Related papers: CAESAR: Space Weather archive prototype for ASPIS
The characterization of the optical properties of the atmosphere in the near UV, in particular the tropospheric aerosol stratification, clouds optical depth and spatial distribution are common in the field of atmospheric physics, due to…
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical…
The Earth's magnetosphere is formed as a consequence of interaction between the planet's magnetic field and the solar wind, a continuous plasma stream from the Sun. A number of different solar wind phenomena have been studied over the past…
SuperAGILE (SA) is a detection system on board of the AGILE satellite (Astro-rivelatore Gamma a Immagini LEggero), a Gamma-ray astronomy mission approved by the Italian Space Agency (ASI) as first project for the Program for Small…
One of the key features of sixth-generation (6G) mobile communications will be integrated sensing and communication (ISAC). While the main goal of ISAC in standardization efforts is to detect objects, the byproducts of radar operations can…
The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the…
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing…
Planetary Climate Models (PCMs) are developed to explore planetary climates other than the Earth. Therefore, the methods implemented need to be suitable for a large diversity of conditions. Every planet with a significant atmosphere has…
Academic and research cyberinfrastructures (AR-CIs) present unique security challenges due to their collaborative nature, heterogeneous components, and the lack of practical security assessment frameworks tailored to their needs. We propose…
The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets. General circulation model show capability to simulate these phenomena, but is…
Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present Solaris, the first foundation model for forecasting the…
The JASMIN super-data-cluster is being deployed to support the data analysis requirements of the UK and European climate and earth system modelling community. Physical colocation of the core JASMIN resource with significant components of…
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data…
ASTRI (Astrofisica a Specchi con Tecnologia Replicante Italiana) is a "Flagship Project" financed by the Italian Ministry of Instruction, University and Research and led by the Italian National Institute of Astrophysics. It represents the…
Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior…
Earth system science is producing increasingly large, high-dimensional datasets from physics based Earth system models to AI-based weather and climate models. Embedding-based representations can make these data searchable through similarity…
Aircraft trajectory prediction requires the determination of the atmospheric properties (pressure, temperature, and density) encountered by the aircraft during its flight. This is accomplished by employing a tabulated prediction published…
The dominant paradigm in machine learning is to assess model performance based on average loss across all samples in some test set. This amounts to averaging performance geospatially across the Earth in weather and climate settings, failing…
One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed…
Landslides pose a significant threat to public safety, but their dynamic processes are difficult to analyze from post-event observation alone. Computational simulation is therefore essential, but it generates vast, abstract datasets that…