Articles | Volume 15, issue 10
https://doi.org/10.5194/acp-15-5835-2015
© Author(s) 2015. 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-15-5835-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Wind extraction potential from ensemble Kalman filter assimilation of stratospheric ozone using a global shallow water model
Remote Sensing Division, Naval Research Laboratory, Washington, DC, USA
K. W. Hoppel
Remote Sensing Division, Naval Research Laboratory, Washington, DC, USA
D. D. Kuhl
Remote Sensing Division, Naval Research Laboratory, Washington, DC, USA
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Cited
9 citations as recorded by crossref.
- All-Sky Microwave Radiances Assimilated with an Ensemble Kalman Filter M. Bonavita et al. 10.1175/MWR-D-19-0413.1
- Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan S. Yang et al. 10.1016/j.atmosenv.2022.119064
- Hybrid 4DVAR with a Local Ensemble Tangent Linear Model: Application to the Shallow-Water Model D. Allen et al. 10.1175/MWR-D-16-0184.1
- Challenges of Increased Resolution for the Local Ensemble Tangent Linear Model D. Allen et al. 10.1175/MWR-D-20-0016.1
- All‐sky satellite data assimilation at operational weather forecasting centres A. Geer et al. 10.1002/qj.3202
- Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM D. Allen et al. 10.5194/acp-18-2999-2018
- Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model D. Allen et al. 10.5194/acp-16-8193-2016
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
- The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
9 citations as recorded by crossref.
- All-Sky Microwave Radiances Assimilated with an Ensemble Kalman Filter M. Bonavita et al. 10.1175/MWR-D-19-0413.1
- Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan S. Yang et al. 10.1016/j.atmosenv.2022.119064
- Hybrid 4DVAR with a Local Ensemble Tangent Linear Model: Application to the Shallow-Water Model D. Allen et al. 10.1175/MWR-D-16-0184.1
- Challenges of Increased Resolution for the Local Ensemble Tangent Linear Model D. Allen et al. 10.1175/MWR-D-20-0016.1
- All‐sky satellite data assimilation at operational weather forecasting centres A. Geer et al. 10.1002/qj.3202
- Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM D. Allen et al. 10.5194/acp-18-2999-2018
- Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model D. Allen et al. 10.5194/acp-16-8193-2016
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
- The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
Latest update: 02 Apr 2025
Short summary
While direct wind observations are routinely made in the troposphere (0-10km), in the stratosphere (above 10km) wind observations are sparse. This study examines the potential of using ozone observations to infer stratospheric wind. This novel approach is tested with a data assimilation system based on a simplified model of the atmosphere, the so-called "shallow water model". It is shown that assimilation of ozone observations significantly benefits winds, particularly in the tropics.
While direct wind observations are routinely made in the troposphere (0-10km), in the...
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