10 citations as recorded by crossref.

1. Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10) G. Fu et al. https://doi.org/10.5194/gmd-10-1751-2017
2. Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption G. Fu et al. https://doi.org/10.5194/acp-17-1187-2017
3. Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation A. Yarce Botero et al. https://doi.org/10.3390/atmos12121633
4. Reconstructing tephra fall deposits via ensemble-based data assimilation techniques L. Mingari et al. https://doi.org/10.5194/gmd-16-3459-2023
5. On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. https://doi.org/10.1007/s11356-020-08268-4
6. An overview of the effects of algorithm use on judgmental biases affecting forecasting A. Chacon & E. Kaufmann https://doi.org/10.1016/j.ijforecast.2024.09.007
7. Estimation of volcanic ash emissions through assimilating satellite data and ground‐based observations S. Lu et al. https://doi.org/10.1002/2016JD025131
8. Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption F. Pardini et al. https://doi.org/10.3390/atmos11040359
9. Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. https://doi.org/10.1175/MWR-D-16-0289.1
10. Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al. https://doi.org/10.5194/acp-22-1773-2022