Related papers: Understanding cirrus clouds using explainable mach…
Optical turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradation affects both astronomical observations…
A one-dimensional method for reconstructing the structure of prestellar and protostellar clouds is presented. The method is based on radiative transfer computations and a comparison of theoretical and observed intensity distributions at…
This paper aims to introduce a method for simulating with a real time performance the automotive LIDAR disturbance by dust clouds caused by natural phenomena, mechanical or man-made processes like a traveling vehicle. In this study, we are…
Ice storms are extreme weather events that can have devastating implications for the sustainability of natural ecosystems as well as man made infrastructure. Ice storms are caused by a complex mix of atmospheric conditions and are among the…
We present a large catalogue of molecular clouds with accurate distance estimates in the southern sky. Based on the three-dimensional (3D) dust extinction map and the best-fit extinction and distance information of over 17 million stars…
Insight into the formation and global distribution of cloud particles in exoplanet atmospheres continues to be a key problem to tackle going into the JWST era. Understanding microphysical cloud processes and atmospheric feedback mechanisms…
Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth fine-scale structures and underestimate extremes. While…
A conditional random field (CRF) model for cloud detection in ground based sky images is presented. We show that very high cloud detection accuracy can be achieved by combining a discriminative classifier and a higher order clique potential…
The existence of Galactic optical cirrus poses a challenge for observing faint objects within our Galaxy and dim extragalactic structures. To investigate individual cirrus filaments in the Hyper Suprime-Cam Subaru Strategic Program public…
We investigate the interaction between a shock-driven hot wind and a cold multi-cloud layer, for conditions commonly found in interstellar and circumgalactic gas. We present a method for identifying distinct clouds using a…
Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the…
A number of transiting exoplanets have featureless transmission spectra that might suggest the presence of clouds at high altitudes. A realistic cloud model is necessary to understand the atmospheric conditions under which such…
The amount of dust estimated from infrared to sub-millimetre (submm) observations strongly depends on assumptions of different grain sizes, compositions and optical properties. Here we use a simple model of thermal emission from cold…
Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a…
Forecast of optical turbulence and atmospheric parameters relevant for ground-based astronomy is becoming an important goal for telescope planning and AO instruments optimization in several major telescope. Such detailed and accurate…
The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these…
Atmosphere modelling applications become increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of…
Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior…
Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and…
Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is…