| Popis: |
Coastal areas host 53% of the world's population increasingly subject to risks related to climate change. In order to understand and prevent these risks, the prediction of coastal hydrodynamic and morphological evolution is essential. Bathymetry is a key geophysical variable in A key parameter to improve numerical coastal hydrodynamics models is the bathymetry. High resolution satellites now allow to observe coastal areas at a regional to global scale in a most cost-efficient way rather than local traditional echo sounding bathymetry measurements. We present S2Shores (Satellite to Shores), a new state-of-the-art python library developed to estimate wave field characteristics such as wavelength, period, direction, celerity to derive bathymetry from satellites. The core code is optimized to process optical satellite imagery, moreover the library is built in an object-oriented structure allowing efficient and agile manipulation of the modules developed from input and output handling to post-precessing. S2Shores is optimized, using parallel computing with Dask python library, to compute large spatial scale, or time series evolution bathymetry as well on HPC cluster as on local computer. For example a bathymetry of 4400 km² of ocean, around the French Gironde estuary, can be computed in 90 sec (using 30 cores and 9.5 Go memory usage) with a 500 meters output grid resolution. We present a work in progress new python library for bathymetry estimation that will be open to the public to use and propose collaborative improvements. |
