Engineering topics

Emily L. Mason

Publications and source records attributed to Emily L. Mason.

3 records

NASA NTRS2023

Dust Lifting Observations with the Mars Science Laboratory Navigation Cameras

Martian dust lifting is believed to occur through two primary mechanisms: dust devils and wind stress forced dust lifting. Gale Crater's varied terrain and meteorology provide a unique in situ perspective on Martian dust lifting, with the Mars Science Laboratory Curiosity rover passing through both conditions and locations detrimental to dust lifting (e.g., the crater floor) and those with active sand motion and frequent dust lifting (e.g., the Bagnold Dunes). Between L s = 248° in Mars Year 33 and L s = 51° in Mars Year 37, over ∼3.5 Mars years and 2,300 sols, the rover's Navigation Cameras took 1,260 dedicated image sequences to search for dust lifting. Approximately 42.7% of all sequences, and 9.5% of the total images have shown active dust lifting, both dust devils and linear/straight-line wind stress dust lifting. 79% of dust lifting events are classified as dust devils, while ∼16% are linear wind stress dust lifting and the remainder are of an indeterminate type. We analyze this large catalog of dust lifting events to provide ground truth on theoretical and model expectations of dust lifting and show that dust lifting in Gale Crater occurs throughout the Martian year, is strongly peaked in frequency near solar noon (even after accounting for observational biases), and that dust lifting shows an affinity for sand-covered surfaces which highlights the importance of saltating sand grains for Martian dust lifting in both dust devils and wind stress forced lifting.

Scott D. Guzewich↗

NASA NTRS2023

The Mars Atmosphere Water Ice Aerosol Climatology by MRO/CRISM: 5 Mars Years of Observations

We use near-infrared spectra returned from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO) to provide retrievals of the column-integrated optical depth of water ice aerosols for more than 5 Martian years between Mars Year (MY) 28 at Ls = 112° (27 September 2006) and MY 33 at Ls = 213° (30 August 2016). We have developed a radiative transfer model for this purpose that retrieves aerosol opacity from the water ice aerosol feature near 3.3 μm using CRISM hyperspectral observations for the first time. The resulting retrievals well depict the main features in the water ice aerosol climatology with repeatable patterns every Martian year. The aphelion cloud belt (ACB) is observed between 10°S and 30°N, with peak optical depth around Ls = 90°. Apart from the global dust storm during MY 28, modest interannual variability in the water ice aerosol optical depth is observed in the ACB. The north polar hood is observed at latitudes poleward of 45°N throughout northern spring and summer seasons and reappears in northern winter. The south polar hood is observed in early to mid-southern autumn, reaching latitudes around 60°S, and re-emerging during southern winter between 45°S and 60°S. A lack of water ice aerosols is repeatedly seen in the southern hemisphere throughout southern spring and summer. High optical depth in the ACB is observed over the volcanoes of Olympus Mons, Elysium, and the Tharsis bulge, as well as over Hellas Basin.

Mars↗

NASA NTRS2022

Developing Data-Driven Artificial Neural Network for A High Throughput Retrieval of Aerosol Optical Depth and Surface Temperature of Mars

In this work, we aim to developartificial neural network(ANN)techniquesto reproduce the retrieval results ofphysical quantities from spacecraft observations of solar system bodiesusing radiative transfer methods. The particular application here is the retrieval of dust optical depth, water-ice optical depth, and surface temperatureonMarsusingdaytime observationsobtained by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor.Compared against the results obtained from traditional radiative transfer retrieval techniques, our ANN successfully recoveredthe three quantitiesusingdaytime observations. The principal advantage of thesemachine learning(ML) algorithms istheir complete automation and highthroughput. Therefore, the algorithms presented here would be useful for very large datasets andwould make practical the sampling of many different approximations or boundary conditions related to a given observation dataset and retrieval problem.

Rafael Moreno↗