Publications related to Data assimilation

Assessing the simulation of snowfall at Dumont d'Urville, Antarctica, during the YOPP-SH special observing campaign

Alexis Berne, Etienne Gabriel Henri Vignon

The performance of a set of atmospheric models and meteorological reanalyses in the representation of precipitation days in Antarctica is assessed using ground-based observations such as a precipitation gauge and a Micro Rain Radar during the Year Of Polar ...

WILEY2023

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Michael Lehning, Wolf Hendrik Huwald, Jérôme François Sylvain Dujardin, Franziska Gerber, Fanny Kristianti

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

2023

An evaluation of new particle formation events in Helsinki during a Baltic Sea cyanobacterial summer bloom

Lubna Dada, Marjan Marbouti

Several studies have investigated new particle formation (NPF) events from various sites ranging from pristine locations, including forest sites, to urban areas. However, there is still a dearth of studies investigating NPF processes and subsequent aerosol ...

COPERNICUS GESELLSCHAFT MBH2022

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Damien Bouffard, Camille Roland Marie Minaudo, Cintia Luz Ramon Casanas, Firat Ozdemir

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics packa ...

COPERNICUS GESELLSCHAFT MBH2022

Learning Decoupled Representations for Human Pose Forecasting

Alexandre Massoud Alahi, Yuejiang Liu, Saeed Saadatnejad, Taylor Ferdinand Mordan, Behnam Parsaeifard

Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not eff ...

IEEE2021

Robustness via Cross-Domain Ensembles

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong predi ...

IEEE2021

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau

This article presents the methodology developed to generate and use dosimeter covariances and to estimate nuisance parameters for the PETALE experimental programme. In anticipation of the final experimental results, this work investigates the consideration ...

2020

Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

Alfred Johny Wüest, Damien Bouffard, Theo Baracchini

The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simul ...

2020

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Data assimilation

Good enough: Intermediate complexity atmospheric models improve the representation of processes affecting seasonal snow

Machine Learning Security Against Data Poisoning: Are We There Yet?

Assessing the simulation of snowfall at Dumont d'Urville, Antarctica, during the YOPP-SH special observing campaign

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

An evaluation of new particle formation events in Helsinki during a Baltic Sea cyanobacterial summer bloom

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Learning Decoupled Representations for Human Pose Forecasting

Robustness via Cross-Domain Ensembles

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

Graph Chatbot

Chat with Graph Search

Assessing the simulation of snowfall at Dumont d'Urville, Antarctica, during the YOPP-SH special observing campaign

Machine Learning Security Against Data Poisoning: Are We There Yet?

Good enough: Intermediate complexity atmospheric models improve the representation of processes affecting seasonal snow

An evaluation of new particle formation events in Helsinki during a Baltic Sea cyanobacterial summer bloom

Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

Robustness via Cross-Domain Ensembles

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Learning Decoupled Representations for Human Pose Forecasting

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation