Advance Dust Devil Detection with AI using Mars2020 MEDA instrument

Abstract

Mars’ dust cycle is a critical factor that drives the weather and climate of the planet. Airborne dust affects the energy balance that drives the atmospheric dynamic. Therefore, for studying the present-day and recent-past climate of Mars we need to observe and understand the different processes involved in the dust cycle. To this end, the Mars Environmental Dynamics Analyser (MEDA) station [1] includes a set of sensors capable of measuring the radiance fluxes, the wind direction and velocity, the pressure, and the humidity over the Martian surface. Combining these observations with radiative transfer (RT) simulations, airborne dust particles can be detected and characterized (optical depth, particle size, refractive index) along the day. The retrieval of these dust properties allows us to analyze dust storms or dust-lifting events, such as dust devils, on Mars [2][3]. Dust devils are thought to account for 50% of the total dust budget, and they represent a continuous source of lifted dust, active even outside the dust storms season. For these reasons, they have been proposed as the main mechanism able to sustain the ever-observed dust haze of the Martian atmosphere. Our radiative transfer simulations indicate that variations in the dust loading near the surface can be detected and characterized by MEDA radiance sensor RDS [4]. This study reanalyzes the dataset of dust devil detections obtained in [3] employing artificial intelligence techniques including anomaly detection based on autoencoders [5] and deep learning models [6] to analyze RDS and pressure sensor data. As we will show, preliminary results indicate that our AI models can successfully identify and characterize these phenomena with high accuracy. The final aim is to develop a powerful tool that can improve the database for the following sols of the mission, and subsequently extend its use for other atmospheric studies.