Posters
This year, given the virtual nature of the conference we were called to adapt the poster format. As such, authors were not required to provide a poster, but instead a small (we suggest 6 slides long) powerpoint-style presentation .pdf.
Slack here. (Or here)
The posters can be found here.
Paper ID | Paper Title | Presenting author |
2 | A data-driven framework for the stochastic parametrization and reconstruction of small-scale features in climate models | Themis Sapsis |
3 | Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks | Bastien FRANCOIS |
4 | A Hybrid Deep Generative Approach to Simulate Spatial Patterns of Daily Temperature and Rainfall | Adway Mitra |
5 | Application of machine learning techniques for regional bias correction of SWE estimates in Ontario, Canada | Fraser King |
6 | Exploring Deep Learning techniques to detect and track Tropical Cyclones | Ashwin Samudre |
7 | A DEEP LEARNING APPROACH TO SHORT-TERM QUANTITATIVE PRECIPITATION FORECASTING | Nishant Yadav |
9 | CNN-Based Forecasting of Intraseasonal Mean and Active/Break Spells for Indian Summer Monsoon | Moumita Saha |
10 | Intra-hour Forecasting of Solar Irradiance with a Hybrid Random Forest-Persistence Model | Moumita Saha |
11 | Data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations | Maxime Beauchamp |
12 | Toward Enhanced Seasonal Forecasts: Characterization of the Polar Vortex Variability | Raphaël de Fondeville |
13 | fcPrecipNet: Deep Learning based Rainfall Prediction using surface observation data | Yeji Choi |
14 | Learning Chaotic and Stochastic Dynamics from Noisy and Partial Observation using Variational Deep Learning | Duong Van Nguyen |
15 | Stochastic simulation of cloud-aerosol tracks | Lekha Patel |
16 | Boosting performance in machine learning of geophysical flows via scale separation | Davide Faranda |
17 | Artificial intelligence reconstructs missing climate information | Christopher Kadow |
19 | RETRIEVING RAIN RATES FROM SPACE BORNE MICROWAVE SENSORS USING U-NETS. | Nicolas Viltard |
21 | Heat Waves and Myocardial Infarctions: Towards Modelling Health Impacts of Climate Change | Lennart C Marien |
22 | A Gaussian process state-space model for atmospheric CO2 and sea surface temperature index reconstruction from boron isotope and planktonic δ18O proxies | Taehee Lee |
25 | Towards dynamical adjustment of the full temperature distribution | Edoardo Vignotto |
27 | Examining Internal Cloud Cluster Variability | Alex Schuddeboom |
28 | Wintertime Atmospheric Circulation Regimes in the Euro-Atlantic Sector: Persistence and Non-Stationarity | Swinda K.J. Falkena |
30 | A Big, Public Dataset of Marine Heat Wave Events for Studies of Climate Change | J. Xavier Prochaska |
31 | Quantification of sensitivities for aerosol-cloud interactions in marine boundary layer clouds with machine learning | Lukas Zipfel |
32 | Detecting Marine Heat Waves using Deep Learning | Kamil J Kisielewicz |
33 | Rain Nowcasting using U-Net neural networks | Vincent Bouget |
35 | Testing the Suitability of Tree-based Models for Climate Statistical Downscaling | Mikel N Legasa |
36 | Learning Patterns of Climate-induced Sea Level Changes with Gaussian Processes | Veronica Nieves |
37 | SOUTHERN OCEAN FRONT DETECTION | Simon D. A. Thomas |
38 | Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures | Thomas Opitz |
39 | On the Ability of Deep Learning to Classify Convective Storms of a Future Climate | Maria J Molina |
40 | Data-driven approaches to upsample Sentinel-5P satellite methane column density observations | Kalai Ramea |
41 | Classification of Eddy Signatures in SST images with CNNs | Evangelos Moschos |
42 | Detection and attribution of reduced satellite-observed aerosol loading to COVID-19 with machine learning | Hendrik Andersen |
44 | Observation-driven attribution of air pollution determinants | Jan Cermak |
46 | Discovering causal factors of drought in Ethiopia | Mohammad Noorbakhsh |
47 | The Antarctic Slope Front position estimated from hydrography | Etienne Pauthenet |
49 | Spatial and Temporal Performance of Deep Generative Models | Shahine Bouabid |
50 | Understanding Deep Learning Decisions in Statistical Downscaling Models | Jorge Baño-Medina |
51 | Towards a generalized Framework for Gapfilling complex Earth observations | Verena Bessenbacher |
52 | WEAK SUPERVISION LEARNING FOR SEMANTIC SEGMENTATION OF METOCEANIC PROCESSES | Aurélien Colin |
53 | Learning missing part of physics-based models within a variational data assimilation scheme | Arthur Filoche |
54 | Investigating a Satellite Precipitation Product's Ability to Reproduce Summer Precipitation Extremes in the Northern US Rocky Mountains | Brook Russell |
55 | Linkages between Arctic Sea Ice decline and atmospheric circulation regimes using a Multinomial logistic regression approach | Johannes Riebold |
57 | Modeling Watershed Nutrient Concentrations with AutoML | Grace E Kim |
59 | Unsupervised Learning for Known and Unknown Dynamics | Marie Déchelle |
61 | SELECTION OF DYNAMICAL MODEL USING ANALOG DATA ASSIMILATION | Juan J Ruiz |
62 | Improving weather and climate predictions by training of supermodels | Francine J Schevenhoven |
63 | Creating Bias-Corrected Diffuse and Direct Beam Components of Hourly Solar Radiation from 1980-2019 using Ensembles of Decision Trees | TC Chakraborty |
64 | A multi-forcing ensemble of data-driven global runoff reconstructions using machine learning | Lukas Gudmundsson |
65 | Causal Discovery as a novel approach for CMIP6 climate model evaluation | Kevin DEBEIRE |
66 | QUANTIFICATION OF FORECAST UNCERTAINTYUSING NEURAL NETWORK | Maximiliano A Sacco |
67 | The Evolution of Marine Heat Waves through Extremal Networks | Katerina Giamalaki |
68 | LEARNING TO COMPARE VISIBILITY ON WEBCAM IMAGES | Pierre Lepetit |
69 | Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces | Maike Sonnewald |
70 | Understanding global vegetation dynamics from a nonlinear spatio-temporal dimensionlity reduction | Diego Bueso Acevedo |
72 | A Generalized Spatio-Temporal Threshold Clustering Method for Identification of Extreme Event Patterns | vitaly kholodovsky |
73 | Predicting Infrastructure Network Damage from Forecasts of Hurricanes Using Large-Ensemble Outputs | Derek Chang |
74 | Generative Modeling of Atmospheric Convection | Griffin S Mooers |
76 | VARIATIONAL AUTOENCODER ANOMALY-DETECTION OF AVALANCHE DEPOSITS IN SATELLITE SAR IMAGERY | Saumya Sinha |
77 | Subseasonal Forecasts of Arctic Sea Ice Using Random Forest Models | Marie C McGraw |
78 | Towards emulating high-resolution climate dynamics using deep generative models | Rishabh Gupta |
80 | Machine Learning for Long-lead Forecast of Heatwaves in the United States | Negin Sobhani |
81 | A NEURAL-BASED BIO-REGIONALIZATION OF THE MEDITERRANEAN SEA USING SATELLITE AND ARGO-FLOAT RECORDS | Roy El Hourany |
82 | Climate Data Hub for Scalable Action | Sumeet Sandhu |
83 | Deep Learning of subgrid parameterization of momentum forcing in a high-resolution coupled model | Arthur Guillaumin |
84 | LEXICON-BASED SENTIMENT ANALYSIS AND EMOTION CLASSIFICATION OF THE GLOBAL YOUTH CLIMATE PROTEST ON TWITTER | Temitayo M. Fagbola |
86 | TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall | Rilwan Adewoyin |
88 | A COMPLETE CAUSAL DISCOVERY FRAMEWORK | Peter Jan van Leeuwen |