Articles | Volume 6, issue 6
https://doi.org/10.5194/gmd-6-2087-2013
© 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/gmd-6-2087-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
16 Dec 2013
Development and technical paper |
| 16 Dec 2013
Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation
A. Lipponen
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
V. Kolehmainen
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
S. Romakkaniemi
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
H. Kokkola
Finnish Meteorological Institute, Kuopio Unit, P.O. Box 1627, 70211 Kuopio, Finland
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Cited
13 citations as recorded by crossref.
- Assessing the climate and air quality effects of future aerosol mitigation in India using a global climate model combined with statistical downscaling T. Miinalainen et al. https://doi.org/10.5194/acp-23-3471-2023
- Estimation of the bubble size and bubble loading in a flotation froth using electrical resistance tomography A. Nissinen et al. https://doi.org/10.1016/j.mineng.2014.07.001
- Technical note: Parameterising cloud base updraft velocity of marine stratocumuli J. Ahola et al. https://doi.org/10.5194/acp-22-4523-2022
- Model-enforced post-process correction of satellite aerosol retrievals A. Lipponen et al. https://doi.org/10.5194/amt-14-2981-2021
- Correction of Model Reduction Errors in Simulations A. Lipponen et al. https://doi.org/10.1137/15M1052421
- Contrast enhancement in EIT imaging of the brain A. Nissinen et al. https://doi.org/10.1088/0967-3334/37/1/1
- Physically regularized machine learning emulators of aerosol activation S. Silva et al. https://doi.org/10.5194/gmd-14-3067-2021
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
- Changes in urban air pollution after a shift in anthropogenic activity analysed with ensemble learning, competitive learning and unsupervised clustering M. Hulkkonen et al. https://doi.org/10.1016/j.apr.2022.101393
- RETRACTED: A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. https://doi.org/10.1029/2023WR034630
- Maximuma posterioriestimates in linear inverse problems with log-concave priors are proper Bayes estimators M. Burger & F. Lucka https://doi.org/10.1088/0266-5611/30/11/114004
- A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. https://doi.org/10.1029/2022WR033318
- Constructing reduced model for complex physical systems via interpolation and neural networks* X. Lai et al. https://doi.org/10.1088/1674-1056/abd92e
13 citations as recorded by crossref.
- Assessing the climate and air quality effects of future aerosol mitigation in India using a global climate model combined with statistical downscaling T. Miinalainen et al. https://doi.org/10.5194/acp-23-3471-2023
- Estimation of the bubble size and bubble loading in a flotation froth using electrical resistance tomography A. Nissinen et al. https://doi.org/10.1016/j.mineng.2014.07.001
- Technical note: Parameterising cloud base updraft velocity of marine stratocumuli J. Ahola et al. https://doi.org/10.5194/acp-22-4523-2022
- Model-enforced post-process correction of satellite aerosol retrievals A. Lipponen et al. https://doi.org/10.5194/amt-14-2981-2021
- Correction of Model Reduction Errors in Simulations A. Lipponen et al. https://doi.org/10.1137/15M1052421
- Contrast enhancement in EIT imaging of the brain A. Nissinen et al. https://doi.org/10.1088/0967-3334/37/1/1
- Physically regularized machine learning emulators of aerosol activation S. Silva et al. https://doi.org/10.5194/gmd-14-3067-2021
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
- Changes in urban air pollution after a shift in anthropogenic activity analysed with ensemble learning, competitive learning and unsupervised clustering M. Hulkkonen et al. https://doi.org/10.1016/j.apr.2022.101393
- RETRACTED: A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. https://doi.org/10.1029/2023WR034630
- Maximuma posterioriestimates in linear inverse problems with log-concave priors are proper Bayes estimators M. Burger & F. Lucka https://doi.org/10.1088/0266-5611/30/11/114004
- A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. https://doi.org/10.1029/2022WR033318
- Constructing reduced model for complex physical systems via interpolation and neural networks* X. Lai et al. https://doi.org/10.1088/1674-1056/abd92e
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