地球与行星物理论评（中英文）2024，Vol.55Issue(4)：479-492,14.DOI:10.19975/j.dqyxx.2023-057
基于深度学习的火星12小时全球尘埃分布预测
Deep learning-based 12-hour global dust distribution forecasting on Martian
摘要
Abstract
Martian dust storms have a profound impact on atmospheric structure,pose multiple risks to Mars landers,and greatly affect the accuracy of sounders.This makes the accurate short-term prediction of dust storms extremely important for future Mars exploration missions.However,traditional statistical analyses fail to accu-rately capture the variation patterns of dust.Here,we show that the ConvGRU-Seq2Seq model can fully utilize the data to achieve a 12-h forecast of global dust.We found that considering multiple interconnected meteorological elements,particularly the wind field,and accounting for seasonal variations can enhance forecast accuracy.The ad-dition of the Seq2Seq structure reduced the mean squared error(MSE)by 85.3%and the mean absolute error(MAE)by 75.07%,compared with the original ConvGRU model.Among the six models compared,the ConvGRU-Seq2Seq model exhibited the best test performance,with MSE,MAE,and R2 values of 8.73×10-4,13.48×10-3,and 98.12×10-2,respectively.The model exhibited stable and reliable prediction performance and a more concentrated and accurate spatial distribution of errors.We achieved a rapidly changing dust activity forecast within 12 h with<10%mean absolute percentage error(MAPE).This study presents the first deep learning model for short-term forecasting of Martian dust storms,providing a reference for future Mars exploration missions.关键词
大气/火星尘埃/深度学习/预测Key words
atmosphere/Mars dust/deep learning/prediction分类
天文与地球科学引用本文复制引用
何泽锋,张杰,盛峥,唐满..基于深度学习的火星12小时全球尘埃分布预测[J].地球与行星物理论评（中英文）,2024,55(4):479-492,14.基金项目
This research was supported by the National Natural Science Foundation of China(Grant No.42275060)and National Natural Sci-ence Foundation for Young Scientists of China(Grant No.2021JJ10048) （Grant No.42275060）
