Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data

• URL: http://arxiv.org/abs/2310.04540v1
• Date: Fri, 6 Oct 2023 19:06:43 GMT
• Title: Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data
• Authors: Saumya Sinha, John Fasullo, R. Steven Nerem, Claire Monteleoni
• Abstract summary: We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm/year) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2-degree spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge, and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions.

Related papers

• Regional Ocean Forecasting with Hierarchical Graph Neural Networks [1.4146420810689422]
  We introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high spatial resolution using the operational numerical model of the Mediterranean Sea provided by the Copernicus Marine Service.
  arXiv  Detail & Related papers  (2024-10-15T17:34:50Z)
• SEA-ViT: Sea Surface Currents Forecasting Using Vision Transformer and GRU-Based Spatio-Temporal Covariance Modeling [0.0]
  This paper introduces SEA-ViT, an advanced deep learning model that integrates Vision Transformer with Gate Recurrent Units. SEA-ViT is designed to unravel complex dependencies by leveraging a rich dataset spanning over 30 years.
  arXiv  Detail & Related papers  (2024-09-14T09:23:48Z)
• A Computer Vision Approach to Estimate the Localized Sea State [45.498315114762484]
  This research focuses on utilizing sea images in operational envelopes captured by a single stationary camera mounted on the ship bridge. The collected images are used to train a deep learning model to automatically recognize the state of the sea based on the Beaufort scale.
  arXiv  Detail & Related papers  (2024-07-04T09:07:25Z)
• OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
  Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
  arXiv  Detail & Related papers  (2024-05-12T09:32:40Z)
• A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance [59.21686775951903]
  Various animals exhibit accurate navigation using environment cues. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps.
  arXiv  Detail & Related papers  (2024-02-06T13:20:56Z)
• Sea level Projections with Machine Learning using Altimetry and Climate Model ensembles [0.6882042556551609]
  Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year) We use machine learning (ML) to investigate future patterns of sea level change. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections.
  arXiv  Detail & Related papers  (2023-08-02T19:18:38Z)
• Transforming Observations of Ocean Temperature with a Deep Convolutional Residual Regressive Neural Network [0.0]
  Sea surface temperature (SST) is an essential climate variable that can be measured via ground truth, remote sensing, or hybrid model methodologies. Here, we celebrate SST surveillance progress via the application of a few relevant technological advances from the late 20th and early 21st century. We develop our existing water cycle observation framework, Flux to Flow (F2F), to fuse AMSR-E and MODIS into a higher resolution product. Our neural network architecture is constrained to a deep convolutional residual regressive neural network.
  arXiv  Detail & Related papers  (2023-06-16T17:35:11Z)
• Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting [52.77986479871782]
  Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts. In this work, we investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions.
  arXiv  Detail & Related papers  (2022-10-17T09:14:35Z)
• Activation Regression for Continuous Domain Generalization with Applications to Crop Classification [48.795866501365694]
  Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. We model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem. We develop a dataset spatially distributed across the entire continental United States.
  arXiv  Detail & Related papers  (2022-04-14T15:41:39Z)
• Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring [1.8689461238197955]
  We train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location. We apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network.
  arXiv  Detail & Related papers  (2021-10-30T00:13:46Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.