Preprints
https://doi.org/10.5194/egusphere-2023-718
https://doi.org/10.5194/egusphere-2023-718
01 Jun 2023
 | 01 Jun 2023

DELWAVE 1.0: Deep-learning surrogate model of surface wave climate in the Adriatic Basin

Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer

Abstract. We propose a new point-prediction DEep Learning WAVe Emulating model (DELWAVE) which successfully emulates the behaviour of a numerical surface ocean wave model (SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE training inputs consist of 6-hourly surface COSMO-CLM wind fields during period 1971–1998, while its targets are significant wave height, mean wave period and mean wave direction. Testing input set consists of surface winds during 1998–2000 and cross-validation period is the far-future climate timewindow of 2071–2100. DELWAVE is constructed to have a convolution-based atmospheric encoder block, followed by a temporal collapse block and finally a regression block. Random importance-sampling was performed to better model underpopulated tails of variable data distributions. Detailed ablation studies were performed to determine optimal performance regarding input fields, temporal horizon of the training set and network architecture. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) between 5 and 10 cm, mean wave directions with a MAE of 10°–25° and mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions, related to dominant wind regimes in the basin. We use wave power analysis from linearized wave theory to explain prediction errors in the long-period limit during southeasterly conditions, indicating, as expected, that non-local generation of swell poses a more difficult challenge during long-fetched Scirocco than during cross-basin Bora flow. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared against each other in the end-of-century scenario (2071–2100), and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤5 %), though systematic, underestimate of 99th percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modeling is substantially weaker than the climate change signal.

Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-718', Haoyu Jiang, 17 Jul 2023
    • CC2: 'Reply on CC1', Haoyu Jiang, 17 Jul 2023
  • RC1: 'Comment on egusphere-2023-718', Giacomo Capodaglio, 09 Oct 2023
  • RC2: 'Comment on egusphere-2023-718', Anonymous Referee #2, 06 Nov 2023
  • AC1: 'Comment on egusphere-2023-718', Peter Mlakar, 01 Dec 2023
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer

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Short summary
We propose a new point-prediction DEep Learning WAVe Emulating model (DELWAVE) which successfully emulates the ocean wave model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modeling is substantially weaker than the climate change signal.