60 citations as recorded by crossref.

1. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg https://doi.org/10.1029/2021MS002554
2. A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives H. Snoun et al. https://doi.org/10.1007/s41207-023-00354-6
3. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model J. Brajard et al. https://doi.org/10.1016/j.jocs.2020.101171
4. A comparison of combined data assimilation and machine learning methods for offline and online model error correction A. Farchi et al. https://doi.org/10.1016/j.jocs.2021.101468
5. Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. https://doi.org/10.1016/j.agrformet.2024.110264
6. Differential equation model of financial market stability based on big data L. Hao https://doi.org/10.2478/amns.2021.2.00146
7. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. https://doi.org/10.1088/1748-9326/ac0eb0
8. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. https://doi.org/10.1002/qj.4116
9. Model selection of chaotic systems from data with hidden variables using sparse data assimilation H. Ribera et al. https://doi.org/10.1063/5.0066066
10. Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India A. Dhandapani et al. https://doi.org/10.1016/j.chemosphere.2023.139966
11. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation R. Wang & Z. Shen https://doi.org/10.3390/jmse12010108
12. Data-driven reconstruction of partially observed dynamical systems P. Tandeo et al. https://doi.org/10.5194/npg-30-129-2023
13. Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation G. Gottwald & S. Reich https://doi.org/10.1016/j.physd.2021.132911
14. The future of Earth system prediction: Advances in model-data fusion A. Gettelman et al. https://doi.org/10.1126/sciadv.abn3488
15. ON THE HOPF BIFURCATION AND EQUILIBRIUM POINTS OF PARTIAL BENEFIT SYMBIOSIS OF FINANCIAL MARKET USING DELAY DIFFERENTIAL EQUATIONS D. LI et al. https://doi.org/10.1142/S0218348X25401607
16. Data assimilation method for improving the global spatiotemporal predictions of epidemic dynamics yielded by an ensemble Kalman filter and Metropolis–Hastings sampling F. Liu et al. https://doi.org/10.1007/s11071-023-08632-2
17. Coupling theK-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation M. Fan et al. https://doi.org/10.1515/geo-2020-0312
18. Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning S. Ouala et al. https://doi.org/10.1016/j.physd.2022.133630
19. Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al. https://doi.org/10.5194/tc-17-2965-2023
20. Law of interest rate changes in financial markets based on the differential equation model of liquidity P. Wan & A. Arbad https://doi.org/10.2478/amns.2021.1.00106
21. Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko https://doi.org/10.3103/S1068373924040010
22. Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows P. Clark Di Leoni et al. https://doi.org/10.1103/PhysRevX.10.011023
23. A Physics-Enhanced Network for Predicting Sequential Satellite Images of Typhoon Clouds J. Yuan et al. https://doi.org/10.1109/JSTARS.2025.3574201
24. Super-resolution data assimilation S. Barthélémy et al. https://doi.org/10.1007/s10236-022-01523-x
25. Combining data assimilation and machine learning to build data‐driven models for unknown long time dynamics—Applications in cardiovascular modeling F. Regazzoni et al. https://doi.org/10.1002/cnm.3471
26. A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. https://doi.org/10.1029/2023MS003687
27. ISANet: Deep Neural Network Approximating Image Sequence Assimilation for Tracking Fluid Flows L. Li & J. Ma https://doi.org/10.1109/TGRS.2023.3334612
28. Inferring turbulent environments via machine learning M. Buzzicotti & F. Bonaccorso https://doi.org/10.1140/epje/s10189-022-00258-3
29. Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach A. Sayeed et al. https://doi.org/10.1029/2022EA002375
30. Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation M. Fan et al. https://doi.org/10.1109/ACCESS.2021.3120482
31. RETRACTED: A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. https://doi.org/10.1029/2023WR034630
32. On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation M. Sacco et al. https://doi.org/10.1002/qj.4743
33. Learning latent dynamics for partially observed chaotic systems S. Ouala et al. https://doi.org/10.1063/5.0019309
34. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich https://doi.org/10.1063/5.0066080
35. ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting Y. Xiang et al. https://doi.org/10.1029/2024MS004839
36. Statistical Post-Processing for Gridded Temperature Prediction Using Encoder–Decoder-Based Deep Convolutional Neural Networks A. KUDO https://doi.org/10.2151/jmsj.2022-011
37. Marine data assimilation in the UK: the past, the present, and the vision for the future J. Skákala et al. https://doi.org/10.5194/os-21-1709-2025
38. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. https://doi.org/10.1029/2022MS003474
39. Data Assimilation Networks P. Boudier et al. https://doi.org/10.1029/2022MS003353
40. Learning about structural errors in models of complex dynamical systems J. Wu et al. https://doi.org/10.1016/j.jcp.2024.113157
41. CNN-based model for the spatial imputation (CMSI version 1.0) of in-situ ozone and PM2.5 measurements A. Sayeed et al. https://doi.org/10.1016/j.atmosenv.2022.119348
42. Bridging data and dynamics: neural ODEs for epidemic modeling A. Zidan et al. https://doi.org/10.1007/s11227-025-08197-4
43. Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A. Wikner et al. https://doi.org/10.1063/5.0048050
44. Autodifferentiable Ensemble Kalman Filters Y. Chen et al. https://doi.org/10.1137/21M1434477
45. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. https://doi.org/10.3934/fods.2020015
46. Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case R. Mojgani et al. https://doi.org/10.1063/5.0091282
47. Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0) S. Rasp https://doi.org/10.5194/gmd-13-2185-2020
48. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada https://doi.org/10.5194/gmd-14-5623-2021
49. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet https://doi.org/10.3389/fams.2023.1133226
50. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization M. Bocquet et al. https://doi.org/10.3934/fods.2020004
51. Improving surrogate model robustness to perturbations for dynamical systems through machine learning and data assimilation A. Ajayakumar & S. Raha https://doi.org/10.1098/rspa.2025.0168
52. CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. https://doi.org/10.5194/gmd-15-4569-2022
53. SPLNet: A sequence-to-one learning network with time-variant structure for regional wind speed prediction R. Ye et al. https://doi.org/10.1016/j.ins.2022.07.002
54. A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. https://doi.org/10.1029/2022WR033318
55. Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows S. Pawar et al. https://doi.org/10.1063/5.0012853
56. A machine‐learning and data assimilation forecasting framework for surface waves P. Pokhrel et al. https://doi.org/10.1002/qj.4631
57. Learning earth system models from observations: machine learning or data assimilation? A. Geer https://doi.org/10.1098/rsta.2020.0089
58. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. https://doi.org/10.1063/5.0230837
59. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. https://doi.org/10.1098/rsta.2020.0086
60. Law of interest rate changes in financial markets based on the differential equation model of liquidity P. Wan & A. Hamad Arbad https://doi.org/10.2478/amns.2021.1.00081