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

火星沙尘暴对大气结构影响深远,对火星着陆器构成多重风险,并极大地影响探测仪的精度,因此对沙尘暴进行准确的短期预测对未来的火星探测任务极为重要.然而,传统的统计分析无法准确捕捉沙尘的变化规律.本文展示了Conv-GRU-Seq2Seq模型可以充分利用数据实现全球沙尘12小时预报.研究发现,考虑多个相互关联的气象要素,尤其是风场,并考虑季节性变化,可以提高预报的准确性.与最初的ConvGRU模型相比,加入Seq2Seq结构后,MSE降低了85.3%,MAE降低了75.07%.在比较的六个模型中,ConvGRU-Seq2Seq模型的测试性能最好,MSE、MAE和R2值分别为8.73×10^(-4)、13.48×10^(-3)和98.12×10^(-2).该模型的预测性能稳定可靠,误差空间分布更加集中准确.本文实现了12小时内快速变化的沙尘活动预报,MAPE小于10%.该研究首次提出了火星沙尘暴短期预报的深度学习模型,为未来的火星探测任务提供了重要的气象保障.

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 accurately 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 addition 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 ConvGRUSeq2Seq model exhibited the best test performance,with MSE,MAE,and R^(2)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.