29 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. Regularized variational data assimilation for bias treatment using the Wasserstein metric S. Tamang et al. 10.1002/qj.3794
10. 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
11. Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest X. Zhang et al. 10.3390/rs15071809
12. Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles A. Nair et al. 10.1029/2021GL094133
13. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
14. 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
15. 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
16. 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
17. 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
18. Inverse modeling of the 2021 spring super dust storms in East Asia J. Jin et al. 10.5194/acp-22-6393-2022
19. 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
20. A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
21. A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130
22. 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
23. Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
24. 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
25. A Bayesian data assimilation method to enhance the time sequence prediction ability of data-driven models Y. Li et al. 10.1063/5.0119688
26. 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
27. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
28. Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias J. Durazo et al. 10.3389/fams.2021.679477
29. Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066