Articles | Volume 13, issue 6
https://doi.org/10.5194/acp-13-3481-2013
© Author(s) 2013. 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-13-3481-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Mar 2013
Research article |
| 27 Mar 2013
Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data
V. M. Khade
University Corporation for Atmospheric Research, Visiting Scientist Program, Boulder, CO 80307, USA
J. A. Hansen
Naval Research Laboratory, Monterey, CA 93943, USA
J. S. Reid
Naval Research Laboratory, Monterey, CA 93943, USA
D. L. Westphal
Naval Research Laboratory, Monterey, CA 93943, USA
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17 citations as recorded by crossref.
- Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. 10.5194/acp-18-10615-2018
- Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system A. Tsikerdekis et al. 10.5194/acp-21-2637-2021
- Global simulations of aerosol amount and size using MODIS observations assimilated with an Ensemble Kalman Filter J. Rubin & W. Collins 10.1002/2014JD021627
- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting J. Rubin et al. 10.5194/acp-16-3927-2016
- Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al. 10.5194/acp-19-10009-2019
- 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
- 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
- Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar J. Jin et al. 10.1016/j.atmosenv.2018.05.060
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol? M. Kahnert & E. Andersson 10.5194/acp-17-3423-2017
- JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. 10.1029/2022MS003232
- Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
- 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
- Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs) A. Tsikerdekis et al. 10.5194/gmd-15-3253-2022
- Detecting Dependence in the Sensitive Parameter Space of a Model Using Statistical Inference and Large Forecast Ensembles J. McLay & M. Liu 10.1175/MWR-D-13-00340.1
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