Articles | Volume 17, issue 2
Atmos. Chem. Phys., 17, 1187–1205, 2017
https://doi.org/10.5194/acp-17-1187-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 1187–1205, 2017
https://doi.org/10.5194/acp-17-1187-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article 25 Jan 2017
Research article | 25 Jan 2017
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption
Guangliang Fu et al.
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Cited
16 citations as recorded by crossref.
- 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
- Impact of synthetic space-borne NO<sub>2</sub> observations from the Sentinel-4 and Sentinel-5P missions on tropospheric NO<sub>2</sub> analyses R. Timmermans et al. 10.5194/acp-19-12811-2019
- Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud F. Beckett et al. 10.3390/atmos11040352
- 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. 10.3390/atmos11040359
- FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation A. Prata et al. 10.5194/gmd-14-409-2021
- Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0) S. Osores et al. 10.5194/gmd-13-1-2020
- 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. 10.5194/gmd-10-1751-2017
- Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System S. Valade et al. 10.3390/rs11131528
- Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
- Atmospheric processes affecting the separation of volcanic ash and SO<sub>2</sub> in volcanic eruptions: inferences from the May 2011 Grímsvötn eruption F. Prata et al. 10.5194/acp-17-10709-2017
- Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge S. Lopez-Restrepo et al. 10.1007/s10596-021-10035-4
- Ash Metrics for European and Trans-Atlantic Air Routes During the Eyjafjallajökull Eruption 14 April to 23 May 2010 A. Prata et al. 10.1002/2017JD028199
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
- 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
- Estimation of volcanic ash emissions through assimilating satellite data and ground-based observations S. Lu et al. 10.1002/2016JD025131
14 citations as recorded by crossref.
- 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
- Impact of synthetic space-borne NO<sub>2</sub> observations from the Sentinel-4 and Sentinel-5P missions on tropospheric NO<sub>2</sub> analyses R. Timmermans et al. 10.5194/acp-19-12811-2019
- Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud F. Beckett et al. 10.3390/atmos11040352
- 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. 10.3390/atmos11040359
- FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation A. Prata et al. 10.5194/gmd-14-409-2021
- Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0) S. Osores et al. 10.5194/gmd-13-1-2020
- 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. 10.5194/gmd-10-1751-2017
- Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System S. Valade et al. 10.3390/rs11131528
- Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
- Atmospheric processes affecting the separation of volcanic ash and SO<sub>2</sub> in volcanic eruptions: inferences from the May 2011 Grímsvötn eruption F. Prata et al. 10.5194/acp-17-10709-2017
- Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge S. Lopez-Restrepo et al. 10.1007/s10596-021-10035-4
- Ash Metrics for European and Trans-Atlantic Air Routes During the Eyjafjallajökull Eruption 14 April to 23 May 2010 A. Prata et al. 10.1002/2017JD028199
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
2 citations as recorded by crossref.
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Latest update: 22 Sep 2021
Short summary
A Satellite Observational Operator (SOO) is proposed to translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The SOO makes the analysis step of assimilation comparable in the 3-D model space, and thus it avoids the artificial vertical correlations by not involving the integral operator in directly assimilating 2-D data. The results show that satellite data assimilation with SOO can efficiently improve the estimate of volcanic ash state and the forecast.
A Satellite Observational Operator (SOO) is proposed to translates satellite-retrieved 2-D...
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