<div>The ~55 hour orbit of the Emirates Mars Mission (EMM) or the “Hope'" orbiter enables it to achieve a near-global coverage of the planet every 4 orbits, or ~9 sols. The Emirates Mars Infrared Spectrometer (EMIRS) instrument on board EMM is used to retrieve surface temperatures. We study the geographical and temporal variation of surface temperature on diurnal and seasonal timescales. We compare these measurements with NASA’s rover measurements —  from the Rover Environmental Monitoring Station (REMS) suite on board the Mars Science Laboratory (MSL) "Curiosity" rover, and the Mars Environmental Dynamics Analyzer (MEDA) suite on board the Mars 2020 "Perseverance” rover. We also compare these measurements with the Mars Climate Database (MCD), identify anomalies in surface temperature and discuss the role of thermal inertia. We discuss other implications of these findings leading to a better understanding of temperature variation on Mars and its impact on weather and climate.  </div> <p> </p>

<p>Auroras are an important probe for characterizing the interaction of solar wind with the induced magnetosphere of Mars and understanding the evolution of Mars’s atmosphere. Since their first discovery in 2005, Mars auroras have been studied extensively, particularly using the observations from NASA’s Mars Atmosphere and Volatile Evolution (MAVEN). Electron auroras with discrete and diffuse morphology are observed on the nightside of Mars whereas proton auroras are observed mainly on the dayside of Mars. Recently the Emirates Mars UV Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) has discovered new morphologies of sinuous electron auroras and patchy proton auroras on Mars. In this work, we perform comprehensive statistical analyses of aurora observations to understand the processes responsible for the varied auroral activity on Mars. We systematically isolate electron aurora regions from the nightside EMUS observations and characterize their occurrences and emissions with respect to the crustal magnetic fields, IMF, and electron energies measured by MAVEN. We also develop a purely data-driven model of proton auroras on Mars using MAVEN in-situ observations and UV limb scans between 2014-2022 to train an artificial neural network (ANN). We show that the ANN faithfully reconstructs the observed proton aurora limb scans profiles. We use the trained ANN to analyze the influence of Mars’ crustal magnetic field and IMF on the occurrence rates of the proton auroras using gradient-based attribution maps. </p>