36 citations as recorded by crossref.

1. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites H. Zhou et al. 10.1016/j.atmosenv.2020.117905
2. Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders Y. Wang et al. 10.1175/MWR-D-21-0288.1
3. How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent J. Jin et al. 10.5194/acp-23-1641-2023
4. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
5. A comprehensive investigation of the causes of drying and increasing saline dust in the Urmia Lake, northwest Iran, via ground and satellite observations, synoptic analysis and machine learning models N. Hossein Hamzeh et al. 10.1016/j.ecoinf.2023.102355
6. Training a convolutional neural network to conserve mass in data assimilation Y. Ruckstuhl et al. 10.5194/npg-28-111-2021
7. Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0 J. Jin et al. 10.5194/gmd-14-5607-2021
8. Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows S. Pawar et al. 10.1063/5.0012853
9. A new global daily sea‐surface temperature analysis system at Environment and Climate Change Canada S. Skachko et al. 10.1002/qj.4796
10. Regularized variational data assimilation for bias treatment using the Wasserstein metric S. Tamang et al. 10.1002/qj.3794
11. Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models F. Yu et al. 10.1029/2022GL098551
12. Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas C. Edwards et al. 10.3389/fmars.2024.1458036
13. Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest X. Zhang et al. 10.3390/rs15071809
14. Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles A. Nair et al. 10.1029/2021GL094133
15. Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India S. Wang et al. 10.1016/j.atmosenv.2024.120834
16. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
17. AN EXPLORATORY SEQUENTIAL SENTIMENT ANALYSIS OF ONLINE LEARNING DURING THE MOVEMENT CONTROL ORDER IN MALAYSIA N. Abdul Rahman et al. 10.32890/mjli2021.18.2.9
18. Sequential model identification with reversible jump ensemble data assimilation method Y. Huan & H. Lin 10.1007/s11222-024-10499-1
19. On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. 10.1007/s11356-020-08268-4
20. Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran Z. Ebrahimi-Khusfi et al. 10.1016/j.apr.2020.08.029
21. An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping F. Aghazadeh et al. 10.1016/j.apr.2023.101689
22. Inverse modeling of the 2021 spring super dust storms in East Asia J. Jin et al. 10.5194/acp-22-6393-2022
23. Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China J. Jin et al. 10.5194/acp-20-15207-2020
24. A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
25. A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130
26. Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China W. Chang et al. 10.5194/acp-21-4403-2021
27. Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
28. Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China L. Fang et al. 10.5194/gmd-15-7791-2022
29. A Bayesian data assimilation method to enhance the time sequence prediction ability of data-driven models Y. Li et al. 10.1063/5.0119688
30. A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework X. Zhang et al. 10.1109/TGRS.2024.3427434
31. Next-generation remote sensing and prediction of sand and dust storms: State-of-the-art and future trends P. Jiao et al. 10.1080/01431161.2021.1912433
32. Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting M. Pang et al. 10.5194/gmd-17-8223-2024
33. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
34. Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
35. Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias J. Durazo et al. 10.3389/fams.2021.679477
36. Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066