A Simplified Assimilation Scheme for a Coastal Wave Model Using Concepts of Particle Filter

Abstract

In this work, observations of ocean wave height from satellite altimeters are assimilated into the coastal wave model simulating waves nearshore (SWAN) operating in the Indian coastal waters. The study has two distinctive features. The most important is the use of certain concepts of the modern particle filter technique, which does not represent the model probability density function (PDF) by a Gaussian. The other feature is the joint assimilation of data from three altimeters. The method starts by generating an initial ensemble by the bootstrap technique in which the significant wave height (SWH) field of a control run is perturbed by randomly adding bias to produce a member of the ensemble. At the first assimilation time, a weight-based resampling of the individual members, known as particles, is performed. Stronger particles are retained, while the weaker ones are discarded. In order to keep the ensemble size constant, the algorithm replicates a few strong members. After this resampling, a single model run is employed in the forecast step using a kind of averaging. At the next assimilation time, a synthetic ensemble is again formed by the same bootstrapping, and observations are assimilated. The forecast–assimilation cycle is repeated until the observations are exhausted. Assimilation experiments were conducted for 6 months from February through July in 2016. The power of the technique is evaluated by validating results with altimeter data as well as independent data sets from moored buoys. The results are found to be extremely encouraging for the use of this method in carrying out coastal wave forecasting.