Abstract
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( H s and H s 50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( U b a r and V b a r ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— N R M S E of less than 16%) is the first step in demonstrating the robustness of the method.
Highlights
An operational quantification of water turbidity is essential in many aspects of ocean and coastal management
The present work focuses on two different experiments of constructing long-term suspended particulate inorganic matter (SPIM) profiles using the PROFHMM method
In the English Channel, we demonstrated the feasibility of deriving vertical SPIM profiles over a long time period (≈11 years) while only using three local and average hydrodynamic parameters (Hs, Ubar and Vbar)
Summary
An operational quantification of water turbidity is essential in many aspects of ocean and coastal management. An HMM time-series analysis is combined with a previous classification of modeled turbidity profiles This classification is performed by self-organizing maps (SOMs), which is an efficient means of interpreting similar patterns in complex multivariate data sets [25]. The purpose of the present investigation is to benefit from the hindcast output of a coupled model, previously validated in a research laboratory, and to combine it with the near-real-time (NRT) output of purely hydrodynamical models and satellite measurements, in order to produce an operational system of NRT forecasts (Figure 1). The idea is to extend the application of hindcast results towards an operational and statistical system that performs error processing and error output This concept is tested here on time series of suspended particulate inorganic matter (SPIM) vertical profiles at four locations in the English Channel (western Europe; Figure 2). The results of these two experiments are displayed and discussed in Section 4, followed by the conclusions
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