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

Physical modeling of gelatinous zooplankton sinking in the deep global ocean

Contents: The solver: microbial_decay_odeint_global.py. Run simply as ./microbial_decay_odeint_global.py Temperature global ocean climatologies, remapped to 1 degree grid: rmp_1deg_mercatorglorys12v1_gl12_mean_1993_2016_01.nc rmp_1deg_mercatorglorys12v1_gl12_mean_1993_2016_06.nc Metadata of Temperature Climatology: // global attributes: :CDI = "Climate Data Interface version 2.0.4 (https://mpimet.mpg.de/cdi)" ; :Conventions = "CF-1.6" ; :source = "MERCATOR GLORYS12V1" ; :institution = "Mercator Ocean" ; :references = "http://marine.copernicus.eu" ; :credit = "E.U. Copernicus Marine Service Information (CMEMS)" ; :licence = "http://marine.copernicus.eu/services-portfolio/service-commitments-and-licence/" ; :contact = "[email protected]" ; :producer = "CMEMS - Global Monitoring and Forecasting Centre" ; :area = "GLOBAL" ; :product = "GLOBAL_REANALYSIS_001_030" ; :product_user_manual = "http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-030.pdf" ; :quality_information_document = "http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-030.pdf" ; :title = "Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030" ; :dataset = "global-reanalysis-phy-001-030-monthly-climatology" ; :history = "Wed May 17 14:13:17 2023: cdo remapbil,remap_grid_1deg.txt mercatorglorys12v1_gl12_mean_1993_2016_06.nc rmp_1deg_mercatorglorys12v1_gl12_mean_1993_2016_06.nc" ; :CDO = "Climate Data Operators version 2.0.4 (https://mpimet.mpg.de/cdo)" ; } The remapped global bathymetry: bathy_rmp.pkl: python pickle of a remapped ETOPO2 bathymetry to 1 degree global grid. ETOPO2 bathymetry is publicly available at NOAA National Geophysical Data Center. 2006: 2-minute Gridded Global Relief Data (ETOPO2) v2. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5J1012Q

A convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations (DINCAE)

A method to reconstruct missing data in satellite 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. However, it is unclear how to handle missing data (or data with variable accuracy) in a neural network when using incomplete satellite data in the training phase. The present work shows a consistent approach which uses essentially the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The approach is motivated by the way models and observations are combined in the frame of data assimilation. The neural network is trained by maximizing the likelihood of the observed value. The corresponding error variances are estimated during training and do not need to be known a priori. The approach, called DINCAE (Data-Interpolating Convolutional Auto-Encoder) is applied to a relatively long time-series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data based on an EOF 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. The resulting error estimates are also validated using the cross-validation data and they follow closely the expected Gaussian distribution

Coastal HF radars in the Mediterranean: status of operations and a framework for future development

Abstract. Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with High-Frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near real-time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavours between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to: i) harmonize deployment and maintenance practices; ii) standardize data, metadata and quality control procedures; iii) centralize data management, visualization and access platforms; iv) develop practical applications of societal benefit, that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events or the investigation of transport processes and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socio-economic, technical and scientific challenges to be faced during the implementation of this integrated HFR regional network