Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm
  Surge Forecasting
Patrick Ebel Brandon Victor Peter Naylor Gabriele Meoni Federico Serva Rochelle Schneider
Hurricanes and coastal floods are among the most disastrous natural hazards.
Both are intimately related to storm surges, as their causes and effects,
respectively. However, the short-term forecasting of storm surges has proven
challenging, especially when targeting previously unseen locations or sites
without tidal gauges. Furthermore, recent work improved short and medium-term
weather forecasting but the handling of raw unassimilated data remains
non-trivial. In this paper, we tackle both challenges and demonstrate that
neural networks can implicitly assimilate sparse in situ tide gauge data with
coarse ocean state reanalysis in order to forecast storm surges. We curate a
global dataset to learn and validate the dense prediction of storm surges,
building on preceding efforts. Other than prior work limited to known gauges,
our approach extends to ungauged sites, paving the way for global storm surge
forecasting.