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Solar flares significantly impact the conditions of the Earth's ionosphere. In particular, the sudden increase in X-ray flux during a flare penetrates down to the lowest-lying D-region and dominates ionization at these altitudes (60-100 km). Measurements of very low frequency (VLF: 3-30kHz) radio waves that reflect at D-region altitudes provide a unique remote-sensing probe to investigate the D-region response to solar flare emissions. Here, using a combination of VLF amplitude measurements at 24kHz together with X-ray observations from the Geostationary Operational Environment Satellite (GOES) X-ray sensor, we present a large-scale statistical study of 334 solar flare events and their impacts on the D-region over the past solar cycle. Focusing on both GOES broadband X-ray channels, we investigate how the flare peak fluxes and position on the solar disk dictate an ionospheric response and extend this to investigate the characteristic time delay between incident X-ray flux and the D-region response. We show that the VLF amplitude linearly correlates with both the 1-8 A and 0.5-4 A channels, with correlation coefficients of 0.80 and 0.79, respectively. Unlike higher altitude ionospheric regions for which the location of the flare on the solar disk affects the ionospheric response, we find that the D-region response to solar flares does not depend on the flare location. By comparing the time delays between the peak X-ray fluxes in both GOES channels and VLF amplitudes, we find that there is an important difference between the D-region response and the X-ray spectral band. We also demonstrate for several flare events that show a negative time delay, the peak VLF amplitude matches with the impulsive 25-50 keV hard X-ray fluxes measured by the Ramaty High Energy Solar Spectroscopic Imager (RHESSI)., Comment: 19 pages, 8 figures, accepted in Solar Physics

The Sun is an active star that can launch large eruptions of magnetised plasma into the heliosphere, called coronal mass ejections (CMEs). These ejections can drive shocks that accelerate particles to high energies, often resulting in radio emission at low frequencies (, 31 pages, 6 figures

Forecasting solar flares based on while-light images and photospheric magnetograms of sunspots is notoriously challenging, while accurate forecasting of coronal mass ejections (CME) is still in its infancy. That said, the chances of a CME being launched is more likely following a flare. CMEs launched from the western hemisphere and “halo” CMEs are the most likely to be geomagnetically impactful, but forecasting their arrival and impact at Earth depends on how well their velocity is known near the Sun, the solar wind conditions between the Sun and the Earth, the accuracy of theoretical models and on the orientation of the CME magnetic field. In this presentation, we describe a well observed active region, flare, CME, radio burst and sudden geomagnetic impulse that was observed on December 7-10, 2020 by a slew of instruments (SDO, ACE, DSCOVR, PSP, US and European magnetometers). This was a solar eruption that was not expected, but the CME and resulting geomagnetic impact should have been straight-forward to model and forecast. What can we learn from our failure to forecast this simple event and its impacts at Earth?

The Met Office Space Weather Operations Centre was founded in 2014 and part of its remit is a daily Space Weather Technical Forecast to help the UK build resilience to space weather impacts; guidance includes 4 day geomagnetic storm forecasts (GMSF) and X-ray flare forecasts (XRFF). It is crucial for forecasters, users, modelers, and stakeholders to understand the strengths and weaknesses of these forecasts; therefore, it is important to verify against the most reliable truth data source available. The present study contains verification results for XRFFs using Geo-Orbiting Earth Satellite 15 satellite data and GMSF using planetary K-index (Kp) values from the GFZ Helmholtz Centre. To assess the value of the verification results, it is helpful to compare them against a reference forecast and the frequency of occurrence during a rolling prediction period is used for this purpose. An analysis of the rolling 12 month performance over a 19 month period suggests that both the XRFF and GMSF struggle to provide a better prediction than the reference. However, a relative operating characteristic and reliability analysis of the full 19 month period reveals that although the GMSF and XRFF possess discriminatory skill, events tend to be overforecast.

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms; forecasts for each active region on the solar disk over the next 24 hours, and full-disk forecasts for the next four days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results, and forecasting skill decreasing over longer forecast lead times.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project. NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in the Department of Astronomy at the University of Washington; STFC studentshipScience & Technology Facilities Council (STFC) [ST/N504336/1]; STFC grantScience & Technology Facilities Council (STFC) [ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American Astronomical Society; Space program The following individuals recognize support for their personal contributions. B.M.S. is supported by the NSF grant AST-1715122 and acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. D.S. was supported by STFC studentship ST/N504336/1 and STFC grant ST/N000692/1.; We acknowledge financial contributions from Google as part of the Google Summer of Code program and from the Space program. We acknowledge financial contributions from NumFocus for improving the usability of SunPy's Data Downloader. Additionally, we acknowledge current and future funding from the Solar Physics Division of the American Astronomical Society for SunPy workshops and tutorials at annual meetings.; This work has made use of data from the European Space Agency (ESA) mission Gaia,80 processed by the Gaia Data Processing and Analysis Consortium (DPAC).81 Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement.

The space physics community continues to grow and become both more interdisciplinary and more intertwined with commercial and government operations. This has created a need for a framework to easily identify what projects can be used for specific applications and how close the tool is to routine autonomous or on-demand implementation and operation. We propose the Application Usability Level (AUL) framework and publicizing AULs to help the community quantify the progress of successful applications, metrics, and validation efforts. This framework will also aid the scientific community by supplying the type of information needed to build off of previously published work and publicizing the applications and requirements needed by the user communities. In this paper, we define the AUL framework, outline the milestones required for progression to higher AULs, and provide example projects utilizing the AUL framework. This work has been completed as part of the activities of the Assessment of Understanding and Quantifying Progress working group which is part of the International Forum for Space Weather Capabilities Assessment.

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multi-day periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods, and conversely, of correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event and event/no-event), and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day-1 remains high/low on day-2 even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line., Comment: 33 pages, 13 figures, accepted for publication in ApJ

Coronal holes (CHs) are regions of open magnetic flux which are the source of high speed solar wind (HSSW) streams. To date, it is not clear which aspects of CHs are of most influence on the properties of the solar wind as it expands through the Heliosphere. Here, we study the relationship between CH properties extracted from AIA (Atmospheric Imaging Assembly) images using CHIMERA (Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm) and HSSW measurements from ACE (Advanced Composition Explorer) at L1. For CH longitudinal widths $\Delta\theta_{CH}$67$^{\circ}$), $v_{max}$ is found to tend to a constant value ($\sim$710~km~s$^{-1}$). Furthermore, we find that the duration of HSSW streams ($\Delta t$) are directly related to the longitudinal width of CHs ($\Delta t_{SW}$~$\approx$~0.09$\Delta\theta_{CH}$) and that their longitudinal expansion factor is $f_{SW}~\approx 1.2~\pm 0.1$. We also derive an expression for the coronal hole flux-tube expansion factor, $f_{FT}$, which varies as $f_{SW} \gtrsim f_{FT} \gtrsim 0.8$. These results enable us to estimate the peak speeds and durations of HSSW streams at L1 using the properties of CHs identified in the solar corona., Comment: 8 pages, 4 figures, Accepted for publication in The Astrophysical Journal Letters (APJL) on 25th November 2018

The space weather community has begun to use frontier methods such as data assimilation, machine learning, and ensemble modeling to advance current operational forecasting efforts. This was highlighted by a multi-disciplinary session at the 2017 American Geophysical Union Meeting, 'Frontier Solar-Terrestrial Science Enabled by the Combination of Data-Driven Techniques and Physics-Based Understanding', with considerable discussion surrounding ensemble techniques. Here ensemble methods are described in detail; using a set of predictions to improve on a single-model output, for example taking a simple average of multiple models, or using more complex techniques for data assimilation. They have been used extensively in fields such as numerical weather prediction and data science, for both improving model accuracy and providing a measure of model uncertainty. Researchers in the space weather community have found them to be similarly useful, and some examples of success stories are highlighted in this commentary. Future developments are also encouraged to transition these basic research efforts to operational forecasting as well as providing prediction errors to aid end-user understanding., Comment: Accepted for publication as invited Commentary in Space Weather. 10 pages, 3 figures

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single Extreme Ultra-Violet passband and magnetogram images to extract CH information. Here, the coronal hole identification via multi-thermal emission recognition algorithm (CHIMERA) is described, which analyses multi-thermal images from the atmospheric image assembly (AIA) onboard the solar dynamics observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 Å, 193 Å, and 211 Å). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms.

Data assimilation procedures have been developed for thermospheric models using satellite density measurements as part of the EU Framework Package 7 ATMOP Project. Two models were studied; one a general circulation model, TIEGCM, and the other a semi-empirical drag temperature model, DTM. Results of runs using data assimilation with these models were compared with independent density observations from CHAMP and GRACE satellites throughout solar cycles 23 and 24. Time periods of 60 days were examined at solar minimum and maximum, including the 2003 Hallowe'en storms. The differences between the physical and the semi-empirical models have been characterised. Results indicate that both models tend to show similar behaviour; underestimating densities at solar maximum, and overestimating them at solar minimum. DTM performed better at solar minimum, with both models less accurate at solar maximum. A mean improvement of ~4% was found using data assimilation with TIEGCM. With further improvements, the use of general circulation models in operational space weather forecasting (in addition to empirical methods currently used) is plausible. Future work will allow near-real-time assimilation of thermospheric data for improved forecasting., Comment: Accepted for publication in Space Weather. 11 pages, 6 figures, 1 table