Tootfinder

Improving Solar Flare Nowcasting with the Hot Onset Precursor Event (HOPE) Technique
Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
https://arxiv.org/abs/2509.05234 https…

Improving Solar Flare Nowcasting with the Hot Onset Precursor Event (HOPE) Technique
This study investigates the statistical behavior of plasma properties during Hot Onset Precursor Events (HOPEs) of solar flares and evaluates their potential for improving flare nowcasting. Two datasets are analyzed: (a) new Soft X-Ray (SXR) spectra of 25 flares (C2.6 to M1.0) obtained from the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS), and (b) SXR irradiance data from 137 flares (C5.0 to X7.1) recorded by the X-Ray Sensor on the Geostationary Operational Environmental Satellite (GOES…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
Jordan Brook, Hamish McGowan, Matt Mason, Joshua Soderholm, Alain Protat

From Local Earthquake Nowcasting to Natural Time Forecasting: A Simple Do-It-Yourself (DIY) Method
John B Rundle, Ian Baughman, Andrea Donnellan, Lisa Grant, Geoffrey Fox
https://arxiv.org/abs/2510.02467

From Local Earthquake Nowcasting to Natural Time Forecasting: A Simple Do-It-Yourself (DIY) Method
Previous papers have outlined nowcasting methods to track the current state of earthquake hazard using only observed seismic catalogs. The basis for one of these methods, the "counting method", is the Gutenberg-Richter (GR) magnitude-frequency relation. The GR relation states that for every large earthquake of magnitude greater than MT , there are on average NGR small earthquakes of magnitude MS. In this paper we use this basic relation, combined with the Receiver Operating Characteristic (ROC)…

Nowcasting and aggregation: Why small Euro area countries matter
Andrii Babii, Luca Barbaglia, Eric Ghysels, Jonas Striaukas
https://arxiv.org/abs/2509.24780 https://

Nowcasting and aggregation: Why small Euro area countries matter
The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when including the COVID-1…

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
Jordan Brook, Hamish McGowan, Matt Mason, Joshua Soderholm, Alain Protat
https://arxiv.org/abs/2509.02929

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
We present the Australian Identification, Nowcasting and Tracking (AINT) algorithm, a radar-based system for detecting, characterising, and archiving thunderstorm activity across the Australian continent. AINT employs a two-step hysteresis-based identification scheme to separate embedded convective cells, followed by a phase-correlation and optimisation framework for storm tracking and cell matching. Post-processing routines address challenges of ground clutter using clustering and topography-a…

Deep Learning for Operational High-Resolution Nowcasting in Switzerland Using Graph Neural Networks
Oph\'elia Miralles, Daniele Nerini, Jonas Bhend, Baudouin Raoult, Christoph Spirig
https://arxiv.org/abs/2509.00017

Deep Learning for Operational High-Resolution Nowcasting in Switzerland Using Graph Neural Networks
Recent advances in neural weather forecasting have shown significant potential for accurate short-term forecasts. However, adapting such gridded approaches to smaller, topographically complex regions like Switzerland introduces computational challenges, especially when aiming for high spatial (1 km) and temporal (10 minutes) resolution. This paper presents a Graph Neural Network (GNN)-based approach for high-resolution nowcasting in Switzerland using the Anemoi framework and observational input…

Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
Da Fan, David John Gagne II, Steven J. Greybush, Eugene E. Clothiaux, John S. Schreck, Chaopeng Shen
https://arxiv.org/abs/2507.16219

Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
This study evaluated the probability and uncertainty forecasts of five recently proposed Bayesian deep learning methods relative to a deterministic residual neural network (ResNet) baseline for 0-1 h convective initiation (CI) nowcasting using GOES-16 satellite infrared observations. Uncertainty was assessed by how well probabilistic forecasts were calibrated and how well uncertainty separated forecasts with large and small errors. Most of the Bayesian deep learning methods produced probabilist…