Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 16
Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 16
Development and technical paper
 | 
27 Aug 2024
Development and technical paper |  | 27 Aug 2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Tianning Su and Yunyan Zhang

Data sets

ARM Best Estimate Data Products (ARMBEATM) Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1333748

Active Remote Sensing of CLouds (ARSCL1CLOTH) Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1996113

MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4 (M2T1NXRAD) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/Q9QMY5PBNV1T

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach https://doi.org/10.24381/cds.bd0915c6

Model code and software

Codes and Package of Deep Learning Driven Simulations of Boundary Layer Cloud over the US Southern Great Plains Tianning Su https://doi.org/10.5281/zenodo.10719342

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Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
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