DOI: 10.57760/sciencedb.01536
title: SOM-HMM
title: SOM-HMM

description: Code and data of "Data-Driven Method with Numerical Model: A Combining Framework for Predicting Subtropical River Plumes" submitted to JGR: Oceans.We introduced a novel data-driven framework to study the Minjiang River Plume (MJRP). The framework combines Self-Organizing Map (SOM) clustering with a Hidden Markov Model (HMM). A three-dimensional Regional Ocean Model System for MJRP is first configurated with realistic atmospheric, oceanic lateral, riverine forcings.By applying SOM clustering to the modeled sea surface salinity (SSS) with ~2000 two-day averaged records from 2010 to 2020, we identify six major patterns of MJRP. Each pattern exhibits distinct circulation and plume structures. These MJRP patterns contain not only seasonal signals, but also rich short-term variabilities driven by the riverine inputs and oceanic dynamics. Then, the SOM-HMM method was applied to predict the future of the hidden state (i.e., patterns of MJRP) from the observable states (wind and river runoff). With a hypothetic SSS product from a geostationary satellite as the ground truth, we show that the SOM-HMM method can improve the prediction of MJRP patterns for ~10% with considerable computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. The new framework is capable to delineate the MJRP into simple states to understand and predict complex estuarine dynamics, by reducing the dimension of the modeled SSS. Combining a conventional numerical model with a data-driven method, this approach can be promisingly applied in the short-term marine forecast to support the utilization and management of other estuaries.
description: Code and data of "Data-Driven Method with Numerical Model: A Combining Framework for Predicting Subtropical River Plumes" submitted to JGR: Oceans.We introduced a novel data-driven framework to study the Minjiang River Plume (MJRP). The framework combines Self-Organizing Map (SOM) clustering with a Hidden Markov Model (HMM). A three-dimensional Regional Ocean Model System for MJRP is first configurated with realistic atmospheric, oceanic lateral, riverine forcings.By applying SOM clustering to the modeled sea surface salinity (SSS) with ~2000 two-day averaged records from 2010 to 2020, we identify six major patterns of MJRP. Each pattern exhibits distinct circulation and plume structures. These MJRP patterns contain not only seasonal signals, but also rich short-term variabilities driven by the riverine inputs and oceanic dynamics. Then, the SOM-HMM method was applied to predict the future of the hidden state (i.e., patterns of MJRP) from the observable states (wind and river runoff). With a hypothetic SSS product from a geostationary satellite as the ground truth, we show that the SOM-HMM method can improve the prediction of MJRP patterns for ~10% with considerable computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. The new framework is capable to delineate the MJRP into simple states to understand and predict complex estuarine dynamics, by reducing the dimension of the modeled SSS. Combining a conventional numerical model with a data-driven method, this approach can be promisingly applied in the short-term marine forecast to support the utilization and management of other estuaries.