Related papers: Expanding Mars Climate Modeling: Interpretable Mac…
Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and…
This paper presents an application of artificial intelligence on mass spectrometry data for detecting habitability potential of ancient Mars. Although data was collected for planet Mars the same approach can be replicated for any…
The midlatitude climate and weather are shaped by storms, yet the factors governing their predictability remain insufficiently understood. Here, we use a Convolutional Neural Network (CNN) to predict and quantify uncertainty in the…
Post-Noachian Martian paleochannels indicate the existence of liquid water on the surface of Mars after about 3.5 Gya (Irwin et al., 2015; Palucis et al., 2016). In order to explore the effects of variations in CO$_{2}$ partial pressure and…
Here we discuss one of the current reservoirs of water on Mars, the regolith and rocks exposed at the surface. This reservoir is characterized by the presence of H_{2}O- and OH- bearing phases that produce a broad absorption at a wavelength…
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the…
The present Martian climate is characterized by a cold and dry environment with a thin atmosphere of carbon dioxides (CO2). In such conditions, the planetary climate and habitability are determined by the distribution of CO2 between…
We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We…
Near-surface wind fields on Mars are profoundly modulated by complex topography, yet fine-scale wind field characteristics remain poorly resolved for key geomorphological units such as deltas, valleys, and impact craters, due to the spatial…
The dynamics of Mars' obliquity are believed to be chaotic, and the historical ~3.5 Gyr (late-Hesperian onward) obliquity probability density function (PDF) is high uncertain and cannot be inferred from direct simulation alone. Obliquity is…
The problem of local (e.g., interplanetary) Hubble expansion is studied for a long time but remains a controversial subject till now; and of particular interest is a plausible value of the local Hubble parameter at the scale of the Solar…
Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed. However, the ability of these ML models to generalize beyond the training…
We use a hierarchy of numerical models (a 3-D Global Climate Model, a 1-D radiative-convective model and a 2-D Mantle Dynamics model) to explore the environmental effects of very large impacts on the atmosphere, surface and interior of…
Reducing atmospheres have recently emerged as a promising scenario to warm the surface of early Mars enough to drive the formation of valley networks and other ancient aqueous features that have been detected so far on the surface of Mars.…
Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods to…
While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs,…
Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to…
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale…
The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James…
Artificial Intelligence (AI) weather models are improving rapidly, and their forecasts are already competitive with long-established traditional Numerical Weather Prediction (NWP). To build confidence in this new methodology, it is critical…