Paper Info
Title
DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network
Abstract
To make weather/climate modeling computationally affordable, subgrid-scale physical processes in the numerical models are usually represented by semi-empirical parameterization schemes. For example, planetary boundary layer (PBL) parameterizations are used in atmospheric models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer-the lowest part of the atmosphere. We consider the problem of developing an accurate alternative to physics-based PBL parameterizations for speeding up the operation of atmosphere modeling. Our contributions are twofold. The first contribution is to propose a deep neural network emulator, called DeepPE, that focuses on simulating nonlocal closures in the PBL to capture cross-layer large eddies. We also explore a transfer method to maintain accuracy when applying a trained model to systems with different external forcing. We provide a comparison with three data-driven approaches as well as multi-task fine-tuning in predicting the PBL vertical profiles outputted by the Yonsei University (YSU) parameterization in the Weather Research Forecast (WRF) climate model over 16 locations. The experiment results show that our method can better simulate the vertical profiles within the boundary layer of velocities, temperature, wind speed, and water vapor over the entire cycle. And they also indicate that it achieves a comparable generalization performance with less computational cost.
Year
DOI
Venue
2021
10.1007/978-3-030-86517-7_6
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: APPLIED DATA SCIENCE TRACK, PT V
Keywords
DocType
Volume
Neural networks, Supervised learning, Environmental sciences
Conference
12979
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Fengyang Xu100.68
Wencheng Shi200.34
Yunfei Du3879.33
Zhiguang Chen487.25
Yutong Lu530753.61