Blog

Eddy Detecting Neural Networks: harnessing visible satellite imagery and altimetry for operational oceanography – Evangelos Moschos

Our next seminar will be held on Wednesday the 11th of May 2022, at 14h00 ECT, at the Pierre et Marie Curie campus of Sorbonne Université, in seminar room 105 of LiP6, located on the first floor of the corridor 25/26 (easier access through tower 26).


The seminar can also be followed remotely through zoom here: https://zoom.us/j/98859268451 

You can ask questions during and after the talk in the slack channel: https://tinyurl.com/AI4CLIMATESLACK


Evangelos Moschos’ talk is entitled:

« Eddy Detecting Neural Networks: harnessing visible satellite imagery and altimetry for operational oceanography »

Mesoscale Eddies are oceanic vortices with a typical radius of the order of 20–80 km. They live for days, months or even years, trapping and transporting heat, salt, pollutants, and various biogeochemical components from their regions of formation to remote areas. Eddies have a primordial role in the oceanic circulation modifying the surface currents as well as the mixed layer depth of the ocean. Thus, eddy detection and tracking is an emergent thematic of operational oceanography with advances in the last 10 years.

Eddies can be tracked on altimetry-derrived Sea Surface Height (SSH) and geostrophic velocity currents through standard detection methods. However the strong spatio-temporal interpolation of the altimetric observations raises uncertainty of detection. Satellite imagery, on the visible and infrared spectrum, contains signatures of eddies, which despite their fine-scale resolution are too complex to be processed by geometric based methods.

Machine Learning/Computer Vision methods have proved very prominent in exploiting complex remote sensing information, such as the eddy signatures on satellite imagery. We build a Convolutional Neural Network which can accurately detect the position, shape and form of mesoscale eddies in satellite Sea Surface Temperature (SST) images. Our CNN only misses 3% of coherent structures, with more than 20km radius and a clear signature on the Sea Surface Temperature, compared with a 34% miss rate of standard eddy detection methods on altimetric maps. Additionally, while standard altimetric detection has a 10% false positive rate (“ghost eddies”) the neural network detects less than 1% of ghosts.

By combining detections on data stemming from different sensors (here SSH & SST) we do also provide a set of reference nowcast (real-time) detections with almost zero uncertainty. We focus here on an application of validation of operational numerical oceanographic models in the Mediterranean Sea through the comparison of their outputs with the reference detections, allowing to quantify their nowcast error and pick-and-choose between different numerical on a certain region.

Biographic notice:
Evangelos Moschos is a PhD student at the Laboratoire de Météorologie Dynamique, Ecole Polytechnique, France. After an engineering diploma in the Athens Polytechnic, he developed a keen interest in bridging computer vision methods with earth observation and operational oceanography. Co-founder of Amphitrite, a start-up harnessing AI to provide maritime stakeholders with real-time, reliable oceanic data.

Towards the combination of physical and data-driven forecasts for Earth system prediction – Eviatar Bach (ENS Paris)

The seminar is on January the 26th, at 14:00 (CET) remotely and in person. The in-person meeting will be held in the SCAI conference room (map at the end of the post).

If you like to attend online, here is the link for zoom: https://us02web.zoom.us/j/84372436675?pwd=eWhwR2NXYllKUUpWOGU3OGEvUVR3Zz09

Eviatar Bach’s presentation is entitled:

«Towards the combination of physical and data-driven forecasts for Earth system prediction»

Abstract:

Due to the recent success of machine learning (ML) in many prediction problems, there is a high degree of interest in applying ML to Earth system prediction. However, because of the high dimensionality of the system, it is critical to use hybrid methods which combine data-driven models, physical models, and observations. I will present two such hybrid methods: Ensemble Oscillation Correction (EnOC) and multi-model data assimilation (MM-DA).

Oscillatory modes of the climate system are one of its most predictable features, especially at intraseasonal timescales. It has previously been shown that these oscillations can be predicted well with statistical methods, often with better skill than dynamical models. However, they only represent a portion of the signal, and a method for beneficially combining them with dynamical forecasts of the full system has not previously been developed. Ensemble Oscillation Correction (EnOC) is a method which corrects oscillatory modes in ensemble forecasts from dynamical models. We show results of EnOC applied to chaotic toy models with significant oscillatory components, as well as to forecasts of South Asian monsoon rainfall.

A more general method for combining multiple models and observations is multi-model data assimilation (MM-DA). MM-DA generalizes the variational or Bayesian formulation of the Kalman filter. However, previous implementations of this approach have not estimated the model error, and have therewith not been able to correctly weight the separate models and the observations. Here, we show how multiple models can be combined for both forecasting and DA by using an ensemble Kalman filter with adaptive model error estimation. This methodology is applied to multiscale chaotic models and results in significant error reductions compared to the best model and to an unweighted multi-model ensemble. Lastly, I will discuss the potential of this method for combining physical model forecasts, ML, and observations.

Bio

Eviatar Bach is a Make Our Planet Great Again (MOPGA) postdoctoral fellow in Michael Ghil’s group at the École Normale Supérieure in Paris. Previously, he obtained his PhD at the University of Maryland, College Park with Eugenia Kalnay and Safa Mote. He is currently working on improving geophysical forecasts with data assimilation and data-driven prediction methods, and is interested in understanding the nonlinear dynamics and predictability of the climate system.

What is the cost? Calculating the environmental impact of scientific calculus – Anne-Laure Ligozat

The seminar is scheduled for Friday the 3rd of December 2021 at 10:00 CET by Anne-Laure Ligozat and will be held both remotely and in person, in English, with slides in English. The in-person meeting will be held in the SCAI conference room (map at the end of the post).

Link to the zoom session: https://zoom.us/j/96346324217

Anne-Laure Ligozat’s presentation is entitled:

«Calculer quoi qu’il en coûte ? Coût environnemental du calcul scientifique »
(«What is the cost? Calculating the environmental impact of scientific calculus.»)

Abstract:

Dans ce séminaire, je présenterai les différents impacts
environnementaux dus au numérique, pour introduire ceux du calcul
scientifique. Les impacts seront envisagés aussi bien au niveau des
programmes informatiques, en indiquant les outils existants pour le
calcul de leur empreinte carbone et leurs limitations, qu’au niveau d’un
laboratoire de recherche.

/

In this seminar, I will be presenting the different environmental impacts of numerical computations, before focusing on those of scientific calculus. These impacts are analyzed both at the coding and program execution level, by showcasing the tools for monitoring their carbon impact and their limitations, as well as at the laboratory level.

Short bio:

Anne-Laure Ligozat est maîtresse de conférence en informatique à l’ENSIIE et au LISN à Saclay. Son thème de recherche est l’impact environnemental du numérique.
Anne-Laure Ligozat is an assistant professor in informatics at ENSIIE and at the LISN lab of Saclay. Her research interest is the environmental impact of informatics.

New ways for dynamical prediction of extreme heat waves: rare event simulations and machine learning with deep neural networks. – Freddy Bouchet (ENS Lyon)

The seminar is on October the 19th, at 14:00 (CEST)both in-person and remotely

Place of the seminar: « Campus Pierre & Marie Curie » of Sorbonne University. It will take place in SCAI seminar room, building « Esclangon », 1st floor

If you like to attend online, here is the link for zoom: https://us02web.zoom.us/j/82605468661

Freddy Bouchet’s presentation is entitled:

«New ways for dynamical prediction of extreme heat waves: rare event simulations and machine learning with deep neural networks.»

Abstract:

In the climate system, extreme events or transitions between climate attractors are of primarily importance for understanding the impact of climate change. Recent extreme heat waves with huge impacts are striking examples. However, it is very hard to study those events with conventional approaches, because of the lack of statistics, because they are too rare for historical data and because realistic models are too complex to be run long enough.

We cope with this lack of data issue using rare event simulations. Using some of the best climate models, we oversample extremely rare events and obtain several hundreds more events than with usual climate runs, at a fixed numerical cost. Coupled with deep neural networks this approach improves drastically the prediction of extreme heat waves.

This shed new light on the fluid mechanics processes which lead to extreme heat waves. We will describe quasi-stationary patterns of turbulent Rossby waves that lead to global teleconnection patterns in connection with heat waves and analyze their dynamics. We stress the relevance of these patterns for recently observed extreme heat waves and the prediction potential of our approach.

Climate Modeling in the Age of Machine Learning – Laure Zanna (NYU)

The seminar is on June the 23th, at 15:00 and will be held remotely, in english.

Link to the zoom session: https://us02web.zoom.us/j/85178591120

Laure Zanna’s presentation is entitled:

«Climate Modeling in the Age of Machine Learning »

Abstract:

Numerical simulations used for weather and climate predictions solve approximations of the governing laws of fluid motions on a grid. Ultimately, uncertainties in climate predictions originate from the poor or lacking representation of processes, such as ocean turbulence and clouds that are not resolved on the grid of global climate models. The representation of these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? In this talk, I will discuss the current state of climate modeling and its future, focusing on the advantages and challenges of using machine learning for climate projections. I will present some of our recent work in which we leverage tools from machine learning and deep learning to learn representations of unresolved ocean processes and improve climate simulations. Our work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions.

Short bio:

Laure Zanna is a Professor in Mathematics & Atmosphere/Ocean Science at the Courant Institute, New York University.  Her research focuses on the role of ocean dynamics in climate change. Prior to NYU, she was a faculty member at the University of Oxford until 2019 and obtained her PhD in 2009 in Climate Dynamics from Harvard University. She was the recipient of the 2020 Nicholas P. Fofonoff Award from the American Meteorological Society “For exceptional creativity in the development and application of new concepts in ocean and climate dynamics”. She is the lead principal investigator of M²LInES, an international effort supported by Schmidt Futures to improve climate models with scientific machine learning. 

Working group 4: Marie Dechelle – Bridging Dynamical Models and Deep Networks to Solve Forward and Inverse Problems

 21 Juin à 10h
Campus de Jussieu, Salle de réunion SCAI,
Bâtiment Esclangon 1er étage
Zoom: https://zoom.us/j/98265750503

Partially observed dynamical systems embrace a wide class of phenomena and represent an overwhelming majority of Earth science modeling, traditionally relying on ordinary or partial differential equations. Recent trends consider Machine Learning as an alternative or complementary approach to traditional physical models, allowing the integration of observations and potentially faster computations through model reduction. In this regard, latest works study the learning of the decomposition between model-based (MB) and data driven (ML) dynamical representations. However, learning such a decomposition with the sole supervision on the trajectories is ill-posed.

We introduce a learning algorithm to bridge model-based prediction and data-based algorithms, while solving the ill-posedness. This one relies on a cost function based on the computation of an upper bound of the prediction error, which enables us to minimize the contribution of the data driven algorithm while recovering physical parameters of the MB part. We evidence the soundness of our approach on a physical dataset based on simplified Navier-Stokes equations. We also present preliminary results on outputs of the ocean model NATL60.

L’Atelier interne « SCAI & AI4Climate » réunit les chercheurs, ingénieurs, doctorants, post doctorants concernés par les thématiques liées à conception et l’utilisation de nouvelles méthodes d’Intelligence Artificielle pour l’étude de l’environnement, allant du modèle à l’observation. Les premières réunions seront consacrées aux travaux des doctorants. L’exposé sera suivi d’une discussion avec les participants sur l’approche et les perspectives possibles du travail. 


Filling gaps in ocean satellite data -Aida Alvera-Azcárate & Alexander Barth

Link to the slides.

The seminar is on May the 6th, at 14:00 and will be held remotely, in english.

Link to the zoom session: https://zoom.us/j/93683521817

Aida Alvera-Azcárate’s presentation is entitled:

« Filling gaps in ocean satellite data »

Abstract:

 Satellite data offer an unequalled 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.

Short bios:

Aida Alvera-Azcárate is a researcher at the GHER (GeoHydrodynamics and Environment Research) of the University of Liege in Belgium. She did a PhD in Science at the University of Liege and made a post-doc at the University of South Florida (US) before joining the GHER in 2007 where she studies the ocean using satellite and in situ data and works in the development of interpolation techniques to reconstruct satellite data.

Alexander Barth is a researcher working at the University of Liege (Belgium) in the GHER group (GeoHydrodynamics and Environment Research). He did a PhD on nested numerical ocean models and data assimilation. Currently he is working on variational analysis schemes for climatologies and neural networks to reconstruct missing data.

Narrowing uncertainties of climate projections using data science tools? -Pierre Tandeo

The seminar is on March the 26th, at 10 o’clock and will be held remotely, in english.

The slides can be found here. We are currently uploading the video of the talk and will be adding a link to it as soon as it uploads.

Link to the zoom session: https://zoom.us/j/94170014183

Pierre Tandeo’s presentation is entitled:

« Narrowing uncertainties of climate projections using data science tools? »

Abstract:

 Climate indices show large variability in CMIP climate predictions. In this presentation, we propose to weight multi-model climate simulations to reduce the uncertainty in climate predictions, and better estimate the future evolution of climate indices. The proposed methodology is based on advanced data science tools (i.e, data assimilation, analog forecasting, model evidence metrics), to accurately compute distances between current observations and simulated climate indices. This low-cost procedure is tested on a simplified climate model. The results show that the methods can be applied locally and is able to identify relevant parameterizations.

Short bio:

Pierre Tandeo an associate professor at IMT Atlantique (Brest, France) and an associate researcher at the Data Assimilation Research Team, RIKEN Center for Computational Science (Kobe, Japan). More information:  https://tandeo.wordpress.com/.

Working group 3: Pierre Lepetit – Estimation of visibility and snow height on webcam images with learning to rank approach

L’Atelier interne « SCAI & AI4Climate » réunit les chercheurs, ingénieurs, doctorants, post doctorants concernés par les thématiques liées à conception et l’utilisation de nouvelles méthodes d’Intelligence Artificielle pour l’étude de l’environnement, allant du modèle à l’observation. Les premières réunions seront consacrées aux travaux des doctorants. L’exposé sera suivi d’une discussion avec les participants sur l’approche et les perspectives possibles du travail. 

16 Mars à 10h
sur le campus de Jussieu,
Salle de réunion SCAI
Batiment Esclangon 1er étage

Participer à la réunion Zoom
(voir information de connexion ci-dessous)

  • The image-based estimation of meteorological parameters provides clear benefits for surface weather observation. When a local event arises, as a dense fog or a snow settling, webcams and CCTV cameras are sources of valuable information. These images actually inform about the class of weather (sunny, rainy, foggy, snowy, etc). They also enable to gauge quantitative parameters as the horizontal visibility (the farest you can see), the snow height, the precipitation rate, etc, with a variable precision.
  • Recently, the weather classification task has been successfully addressed by deep learning approaches. However, the quantitative estimation faces a strong difficulty: the existing data sets that contain both images and precise weather measurements are rare and involve only few different outdoor scenes. It is virtually impossible for an expert to assign image-wise quantitative labels, but it is possible to compare two images from the same webcam and therefore assign pairwise labels. An “uncomparable” label being assigned to couples for which the expert is not able to distinguish the two images with respect to the parameter.
  • This analysis gives the starting point of the workshop. The discussion will deal with the methods of labeling, learning to rank and calibration that may help to yield such comparisons and to predict ordinal or quantitative estimations of visibility and snow height. The way uncomparable pairs could lead to predict an image-wise uncertainty will also be addressed.

Participer à la réunion Zoom

https://zoom.us/j/98278319724

ID de réunion : 982 7831 9724

Trouvez votre numéro local : https://zoom.us/u/agSnuNJYM