Our People
Dr Yumeng Chen
Data Assimilation Scientific Programmer
Data Assimilation
Research interests
I am interested in the implementation of data assimilation and its applications to climate systems. I work on the parameter estimations in sea ice models, the data assimilation library, parallel data assimilation framework (PDAF), and its application to ocean biogeochemistry models and other fields.
Recent publications
A data-driven sea-ice model with generative deep learning. 2024-11-27
DOI: https://doi.org/10.5194/egusphere-egu24-11908
Multivariate state and parameter estimation using data assimilation in a Maxwell-Elasto-Brittle sea ice model. 2024-11-27
DOI: https://doi.org/10.5194/egusphere-egu24-2377
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble. 2024-09-01
DOI: https://doi.org/10.1063/5.0230837
Tailoring data assimilation to discontinuous Galerkin models. 2024-07
DOI: https://doi.org/10.1002/qj.4737
A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.0. 2024-06-11
DOI: https://doi.org/10.5194/egusphere-2024-1078
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology. 2024-05-14
DOI: https://doi.org/10.5194/tc-18-2381-2024
DAPPER: Data Assimilation with Python: a Package for Experimental Research. 2024-02-29
DOI: https://doi.org/10.21105/joss.05150
Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology. 2023-10-16
DOI: https://doi.org/10.5194/egusphere-2023-1809
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses. 2023-07-27
DOI: https://doi.org/10.5194/gmd-16-4233-2023
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology. 2023-07-21
DOI: https://doi.org/10.5194/tc-17-2965-2023
Ensemble Data Assimilation in NEMO using PDAF. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-3468
Simplified Kalman smoother and ensemble Kalman smoother for improvingocean forecasts and reanalyses. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-17579
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020. 2023-04-25
DOI: https://doi.org/10.5194/tc-17-1735-2023
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses. 2023-03-15
DOI: https://doi.org/10.5194/egusphere-2023-337
Deep learning of subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell-Elasto-Brittle rheology. 2023-01-02
DOI: https://doi.org/10.5194/egusphere-2022-1342
Novel Arctic sea ice data assimilation combining ensemble Kalman filter with a Lagrangian sea ice model. 2022-08-16
DOI: https://doi.org/10.5194/egusphere-2022-627
Learning and screening of neural networks architectures for sub-grid-scale parametrizations of sea-ice dynamics from idealised twin experiments. 2022-03-27
DOI: https://doi.org/10.5194/egusphere-egu22-5910
Inferring the instability of a dynamical system from the skill of data assimilation exercises. 2021-12-23
DOI: https://doi.org/10.5194/npg-28-633-2021
Extending legacy climate models by adaptive mesh refinement for single-component tracer transport: a case study with ECHAM6-HAMMOZ (ECHAM6.3-HAM2.3-MOZ1.0). 2021-05-03
DOI: https://doi.org/10.5194/gmd-14-2289-2021
Comparison of dimensionally split and multi-dimensional atmospheric transport schemes for long time steps. 2017-10
