50 citations as recorded by crossref.

1. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg 10.1029/2021MS002554
2. A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives H. Snoun et al. 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. 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. 10.1016/j.jocs.2021.101468
5. Differential equation model of financial market stability based on big data L. Hao 10.2478/amns.2021.2.00146
6. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
7. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. 10.1002/qj.4116
8. Model selection of chaotic systems from data with hidden variables using sparse data assimilation H. Ribera et al. 10.1063/5.0066066
9. Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India A. Dhandapani et al. 10.1016/j.chemosphere.2023.139966
10. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation R. Wang & Z. Shen 10.3390/jmse12010108
11. Data-driven reconstruction of partially observed dynamical systems P. Tandeo et al. 10.5194/npg-30-129-2023
12. Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation G. Gottwald & S. Reich 10.1016/j.physd.2021.132911
13. The future of Earth system prediction: Advances in model-data fusion A. Gettelman et al. 10.1126/sciadv.abn3488
14. 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. 10.1007/s11071-023-08632-2
15. Coupling theK-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation M. Fan et al. 10.1515/geo-2020-0312
16. Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning S. Ouala et al. 10.1016/j.physd.2022.133630
17. Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al. 10.5194/tc-17-2965-2023
18. Law of interest rate changes in financial markets based on the differential equation model of liquidity P. Wan & A. Arbad 10.2478/amns.2021.1.00106
19. Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows P. Clark Di Leoni et al. 10.1103/PhysRevX.10.011023
20. Super-resolution data assimilation S. Barthélémy et al. 10.1007/s10236-022-01523-x
21. Combining data assimilation and machine learning to build data‐driven models for unknown long time dynamics—Applications in cardiovascular modeling F. Regazzoni et al. 10.1002/cnm.3471
22. A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
23. ISANet: Deep Neural Network Approximating Image Sequence Assimilation for Tracking Fluid Flows L. Li & J. Ma 10.1109/TGRS.2023.3334612
24. Inferring turbulent environments via machine learning M. Buzzicotti & F. Bonaccorso 10.1140/epje/s10189-022-00258-3
25. Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach A. Sayeed et al. 10.1029/2022EA002375
26. Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation M. Fan et al. 10.1109/ACCESS.2021.3120482
27. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
28. On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation M. Sacco et al. 10.1002/qj.4743
29. Learning latent dynamics for partially observed chaotic systems S. Ouala et al. 10.1063/5.0019309
30. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich 10.1063/5.0066080
31. Statistical Post-Processing for Gridded Temperature Prediction Using Encoder–Decoder-Based Deep Convolutional Neural Networks A. KUDO 10.2151/jmsj.2022-011
32. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. 10.1029/2022MS003474
33. Data Assimilation Networks P. Boudier et al. 10.1029/2022MS003353
34. Learning about structural errors in models of complex dynamical systems J. Wu et al. 10.1016/j.jcp.2024.113157
35. CNN-based model for the spatial imputation (CMSI version 1.0) of in-situ ozone and PM2.5 measurements A. Sayeed et al. 10.1016/j.atmosenv.2022.119348
36. Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A. Wikner et al. 10.1063/5.0048050
37. Autodifferentiable Ensemble Kalman Filters Y. Chen et al. 10.1137/21M1434477
38. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
39. Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case R. Mojgani et al. 10.1063/5.0091282
40. 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 10.5194/gmd-13-2185-2020
41. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada 10.5194/gmd-14-5623-2021
42. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet 10.3389/fams.2023.1133226
43. CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. 10.5194/gmd-15-4569-2022
44. SPLNet: A sequence-to-one learning network with time-variant structure for regional wind speed prediction R. Ye et al. 10.1016/j.ins.2022.07.002
45. A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. 10.1029/2022WR033318
46. Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows S. Pawar et al. 10.1063/5.0012853
47. A machine‐learning and data assimilation forecasting framework for surface waves P. Pokhrel et al. 10.1002/qj.4631
48. Learning earth system models from observations: machine learning or data assimilation? A. Geer 10.1098/rsta.2020.0089
49. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
50. Law of interest rate changes in financial markets based on the differential equation model of liquidity P. Wan & A. Hamad Arbad 10.2478/amns.2021.1.00081