Found: 15
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Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset.
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- Machine Learning & Knowledge Extraction, 2022, v. 4, n. 1, p. 150, doi. 10.3390/make4010008
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- Article
SALSA2.0: The sectional aerosol module of the aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0.
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- Geoscientific Model Development Discussions, 2018, p. 1, doi. 10.5194/gmd-2018-47
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- Article
Isoprene derived secondary organic aerosol in a global aerosol chemistry climate model.
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- Geoscientific Model Development Discussions, 2017, p. 1, doi. 10.5194/gmd-2017-244
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- Article
The Chemistry Climate Model ECHAM6.3-HAM2.3-MOZ1.0.
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- Geoscientific Model Development Discussions, 2017, p. 1, doi. 10.5194/gmd-2017-191
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- Article
Ozone impacts of gas--aerosol uptake in global chemistry transport models.
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- Atmospheric Chemistry & Physics, 2018, v. 18, n. 5, p. 3147, doi. 10.5194/acp-18-3147-2018
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- Article
Temperature forecasting by deep learning methods.
- Published in:
- Geoscientific Model Development, 2022, v. 15, n. 23, p. 8931, doi. 10.5194/gmd-15-8931-2022
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- Article
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties.
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- Geoscientific Model Development, 2022, v. 15, n. 11, p. 4331, doi. 10.5194/gmd-15-4331-2022
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- Publication type:
- Article
SALSA2.0: The sectional aerosol module of the aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0.
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- Geoscientific Model Development, 2018, v. 11, n. 9, p. 3833, doi. 10.5194/gmd-11-3833-2018
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- Publication type:
- Article
Isoprene-derived secondary organic aerosol in the global aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0.
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- Geoscientific Model Development, 2018, v. 11, n. 8, p. 3235, doi. 10.5194/gmd-11-3235-2018
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- Article
The chemistry-climate model ECHAM6.3-HAM2.3-MOZ1.0.
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- Geoscientific Model Development, 2018, v. 11, n. 5, p. 1695, doi. 10.5194/gmd-11-1695-2018
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- Publication type:
- Article
Temperature forecasting by deep learning methods.
- Published in:
- Geoscientific Model Development Discussions, 2022, p. 1, doi. 10.5194/gmd-2021-430
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- Publication type:
- Article
Global, high-resolution mapping of tropospheric ozone - explainable machine learning and impact of uncertainties.
- Published in:
- Geoscientific Model Development Discussions, 2022, p. 1, doi. 10.5194/gmd-2022-2
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- Publication type:
- Article
Ozone Impacts of Gas-Aerosol Uptake in Global Chemistry Transport Models.
- Published in:
- Atmospheric Chemistry & Physics Discussions, 2017, p. 1, doi. 10.5194/acp-2017-566
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- Publication type:
- Article
AQ-Bench: a benchmark dataset for machine learning on global air quality metrics.
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- Earth System Science Data, 2021, v. 13, n. 6, p. 3013, doi. 10.5194/essd-13-3013-2021
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- Publication type:
- Article
AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics.
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- Earth System Science Data Discussions, 2021, p. 1, doi. 10.5194/essd-2020-380
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- Publication type:
- Article