NP5.1

Inverse problems, Predictability, and Uncertainty Quantification in Geosciences using data assimilation and its combination with machine learning

NP5.1

Inverse problems, Predictability, and Uncertainty Quantification in Geosciences using data assimilation and its combination with machine learning

Convener: Javier Amezcua | Co-conveners: Natale Alberto Carrassi, Sergey Frolov, Tijana Janjic, Lars Nerger, Olivier Talagrand

vPICO presentations

| Tue, 27 Apr, 09:00–12:30 (CEST)

vPICO presentations: Tue, 27 Apr

Chairpersons: Javier Amezcua, Olivier Talagrand, Natale Alberto Carrassi

09:00–09:05

5-minute convener introduction

Covariance matrices and covariance models

EGU21-1778

solicited

Dynamically informed covariance modelling in data assimilation

Ross Bannister and Ruth Petrie

EGU21-13764

ECS

Insights about the hybrid error covariance models

Francesco Sardelli and Craig Bishop

EGU21-9169

ECS

Empirical determination of the covariance of forecast errors: an empirical justification and reformulation of Hybrid covariance models

Diego Saul Carrio Carrio et al.

EGU21-13672

Including the spatial observation error correlation in data assimilation of AMSU-A radiances

Koji Terasaki and Takemasa Miyoshi

Mathematics and methods

EGU21-16125

ECS

Score matching filters

Marie Turčičová et al.

EGU21-2463

Ensemble data assimilation for systems with different degrees of nonlinearity with a hybrid nonlinear-Kalman ensemble transform filter

Lars Nerger

EGU21-10591

ECS

Assimilation of Nonlinear Observations with the Maximum Likelihood Ensemble Filter

Saori Nakashita and Takeshi Enomoto

EGU21-129

Homotopy Particle Filter and Data Assimilation

Juan Restrepo and Jorge Ramirez

EGU21-4414

ECS

Randomised preconditioning for the forcing formulation of weak constraint 4D-Var

Ieva Dauzickaite et al.

EGU21-7510

Sensitivity Operator Inverse Modeling Framework for Advection-Diffusion-Reaction Models with Heterogeneous Measurement Data

Alexey Penenko et al.

EGU21-13994

ECS

Triangular grid-based common inversion framework for different geophysical data to improve subsurface imaging

Vishnu Kant Verma and Anand Singh

EGU21-16403

Ensemble Kalman Filter for non-conservative moving mesh solvers with a joint physics and mesh location update

Christian Sampson et al.

Uncertainty Quantification

EGU21-13077

A test of an alternative approach for uncertainty representation in weather forecasting

Tijana Janjic et al.

EGU21-16199

ECS

LESbrary: A library of large eddy simulation data for the calibration and uncertainty quantification of ocean surface boundary layer turbulence parameterizations

Gregory Wagner et al.

EGU21-9254

Machine learning-based uncertainty quantification for data assimilation: a simple model experiment

Juan Ruiz et al.

EGU21-4807

Enabling efficient uncertainty quantification for seismic modeling via projection-based model reduction

Francesco Rizzi et al.

Novel applications

EGU21-10424

ECS

Improving Ocean Circulations Using Lagrangian Data Assimilation of Surface Drifters During Grand Lagrangian Deployments | Highlight

Luyu Sun et al.

EGU21-9947

ECS

Parameter estimation for ocean biogeochemical component in a global model using Ensemble Kalman Filter: a twin experiment

Tarkeshwar Singh et al.

EGU21-3851

ECS

Extraction of Nonlinear Dynamics of Heterogeneous Reactions Based on Sparse Modeling

Masaki Ito et al.

EGU21-7057

ECS

A Statistical Reconstruction of Sea-Surface Temperature and Sea-Ice Concentration for the Last Glacial Maximum | Highlight

Lachlan Astfalck et al.

EGU21-3880

ECS

Assimilating sea ice deformation observations using a multiscale alignment ensemble data assimilation method | Highlight

Yue Ying and Laurent Bertino

EGU21-14539

Approaches to improve the robustness of the Comparison Model Method for the inverse problem of groundwater hydrology

Alessandro Comunian and Mauro Giudici

09:57–10:30

Meet the authors in their breakout text chats

Chairpersons: Lars Nerger, Sergey Frolov, Tijana Janjic

Coupled data assimilation

EGU21-3316

ECS

solicited

Air-sea strongly coupled data assimilation for tropical cyclone prediction

Xingchao Chen

EGU21-3170

ECS

The role of flow-dependent oceanic background-error covariance information in air-sea coupled data assimilation during tropical cyclones: a case study

Tsz Yan Leung et al.

EGU21-14181

ECS

Strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM: comparison with the weakly coupled data assimilation

Qi Tang et al.

EGU21-9306

Strongly coupled ensemble transform Kalman filter estimation of ocean optical parameters in a coupled GCM

Vassili Kitsios et al.

EGU21-8360

ECS

Strongly Coupled Assimilation of a Hypothetical Ocean Current Observing Network within a Regional Ocean-Atmosphere Coupled Model: An OSSE Case Study of Typhoon Hato

Luke Phillipson et al.

Machine learning

EGU21-10475

ECS

A machine learning approach to the observation operator for satellite radiance data assimilation

Jianyu Liang et al.

EGU21-7432

ECS

State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics

Quentin Malartic et al.

EGU21-15678

Jointly learning variational data assimilation models and solvers for geophysical dynamics

Ronan Fablet et al.

EGU21-2585

ECS

High-resolution Ensemble Kalman Fiter with a low-resolution model using a machine learning super-resolution approach.

Sébastien Barthélémy et al.

EGU21-3560

Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation | Highlight

Georg Gottwald and Sebastian Reich

EGU21-14158

ECS

Inversion of One Dimensional Electrical Resistivity Data in Anisotropic Media via Artificial Neural Networks

Doğukan Durdağ and Ertan Pekşen

EGU21-913

A Neural Network-Based Observation Operator for Coupled Ocean-Acoustic Variational Data Assimilation

(withdrawn)

Andrea Storto et al.

EGU21-4350

ECS

Training a convolutional neural network to conserve mass in data assimilation

Yvonne Ruckstuhl et al.

EGU21-10932

Correcting model error with an online Artificial Neural Network

Marcin Chrust et al.

EGU21-9566

Machine learning based conditional mean filter: a non-linear extension of the ensemble Kalman filter

Truong-Vinh Hoang et al.

EGU21-6036

ECS

Using machine learning techniques to generate analog ensembles for data assimilation

Lucia Yang and Ian Grooms

11:40–12:30

Meet the authors in their breakout text chats