Daily Subsurface Ocean Temperature Climatology Using Multiple Data Sources: New Methodology

Hemming, Michael; Roughan, Moninya; Schaeffer, Amandine

doi:10.3389/fmars.2020.00485

Cited by 6 publications

(21 citation statements)

References 26 publications

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“…The Port Hacking National Reference Station (151.2 • E, 34.1 • S) is located in 100 m of water, ∼8.5 km off the coast of Sydney Australia where tidal influences are weak. See Roughan et al (2010Roughan et al ( , 2013Roughan et al ( , 2015, and Hemming et al (2020) for more information on the long term sampling at Port Hacking. The water samples are collected in bottles at a range of depths through the water column, nominally, at 10, 20, 30, 40, 50, 75, and 100 m in 100 m of water.…”

Section: Prediction Based On Co-variatesmentioning

confidence: 99%

“…The data collection began in 1953 with a frequency of weekly to monthly but due to the nature of manual collection consists of many gaps and irregular sampling intervals. See Hemming et al (2020) their Figure 3 and Table 1 for a full description of the data collection.…”

Section: Prediction Based On Co-variatesmentioning

confidence: 99%

“…QC includes but is not limited to checking for impossible date, position, depth range (based on global and regional values) correlation with adjacent data (above, below, before, and after). All steps including the use of the IMOS data toolbox for QC are described in Hemming et al (2020). The data used here were flagged as either "good data" or "probably good data" following UNESCO Intergovernmental Oceanographic Commission (IOC) protocol (flags 1 and 2, respectively) (Lynch et al, 2014;Morello et al, 2014).…”

Section: Prediction Based On Co-variatesmentioning

confidence: 99%

“…Oceans play a pivotal role in the global weather and climate systems and support a multi-billion dollar blue economy, hence continuous monitoring of ocean conditions, in coastal marine areas in particular, is important to a wide range of stakeholders (UNESCO, 2019). Although many different types of observation platforms can measure the same ocean variable (e.g., temperature), there can be fundamental differences between them due to the instrumentation and the spatiotemporal sampling making comparison difficult (Hemming et al, 2020). For example, satellite observations are restricted to the surface and represent area-averages (often over many kilometers) while data from in situ observations represents a point-based observation in space (Lee et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…These data gaps and inconsistencies make ocean observations inaccessible to non-expert users, and even expert users (e.g., ocean modelers) typically take the path of least resistance when accessing ocean data. It is not a straightforward exercise to compare, for example, recent high frequency (e.g., 5 min) ocean temperature observations from a mooring with sparse (e.g., monthly) bottle data collected in the 1950s (Hemming et al, 2020). Furthermore, timeseries gaps can significantly affect trend analysis (Wynn and Wickwar, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Contractor

Roughan

2021

Front. Mar. Sci.

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Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making.

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Section: Prediction Based On Co-variatesmentioning

confidence: 99%

Section: Prediction Based On Co-variatesmentioning

confidence: 99%

Section: Prediction Based On Co-variatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Contractor

Roughan

2021

Front. Mar. Sci.

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Quantifying coastal freshwater extremes during unprecedented rainfall using long timeseries multi-platform salinity observations

Malan,

Roughan,

Hemming

et al. 2024

Nat Commun

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During 2022, extreme rainfall occurred across southeast Australia, making it the wettest year on record. The oceanic impact of extreme rainfall events in normally ‘dry’ regions is not well understood, as their effects are challenging to observe. Here, we use unique multi-platform timeseries and spatial data from 36 autonomous ocean glider missions over 13 years, and we define an extreme salinity threshold inshore of the East Australian Current. We show that the freshwater plume extended fivefold further than previously thought. The compound effect of multiple large rainfall events resulted in a newly observed stratification (‘double-stacking’) dynamic, with the stratification being largely controlled by salinity. Extreme salinity events are known to be important for species composition of local fisheries as well as detrimental for coastal water quality. Such events and their impacts may become more common as extreme rainfall events are projected to become more frequent in a changing climate. Hence, comprehensive observing strategies facilitating identification of salinity extremes are essential.

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Multi-decadal ocean temperature time-series and climatologies from Australia’s long-term National Reference Stations

et al. 2022

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Multi-decadal ocean time-series are fundamental baselines for assessing the impacts of environmental change, however, compiling and quality controlling historic data from multiple sources remains challenging. Here we aggregate, document, and release a number of long time-series temperature products and climatologies compiled from data obtained at 4 monitoring sites around Australia where sub-surface ocean temperature has been recorded nominally weekly to monthly since the 1940s/50s. In recent years, the sampling was augmented with data obtained from moored sensors, vertical profiles and satellite-derived data. The temperature data have been quality controlled, and combined using a rigorously tested methodology. We have packaged the multi-decadal, multi-depth, multi-platform temperature time-series at each site and produced a range of daily temperature climatologies from different data combinations and time periods. The 17 data products are provided as CF-compliant NetCDF files and will be updated periodically. The long-term temperature time-series will be useful for studies of ocean temperature variability, trends, anomalies and change. The data collection is supported by Australia’s Integrated Marine Observing System and data are open-access.

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Daily Subsurface Ocean Temperature Climatology Using Multiple Data Sources: New Methodology

Cited by 6 publications

References 26 publications

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Quantifying coastal freshwater extremes during unprecedented rainfall using long timeseries multi-platform salinity observations

Multi-decadal ocean temperature time-series and climatologies from Australia’s long-term National Reference Stations

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