Articles | Volume 17, issue 2
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.
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
Delft University of Technology, Delft Institute of Applied Mathematics,
Mekelweg 4, 2628 CD Delft, the Netherlands
Fred Prata
Nicarnica Aviation AS, Gunnar Randers vei 24, 2007 Kjeller, Norway
Hai Xiang Lin
Delft University of Technology, Delft Institute of Applied Mathematics,
Mekelweg 4, 2628 CD Delft, the Netherlands
Arnold Heemink
Delft University of Technology, Delft Institute of Applied Mathematics,
Mekelweg 4, 2628 CD Delft, the Netherlands
Arjo Segers
TNO, Department of Climate, Air and Sustainability, P.O. box 80015,
3508 TA Utrecht, the Netherlands
Delft University of Technology, Delft Institute of Applied Mathematics,
Mekelweg 4, 2628 CD Delft, the Netherlands
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22 citations as recorded by crossref.
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- 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
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al. 10.5194/acp-22-1773-2022
- 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
- Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 A. Folch et al. 10.3389/feart.2021.741841
- Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019 A. Capponi et al. 10.5194/acp-22-6115-2022
- 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
- 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
19 citations as recorded by crossref.
- Reconstructing tephra fall deposits via ensemble-based data assimilation techniques L. Mingari et al. 10.5194/gmd-16-3459-2023
- 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
- A Knowledge-Aided Robust Ensemble Kalman Filter Algorithm for Non-Linear and Non-Gaussian Large Systems S. Lopez-Restrepo et al. 10.3389/fams.2022.830116
- 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
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al. 10.5194/acp-22-1773-2022
- 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
- Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 A. Folch et al. 10.3389/feart.2021.741841
- Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019 A. Capponi et al. 10.5194/acp-22-6115-2022
- 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
3 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
- Estimation of volcanic ash emissions through assimilating satellite data and ground‐based observations S. Lu et al. 10.1002/2016JD025131
- 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
Saved (final revised paper)
Saved (preprint)
Latest update: 23 Nov 2024
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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