Related papers: Using uncertainty-aware machine learning models to…
This research paper presents an approach to enhancing the predictive capability of architects in the design and assurance of systems, focusing on systems operating in dynamic and unpredictable environments. By adopting a systems approach,…
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly…
In a changing climate, artificial intelligence (AI) weather models have the potential to provide cheaper, faster, and more accurate forecasts of high-impact weather events. To realize this potential and gauge trustworthiness, there is a…
We are interested in the influence of cloudy atmospheres on the thermal radius evolution of warm exoplanets from an interior modelling perspective. By applying a physically motivated but simple parameterized cloud model, we obtain the…
Stratospheric aerosol injection (SAI), a possible climate engineering strategy where reflective particles are injected into the stratosphere, has been explored to mitigate global warming and its associated risks, such as the intensification…
Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply…
Stratospheric aerosol injection (SAI) has been proposed as a geoengineering strategy to mitigate global warming by increasing Earth's albedo. Silica-based materials, such as diamond-doped silica aerogels, have shown promising optical…
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of…
The dipolar interaction is known to substantially affect the properties of magnetic nanoparticles. This is particularly important when the particles are kept in a fluid suspension or packed inside nano-carriers. In addition to its usual…
Studying the ambient solar wind, a continuous pressure-driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve…
Compositional convection is atmospheric mixing driven by density variations caused by compositional gradients. Previous studies have suggested that compositional gradients of atmospheric trace species within planetary atmospheres can impact…
Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with…
Dust storms are common in arid zones on the earth and others planets such as Mars. The impact of dust storms on solar radiation has significant implications for solar power plants and autonomous vehicles powered by solar panels. This paper…
HCI work has explored the effective integration of AI/ML tools across "application domains" from healthcare to finance to transportation. We add to this literature with an analysis of AI/ML tools in meteorology, a domain that already uses…
Turbulent mixing and entrainment at the boundary of a cloud is studied by means of direct numerical simulations that couple the Eulerian description of the turbulent velocity and water vapor fields with a Lagrangian ensemble of cloud water…
Owing to their wavelengths dependent absorption and scattering properties, clouds have a strong impact on the climate of planetary atmospheres. Especially, the potential greenhouse effect of CO2 ice clouds in the atmospheres of terrestrial…
The paper contains the review of a number of wide-angle polarization CCD-measurements of the twilight sky in V and R color bands with effective wavelengths equal to 550 and 700 nm. The basic factors effecting (usually decreasing) on the…
In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS observation protocol that is…
Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Satellite imaginary data are in increasingly higher resolution, both spatially and temporally, making it impossible…
Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as…