Data

Assimilation of Satellite Observations into Coastal Biogeochemical Models

Australian Ocean Data Network
Turner, Matthew
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://catalogue.aodn.org.au:443/geonetwork/srv/api/records/980e35f0-29e5-11dc-b961-00188b4c0af8&rft.title=Assimilation of Satellite Observations into Coastal Biogeochemical Models&rft.identifier=https://catalogue.aodn.org.au:443/geonetwork/srv/api/records/980e35f0-29e5-11dc-b961-00188b4c0af8&rft.description=This thesis investigates the improvement of forecasting water temperature in a coastal embayment through the assimilation of satellite sea surface temperature (SST). Port Phillip Bay (PPB) in southeastern Australia was used as a case study, where temperature forecasts could be compared against in situ temperature measurements. Over the long term satellite derived SST observations were found to have negligible bias, however a strong diurnal bias was apparent. The model of PPB replicated the main features of PPB well, although the temperature prediction was warm biased. The actual assimilation of SST data was contrasted against a climatology forecast of PPB temperature. The assimilation of SST, without any specific accounting for the diurnal bias improved the forecast, although errors due to observational bias were noted. Attempts to remove this bias using diurnal correction algorithms failed, owing to a larger than expected cool skin. Conditional merging, which combines spatial and in situ observations, was applied to the SST observations and improved forecast accuracy by reducing the observation bias. This work demonstrates that forecasting models can be improved through the assimilation of satellite derived observations. An examination of the assimilation innovations indicated where the forecast accuracy could be further improved.Maintenance and Update Frequency: notPlanned&rft.creator=Turner, Matthew &rft.date=2007&rft.coverage=westlimit=144.3; southlimit=-38.3; eastlimit=145.2; northlimit=-37.8&rft.coverage=westlimit=144.3; southlimit=-38.3; eastlimit=145.2; northlimit=-37.8&rft_rights=Contact author or point of contact for access to data&rft_rights=This metadata may be downloaded for use in accordance with the Copyright Act 1968.&rft_subject=biota&rft_subject=COASTAL HABITAT&rft_subject=EARTH SCIENCE&rft_subject=BIOSPHERE&rft_subject=AQUATIC ECOSYSTEMS&rft_subject=SEAFLOOR TOPOGRAPHY&rft_subject=OCEANS&rft_subject=BATHYMETRY/SEAFLOOR TOPOGRAPHY&rft_subject=POTENTIAL TEMPERATURE&rft_subject=OCEAN TEMPERATURE&rft_subject=SEA SURFACE TEMPERATURE&rft_subject=WATER TEMPERATURE&rft_subject=SALINITY&rft_subject=SALINITY/DENSITY&rft_subject=DISCHARGE/FLOW&rft_subject=TERRESTRIAL HYDROSPHERE&rft_subject=SURFACE WATER&rft_subject=TIDAL CURRENTS&rft_subject=TIDES&rft_subject=OCEAN CURRENTS&rft_subject=OCEAN CIRCULATION&rft_subject=OCEAN MIXED LAYER&rft_subject=Port Phillip Bay&rft.type=dataset&rft.language=English Access the data
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Brief description

This thesis investigates the improvement of forecasting water temperature in a coastal embayment through the assimilation of satellite sea surface temperature (SST). Port Phillip Bay (PPB) in southeastern Australia was used as a case study, where temperature forecasts could be compared against in situ temperature measurements. Over the long term satellite derived SST observations were found to have negligible bias, however a strong diurnal bias was apparent. The model of PPB replicated the main features of PPB well, although the temperature prediction was warm biased. The actual assimilation of SST data was contrasted against a climatology forecast of PPB temperature. The assimilation of SST, without any specific accounting for the diurnal bias improved the forecast, although errors due to observational bias were noted. Attempts to remove this bias using diurnal correction algorithms failed, owing to a larger than expected cool skin. Conditional merging, which combines spatial and in situ observations, was applied to the SST observations and improved forecast accuracy by reducing the observation bias. This work demonstrates that forecasting models can be improved through the assimilation of satellite derived observations. An examination of the assimilation innovations indicated where the forecast accuracy could be further improved.

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Maintenance and Update Frequency: notPlanned

Notes

Credit
This thesis was carried out under the supervision of Dr Jeffrey Walker, Dr Peter Oke and Prof Rodger Grayson.
Credit
This thesis was supported by a CSIRO-Melbourne University Collaborative Research Grant and a Melbourne Research Scholarship.
Credit
The contributions of Dr John Parslow, Dr Pavel Sakov and Dr Stuart Corney are gratefully acknowledged.
Credit
Dr Mike Herzfeld and Mr Jason Waring provided assistance with the use of MECO.
Purpose
The purpose of this thesis was to assimilate satellite sea surface temperature (SST) observations into a high resolution hydrodynamic model using Port Phillip Bay as a case study.

Created: 04 07 2007

Data time period: 1993 to 31 12 2003

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145.2,-37.8 145.2,-38.3 144.3,-38.3 144.3,-37.8 145.2,-37.8

144.75,-38.05

text: westlimit=144.3; southlimit=-38.3; eastlimit=145.2; northlimit=-37.8

Subjects
AQUATIC ECOSYSTEMS | BATHYMETRY/SEAFLOOR TOPOGRAPHY | BIOSPHERE | Coastal Habitat | Discharge/flow | EARTH SCIENCE | OCEAN CIRCULATION | Ocean Currents | Ocean Mixed Layer | OCEAN TEMPERATURE | OCEANS | Potential Temperature | Port Phillip Bay | Salinity | SALINITY/DENSITY | Sea Surface Temperature | Seafloor Topography | SURFACE WATER | TERRESTRIAL HYDROSPHERE | Tidal Currents | TIDES | Water Temperature | biota |

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  • global : 980e35f0-29e5-11dc-b961-00188b4c0af8
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