Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Abstract

Numerical models are of fundamental usage for estuarine and coastal sciences. Although numerical simulations are widely applied, analyzing and improving them are often challenging tasks given their large volume and huge parameter space. In this study, a novel data‐driven framework is introduced 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, and riverine forcings. By applying SOM clustering to the modeled sea surface salinity (SSS) with ∼2,000 2‐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 predict MJRP patterns considerably high prediction accuracy and computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. 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.

Framework to Extract Extreme Phytoplankton Bloom Events with Remote Sensing Datasets: A Case Study

“…At the current point, using reconstructed data (as in this study) or reanalyzed ecosystem models (such as the NEMO-PISCES product [57]) can be a feasible solution to this issue. Numerical models should also be applied to fully understand the genesis and environmental effects of the bloom [58][59][60]. Moreover, CHL is an essential climate variable but is not the only variable that can be analyzed with this framework.…”

Fast cleaning method for smart grid data based on sparse self-coding

“…Fast cleaning of the data is mainly done by cluster analysis, by dividing the data into categories according to the distance between the sample data, and cleaning the data according to different categories, and the specific implementation steps are as follows [11][12][13].…”

A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR

“…The adjacent sea has a mean water depth of ~60 m [49], an To illustrate the capability of the OPEN method for wind retrieval for distinct sea states and topography, buoy data from October 2019 to April 2020 near the coasts of Fujian Province, in the East China Sea, is used as an independent data set to validate the proposed wind retrieval method. The adjacent sea has a mean water depth of ~60 m [49], an intensified wind jet in winter with wind speed >10 m/s [50], and resultant high sea states. Therefore, it is a question to be answered to what extent the method can be applied in wind retrieval for distinct environments.…”

“…intensified wind jet in winter with wind speed >10 m/s [50], and resultant high sea states. Therefore, it is a question to be answered to what extent the method can be applied in wind retrieval for distinct environments.…”