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

Jiali Wang, Prasanna Balaprakash, Veerabhadra Rao Kotamarthi

Geoscientific model development 2019 article cited by 60

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Meteorological Phenomena and Simulations weather forecasting stage: research

Problem

Physics-based planetary boundary layer (PBL) parameterizations in numerical weather forecast models are computationally expensive.

Why it matters

Expensive parameterizations slow the weather forecasts that society depends on for planning and safety.

What the AI did

Deep neural networks were trained on Weather Research Forecast (WRF) model output to emulate the PBL parameterization, and evaluated as an alternative to the physics-based computation.

Results

not stated in source (abstract truncated before quantitative results)

Impact

Peer-reviewed demonstration of neural emulation of a physics parameterization in a weather forecast model.

Record

Venue	Geoscientific model development
Published	2019
Work type	article
Authors	Jiali Wang, Prasanna Balaprakash, Veerabhadra Rao Kotamarthi
Citations	60 (OpenAlex, at import time)
References	27
Model or method	deep neural network emulator
Organization	not stated in source
Primary source	https://doi.org/10.5194/gmd-12-4261-2019
Open access	gold
Keywords	neural network emulation, WRF, boundary layer, parameterization, weather forecasting

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states PBL parameterizations are computationally expensive and critical to weather/climate models; neural emulation could speed forecasts, but no impact scale quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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