Temporal and Spatial Prediction of Column Dust Optical Depth Trend on Mars Based on Deep Learning

Abstract

Dust storms, as an important extreme weather event on Mars, have significant impacts on the Martian atmosphere and climate and the activities of Martian probes. Therefore, it is necessary to analyze and predict the activity trends of Martian dust storms. This study uses historical data on global Column Dust Optical Depth (CDOD) from the Martian years (MYs) 24–36 (1998–2022) to develop a CDOD prediction method based on deep learning and predicts the spatiotemporal trends of dust storms in the landing areas of Martian rovers at high latitudes, the tropics, and the equatorial region. Firstly, based on a trained Particle Swarm Optimization (PSO) Long Short-Term Memory (LTSM)-CDOD network, the rolling predictions of CDOD average values for several sols in the future are performed. Then, an evaluation method based on the accuracy of the test set gives the maximum predictable number of sols and categorizes the predictions into four accuracy intervals. The effective prediction time of the model is about 100 sols, and the accuracy is higher in the tropics and equatorial region compared to at high latitudes. Notably, the accuracy of the Zhurong landing area in the north subtropical region is the highest, with a coefficient of determination (R2) and relative mean error (RME) of 0.98 and 0.035, respectively. Additionally, a Convolutional LSTM (ConvLSTM) network is used to predict the spatial distribution of CDOD intensity for different latitude landing areas of the future sol. The results are similar to the time predictions. This study shows that the LSTM-based prediction model for the intensity of Martian dust storms is effective. The prediction of Martian dust storm activity is of great significance to understanding changes in the Martian atmospheric environment and can also provide a scientific basis for assessing the impact on Martian rovers’ landing and operations during dust storms.

Keywords

• Martian dust storm
• column dust optical depth (CDOD)
• deep learning
• long short-term memory (LTSM)
• prediction