|
831 |
Transferability of machine-learning-based global calibration models for NO<sub>2</sub> and NO low-cost sensors
Enthalten in Atmospheric measurement techniques Bd. 17, 2024, Nr. 13: 3917-3931. 15 S.
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|
832 |
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 11: 4689-4703. 15 S.
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|
833 |
Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs
Enthalten in Atmospheric measurement techniques Bd. 17, 2024, Nr. 10: 3103-3119. 17 S.
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834 |
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 9: 4017-4029. 13 S.
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835 |
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 9: 3667-3685. 19 S.
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836 |
Diagnosing drivers of PM<sub>2.5</sub> simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 9: 3617-3629. 13 S.
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837 |
CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery
Enthalten in Atmospheric measurement techniques Bd. 17, 2024, Nr. 9: 2583-2593. 11 S.
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838 |
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 8: 3175-3197. 23 S.
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839 |
Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 7: 2663-2682. 20 S.
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840 |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Enthalten in Geoscientific model development Bd. 17, 2024, Nr. 7: 2641-2662. 22 S.
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