Related papers: Ensemble modeling of CMEs using the WSA-ENLIL+Cone…
By 2050, we expect that CME models will accurately describe, and ideally predict, observed solar eruptions and the propagation of the CMEs through the corona. We describe some of the present known unknowns in observations and models that…
A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal…
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or…
Predicting the impacts of coronal mass ejections (CMEs) is a major focus of current space weather forecasting efforts. Typically, CME properties are reconstructed from stereoscopic coronal images and then used to forward model a CME's…
There have been few attempts in the past to understand the collision of individual cases of interacting Coronal Mass Ejections (CMEs). We selected 8 cases of interacting CMEs and estimated their propagation and expansion speeds, direction…
Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation…
Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, especially when it comes to prediction accuracy. However, in complex real-world scenarios, particularly in safety-critical applications, high accuracy alone…
We examine the propagation and interaction properties of three successive coronal mass ejections (CMEs) from 2001 November 21-22, with a focus on their connection with the behaviors of the associated long-duration complex type II radio…
A study of the kinematics and arrival times of CMEs at Earth, derived from time-elongation maps (J-maps) constructed from STEREO/Heliospheric Imager (HI) observations, provides an opportunity to understand the heliospheric evolution of CMEs…
Long-range ensemble forecasts are typically verified as anomalies with respect to a lead-time dependent climatological mean to remove the influence of systematic biases. However, common methods for calculating anomalies result in…
The NASA STEREO mission opened up the possibility to forecast the arrival times, speeds and directions of solar transients from outside the Sun-Earth line. In particular, we are interested in predicting potentially geo-effective…
Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able…
Motivated by applications to 3D printing, this paper presents two algorithms for calculating an ensemble of solutions to heat conduction problems. The ensemble average is the most likely temperature distribution and its variance gives an…
Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multiple models and selecting the most probable label. This idea…
We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is comprised of a system of ordinary differential equations…
Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with…
Simulating coronal mass ejections (CMEs) from their origin in active regions (ARs) to their propagation to Earth remains challenging, particularly when aiming to resolve AR scales and employ realistic magnetic field strengths without…
We study the clustering properties of fast Coronal Mass Ejections (CMEs) that occurred during solar cycles 23 and 24. We apply two methods: the Max spectrum method can detect the predominant clusters and the de-clustering threshold time…
The stochastic nature of chemical reactions involving randomly fluctuating population sizes has lead to a growing research interest in discrete-state stochastic models and their analysis. A widely-used approach is the description of the…
Meteorological ensembles are a collection of scenarios for future weather delivered by a meteorological center. Such ensembles form the main source of valuable information for probabilistic forecasting which aims at producing a predictive…