How to cite: Irie, R., Stewart, H., Kura, T., Hisada, M., and Yaguchi, T.: Parameterizing ocean vertical mixing using deep learning trained from high-resolution simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2297, https://doi.org/10.5194/egusphere-egu24-2297, 2024.

The Coupled Model Intercomparison Project, now in its sixth phase (CMIP6), is a global effort to project future climate scenarios on following certain shared socioeconomic pathways (SSP). For the period 1950-2014, CMIP6 provides a historical model output. From 2015 future projections with four different SSP scenarios, viz. SSP126, 245, 370 and 585 are available. From 2015-2023, we also have reanalysis of the actual ocean and atmosphere variables in these years. From this data, it is observed that CMIP6 future projections of ocean variables have a root mean square error (RMSE) of 1.22 psu in sea surface salinity, 1.24 °C in sea surface temperature, 2.23 m/s in the zonal ocean velocity component, 1.34 m/s in the meridional ocean velocity component. Similarly, the atmospheric variables have a RMSE of 1.34 °C in temperature at 2-meter height, 2.12 m/s in the zonal, and 1.321 m/s meridional wind component. Our goal is to develop an accurate method to correct this bias and provide updated future projections for scientific analysis. To this end, we developed a two phase deep neural network model that accepts monthly fields from the CMIP6 projections (all four SSP scenarios), and outputs a bias corrected field. In the first phase, a deep neural model, which we call as Atmospheric-Ocean Network 1 (AONet1) is used to obtain bias corrected fields for each of the four SSP separately. The AONet1 is trained and validated using the historical CMIP6 data (1950-2014) as input and ORAS5 and ERA5 data as the output (the bias corrected field). In the second phase, the four bias-corrected SSP fields are fed to AONet2 and the final bias corrected single field is produced. The AONet2 is trained and validated using future projection data from 2015-2021 as input and ORAS5 and ERA5 from the same period as output. The testing of the two phase model is performed for years 2022 and 2023, before bias corrected future fields are produced. Results are compared to the statistical EDCDF method using different Image Quality Assessment metrics such as Data structural similarity index measure (DSSIM), Multi-Scale SSIM, and Visual information fidelity. On test data, the RMSE after bias reduction using the two phase AONet model is 40% lower. Image assessment metric values surpassed the EDCDF approach as well.

How to cite: Pasula, A. and Subramani, D.: A two-phase Neural Model for CMIP6 bias correction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18857, https://doi.org/10.5194/egusphere-egu24-18857, 2024.

The ICT+ project:” Transforming ocean surveying by the power of DL and statistical methods” hosted by UiT The Artic University of Norway, aims at employing machine learning techniques in improving and streamlining methods currently used in ocean surveying by private sector partners to the project, MultiConsult and Argeo. The tasks include detection and identification of µm (e.g. foraminifera, microplastics) to m (e.g. boulders, shipwrecks) sized objects and elements at and in the seabed in data that presently is processed manually by skilled workers, but ideally could be wholly or partially processed using an automated approach.

Here we present preliminary work and results related to application of the YOLO (You Only Look Once) algorithms in detection and identification of meio fauna (foraminifera) in - and macro (mollusc) fauna at the seabed. Both proxies are used in evaluation of the environmental state of the seabed. YOLO is a real-time object detection deep learning algorithm that efficiently identifies and locates objects in images or videos in a single pass through the neural network.

Presently the year on year growth or shrinkage of protected mollusc banks in northern Norwegian fjords is manually evaluated via video observation in seabed video sequences annually captured via remotely operated vehicles. The preliminary results suggest that upon moderate training the YOLO algorithm can identify presence/absence of mollusc bank formations in set video sequences, thus supporting and eventually minimizing the task of inspecting the video footage manually.      

Foraminifera are abundant marine meiofauna living in the water column or at and in the seabed. Foraminifera are utilized in research into both modern and past marine environments as they have high turnover rates and individual shells have high preservation potential. Foraminiferal shells are accumulated in the sediments and after sample processing, they subsequently can be manually detected and identified via microscope. This work is very labour-intensive and demands skilled expertise but suffers from errors by and bias of the individual expert.

Preliminary results show that a YOLO network, trained on ca 4100 individuals (20 subgroups; benthic calcareous foraminifera (n=19), Planktic foraminifera (n=1)) in 346 images have model performances of up to 0.96 mAP (mean average precision) when trained, validated and tested on the training set. These promising results will be tested on real world samples. This testing is complicated by real world samples containing many more foraminiferal species/groups that were not trained upon, overlapping or closely set specimens and presence of non-foraminiferal material (e.g. sediment grains, other meio-fauna or –flora, etc.). Thus, additional training with focus on set complicating aspects will likely be necessary and most recent result will be presented.

How to cite: Aagaard Sørensen, S., Myrvoll-Nielsen, E., Martinsen, I., Godtliebsen, F., Galata, S., Junttila, J., and Vassdal, T.: Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18759, https://doi.org/10.5194/egusphere-egu24-18759, 2024.

Coral reefs, which host more than a third of the ocean’s biodiversity on less than 0.1% of its surface, are existentially threatened by climate change and other human activities. This necessitates methods for evaluating the state of coral reefs that are efficient, scalable, and low-cost. Current digital reef monitoring tools typically rely on conventional Structure-from-Motion photogrammetry, which can limit the scalability, and current datasets for training semantic segmentation systems are either sparsely labeled, domain-specific, or very small. We describe the first deep-learning-based 3D semantic mapping approach, which enables rapid mapping of coral reef transects by leveraging the synergy between self-supervised deep learning SLAM systems and neural network-based semantic segmentation, even when using low-cost underwater cameras. The 3D mapping component learns to tackle the challenging lighting effects of underwater environments from a large dataset of reef videos. The transnational data-collection initiative was carried out in Djibouti, Sudan, Jordan, and Israel, with over 150 hours of collected video footage for training the neural network for 3D reconstruction. The semantic segmentation component is a neural network trained on a dataset of video frames with over 80’000 annotated polygons from 36 benthic classes, down to the resolution of prominent visually identifiable genera found in the shallow reefs of the Red Sea. This research paves the way for affordable and widespread deployment of the method in analysis of video transects in conservation and ecology, highlighting a promising intersection with machine learning for tangible impact in understanding these oceanic ecosystems. 

How to cite: Sauder, J., Banc-Prandi, G., Perna, G., Meibom, A., and Tuia, D.: Scalable 3D Semantic Mapping of Coral Reefs with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5552, https://doi.org/10.5194/egusphere-egu24-5552, 2024.

Microfossils are important in climate analysis and in exploration of subsea energy resources. The abundance and distribution of species found in sediment cores provide valuable information, but the analysis is difficult and time consuming as it is based on manual work by human experts. It is also a challenge to have enough labelled data to train a standard deep learning classifier on microfossil images. We propose an efficient pipeline for processing and grouping fossils by species from microscope slides using self-supervised learning. First we show how to efficiently extract crops from whole slide images by adapting previously trained object detection algorithms. Second, we provide a comparison of a range of contrastive self-supervised learning methods to classify and identify microfossil from very few labels. We obtain excellent results with convolutional neural networks and vision transformers fine-tuned by self-supervision.

How to cite: Martinsen, I., Wade, D., Ricaud, B., and Godtliebsen, F.: A deep learning pipeline for automatic microfossil analysis and classification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21554, https://doi.org/10.5194/egusphere-egu24-21554, 2024.

The assessment and monitoring of microbial plankton biodiversity are essential to obtain a robust evaluation of the health status of marine environments. The PETRI-MED project addresses this imperative by developing novel strategies to monitor the microbial plankton community composition and function, based on satellite observations. PETRI-MED will focus on the Mediterranean Sea as a global biodiversity hotspot with profound ecological and cultural importance. The primary objectives of PETRI-MED project encompass (i) the development of innovative satellite-based indicators to determine the biodiversity status and trends of microbial plankton community, (ii) the identification of spatio-temporal patterns in microbial plankton distribution and diversity, and (iii) the elucidation of key controls of biodiversity patterns, including ecological connectivity, natural and human-related forcings, by focusing on key indicators of ocean’s health and/or biogeochemical state. To do so, PETRI-MED will largely rely on satellite optical radiometric measurements (i.e, Ocean Colour, OC), exploiting the combined temporal and spatial characteristics of latest OC European datasets (i.e., Copernicus Sentinel-3 and European Space Agency OC-CCI) with state-of-the-art remote sensing observations and biogeochemical models (as provided by Copernicus Marine), marine currents modelling, and genomic techniques. To achieve the ambitious goal of merging remote sensing, biogeochemical/physical modelling, and in situ omics measurements, PETRI-MED will rely on Artificial Intelligence (AI). The overarching goal of PETRI-MED is to empower policymakers and stakeholders with the necessary knowledge to adopt prioritization approaches for ecosystem management based on quantitative, real-time metrics. This includes the design and implementation of protection strategies and policies to safeguard biodiversity, quantifying the impact of implemented actions at various levels, and enabling systematic, fact-supported management of Marine Protected Areas (MPAs), Key Biodiversity Areas, and Ecologically or Biologically Significant Marine Areas. Furthermore, PETRI-MED seeks to evaluate the viability of MPA management in response to climate change, ensuring adaptive strategies for the conservation of marine ecosystems in the face of environmental challenges. In summary, PETRI-MED represents a comprehensive and innovative approach to advancing our understanding of microbial plankton biodiversity in the Mediterranean Sea. Through the integration of satellite technology, omics techniques and AI, the project contributes valuable insights and tools for effective marine ecosystem management and conservation strategies.

How to cite: Tinta, T. and the PETRI-MED: PETRI-MED: Advancing Satellite-Based Monitoring for Microbial Plankton Biodiversity in the Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3523, https://doi.org/10.5194/egusphere-egu24-3523, 2024.

Retrievals of ocean color (OC) properties from space are important for better understanding the ocean ecosystem and carbon cycle. The launch of atmospheric hyperspectral instruments such as the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GEMS, provide a unique opportunity to examine the diurnal variability in ocean ecology across various waters in North America and prepare for the future suite of hyperspectral OC sensors. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO OC instrument (OCX), it provides hourly coverage of US coastal regions and great lakes, such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. We will apply our newly developed machine learning (ML) based atmospheric correction approach for OC retrievals to TEMPO data. Our approach begins by decomposing measured hyperspectral radiances into spectral features that explain the variability in atmospheric scattering and absorption as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict OC properties such as chlorophyll concentration derived from collocated MODIS/VIIRS physically-based retrievals. This ML approach compliments the standard radiative transfer-based OC retrievals by providing gap-filling over cloudy regions where the standard algorithms are limited. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate OC properties in less-than-ideal conditions such as lightly to moderately cloudy conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it provides extended spectral measurements at green and red wavelengths which are important particularly for coastal waters. Additionally, our ML technique can be applied to provisional data early in the mission and has potential to demonstrate the value of near real time OC products that are important for monitoring of harmful algae blooms and transient oceanic phenomena.   

How to cite: Fasnacht, Z., Joiner, J., Bandel, M., Haffner, D., Vassilkov, A., Castellanos, P., and Krotkov, N.: Harnessing Machine Learning and Principal Components Techniques for Atmospheric and Glint Correction to Retrieve Ocean Color from Geostationary Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12271, https://doi.org/10.5194/egusphere-egu24-12271, 2024.

4DVarNet algorithm is an AI based variational approach that performs spatiotemporal time-series interpolation. It has been used with success on Ocean Color satellite images to fill in the blank of missing data due to e.g., the satellites trajectories or the clouds covering. 4DVarNet has shown impressive interpolation performances compare to other classical approaches such as DInEOF.
We propose to show that 4DVarNet is a flexible model that learns global dynamics instead of local patterns, thus enabling it to interpolate different type of data, i.e., data from different spatio-temporal domain and/or representing different variables, using the same pre-trained model.

The core of our technique involves extrapolating the learned models to other, somewhat larger geographical areas, including the entire Mediterranean and other regions like the North Sea. We achieve this by segmenting larger areas into smaller and manageable sections, and then choosing a section to train the model. Finally the trained model is applied to each segment and seamlessly integrating the prediction results. This method ensures detailed and accurate coverage over extensive areas, significantly enhancing the predictive power of our models while maintaining low computational costs.

Our results demonstrate that this approach not only outperforms traditional methods in terms of accuracy but also provides a scalable solution, adaptable to various geographical contexts. By leveraging localized training and strategic extrapolation, we offer a robust framework for ocean monitoring, paving the way for advanced satellite image applications in diverse settings.

How to cite: Dorffer, C., Nguyen, T. T. N., Jourdin, F., and Fablet, R.: Spatial Generalization of 4DVarNet in ocean colour Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18688, https://doi.org/10.5194/egusphere-egu24-18688, 2024.

Short-term forecasting (several days in advance) of underwater visibility range is needed for marine and maritime operations involving divers or optical sensors, as well as for recreational activities such as scuba diving (e.g. Chang et al 2013). Underwater visibility mainly depends on water turbidity, which is caused by small suspended particles of organic and mineral origin (Preisendorfer 1986). Modelling the fate of these particles can be complex, encouraging the development of machine learning methods based on satellite data and hydrodynamic simulations (e.g. Jourdin et al 2020). In the field of forecasting visibility, deep learning methods are emerging (Prypeshniuk 2023). Here, in continuation of Vient et al (2022) on the interpolation purpose, this work deals with forecasting subsurface mineral turbidity levels over the French continental shelf of the Bay of Biscay using the deep learning method entitled 4DVarNet (Fablet et al 2021) applied to ocean colour satellite data, with additional data such as bathymetry (ocean depths) and time series of main forcing statistical parameters like wave significant heights and tidal coefficients. Using satellite data alone, results show that 2-day forecasts are accurate enough. When adding bathymetry and forcing parameters in the process, forecasts can go up to 6 days in advance.

References

Chang, G., Jones, C., and Twardowski, M. (2013), Prediction of optical variability in dynamic nearshore environments, Methods in Oceanography, 7, 63-78, https://doi.org/10.1016/j.mio.2013.12.002

Fablet, R., Chapron, B., Drumetz, L., Mémin, E., Pannekoucke, O., and Rousseau, F. (2021), Learning variational data assimilation models and solvers, Journal of Advances in Modeling Earth Systems, 13, e2021MS002572, https://doi.org/10.1029/2021MS002572

Jourdin, F., Renosh, P.R., Charantonis, A.A., Guillou, N., Thiria, S., Badran, F. and Garlan, T. (2021), An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor, IEEE Transactions on Geoscience and Remote Sensing, 59(7), 5423-5433, https://doi.org/10.1109/TGRS.2020.3011742

Preisendorfer, R. W. (1986), Secchi disk science: Visual optics of natural waters, Limnology and Oceanography, 31(5), 909-926, https://doi.org/10.4319/lo.1986.31.5.0909

Prypeshniuk, V. (2023), Ocean surface visibility prediction, Master thesis, Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences, Lviv, Ukraine, 39 pp, https://er.ucu.edu.ua/handle/1/3948?locale-attribute=en

Vient, J.-M., Fablet, R.;, Jourdin, F. and Delacourt, C. (2022), End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations, Remote Sens., 14(16), 4024, https://doi.org/10.3390/rs14164024

How to cite: Vient, J.-M., Jourdin, F., Fablet, R., and Delacourt, C.: Data-driven short-term forecast of suspended inorganic matter as seen by ocean colour remote sensing., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15508, https://doi.org/10.5194/egusphere-egu24-15508, 2024.

The water in the basin of sill fjords is renewed occasionally. In some fjords, this renewal occurs irregularly while in others it has a more regular character. Independently of the renewal period, the renewal mechanism is thought to be common to all sill fjords: subsurface water outside of the fjord mouth lifted above the sill depth will trigger a renewal, provided that the lifted water mass is denser than the water in the basin. In Western Norway, the northerly, upwelling favorable winds that occur during Spring/Summer, provide a forcing for the uplifting of the isopycnals and bring dense, subsurface water to the upper water column, thereby creating the conditions for renewals to occur. The renewal of sill fjord basins is an important aspect of the fjord ecological condition because it supplies oxygen rich water to the fjord basin, whose oxygen is consumed by the degradation of organic matter during the stagnant periods. Byfjorden is the urban fjord in Bergen, Norway. It is heavily urbanized and has been consistently showing lower oxygen levels in its basin, which has ecological implications.

Byfjorden’s basin water is regularly renewed between the months of March and August and a strong link to coastal and atmospheric variability is well known, which makes it an attractive choice for the application of Deep Learning to predict basin water renewal in sill fjords, in the context of the atmospheric and hydrographic setting of the Norwegian coast.

In this work, the prediction of deep water renewal in Byfjorden and the basin’s oxygen levels is investigated with deep learning techniques. After a statistical study of oxygen variability correlation with wind forcing along the Norwegian coast, we develop and test a model to predict renewals and fill gaps in Byfjorden’s oxygenatio record.

How to cite: Bettencourt, J.: Prediction of sill fjord basin water renewals and oxygen levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18627, https://doi.org/10.5194/egusphere-egu24-18627, 2024.

Oxygen is fundamentally essential for all life. Unfortunately, recent research has shown that global ocean deoxygenation has significantly increased over the past 50 years, and the stock of dissolved oxygen (DO) in the ocean has been continuously decreasing. Breathless ocean has led to large-scale death of fish, seriously affecting the marine ecosystem. Moreover, global warming and human activities have further intensified the expansion of dead zones (low-oxygen area) in the ocean.

Hence, it is of vital importance to quantitatively understand and predict the trend of global ocean deoxygenation. However, despite of the accumulation of in-situ DO observation in recent years, global and long-term observation data is still severely sparse, leading to a critical challenge in reconstructing global ocean deoxygenation over a century. Existing works can be categorized into two ways: (1) Physics-informed numerical models. These methods simulate the DO concentration based on climate models without utilizing in-situ observations, e.g., Coupled Model Intercomparison Project Phase 6 (CMIP6). However, these models fail to adjust biased simulation results based on temporal DO observations and cause error propagation. (2) Spatial interpolation methods. These methods reconstruct the global deoxygenation through available observations by geostatistical regression, Kriging, etc. But these ways are unable to capture the complex spatiotemporal heterogeneity and physical-biogeochemical properties, showing inconsistent performance in different areas.

To this end, we propose a knowledge-infused deep graph learning method called 4D Spatio-Temporal Graph HyperNetwork (4D-STGHN) to reconstruct four-dimensional (including time, latitude, longitude, and depth) global ocean deoxygenation from 1920 till now. To capture the spatio-temporal heterogeneity in different regions, 4D-STGHN utilize hypernetwork to generate non-shared parameters by fusing 4D geographic information and observations. Moreover, we design a chemistry-informed gradient norm mechanism as the loss function by integrating the observation of nitrate and phosphate, hereby further improving the performance of DO reconstruction. 4D-STGHN shows promising reconstruction with mean absolute percentage error (MAPE) of only 5.39%, largely outperforming three CMIP6 experiments (CESM2-omip1, CESM2-omip2 and GFDL-ESM4-historical) on dissolved oxygen and other machine learning methods. Further analysis on the global oxygen minimum zones, as well as regional analysis are conducted to evaluate the effectiveness of our proposed methods.

How to cite: Lu, B., Zhao, Z., Han, L., Gan, X., Zhou, Y., Zhou, L., Fu, L., Wang, X., Zhang, J., and Zhou, C.: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5926, https://doi.org/10.5194/egusphere-egu24-5926, 2024.

Tropical cyclones (TCs) are natural disasters originating over tropical or subtropical oceans. Their landfall is generally accompanied by extensive high winds and persistent precipitation, causing severe economic losses and human casualties yearly. Consequently, conducting effective TC landfall intensity forecasts for disaster risk reduction is imperative. The calm center of a TC, known as the TC eye, serves as a vital indicator of its intensity. Hence, precisely locating TC centers is crucial for determining TC intensity. In this study, a deep-learning model was developed to extract TC centers from satellite remote-sensing images automatically.
Space-borne synthetic aperture radar (SAR) imagery plays a critical role in monitoring natural hazards owing to its high spatial resolution, wide coverage, and day-night imaging capabilities. A total of 110 Sentinel SAR images spanning from 2016 to 2019 were used for TC center localization in this paper. They were acquired in interferometric-wide (IW) mode with a pixel size of 10 m and extra-wide (EW) mode with a pixel size of 40 m. They were resampled by spatial averaging to maintain the same pixel size of 80 m. Additionally, we manually annotated the central area of tropical cyclone images as ground truth data.
For the dataset, we initially divided 110 SAR images and the corresponding truth data into training, validation, and testing sets in an 8:1:1 ratio. Subsequently, we partitioned the SAR images into 256 × 256 pixel-sized slices as the model inputs. 32151/4611/3900 samples were extracted as the training/validation/testing dataset. Considering the target samples containing the center position are far less than compared background samples in TCs, we retained all center-containing samples and randomly selected 1.2 times the number of background samples for each image. Consequently, we obtained a final dataset of 2388/257/245 samples for training, validation, and testing.
As is known, deep learning technology excels in learning non-linear relationships and is good at automatically extracting intricate patterns from SAR imagery. The Res-UNet, a U-Net-like model with the weighted attention mechanism and the skip connection scheme that has been proven effective in solving the problem of contrast reduction caused by signal interference, was ultimately determined as the deep learning model for the automatic positioning of tropical cyclone centers in our study.
We calculated the centroid of the central region and compared the model results with ground truth. Our model outputs agreed well with the visually located TC center with a mean intersection over union (IOU) and a mean TC center location error of 0.71/0.70/0.67 and 3.59/2.24/2.20 km for the training/validation/testing dataset. Moreover, our model greatly simplifies the complexity of traditional methods such as using spiral rainbands and background wind fields for center positioning. At the same time, our method can not only obtain the position of the TC center but also extract the central area, thereby obtaining the morphological characteristics of TCs, which is conducive to better monitoring and intensity determination of TC.

How to cite: Mu, S., Wang, H., and Li, X.: A Deep Learning Model for Tropical Cyclone Center Localization Based on SAR Imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6927, https://doi.org/10.5194/egusphere-egu24-6927, 2024.

A Catboost-based intelligent tropical cyclone (TC) intensity-detecting model is built to quantify the intensity of TCs over the Western North Pacific (WNP) with the cloud-top brightness temperature (CTBT) data of Fengyun-2F (FY-2F) and Fengyun-2G (FY-2G) and the best-track data of the China Meteorological Administration (CMA-BST) in recent years (2015-2018). Catboost-based model is featured with the greedy strategy of combination, the ordering principle in optimizing the possible gradient bias and prediction shift problems, and the oblivious tree in fast scoring. Compared with the previous studies based on the pure convolutional neural network (CNN) models, the Catboost-based model exhibits better skills in detecting TC intensity with the root mean square error (RMSE) of 3.74 m s^-1. Besides of the three mentioned model features, there are also two reasons on model design. On one hand, the Catboost-based model uses the method of introducing prior physical factors (e.g., the structure and shape of the cloud, deep convections and background fields) into its training process, on the other hand, the Catboost-based model expands the dataset size from 2342 to 13471 samples by hourly interpolation of the original dataset. Furthermore, this paper investigates the errors of the model in detecting different categories of TC intensity. The results show that the deep learning-based TC intensity-detecting model proposed in this paper has systematic biases, namely, overestimation (underestimation) of intensities in TC which are weaker (stronger) than typhoon level, and the errors of the model in detecting weaker (stronger) TCs are smaller (larger). This implies that more factors than the CTBT should be included to further reduce the errors in detecting strong TCs.

How to cite: Zhong, W., He, H., Wang, S., Sun, Y., and Yao, Y.: A Catboost-based Model for Intensity Detection of Tropical Cyclones over the Western North Pacific Based on Satellite Cloud Images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1277, https://doi.org/10.5194/egusphere-egu24-1277, 2024.

Surface currents influence ship navigation, coastal heat transfer and sediment transport, and thus necessitate robust models that can reliably predict surface current behaviour. However, our ability to make predictions over long time scales are commonly hampered by a lack of long observational datasets. Remote sensing technologies, which include satellite altimetry and high-frequency radar, are often used to measure global surface currents. However, their ability to reveal insights on ocean dynamics at a regional scale remain limited by restrictions related to space-time sampling. Here, we explore the use of AIS data as a means to derive surface currents in the Sunda Shelf Region in equatorial southeast Asia. Firstly, we apply nearest-neighbour interpolation to map relevant AIS information, that includes the ship’s speed over ground, course over ground and heading, onto a grid with a spatial resolution of 100m and an hourly temporal resolution. Next, we applied a gradient descent approach to derive surface currents at the positions of the ships. We then implement a generative model on PyTorch to reconstruct surface currents in the region. The model performance is evaluated by comparing to observational data from drifters and drifting buoys. Lastly, we employed wavelet analysis, a type of nonstationary spectral analysis, to examine the dominant frequencies or periods where surface currents are strong. Our pilot study highlights the potential of AIS data as a credible alternative to traditional methods of measuring surface currents in data scarce areas.

How to cite: Puah, J. Y., Haigh, I. D., Lallemant, D., Fablet, R., Morgan, K., and Switzer, A. D.: Unveiling the Ocean’s Rhythms: Blending Deep Learning and Spectral Analysis Together to Gain Insights into Sunda Shelf Surface Currents using AIS Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8207, https://doi.org/10.5194/egusphere-egu24-8207, 2024.

Rising sea levels are one of many consequences of anthropogenic climate
change. Over the past few decades, several global observational records have
become available that give a more detailed picture of the increasing
impacts. Nevertheless, there continue to be data challenges, such as
sparsity or signal to noise ratio, that need to be dealt with. Machine Learning (ML)
and specifically, Deep Learning (DL) approaches have presented themselves as valuable
tools for such large-scale and complex data sources. To this end, the OceanBench
Benchmark suite was recently developed to provide a
standardized pre-processing and evaluation framework for Sea Surface Height
(SSH) interpolation tasks involving nadir and Surface Water and Ocean Topography
(SWAT) Altimetry Tracks. From the methodological perspective, a reoccurring
issue is the lack of uncertainty quantification for DL applications in Earth
Observation. Therefore, we extend the suite of metrics provided by OceanBench
to probabilistic evaluation metrics and test state-of-the-art uncertainty
quantification models from the DL community. Specifically, we focus on
Conditional Convolutional Neural Processes (ConvCNP) and
Inpainting Diffusion models as methodologies to quantify
uncertainty for the interpolation task and demonstrate their viability and
advantages over other ML methods for both accuracy and probabilistic metrics.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Conditional Generative Models for OceanBench Sea Surface Height Interpolation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15594, https://doi.org/10.5194/egusphere-egu24-15594, 2024.

The Federal Maritime and Hydrographic Agency of Germany performs weekly analyses of sea surface temperatures (SST) for the North Sea and Baltic Sea on an operational basis. The analysis is based on in-situ observations and satellite retrievals. Existing procedures require manual quality control and subjective decisions on plausibility of measurements combined with simple interpolation techniques. This study presents ongoing work to develop new procedures based on a machine learning approach, designed to fill in gaps in observational data fields. The employed inpainting technique makes use of a convolutional neural network (CNN) that is trained with complete SST-fields from high-resolution (~3 km) ocean model simulations and masks derived from satellite retrievals to ignore regions covered by clouds on respective days.

First validation efforts for the North Sea based on reconstructing modelled fields that were excluded from training data indicate very promising results, that is an RMSE of ~ 0.5 K or less for most regions of the North Sea. However, areas with high variance such as waters very close to the coast and the Norwegian Channel exhibit larger errors up to 1 K. Additionally, we can show that errors tend to be larger when less observational data are available, e.g. on days with lots of clouds.

It will be tested if optional features of the algorithm may help to improve results in these cases. Especially the possibility to use “memory” of preceding days – potentially featuring less clouds – seems promising in this respect. Furthermore, it will be elaborated if the option of overwriting existing observational data with values better fitting the patterns learned by the CNN is suitable to improve the overall results and hence may be an alternative to external (manual) quality control and plausibility checking.

The final aim of this study is to establish an approach suitable for the operational analysis of daily SSTs with a horizontal resolution of approx. 5 km and the production of an SST reanalysis of North Sea and Baltic Sea SSTs starting in 1990.

How to cite: Kruschke, T., Kadow, C., Meuer, J., and Hinrichs, C.: Machine-learning-based analysis and reconstruction of high-resolution sea-surface temperatures for the North Sea and Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11061, https://doi.org/10.5194/egusphere-egu24-11061, 2024.

Traditional physics-based numerical models have served and are serving as reliable tools to gain insights into spatiotemporal behavior of ocean states such as water levels and currents. However, they have significant computational demand that often translates to slower forecasting capabilities. Additionally, these models can encounter difficulties in capturing certain physical processes and struggle to effectively bridge various spatial and temporal scales.

Considering these challenges, machine learning-based surrogate models emerge as a promising alternative. Physical surrogate models that learn multiple physics (on different spatial and temporal scales) from large datasets during extensive pretraining (Multiple physics pretraining, MPP) can enable later applications to poorly observed data domains which are common in ocean sciences. Hence, transfer learning capabilities can help improve the oceanographic forecasting, especially in data-limited regimes.

In this work, we explore the capabilities of pretrained transformer models for prediction on a test case for the North Sea. The results from two-dimensional simulations are used for training and fine-tuning. We utilize 2D datasets from publicly available PDEBench together with domain-specific datasets from DHI’s historical records of simulated 2D metocean data. We forecast water levels and currents with pretrained models and evaluate MPP forecast results against in-situ point observations and numerical model results.

Initial findings suggest that pretraining poses potential for generalizing and transferring knowledge to novel regions and relevance in practical application. A challenge is posed by model interpretability, highlighting an area for further development.

How to cite: Cremer, C., Anderson, H., and Mariegaard, J.: Exploring Pretrained Transformers for Ocean State Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17159, https://doi.org/10.5194/egusphere-egu24-17159, 2024.

Large Language Models (LLMs) have made significant strides in language understanding, including natural language processing, summarization, and translation, and they have the potential to be applied to a range of climate-related challenges. For instance, LLMs can be leveraged for data cleaning and transformation, and also assisting scientists/engineers in their daily work tasks.

For the machine learning community, the year 2023 was arguably the year of breakthroughts in LLM use in production. I present in this work the exciting potential for recent advances in LLMs to revolutionize how the ocean science community can interact with computer code, information gathering, dataset finding, etc. Specifically, I will present simple applications of how these advancements in Natural Language Processing (NLP) can assist the NEMO ocean model community. Examples range from using question answering systems for browsing efficiently NEMO documentation to creating conversational agents or chatbots that can assist not only new members wanting to learn about the NEMO model but also confirmed users. 

An important aspect of this work is relying only on open source LLMs, evaluating the performances of several models and discussing the ethical implications of these tools. I also discuss the question of whether using these LLMs blindly without domain knowledge is a good idea, as an important chunk of this work can arguably be easily done by anyone with good computer science skills thanks to the democratization of data science tools and learning materials.

How to cite: Lguensat, R.: Leveraging recent advances in Large Language Models for the ocean science community, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18493, https://doi.org/10.5194/egusphere-egu24-18493, 2024.

Conventional studies of subseasonal-to-seasonal sea ice variability across scales have relied upon computationally expensive physics-based models solving systems of differential equations. IceNet, a deep learning-based sea ice forecasting model under development since 2021, has proven competitive to such state-of-the-art physics-based models, capable of generating daily 25 km resolution forecasts of sea ice concentration across the Arctic and Antarctic at a fraction of the computational cost once trained. Yet, these IceNet forecasts leave room for improvement through three main weaknesses. First, the forecasts exhibit physically unrealistic spatial and temporal blurring characteristic of deep learning methods trained under mean loss objectives. Second, the use of 25 km scale OSISAF data renders local forecasts along coastal regions and in regions surrounding maritime vessels inconclusive. Third, the sole provision of sea ice concentration in forecasts leaves questions about other critical ice properties such as thickness unanswered. We present preliminary results addressing these three challenges, turning to deep generative models to capture forecast uncertainty and improve spatial sharpness; leveraging 3 and 6 km scale AMSR-2 sea ice products to improve spatial resolution; and incorporating auxiliary datasets, chiefly thickness, into the training and inference pipeline to produce multivariate forecasts of sea ice properties beyond simple sea ice concentration. We seek feedback for improvement and hope continued development of IceNet can help answer key scientific questions surrounding the state of sea ice in our changing polar climates.

How to cite: McDonald, A., Smith, J., Yatsyshin, P., Andersson, T., Bowler, E., van Zeeland, L., Ubald, B., Byrne, J., Pérez-Ortiz, M., Turner, R. E., and Hosking, J. S.: Pushing the Limits of Subseasonal-to-Seasonal Sea Ice Forecasting with Deep Generative Modelling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22070, https://doi.org/10.5194/egusphere-egu24-22070, 2024.

Seismic hazard assessment requires a detailed understanding of the evolution of fault systems, rupture processes, and linkage between segments. Identifying and characterizing Quaternary surface faulting features, such as fault scarps, provide valuable morphotectonic data on cumulative displacement over time, especially in regions with moderate to low seismic activity. Although fault cumulative vertical surface offsets have been traditionally measured using topographic profiles, these profiles are unevenly spread along the faults and may not reflect all the morphological changes along them. To address this situation, expanding the analysis to encompass a larger number of profiles is a viable option; nevertheless, manually executing this task would prove significantly time-consuming. Machine Learning (ML) has shown unprecedented capacities to evaluate large datasets in reduced time and provide a wealth valuable information with their related uncertainties. With this in mind, we propose a ML algorithm called ScarpLearn based on Convolutional Neural Networks (CNN) to compute the vertical cumulative displacement and its uncertainty for normal fault scarps. Despite ScarpLearn being initially developed to characterize simple scarps in onshore areas, we have enhanced its capabilities so that it can also be used in offshore areas subject to oceanic processes. This includes, among others, more intense diffusion, or the presence of seabed features such as pockmarks. Additionally, we have improved the code's versatility by providing a method modification that allows it to better characterization of scarps in more complex areas where multiple faults offset the seafloor. To this, we have trained the algorithm using a large database of realistic synthetic bathymetric profiles, including different parameters such as fault dip, slip velocity, scarp spread, scarp diffusion coefficient, and variable resolutions to ensure adaptability to all datasets. These modifications have resulted in the improvement of the ScarpLearn algorithm’s adaptability, elevating its accuracy and reliability in capturing the complexity of marine fault systems, but also applicable to terrestrial systems. We have applied the new ScarpLearn version to the North-South faults of the northern Alboran Sea, contributing to the accurate analysis of this Plio-Quaternary transtensional system and its complex geological structures. This innovative approach has allowed us to refine the morphotectonic analysis of the area and to understand better the geodynamics of the North-South faults system. In this research, we have explored the advances of the CNN method achieved in oceanic environments, considering intensive data compilation, computational time, accuracy, uncertainties, and current limitations. Our advances demonstrate the ScarpLearn ML potential, specifically tailored to analyze marine environments and multiple fault segments both onshore and offshore. Our research results contribute to the progress of marine geosciences by improving morphotectonic analysis using ML algorithms.

Keywords: Convolutional Neural Networks (CNN), Oceanic processes, Normal faults, Multiple scarps.

How to cite: Canari, A., Pousse-Beltran, L., Giffard-Roisin, S., Perea, H., and Martínez – Loriente, S.: Revealing Machine Learning's potential for morphotectonic analysis of marine faults: Application to the North-South faults in the Alboran Sea (Westernmost Mediterranean), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4126, https://doi.org/10.5194/egusphere-egu24-4126, 2024.

Biogeochemical ocean models are usually based on two size classes for particulate organic matter: small classes (1-100 𝜇m) and large classes (100-5000 𝜇m). Based on the measurements of particulate organic carbon (POC) concentration from UVP5 profiles and observations of environmental and ecosystem conditions we estimated an optimal number of size classes for POC that can be introduced in biogeochemical ocean models. 

We identified four size classes based on the correlation between POC concentration and environmental and ecosystem variables. It gives us information on the relationship between POC and surrounding temperature, chlorophyll-a concentration, nitrate, phosphate and oxygen levels as well as plankton functional types (PFTs). 

Further, we applied Machine Learning methods to reconstruct size classes of POC concentration and identify the most important drivers for each class. We showed that the concentration of POC smaller than 0.3 mm mostly depends on environmental characteristics while concentration of POC bigger than 0.3 mm strongly depends on PFTs.  

How to cite: Denvil-Sommer, A., Le Quere, C., Kiko, R., Buitenhuis, E., Racault, M.-F., and Lombard, F.: Size classification of particulate organic carbon concentration and its link to the ecosystem based on Machine Learning techniques., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20799, https://doi.org/10.5194/egusphere-egu24-20799, 2024.