Filling gaps in ocean satellite data: DINEOF and DINCAE

Satellite data offer an unequaled amount of information of the Earth’s surface, including the ocean. However, data measured using visible and infrared wavebands are affected by the presence of clouds and have therefore a large amount of missing data (on average, clouds cover about 75% of the Earth). The spatial and temporal scales of variability in the ocean require techniques able to handle undersampling of the dominant scales of variability. The GHER (GeoHydrodynamics and Environment Research) of the University of Liege in Belgium has been working over the last two decades on interpolation techniques for satellite and in situ ocean data. In this talk we will focus on techniques developed for satellite data. We’ll start with DINEOF - Data Interpolating Empirical Orthogonal Functions- which is a data-driven technique using EOFs to infer missing information in satellite datasets. We will follow with a more recent development, DINCAE - Data Interpolating Convolutional AutoEncoder. Training a neural network with incomplete data is problematic, and this is overcome in DINCAE by using the satellite data and its expected error variance as input. The autoencoder provides the reconstructed field along with its expected error variance as output. We will provide examples of reconstructed satellite data for several variables, like sea surface temperature, chlorophyll concentration, and some recent developments with DINCAE to grid altimetry data to complete fields

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

peer reviewedA method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.MULTI-SYNC project (contract SR/00/359), Consortium des Équipements de Calcul Intensif (CÉCI), funded by the F.R.S.-FNRS under grant no. 2.5020.11, COST action ES1402 – “Evaluation of Ocean Syntheses

Vague de chaleur marine dans le Pacifique Sud-Est causée par une combinaison de facteurs atmosphériques et océaniques

Marine heatwaves (MHWs) are discrete warm-water anomalies events occurring in every ocean around the globe, in both coastal and open ocean, having major impacts on ecosystems, fisheries and aquaculture. They are generally caused by a combination of oceanic and atmospheric conditions that favours the increase of the sea surface temperature and/or reduce the heat transfer from the ocean to the atmosphere. Here we investigated through satellite data the formation of a MHW offshore the Chilean Patagonia in the Southeast Pacific Ocean which lasted from May to October 2016. That MHW was identifiable through the presence of unusually low heat transfer from the ocean to the atmosphere. This lower than usual heat loss from the ocean was due to the temporary reduction of the wind speed, causing reduced oceanic latent heat loss. These factors, added to the advection of anomalously warm waters from the extratropical South Pacific, favoured the development of a long-lasting MHW

Vagues de chaleur marines en Patagonie : de la grande échelle à l'échelle régionale

Marine heatwaves offshore Patagonia and progression of my thesis

Variational analysis of high-frequency radar surface currents using DIVA

DIVA (Data-Interpolating Variational Analysis) is a tool that allows one to interpolate observations on a regular grid in an optimal way. It is commonly applied to in situ observations such as temperature, salinity or nutrient concentration to obtain gridded climatologies. It takes the coastline and bathymetry, ocean currents and the spatial connectivity of water bodies into account to interpolate these tracers, also considering uncertainties on observations. For vector quantities, like ocean surface currents, addition a new set of constraints must be taken into consideration based on the relationship between the components. We have extended DIVA to include additional dynamic constraints relevant to surface currents, including imposing a zero normal velocity at the coastline, imposing a low horizontal divergence of the surface currents, temporal coherence and a simplified dynamics based on the Coriolis force and possibly including a surface pressure gradient. The impact of these constraints is evaluated by cross-validation using the HF (High-Frequency) radar surface current observations in the Ibiza Channel from the Balearic Islands Coastal Ocean Observing and Forecasting System (SOCIB). A small fraction of the radial current observations are set aside to validate the velocity reconstruction. The remaining radial currents from the two radar sites are combined to derive total surface currents using DIVA and then compared to the cross-validation data set. The benefit of the dynamic constraints is assessed relative to a naive variational interpolation ignoring these dynamical constraints. Best results have been obtained when the Coriolis force and the surface pressure gradient are included together

Reconstruction of missing data in satellite images of the Southern North Sea using a convolutional neural network (DINCAE)

peer reviewedA neural network with the architecture of a convolutional autoencoder is used to reconstruct missing data in satellite images of the Southern North Sea. The technique is applied to a multi-satellite data product of chlorophyll-a and total suspended particulate matter (SPM) concentration (representing 20 years of data). The presence of clouds significantly reduces the extent of the ocean that can be measured by satellite sensors using the visible or infrared spectrum. The accuracy of the reconstruction is assessed using cross-validation (i.e. increasing the actual extent of the cloud coverage). The results of the neural network compare favourably the data withheld for cross-validation.MultiSyn

Analysis of 23 Years of Daily Cloud-Free Chlorophyll and Suspended Particulate Matter in the Greater North Sea

Satellite-derived estimates of ocean color variables are available for several decades now and allow performing studies of the long-term changes occurred in an ecosystem. A daily, gap-free analysis of chlorophyll (CHL) and suspended particulate matter (SPM, indicative of light availability in the subsurface) at 1 km resolution over the Greater North Sea during the period 1998–2020 is presented. Interannual changes are described, with maximum average CHL values increasing during the period 1998–2008, a slightly decreasing trend in 2009–2017 and an stagnation in recent years. The typical spring bloom is observed to happen earlier each year, with about 1 month difference between 1998 and 2020. The duration of the bloom (time between onset and offset) appears also to be increasing with time, but the average CHL value during the spring bloom does not show a clear trend. The causes for earlier spring blooms are still unclear, although a rising water temperature can partially explain them through enhanced phytoplankton cell division rates or through increased water column stratification. SPM values during winter months (prior to the development of the spring bloom) do not exhibit a clear trend over the same period, although slightly higher SPM values are observed in recent years. The influence of sea surface temperature in the spring bloom timing appears to be dominant over the influence of SPM concentration, according to our results. The number of satellites available over the years for producing CHL and SPM in this work has an influence in the total amount of available data before interpolation. The amount of missing data has an influence in the total variability that is retained in the final dataset, and our results suggest that at least three satellites would be needed for a good representation of ocean color variability.MultiSyn

SEASTAR: a mission to study ocean submesoscale dynamics and small-scale atmosphere-ocean processes in coastal, shelf and polar seas

High-resolution satellite images of ocean color and sea surface temperature reveal an abundance of ocean fronts, vortices and filaments at scales below 10 km but measurements of ocean surface dynamics at these scales are rare. There is increasing recognition of the role played by small scale ocean processes in ocean-atmosphere coupling, upper-ocean mixing and ocean vertical transports, with advanced numerical models and in situ observations highlighting fundamental changes in dynamics when scales reach 1 km. Numerous scientific publications highlight the global impact of small oceanic scales on marine ecosystems, operational forecasts and long-term climate projections through strong ageostrophic circulations, large vertical ocean velocities and mixed layer re-stratification. Small-scale processes particularly dominate in coastal, shelf and polar seas where they mediate important exchanges between land, ocean, atmosphere and the cryosphere, e.g., freshwater, pollutants. As numerical models continue to evolve toward finer spatial resolution and increasingly complex coupled atmosphere-wave-ice-ocean systems, modern observing capability lags behind, unable to deliver the high-resolution synoptic measurements of total currents, wind vectors and waves needed to advance understanding, develop better parameterizations and improve model validations, forecasts and projections. SEASTAR is a satellite mission concept that proposes to directly address this critical observational gap with synoptic two-dimensional imaging of total ocean surface current vectors and wind vectors at 1 km resolution and coincident directional wave spectra. Based on major recent advances in squinted along-track Synthetic Aperture Radar interferometry, SEASTAR is an innovative, mature concept with unique demonstrated capabilities, seeking to proceed toward spaceborne implementation within Europe and beyond