NOAA Space Weather Prediction Center Research Articles

Heliospheric 3-D MHD ENLIL simulations of multi-CME and multi-spacecraft events

Interpreting multi-spacecraft heliospheric observations of the evolving solar wind (SW) streams with propagating and interacting coronal mass ejections (CMEs) is challenging. Numerical simulations can provide global context and suggest what may and may not be observed. The heliospheric three-dimensional (3D) magnetohydrodynamic (MHD) ENLIL model can provide a near-real-time prediction of heliospheric space weather, and it is used at NASA Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), and UK Meteorological Office (MetOffice). However, this version does not show its full potential, especially in the case of multi-CME events observed by various spacecraft. We describe tools developed to interpret remote observations and in-situ measurements better and apply them to multi-CME events observed by ACE, STEREO-A, Parker Solar Probe (PSP), BepiColombo, and Solar Orbiter. We present some results on 1) global structures of the SW speed and density at the ecliptic, 2) the evolution of SW parameters at the spacecraft, 3) magnetic field connectivity at the spacecraft, 4) automatic detection of shock parameters and alert plots, and 5) synthetic white-light (WL) imaging. This paper is not on model initialization or analyzing specific CME events, but it describes features not used at space weather prediction centers and provided by NASA/CCMC Run-On-Request service. This paper advertises new tools and shows their benefits when applied to selected heliospheric space weather events observed at near-Earth, PSP, Solar Orbiter, and STEREO-A spacecraft.

NOAA Space Weather Prediction Center Radiation Advisories for the International Civil Aviation Organization

AbstractThe National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) issues several solar radiation storm products: the long standing proton Warnings and Alerts that are based on particle intensity levels observed by the Geostationary Operational Environmental Satellites; and the more recent International Civil Aviation Organization (ICAO) radiation advisories which specify effective dose rates at aviation flight levels. SWPC ICAO advisories are supported by the U.S. Federal Aviation Administration (FAA) CARI‐7A model. In this paper we use CARI‐7A modeling results for the Ground Level Enhancement 69 (GLE69) solar radiation storm which occurred on the 20th of January 2005 to demonstrate the ICAO advisory format. For the onset and peak of GLE69, we find that a severe (SEV) radiation advisory would have been issued for altitudes above 32,000 ft, for polar and mid latitude regions of the northern and southern hemisphere. At lower altitudes, down to 25,000 ft, the moderate (MOD) radiation threshold would have been exceeded. In total, 10 ICAO radiation advisories would have been issued over 6.5 hr. From the retrospective modeling of GLE69, and feedback from users, we identify ways in which the ICAO advisories should be improved.

Validation of Ionospheric Modeled TEC in the Equatorial Ionosphere During the 2013 March and 2021 November Geomagnetic Storms

AbstractThis paper presents the validation of modeled total electron content (TEC) from 14 ionospheric models, including empirical, physics‐based, and data assimilation (DA) models, hosted by the NASA/NSF Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL). This study aims to assess the current progress and capability of the CCMC‐hosted ionospheric models in capturing the storm time ionosphere during the low and moderate solar flux years. We focus on the low‐latitude ionosphere (i.e., ±40° in magnetic latitude) and compare the modeled TEC with the Madrigal TEC during the 2013 March and 2021 November storms. Multiple metrics are used to quantitatively assess the models' accuracy, precision, association, bias, and capability in capturing the TEC changes in response to the storms. The skill score based on the metric scores is further proposed to evaluate the overall performance of ionospheric models against the reference model (International Reference Ionosphere 2016; IRI‐2016). The results indicate that the DA model GLObal Total Electron Content and JPL Global Ionospheric Map models show good performance in modeling the TEC and reasonably reflect the storm time TEC changes spatially and temporally. The empirical models IRI‐2016 and 2020 show relatively good performance compared with the physics‐based models regarding the model‐data comparison; however, it is difficult to characterize the TEC changes caused by storms. The physics‐based models can simulate the storm effect in spatial and temporal TEC variations better than the empirical model. The performance of ionospheric models in capturing the storm time TEC anomaly is presented and discussed.

An investigation of the relationship between the CME and the Geomagnetic Storm

The primary objective of this work is to look for any conceivable link between the geomagnetic storm and coronal mass ejection (CME) events. The relevant data for both CME and geomagnetic storm occurrences have been obtained from the Solar and Heliospheric Observatory mission’s LASCO and the NOAA Space Weather Prediction Center, respectively, for the same time span (February 1999 to December 2007). We performed Multivariate Singular Spectral Analysis (MSSA), Semblance Analysis, and Multifractal cross-correlation detrended fluctuation (MFXDFA) analysis to achieve our target. We also applied MSSA to investigate the causal link between the two data sets mentioned above. The analysis identifies the supportiveness factor between the data series and the possibility of a one-way statistical relationship between the data series is also verified. Then, Semblance and MFXDFA were used to detect the presence of any conceivable local phase relationship (time and frequency-dependent) and cross-correlation in the data sets. To make our analysis more persuasive, we must use all three methods. The Semblance analysis reveals positive and negative phase correlation between the signals under investigation at different time sub-intervals, whilst the MFXDFA validates the long-term power-law cross-correlations.

Multifractal cross‐correlation analysis between CME and Planetary K‐index time series

AbstractThis research aims to examine the multiscale‐multifractal correlation properties between the geomagnetic storm and coronal mass ejection (CME) occurrences by analyzing the CME linear speed and Planetary K‐index time series data. The relevant data for both CME and geomagnetic storm occurrences were obtained from the Solar and Heliospheric Observatory mission's LASCO and the NOAA Space Weather Prediction Center, respectively, for the same period (February 1999 to December 2007). We performed MultiFractal cross‐correlation Detrended Fluctuation Analysis (MFXDFA) and Multifractal cross‐correlation Detrending Moving average Analysis (MFXDMA) to investigate and quantify the possible cross‐correlation between the two natural events. The MFXDFA technique is also compared to the backward MFXDMA algorithm's performance. The change in the degree of cross‐correlation over time has been investigated, and the findings are quantitatively analyzed. The existence of significant power‐law cross‐correlations has been discovered within all scaling orders. Furthermore, we also find evident persistence of cross‐correlation with substantial Hurst exponents. In addition, it has been observed that long‐term cross‐correlation has a more considerable degree of multifractality and persistence than short‐term cross‐correlation.

Aurora Detection From Nighttime Lights for Earth and Space Science Applications

AbstractThis research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora‐contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite‐ or ground‐based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar‐orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source.

Observations From NOAA's Newest Solar Proton Sensor

AbstractSpace weather instrumentation on board the National Oceanic and Atmospheric Administration's (NOAA's) newest Geostationary Operational Environmental Satellite (GOES)‐R series includes the Solar and Galactic Proton Sensor (SGPS), which has been collecting data since January 8, 2017. SGPS supports real‐time alerts of solar energetic particle events at the NOAA Space Weather Prediction Center (SWPC) and provides data to the space science community, advancing basic space science research and understanding of space weather effects on satellite systems. The first GOES‐R series spacecraft, GOES‐16, was launched on November 19, 2016. A series of solar particle events in September 2017 enabled cross‐calibration of GOES‐16 SGPS with the Energetic Particle Sensors on GOES‐13 and ‐15. This paper is intended as a resource for SGPS data users, including comparisons with legacy GOES energetic particle measurements, corrections applied to SGPS Level‐2 data, important caveats, background level fluxes, and measurements of trapped magnetospheric protons.

Analysis of fractality and complexity of the planetary K-index

The objective of this research is to explore the inherent complexities and multifractal properties of the underlying distributions in the daily Planetary K-index time series collected from NOAA Space Weather Prediction Center. In this article, non-stationary and nonlinear characteristics of the signal have been explored using Smoothed Pseudo Wigner–Ville Distribution and Delay Vector Variance algorithms, respectively, while Recurrence Plot, 0–1 test, Recurrence Quantification Analysis and correlation dimension analysis have been applied to confirm and measure the chaos in the signal under consideration. Multifractal detrending moving average has been used to evaluate the multifractality and also recognise the singularities of the signal. The result of these analyses validates the nonstationary and nonlinear characteristics of the Planetary K-index signal, while a significant presence of deterministic chaos in it has also been noticed. It has also been confirmed that the Planetary K-index exhibits multifractal nature with positive persistence. The long-range temporal association and also the large pdf are discovered to be the primary factors that contribute to the multifractal behaviour of the Kp-index.

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Magnetohydrodynamics (MHD)-based global space weather models have mostly been developed and maintained at academic institutions. While the “free spirit” approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).

A Daily Determination of BZ Using the Russell‐McPherron Effect to Forecast Geomagnetic Activity

AbstractSince the middle of the last decade, UCSD has incorporated magnetic field data in its Institute for Space‐Earth Environmental Research interplanetary scintillation tomographic analysis. These data are extrapolated upward from the solar surface using the Current Sheet Source Surface model (Zhao & Hoeksema, 1995, https://doi.org/10.1029/94JA02266) to provide predictions of the interplanetary field in RTN coordinates. Over the years this technique has become ever more sophisticated, and allows different types of magnetogram data (SOLIS, Global Oscillation Network Group, etc.,) to be incorporated in the field extrapolations. At Earth, these fields can be displayed in a variety of ways, including Geocentric Solar Magnetospheric (GSM) Bx, By, and Bz coordinates. Displayed daily, the Current Sheet Source Surface model‐derived GSM Bz shows a significant positive correlation with the low‐resolution (few day variation) in situ measurements of the Bz field. The nano‐Tesla variations of Bz maximize in spring and fall as Russell and McPherron (1973, https://doi.org/10.1029/JA078i001p00092) have shown. More significantly, we find that the daily variations are correlated with geomagnetic Kp and Dst index variations, and that a decrease from positive to negative Bz has a high correlation with minor‐to‐moderate geomagnetic storm activity, as defined by NOAA Space Weather Prediction Center planetary Kp values. Here we provide an 11‐year study of the predicted Bz field, from the extrapolation of the Global Oscillation Network Group‐magnetograms. We provide a skill‐score analysis of the technique's geomagnetic storm prediction capability, which allows forecasts of moderate enhanced geomagnetic storm activity. UCSD and the Korean Space Weather Center currently operate a website that predicts this low‐resolution GSM Bz field component variation several days in advance.

The Global Oscillation Network Group Facility—An Example of Research to Operations in Space Weather

AbstractThe Global Oscillation Network Group (GONG) is a system of ground‐based solar observing stations distributed geographically so that the Sun is visible nearly continuously at all times. Originally developed to provide data for research into the solar interior via helioseismology, GONG is now also providing data for operational space weather forecasting as a cost‐effective and reliable alternative to space missions. The data comprise full‐disk magnetograms, H‐α intensity images, and helioseismic maps of activity on the solar far side. These data are provided in near real time to the NOAA Space Weather Prediction Center, the US Air Force, and NASA. GONG is a successful example of transitioning a research facility funded by the NSF into an operational asset.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

Abstract This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

New Federal Government Space Weather Website and Document Repository Launched

AbstractOn Tuesday, 19 September 2017, the NOAA Space Weather Prediction Center and Office of the Federal Coordinator for Meteorology (OFCM) launched the new Space Weather Operations, Research, and Mitigation website SWORM.GOV. The website provides access to the public to Federal activities supporting the Executive Office of the President National Science and Technology Council SWORM Subcommittee as well as other activities and events relevant to the National Space Weather Enterprise. SWORM.GOV was approved by the SWORM Subcommittee, funded by NOAA, and maintained by OFCM.

SPRINTS: A Framework for Solar‐Driven Event Forecasting and Research

AbstractCapabilities to predict onset and time profiles of solar‐driven events, including solar X‐ray flares; solar energetic particles (SEP); coronal mass ejections; and high‐speed streams, are critical in mitigating their potential impacts. We introduce the Space Radiation Intelligence System (SPRINTS). This NASA‐invested technology integrates preeruptive metadata and forecasts from the MAG4 system with posteruptive metadata in order to produce high fidelity and preeruptive to posteruptive transitional forecasts for solar‐driven events. To catalog start and end times of the four solar‐driven events, SPRINTS is capable of generating posteruptive forecasts based on automatic detections employed on 30+ years of GOES X‐ray and particle data as well as 20+ years of ACE and DSCOVR solar wind data. The prediction results for 1986–2016 presented here are from the SPRINTS posteruptive capability for forecasting SEPs leveraging GOES X‐ray metadata. We present onset, peak flux, and time profile SEP forecast metrics and results based on expert‐guided, statistical, and machine‐learned decision tree models. Operating on data from a 20 year period, a machine‐learned decision tree model provided the best results for predicting an S1 event (on the NOAA Space Weather Prediction Center Solar Radiation Scale): 86% probability of detection and 37% false alarm rate. Five flare‐related metadata sets were leveraged in the decision tree modeling. Consistently, flare‐integrated flux, flare heliolongitude, and flare decay‐phase duration were found to be the top three forecasting parameters, while flare magnitude and flare latitude had little to no impact on the SEP forecast model. For the solar‐driven events of March 2012, we demonstrate SPRINTS abilities to forecast solar flares, SEP onset, and SEP evolution.

Validation of the effect of cross‐calibrated GOES solar proton effective energies on derived integral fluxes by comparison with STEREO observations

AbstractThe derivation of integral fluxes from instrument coincidence rates requires accurate knowledge of their effective energies. Recent cross calibrations of GOES with the high‐energy‐resolution Interplanetary Monitoring Platform (IMP) 8 Goddard Medium Energy Experiment (GME) (Sandberg et al., Geophys. Res. Lett, 41, 4435, 2014a) gave significantly lower effective energies than those currently used by the NOAA Space Weather Prediction Center to calculate solar proton integral fluxes from GOES rates. This implies systematically lower integral fluxes than currently produced. This paper quantifies the differences between the current and the cross‐calibrated GOES integral fluxes and validates the latter. Care is taken to rule out the spectral resolution of the measurements or different integration algorithms as major contributors to differences in the magnitudes of the derived integral fluxes. The lower effective energies are validated by comparison with the independent, high‐resolution observations by the STEREO Low‐Energy Telescope (LET) and High‐Energy Telescope (HET) during the December 2006 solar proton events. The current GOES product is similar to the >10 MeV integral fluxes recalculated by using the Sandberg et al. [] effective energies but is substantially greater at higher energies. (The median ratios of the current to the recalculated fluxes are 1.1 at >10 MeV, 1.7 at >30 MeV, 2.1 at >60 MeV, and 2.9 at >100 MeV.) By virtue of this validation, the cross‐calibrated GOES integral fluxes should be considered more accurate than the current NOAA product. The results of this study also demonstrate good consistency between the two long‐term IMP 8 GME and STEREO LET and HET solar proton data sets.

Community‐wide validation of geospace model local K‐index predictions to support model transition to operations

AbstractWe present the latest result of a community‐wide space weather model validation effort coordinated among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), model developers, and the broader science community. Validation of geospace models is a critical activity for both building confidence in the science results produced by the models and in assessing the suitability of the models for transition to operations. Indeed, a primary motivation of this work is supporting NOAA/SWPC's effort to select a model or models to be transitioned into operations. Our validation efforts focus on the ability of the models to reproduce a regional index of geomagnetic disturbance, the local K‐index. Our analysis includes six events representing a range of geomagnetic activity conditions and six geomagnetic observatories representing midlatitude and high‐latitude locations. Contingency tables, skill scores, and distribution metrics are used for the quantitative analysis of model performance. We consider model performance on an event‐by‐event basis, aggregated over events, at specific station locations, and separated into high‐latitude and midlatitude domains. A summary of results is presented in this report, and an online tool for detailed analysis is available at the CCMC.

Forecasting auroras from regional and global magnetic field measurements

Abstract. We use the connection between auroral sightings and rapid geomagnetic field variations in a concept for a Regional Auroral Forecast (RAF) service. The service is based on statistical relationships between near-real-time alerts issued by the NOAA Space Weather Prediction Center and magnetic time derivative (dB∕dt) values measured by five MIRACLE magnetometer stations located in Finland at auroral and sub-auroral latitudes. Our database contains NOAA alerts and dB∕dt observations from the years 2002–2012. These data are used to create a set of conditional probabilities, which tell the service user when the probability of seeing auroras exceeds the average conditions in Fennoscandia during the coming 0–12 h. Favourable conditions for auroral displays are associated with ground magnetic field time derivative values (dB∕dt) exceeding certain latitude-dependent threshold values. Our statistical analyses reveal that the probabilities of recording dB∕dt exceeding the thresholds stay below 50 % after NOAA alerts on X-ray bursts or on energetic particle flux enhancements. Therefore, those alerts are not very useful for auroral forecasts if we want to keep the number of false alarms low. However, NOAA alerts on global geomagnetic storms (characterized with Kp values > 4) enable probability estimates of > 50 % with lead times of 3–12 h. RAF forecasts thus rely heavily on the well-known fact that bright auroras appear during geomagnetic storms. The additional new piece of information which RAF brings to the previous picture is the knowledge on typical storm durations at different latitudes. For example, the service users south of the Arctic Circle will learn that after a NOAA ALTK06 issuance in night, auroral spotting should be done within 12 h after the alert, while at higher latitudes conditions can remain favourable during the next night.

Using citizen science reports to define the equatorial extent of auroral visibility

AbstractAn aurora may often be viewed hundreds of kilometers equatorward of the auroral oval owing to its altitude. As such, the NOAA Space Weather Prediction Center (SWPC) Aurora Forecast product provides a “view line” to demonstrate the equatorial extent of auroral visibility, assuming that it is sufficiently bright and high in altitude. The view line in the SWPC product is based upon the latitude of the brightest aurora, for each hemisphere, as specified by the real‐time oval variation, assessment, tracking, intensity, and online nowcasting (OVATION) Prime (2010) aurora precipitation model. In this study, we utilize nearly 500 citizen science auroral reports to compare with the view line provided by an updated SWPC aurora forecast product using auroral precipitation data from OVATION Prime (2013). The citizen science observations were recorded during March and April 2015 using the Aurorasaurus platform and cover one large geomagnetic storm and several smaller events. We find that this updated SWPC view line is conservative in its estimate and that the aurora is often viewable further equatorward than is indicated by the forecast. By using the citizen reports to modify the scaling parameters used to link the OVATION Prime (2013) model to the view line, we produce a new view line estimate that more accurately represents the equatorial extent of visible aurora. An OVATION Prime (2013) energy flux‐based equatorial boundary view line is also developed and is found to provide the best overall agreement with the citizen science reports, with an accuracy of 91%.

Validation of an operational product to determine L1 to Earth propagation time delays

AbstractWe describe the development and validation of an operational space weather tool to forecast propagation delay times between L1 and Earth using the Weimer and King (2008) tilted phase front technique. A simple flat plane convection delay method is currently used by the NOAA Space Weather Prediction Center (SWPC) to propagate the solar wind from a monitoring satellite located at L1 to a point upstream of the magnetosphere. This technique assumes that all observed solar wind discontinuities, such as interplanetary shocks and interplanetary coronal mass ejection boundaries, are in a flat plane perpendicular to the Sun‐Earth line traveling in the GSE X direction at the observed solar wind velocity. In reality, these phase plane fronts can have significantly tilted orientations, and by relying on a ballistic propagation method, delay time errors of ±15 min are common. In principle, the propagation time delay product presented here should more accurately predict L1 to Earth transit times by taking these tilted phase plane fronts into account. This algorithm, which is based on the work of Weimer and King (2008), is currently running in real time in test mode at SWPC as part of the SWPC test bed. We discuss the current algorithm performance, and via our detailed validation study, show that there is no significant difference between the two propagation methods when run in a real‐time operational environment.

Ensemble Modeling of the 23 July 2012 Coronal Mass Ejection

AbstractOn 23 July 2012 a significant and rapid coronal mass ejection (CME) was detected in situ by the Solar Terrestrial Relations Observatory (STEREO) A. This CME was unusual due to its extremely brief Sun‐to‐1 AU transit time of less than 21 h and its exceptionally high impact speed of 2246 km/s. If this CME had been Earth directed, it would have produced a significant geomagnetic storm with potentially serious consequences. To protect our ground‐ and space‐based assets, there is a clear need to accurately forecast the arrival times of such events using realistic input parameters and models run in near real time. Using Wang‐Sheely‐Arge (WSA)‐Enlil, the operational model currently employed at the NOAA Space Weather Prediction Center, we investigate the sensitivity of the 23 July CME event to model input parameters. Variations in the initial CME speed, angular width, and direction, as well as the ambient solar wind background, are investigated using an ensemble approach to study the effect on the predicted arrival time of the CME at STEREO A. Factors involved in the fast transit time of this large CME are discussed, and potential improvements to modeling such events with the WSA‐Enlil model are presented.