstract

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 with model estimates were used to determine the coefficient that relates the sail and keel measurements. The level ice draft showed a good match with the values extracted from the ULS data, while the sail to keel relationship coefficient seems to vary between a value of 3 during January and February and a value of 7 from March to May. Further studies have to be conducted to understand these variations. The ice concentration estimates from the assimilated model were compared with the ice concentration estimates derived from the images that were obtained during a field survey along the Labrador coast. The results of the ice concentration derived from the images showed a good match with the model values. The results were also compared with the estimates from Canadian Ice Service (CIS) ice charts and Advanced Microwave Scanning Radiometer-Earth observation (AMSR-E).

Item Type: Thesis (Doctoral (PhD))
URI: http://research.library.mun.ca/id/eprint/13355
Item ID: 13355
Additional Information: Includes bibliographical references (pages 153-176).
Keywords: Sea ice modeling, ice-ocean modeling, ice concentration, ice thickness, Upward looking Sonar, Freeboard
Department(s): Engineering and Applied Science, Faculty of
Date: June 2018
Date Type: Submission
Library of Congress Subject Heading: Sea ice--Mathematical modelling; Sea ice--remote sensing.

Actions (login required)

View Item View Item

Downloads

Downloads per month over the past year

View more statistics