HLRB Project pr32to
High Resolution Modelling of Ocean Tides
Deutsches Geodätisches Forschungsinstitut
Proposing Institution
Deutsches Geodätisches Forschungsinstitut
Project Manager
Dr. Wolfgang Bosch
Alfons-Goppel-Str. 11
80539 München
Abstract
All numerical models are subjected to errors and results are hence incomplete. Specifically with respect to tide models there may be many causes for concern, including unknown dissipation parameters, inexact bathymetry, uncertainty in initial and boundary conditions, missing physics, especially in shallow seas. Constraining tide models through geophysical data reduce this deficiency and can help to determine missing physics or uncertain parameters. However conventional data assimilation methods are afflicted with difficulty in handling the rapid growing number of altimeter data and above all in reproducing the non-linear tides in shallow water regions. The tide model HAMTIDE (high resolution tide model) has been developed to overcome those issues, is resource friendly and precise in results. The model allows errors in momentum equations and hence, dynamic residuals to emerge by iteratively solving tide and data equations. The dynamic residuals in turn are footprints of model errors, revealing possible error causes and sources in the assumed dynamics. Dynamic residual energy, derived by the dynamic residuals, is of great importance for the study of tidal dissipation. The dynamic residual energy could be generated by inadequate parameterization of bottom and turbulent frictions, by baroclinic energy conversion from surface tides and possibly by data. The dynamic residuals extract energy from mean flows if bottom and/or eddy frictions are not correctly modeled and therefore, are unable to dissipate energy sufficiently. The same occurs over rough sea-floor topography due to interaction with tidal currents, if proper internal wave drags are not included in the model. Inaccurate and insufficient data may cause additional residual energy to appear. Among all, high-resolution data assimilative model with dense data distribution removes the model deficiencies to a large extent and gives good results in reproducing ocean tides.
