Work

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

Year: 2024

Type: article

Abstract: Accurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Source: Geophysical Research Letters

DOI: 10.1029/2024gl111076

Authors: Jiehong Xie, Pang‐Chi Hsu, Yamin Hu, Hualong Zhang, Mengxi Ye

Institutions: Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China Meteorological Administration, Guangdong Province Environmental Monitoring Center

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,900