Deep learning-based 12-hour global dust distribution forecasting on Martian
He Zefeng
Zhang Jie
Sheng Zheng
Tang Man
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.
Keywords：atmosphereMars dustdeep learningprediction
Publication Date：2024-07-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：14( 479-492 )