Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4261-2019
https://doi.org/10.5194/gmd-12-4261-2019
Development and technical paper
 | 
10 Oct 2019
Development and technical paper |  | 10 Oct 2019

Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi

Related authors

Evaluation of North Atlantic Tropical Cyclones in a Convection-Permitting Regional Climate Simulation
Lara Tobias-Tarsh, Chunyong Jung, Jiali Wang, Vishal Bobde, Akintomide A. Akinsanola, and V. Rao Kotamarthi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1805,https://doi.org/10.5194/egusphere-2025-1805, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Fully Coupled High-Resolution Atmosphere-Ocean-Wave Simulations of Hurricane Henri (2021): Implications for Offshore Load Assessments
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47,https://doi.org/10.5194/wes-2025-47, 2025
Preprint under review for WES
Short summary
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025,https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Evaluation of a High-Resolution Regional Climate Simulation for Surface and Hub-height Wind Climatology over North America
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-13,https://doi.org/10.5194/wes-2025-13, 2025
Revised manuscript under review for WES
Short summary
Performance of wind assessment datasets in United States coastal areas
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115,https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript accepted for WES
Short summary

Related subject area

Earth and space science informatics
DustNet (v1): skilful neural network predictions of dust aerosols over the Saharan desert
Trish E. Nowak, Andy T. Augousti, Benno I. Simmons, and Stefan Siegert
Geosci. Model Dev., 18, 3509–3532, https://doi.org/10.5194/gmd-18-3509-2025,https://doi.org/10.5194/gmd-18-3509-2025, 2025
Short summary
RiverBedDynamics v1.0: a Landlab component for computing two-dimensional sediment transport and river bed evolution
Angel D. Monsalve, Samuel R. Anderson, Nicole M. Gasparini, and Elowyn M. Yager
Geosci. Model Dev., 18, 3427–3451, https://doi.org/10.5194/gmd-18-3427-2025,https://doi.org/10.5194/gmd-18-3427-2025, 2025
Short summary
A GPU parallelization of the neXtSIM-DG dynamical core (v0.3.1)
Robert Jendersie, Christian Lessig, and Thomas Richter
Geosci. Model Dev., 18, 3017–3040, https://doi.org/10.5194/gmd-18-3017-2025,https://doi.org/10.5194/gmd-18-3017-2025, 2025
Short summary
The Earth System Grid Federation (ESGF) Virtual Aggregation (CMIP6 v20240125)
Ezequiel Cimadevilla, Bryan N. Lawrence, and Antonio S. Cofiño
Geosci. Model Dev., 18, 2461–2478, https://doi.org/10.5194/gmd-18-2461-2025,https://doi.org/10.5194/gmd-18-2461-2025, 2025
Short summary
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
Elena Tomasi, Gabriele Franch, and Marco Cristoforetti
Geosci. Model Dev., 18, 2051–2078, https://doi.org/10.5194/gmd-18-2051-2025,https://doi.org/10.5194/gmd-18-2051-2025, 2025
Short summary

Cited articles

Attali, J. G. and Pagès, G.: Approximations of functions by a multilayer perception: A new approach, Neural Networks, 6, 1069–1081, 1997. 
Chen, T. and Chen, H.: Approximation capability to functions of several variables, nonlinear functionals and operators by radial basis function neural networks, Neural Networks, 6, 904–910, 1995a. 
Chen, T. and Chen, H.: Universal approximation to nonlinear operators by neural networks with arbitrary activation function and its application to dynamical systems, Neural Networks, 6, 911–917, 1995b. 
Chevallier, F., Chéruy, F., Scott, N. A., and Chédin, A.: A neural network approach for a fast and accurate computation of longwave radiative budget, J. Appl. Meteorol., 37, 1385–1397, 1998. 
Chevallier, F., Morcrette, J.-J., Chéruy, F., and Scott, N. A.: Use of a neural-network-based longwave radiative transfer scheme in the EMCWF atmospheric model, Q. J. Roy. Meteor. Soc., 126, 761–776, 2000. 
Download
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Share