Validation of genetic algorithm-based optimal sampling for ocean data assimilation

Abstract

Abstract-Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the Genetic Algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root-mean-square-error (RMSE) between the "true" data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A 5-glider optimal sampling study is set up for a 400 km x 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions. Index Terms-Genetic algorithms, ocean technology, optimization methods sampling methods, adaptive sampling, computational ocean modeling, data assimilation, error subspace statistical estimation, OSSE

A future for intelligent autonomous ocean observing systems

“…A similar approach is the error subspace scheme (Lermusiaux 1999a;Wang et al 2009), which keeps track of the time-evolution of a dominant low-rank approximation to the error covariance matrix using nonlinear ensembles and re-runs the ensemble after each candidate assimilation. Other approaches in this category include mixed-integer programming approaches (Yilmaz et al 2008), potential functions (Munafò et al 2011), genetic algorithms (Heaney et al 2007;Frolov, Garau, and Bellingham 2014;Heaney et al 2016), etc. Note that all of the above adopt a Gaussian approximation of the distributions of all the involved state variables, which neglects the non-Gaussian features of the statistics.…”

“…Many ocean ship surveys have consisted of vertical sections and lawnmower sampling patterns, mainly because ocean researchers wanted such estimates (e.g., measure the transport through a strait) or because not all of what researchers knew was utilized in an optimal expert way. Even for pure exploration, some exploitation with path planning or adaptive sampling becomes useful (e.g., Smith et al 2011;Graham and Cortés 2012;Heaney et al 2016). As mentioned earlier, as soon as observations are collected and analyzed, knowledge is acquired and plans should be improved (e.g., Das et al 2015).…”

Plastic Pollution in the Coastal Oceans: Characterization and Modeling

“…It has been used around the world's oceans [49,57,68,23,19,71,6,37,50,52,55]. Applications include monitoring [53]; real-time acoustic predictions and DA [85,43,56,13]; environmental predictions and management [3,7,8]; relocatable rapid response [74,12]; path planning for autonomous vehicles [76,60,59,54]; and, adaptive sampling [48,28,29]. MSEAS has been tested and validated in many real-time forecasting exercises [49,19,71,56,20,51,1,50,52,55,69].…”