Cloud Catalog from Mars Orbiter Laser Altimeter / Mars Global Surveyor Data Using Machine Learning Algorithms
Authors: Caillé Vincent , Määttänen Anni , Spiga Aymeric , Falletti Lola , Neumann Gregory A. 
Published: March 2021

&lt;p&gt;&lt;span&gt; &lt;/span&gt;&lt;span&gt;In the development of Mars climate models, modeling clouds is an important challenge, and especially for CO&lt;/span&gt;&lt;sub&gt;&lt;span&gt;2&lt;/span&gt;&lt;/sub&gt;&lt;span&gt; clouds&lt;/span&gt;&lt;span&gt;. This is due to the complexity of th&lt;/span&gt;&lt;span&gt;e&lt;/span&gt;&lt;span&gt; atmospheric &lt;/span&gt;&lt;span&gt;processes involved&lt;/span&gt;&lt;span&gt; that may imply rethinking microphysical theories, but also to the scarcity of observations. In the la&lt;/span&gt;&lt;span&gt;te&lt;/span&gt;&lt;span&gt; 90&amp;#8217;s, Mars Orbiter Laser Altimeter was one of the instrument&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; aboard the Mars Global Surveyor spacecraft. Its first goal was to build a precise map of Mars&amp;#8217; topography &lt;/span&gt;&lt;span&gt;through laser altimetry but its sensitivity &lt;/span&gt;&lt;span&gt;allowed for cloud observations as well&lt;/span&gt;&lt;span&gt; . Th&lt;/span&gt;&lt;span&gt;us,&lt;/span&gt;&lt;span&gt; previous studies (Neumann &amp; al. 2003 Ivanov &amp; Muhlemann 2001) have &lt;/span&gt;&lt;span&gt;shown&lt;/span&gt;&lt;span&gt; that some &lt;/span&gt;&lt;span&gt;laser &lt;/span&gt;&lt;span&gt;returns were cloud signatures coming from the atmosphere. However, &lt;/span&gt;&lt;span&gt;at that time, the &lt;/span&gt;&lt;span&gt;huge amount of data &lt;/span&gt;&lt;span&gt;was analysed using simple&lt;/span&gt;&lt;span&gt; dis&lt;/span&gt;&lt;span&gt;t&lt;/span&gt;&lt;span&gt;inction criteria.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt; We use K-means clustering algorithms to compu&lt;/span&gt;&lt;span&gt;ta&lt;/span&gt;&lt;span&gt;tionally analyse MOLA data. In order to optimise the method, we first determine the best observed parameters to distinguish the different kinds of returns (surface, noise and clouds). &lt;/span&gt;&lt;span&gt;The b&lt;/span&gt;&lt;span&gt;est number of clusters is determined using three independent methods : elbow, silhouette score and gap statistics. The method is test&lt;/span&gt;&lt;span&gt;ed&lt;/span&gt;&lt;span&gt; on a restricted sample (10&amp;#160;% of the dataset) and then applied to the &lt;/span&gt;&lt;span&gt;full raw&lt;/span&gt;&lt;span&gt; data&lt;/span&gt;&lt;span&gt;set&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;Once &lt;/span&gt;&lt;span&gt;that &lt;/span&gt;&lt;span&gt;cloud cluster identified, we can plot spatial and temporal distributions of t&lt;/span&gt;&lt;span&gt;he cloud return&lt;/span&gt;&lt;span&gt;s &lt;/span&gt;&lt;span&gt;and compare them&lt;/span&gt;&lt;span&gt; with previous results.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt; As mentioned by Neumann &amp; al. (2003), the product of surface reflectivity &lt;/span&gt;&lt;span&gt;and two-way transmissivity of the atmosphere &lt;/span&gt;&lt;span&gt;appears as the best parameter &lt;/span&gt;&lt;span&gt;discriminating&lt;/span&gt;&lt;span&gt; between surface and cloud returns. A unique number of cluster&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; &lt;/span&gt;&lt;span&gt;(6) &lt;/span&gt;&lt;span&gt;is identified by all three optimisation methods. Among those clusters, one clearly identifies cloud returns, while others &lt;/span&gt;&lt;span&gt;represent&lt;/span&gt;&lt;span&gt; noise and surface returns. &lt;/span&gt;&lt;span&gt;Our methods&lt;/span&gt;&lt;span&gt; allows us to &lt;/span&gt;&lt;span&gt;identify&lt;/span&gt;&lt;span&gt; more clouds than previous studies. Our cloud distribution remain&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; coherent with the ones given in &lt;/span&gt;&lt;span&gt;previous&lt;/span&gt;&lt;span&gt; studies, showing the viability of our method. We &lt;/span&gt;&lt;span&gt; will present&lt;/span&gt;&lt;span&gt; a catalog of cloud &lt;/span&gt;&lt;span&gt;returns &lt;/span&gt;&lt;span&gt;coming from MOLA data. &lt;/span&gt;&lt;span&gt;We are now working &lt;/span&gt;&lt;span&gt;to separate different kinds of clouds within these returns (absorptive and reflective clouds, CO&lt;/span&gt;&lt;sub&gt;&lt;span&gt;2&lt;/span&gt;&lt;/sub&gt;&lt;span&gt; / water clouds, dust &amp;#8230;) using machine learning algorithms and a recent MOLA surface reflectivity map (Heavens &amp; al. 2016).&lt;/span&gt;&lt;/p&gt;