Related papers: Towards a Foundation Model for the Martian Atmosph…
Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations, supporting applications in ecosystem monitoring, carbon accounting,…
Space weather observations and modeling at Mars have begun but they must be significantly increased to support the future of Human Exploration on the Red Planet. A comprehensive space weather understanding of a planet without a global…
In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its…
We discuss 3D global simulations of the early Martian climate that we have performed assuming a faint young Sun and denser CO2 atmosphere. We include a self-consistent representation of the water cycle, with atmosphere-surface interactions,…
Since about 25 years ago work has been dedicated to the development of hydrodynamical model atmospheres for cool stars (of A to T spectral type). Despite their obviously sounder physical foundation in comparison with standard hydrostatic…
The composition of an atmosphere has integrated the geological history of the entire planetary body. However, the long-term evolutions of the atmospheres of the terrestrial planets are not well documented. For Earth, there were until…
Global and mesoscale models represent the background (slowly varying) winds on Mars, but short timescale wind variability is not explicitly represented. The local wind erosion and dust deposition model can be useful for more accurate local…
The added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large deep learning models. Inspired by climate model hierarchies,…
CO2+H2 greenhouse warming has recently emerged as a promising scenario to sufficiently warm the early martian surface to allow the formation of valley networks and lakes. Here we present numerical 3-D global climate simulations of the early…
We compare estimates of atmospheric precipitation during the Martian Noachian-Hesperian boundary 3.8 Gyr ago as calculated in a radiative-convective column model of the atmosphere with runoff values estimated from a geomorphological…
Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should…
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data. After introducing this new family of generative…
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity,…
Modeling the outflow of planetary atmospheres is important for understanding the evolution of exoplanet systems and for interpreting their observations. Modern theoretical models of exoplanet atmospheres become increasingly detailed and…
Machine Learning for aviation weather is a growing area of research for providing low-cost alternatives for traditional, expensive weather sensors; however, in the area of atmospheric visibility estimation, publicly available datasets,…
Planet-encircling dust storms fundamentally reshape Martian weather and the near-surface electrostatic environment. We investigate the generation and evolution of electric fields during global dust storms using bimodal dust size…
The research project has been made for mathematical modeling of aerospace system Space-to-Surface for avoid intercepting process by flight objects Surface-to-Air. The research has been completed and created mathematical models which used…
Clouds are important components of the atmosphere. Since it is usually not possible to treat them as ensembles of huge numbers of particles, parameterizations on the basis of averaged quantities (mass and/or number concentration) must be…
As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air temperature data, yet conventional machine learning models…
Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are…