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IAN ROBINSON
2. The different characteristics of data from different sources needs to
be recognised.
The goal should be to harmonise them,
accommodating the differences in a suitable physical model that
parameterises them in terms of measurable and routinely available
quantities.
3. Merging of data from different sources is not necessarily the best
approach. Dialogue with the model assimilation specialists is
essential before decisions are made about this.
4. If data are to be used for assimilation it is essential to provide error
statistics and quality flags in near real time attached to the primary
data.
The error statistics must be based on independent
measurements of the quantities actually represented in the data.
5. The concept of creating a match-up database appears to be a very
useful approach to achieving validation by regularly updated error
statistics for each sensor and data source. To be useful it needs to be
populated in near-real time by matching satellite and in situ data
pairs. However, care must be taken not to utilise data that is already
being used as a means of calibration or fine tuning of processing
algorithms by the agency responsible for the primary processing.
6. Diagnostic data sets, which assemble all the data available from
different sources resampled to a common grid, provide a valuable
resource for evaluation of the products. They will provide the
ground on which to base research for further improvements.
Collaboration is essential between all the players concerned. Handled
properly, this approach should be welcomed by the data producers as a
means for making their products more useful, and also because rapid
feedback from the MDB and DDS provides an external quality control. The
potential users of SST in the modelling community find a group of scientists
within GHRSST-PP ready to work with them. Within this partnership the
modellers should be able to use the data successfully without themselves
needing to become experts in all aspects of the remote sensing methodology.
Finally the scientists are able to contribute more effectively to the
application of their work, which is not only professionally stimulating but
ultimately enhances the profile of this field of research and improves the
prospects for funding of further work.
5.
Conclusion
This chapter has offered a brief introduction to ocean remote sensing and
especially those methods which make measurements of parameters such as
SSH, SST and colour that can usefully contribute to operational monitoring
and forecasting of the ocean. There is considerable potential to enhance the
usefulness of satellite ocean data by assimilating them in near-real time into
IAN ROBINSON
2. The different characteristics of data from different sources needs to
be recognised.
The goal should be to harmonise them,
accommodating the differences in a suitable physical model that
parameterises them in terms of measurable and routinely available
quantities.
3. Merging of data from different sources is not necessarily the best
approach. Dialogue with the model assimilation specialists is
essential before decisions are made about this.
4. If data are to be used for assimilation it is essential to provide error
statistics and quality flags in near real time attached to the primary
data.
The error statistics must be based on independent
measurements of the quantities actually represented in the data.
5. The concept of creating a match-up database appears to be a very
useful approach to achieving validation by regularly updated error
statistics for each sensor and data source. To be useful it needs to be
populated in near-real time by matching satellite and in situ data
pairs. However, care must be taken not to utilise data that is already
being used as a means of calibration or fine tuning of processing
algorithms by the agency responsible for the primary processing.
6. Diagnostic data sets, which assemble all the data available from
different sources resampled to a common grid, provide a valuable
resource for evaluation of the products. They will provide the
ground on which to base research for further improvements.
Collaboration is essential between all the players concerned. Handled
properly, this approach should be welcomed by the data producers as a
means for making their products more useful, and also because rapid
feedback from the MDB and DDS provides an external quality control. The
potential users of SST in the modelling community find a group of scientists
within GHRSST-PP ready to work with them. Within this partnership the
modellers should be able to use the data successfully without themselves
needing to become experts in all aspects of the remote sensing methodology.
Finally the scientists are able to contribute more effectively to the
application of their work, which is not only professionally stimulating but
ultimately enhances the profile of this field of research and improves the
prospects for funding of further work.
5.
Conclusion
This chapter has offered a brief introduction to ocean remote sensing and
especially those methods which make measurements of parameters such as
SSH, SST and colour that can usefully contribute to operational monitoring
and forecasting of the ocean. There is considerable potential to enhance the
usefulness of satellite ocean data by assimilating them in near-real time into