Nansen gjesteforelesning 22.05.2024

Dr. Michael Hart-Davis, Technical University of Munich

«Ocean tides from SWOT: insights in complex coastal regions»

Studying ocean tides from satellite altimetry has traditionally been difficult in coastal regions, mainly due to the complexity of tides in these regions, limited spatial coverage, and land contamination of the radar returns. The Cal/Val phase and the science orbit phase of SWOT provide unique observations which can be exploited for tidal analysis. The nadir data provided by this mission complements other traditional altimetry missions and will serve the refinement of global ocean tide models well in future studies. The KaRIn data, however, is beneficial for evaluating the spatial variability of ocean tides at much smaller scales than previously possible from altimetry or in-situ measurements. In addition, areas very close to the shoreline can also be monitored. Analysing tides in complex coastal regions, such as fjords and inlets, is now also possible thanks to the increased spatial coverage of SWOT.

This presentation evaluates the pixel cloud data of the hydrological product and the ocean product provided by SWOT in three regions. These regions are selected to provide examples of the usefulness of these data in very complex environments. The regions are as follows:

  • The Bristol Channel, on the west coast of the UK.
  • The Sognefjord along the west coast of Norway.
  • The Long Island Sound on the east coast of the USA.

These regions have relatively large tidal ranges and have been challenging for conventional altimetry, resulting in reduced accuracy in available ocean tide models. These regions are also well covered by in-situ measurements and are either covered by the Cal/Val phase or the nominal orbit of the SWOT mission. The resultant estimations will be contrasted with in-situ measurements and state-of-the-art global models.

Når og hvor?

Onsdag, 22.05.24 kl. 14:15 – 15:00.

Copernicus forelesningsrom, 1. etasje, Nansensenteret, Jahnebakken 3, Bergen