A Framework for Designing and Evaluating Solar Flare Forecasting Systems
- URL: http://arxiv.org/abs/2005.02493v1
- Date: Tue, 5 May 2020 21:05:10 GMT
- Title: A Framework for Designing and Evaluating Solar Flare Forecasting Systems
- Authors: T. Cinto (1 and 2), A. L. S. Gradvohl (1), G. P. Coelho (1), A. E. A.
da Silva (1) ((1) School of Technology - FT, University of Campinas -
UNICAMP, Limeira, SP, Brazil, (2) Federal Institute of Education, Science and
Technology of Rio Grande do Sul - IFRS, Campus Feliz, RS, Brazil)
- Abstract summary: Solar flares are the most significant events that can affect the Earth's atmosphere.
This paper proposes a framework to design, train, and evaluate flare prediction systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Disturbances in space weather can negatively affect several fields, including
aviation and aerospace, satellites, oil and gas industries, and electrical
systems, leading to economic and commercial losses. Solar flares are the most
significant events that can affect the Earth's atmosphere, thus leading
researchers to drive efforts on their forecasting. The related literature is
comprehensive and holds several systems proposed for flare forecasting.
However, most techniques are tailor-made and designed for specific purposes,
not allowing researchers to customize them in case of changes in data input or
in the prediction algorithm. This paper proposes a framework to design, train,
and evaluate flare prediction systems which present promising results. Our
proposed framework involves model and feature selection, randomized
hyper-parameters optimization, data resampling, and evaluation under
operational settings. Compared to baseline predictions, our framework generated
some proof-of-concept models with positive recalls between 0.70 and 0.75 for
forecasting $\geq M$ class flares up to 96 hours ahead while keeping the area
under the ROC curve score at high levels.
Related papers
- Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF [0.0]
This study aims to uncover hidden relationships and the evolutionary characteristics of solar flares and their source regions.
Preliminary findings indicate a notable improvement, with an average increase of 5% in both the True Skill Statistic (TSS) and Heidke Skill Score (HSS)
arXiv Detail & Related papers (2024-09-06T18:12:05Z) - Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models [0.08271752505511926]
Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts.
Recently released suite of AI-based weather models produces medium-range forecasts within seconds.
We assess the forecast skill of three top-performing AI-models for convective parameters against reanalysis and ECMWF's operational numerical weather prediction model IFS.
arXiv Detail & Related papers (2024-06-13T07:46:03Z) - Aardvark weather: end-to-end data-driven weather forecasting [30.219727555662267]
Aardvark Weather is an end-to-end data-driven weather prediction system.
It ingests raw observations and outputs global gridded forecasts and local station forecasts.
It can be optimised end-to-end to maximise performance over quantities of interest.
arXiv Detail & Related papers (2024-03-30T16:41:24Z) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset.
Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors.
Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z) - Scaling transformer neural networks for skillful and reliable medium-range weather forecasting [23.249955524044392]
We introduce Stormer, a state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone.
At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals.
On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days.
arXiv Detail & Related papers (2023-12-06T19:46:06Z) - Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch.
In this paper, we collected large-scale load datasets and released a new renewable energy dataset.
We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z) - SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models [13.331224394143117]
Uncertainty quantification is crucial to decision-making.
dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts.
We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data.
arXiv Detail & Related papers (2023-06-24T22:00:06Z) - GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data.
It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.
We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
arXiv Detail & Related papers (2022-12-24T18:15:39Z) - Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global
Weather Forecast [91.9372563527801]
We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast.
For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy.
Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
arXiv Detail & Related papers (2022-11-03T17:19:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.