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The IAGA‐endorsed Polar Cap Indices for the northern and southern hemispheres, PCN and PCS, are compared for 1998–2018, inclusive. Potential effects of the slightly different, and changing, magnetic coordinates of the two magnetic stations employed, Thule (Qaanaaq) in Greenland and Vostok in Antarctica, are investigated. It is shown that the agreement in overall behavior of the two indices is very close indeed but that PCS consistently correlates slightly better with solar wind parameters than PCN. Optimum lags for these correlations are 19 min for 1‐min data and 37 min for hourly averages. The correlations are significantly highest for the predicted magnetopause reconnection voltage, which is a linear predictor of PCN and PCS for all 1‐hr data and for all but the largest 0.1% of 1‐min values. The indices show lower correlation and marked non‐linearity (tending to saturation) at all levels with the estimated magnetopause reconnection electric field or the estimated power input into the magnetosphere. The PCN index is shown to correlate closely with the transpolar voltage measured by the northern‐hemisphere SuperDARN radar network and both PCN and PCS clearly show the Russell‐McPherron effect of dipole tilt and the Y‐component of the interplanetary magnetic field. However the patterns in time‐of‐year and Universal Time (UT) are complicated by lobe reconnection during northward‐IMF, the effect of which on the indices is shown to be predominantly a summer hemisphere phenomenon and gives a UT dependence on the IMF Y‐component that is predicted theoretically. Plain Language Summary: The Polar Cap Indices are generated from geomagnetic recordings made at Thule (Qaanaaq) in Greenland and Vostok in Antarctica and are used as monitors of the coupling of solar wind energy and momentum into Earth's magnetosphere. These stations are at similar locations relative to the nearby magnetic poles of the Earth but there are small differences, the effect of which is investigated. There has been debate about the processing of the data to generate the indices and the extent to which the results from the two hemisphere do ‐ and should ‐ agree with each other. It is shown that the overall agreement of the northern and southern hemisphere indices is very good indeed. It has been proposed that these indices reflect a number of aspects of coupling of the solar wind and Earth's magnetosphere, but it is shown here that they are optimum indicators of the voltage generated by magnetic reconnection between the geomagnetic field and the interplanetary magnetic field. This is shown to be consistent with the variations of the indices with time‐of‐year and time‐of‐day. Key Points: Simultaneous 1‐min values can differ, but the distributions of the north and south polar cap indices over 1998–2018 are very similarBoth indices give significantly higher correlations with the predicted voltage of open flux generation and with observed transpolar voltageBoth indices show a Russell‐McPherron effect plus a northward‐IMF lobe reconnection effect that is predominantly in the summer hemisphere [ABSTRACT FROM AUTHOR]

Geometric modeling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modeling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modeling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modeling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modeling errors are minimised for an observer in the L5 region. Furthermore, geometric modeling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2 ± 1.2 h, 8.3 ± 1.0 h, and 5.8 ± 0.9 h for the average, fast, and extreme CME scenarios. Plain Language Summary: Coronal Mass Ejections (CMEs) are the largest space weather hazard to society. To help manage this hazard, we need to understand how CMEs flow through space and to develop methods to forecast when they will arrive at Earth. To help understand how CMEs flow, a range of geometric models have been developed and are widely used. Geometric models approximate a CME as a simple geometric shape, such as a circle or ellipse, and are used to help interpret CME remote sensing observations from heliospheric imagers. So far, it has been difficult to work out how good the assumptions of geometric models are and how uncertain their predictions are. In this study, we use numerical simulations of the solar wind and CMEs to try and estimate how good the geometrical modeling assumptions are, and the size of the uncertainties on their predictions. We find that because the geometric models don't account for time‐dependent solar wind structure, that they are biased and typically predict that a CME is further out into the solar wind than it actually is. Because of this, geometric models tend to predict early arrival times at Earth. Key Points: We test the performance of geometric models for estimating coronal mass ejection kinematics with a suite of solar wind numerical model runsFor Earth‐directed coronal mass ejection scenarios, geometric modeling errors are minimised for observers in the L5 regionGeometric modeling generally overestimates a coronal mass ejections speed and predicts earlier arrivals at Earth by, on average, 8 hours [ABSTRACT FROM AUTHOR]

Geomagnetically induced currents (GICs) are an impact of space weather that can occur during periods of enhanced geomagnetic activity. GICs can enter into electrical power grids through earthed conductors, potentially causing network collapse through voltage instability or damaging transformers. It would be beneficial for power grid operators to have a forecast of GICs that could inform decision making on mitigating action. Long lead‐time GIC forecasting requires magnetospheric models as drivers of geoelectric field models. However, estimation of the geoelectric field is sensitive to high‐frequency geomagnetic field variations, which operational global magneto‐hydrodynamic models do not fully capture. Furthermore, an assessment of GIC forecast uncertainty would require a large ensemble of magnetospheric runs, which is computationally expensive. One solution that is widely used in climate science is "downscaling," wherein sub‐grid variations are added to model outputs on a statistical basis. We present proof‐of‐concept results for a method that temporally downscales low‐resolution magnetic field data on a 1‐hr timescale to 1‐min resolution, with the hope of improving subsequent geoelectric field magnitude estimates. An analog ensemble (AnEn) approach is used to select similar hourly averages in a historical data set, from which we separate the high‐resolution perturbations to add to the hourly average values. We find that AnEn outperforms the benchmark linear‐interpolation approach in its ability to accurately drive an impacts model, suggesting GIC forecasting would be improved. We evaluated the ability of AnEn to predict extreme events using the FSS, HSS, cost/loss analysis and BSS, finding that AnEn outperforms the "do‐nothing" approach. Plain Language Summary: Forecasting space weather impacts on ground‐based systems, such as power grids, requires the use of computer simulations of the disturbance of the Earth's magnetic field by the solar wind. However, these computer simulations are often too smooth, underestimating small and fast variations in the Earth's magnetic field, which are important for modeling induction hazards that may affect power grids. In this paper we present a proof‐of‐concept scheme that attempts to introduce realistic high‐frequency variations using the idea of looking at how the field has previously behaved in historical events. We test the model and find that it allows for better impact forecasting than if our scheme is not used. Key Points: Operational global MHD models do not fully capture the ground‐level magnetic field variability important for modeling induction hazardsWe provide a proof of concept model to statistically introduce realistic, high‐resolution perturbations with which to drive an impacts modelOur downscaling scheme outperforms a reference linear‐interpolation approach under a range of metrics [ABSTRACT FROM AUTHOR]

Variability in near‐Earth solar wind conditions gives rise to space weather, which can have adverse effects on space‐ and ground‐based technologies. Enhanced and sustained solar wind coupling with the Earth's magnetosphere can lead to a geomagnetic storm. The resulting effects can interfere with power transmission grids, potentially affecting today's technology‐centered society to great cost. It is therefore important to forecast the intensity and duration of geomagnetic storms to improve decision making capabilities of infrastructure operators. The 150 years aaH geomagnetic index gives a substantial history of observations from which empirical predictive schemes can be built. Here we investigate the forecasting of geomagnetic activity with two pattern‐matching forecast techniques, using the long aaH record. The techniques we investigate are an Analogue Ensemble (AnEn) Forecast, and a Support Vector Machine (SVM). AnEn produces a probabilistic forecast by explicitly identifying analogs for recent conditions in the historical data. The SVM produces a deterministic forecast through dependencies identified by an interpretable machine learning approach. As a third comparative forecast, we use the 27 days recurrence model, based on the synodic solar rotation period. The methods are analyzed using several forecast metrics and compared. All forecasts outperform climatology on the considered metrics and AnEn and SVM outperform 27 days recurrence. A Cost/Loss analysis reveals the potential economic value is maximized using the AnEn, but the SVM is shown as superior by the true skill score. It is likely that the best method for a user will depend on their need for probabilistic information and tolerance of false alarms. Plain Language Summary: Space weather has the potential to disrupt society and the economy on a large scale. One such major impact is on power grids, which can be damaged by disturbances in Earth's magnetic field caused by space weather events. As a result, it would be useful to have an accurate forecast of space weather that can help power grid operators make decisions about taking mitigating action. In this work, we test three forecasting techniques which utilize long historical records to exploit patterns in the data and hence predict future disturbances in Earth's magnetic field. We find that all three of the techniques provide valuable information and the best method depends on the individual needs of the forecast user. Key Points: Pattern‐matching techniques are an effective way to forecast geomagnetic activityThe analogue ensemble and support vector machine outperform 27 days recurrence and climatologyThe best forecast approach for the end user will depend on their need for probabilistic forecast information [ABSTRACT FROM AUTHOR]

Variability in the near-Earth solar wind conditions can adversely affect a number of ground- and space-based technologies. Such space-weather impacts on ground infrastructure are expected to increase primarily with geomagnetic storm intensity, but also storm duration, through time-integrated effects. Forecasting storm duration is also necessary for scheduling the resumption of safe operating of affected infrastructure. It is therefore important to understand the degree to which storm intensity and duration are correlated. The long-running, global geomagnetic disturbance index, aa , has recently been recalibrated to account for the geographic distribution of the component stations. We use this aa H index to analyse the relationship between geomagnetic storm intensity and storm duration over the past 150 years, further adding to our understanding of the climatology of geomagnetic activity. Defining storms using a peak-above-threshold approach, we find that more intense storms have longer durations, as expected, though the relationship is nonlinear. The distribution of durations for a given intensity is found to be approximately log-normal. On this basis, we provide a method to probabilistically predict storm duration given peak intensity, and test this against the aa H dataset. By considering the average profile of storms with a superposed-epoch analysis, we show that activity becomes less recurrent on the 27-day timescale with increasing intensity. This change in the dominant physical driver, and hence average profile, of geomagnetic activity with increasing threshold is likely the reason for the nonlinear behaviour of storm duration. [ABSTRACT FROM AUTHOR]

Routine in situ solar wind observations from L5, located 60° behind Earth in its orbit, would provide a valuable input to space weather forecasting. One way to utilize such observations is to assume that the solar wind is in perfect steady state over the 4.5 days it takes the Sun to rotate 60°, and thus, near‐Earth solar wind in 4.5 days time would be identical to that at L5 today. This corotation approximation is most valid at solar minimum when the solar wind is slowly evolving. Using STEREO data, it has been possible to test L5‐corotation forecasting for a few months mostly at solar minimum, but the various contributions to forecast error cannot be disentangled. This study uses 40+ years of magnetogram‐constrained solar wind simulations to isolate the effect of latitudinal offset between L5 and Earth due to the inclination of the ecliptic plane to the solar rotational equator. Latitudinal offset error is found to be largest at solar minimum, due to the latitudinal ordering of solar wind structure. It is also a strong function of time of year: maximum at the solstices and very low at equinoxes. At solstice, the latitudinal offset alone means L5‐corotation forecasting is expected to be less accurate than numerical solar wind models, even before accounting for time‐dependent solar wind structures. Thus, a combination of L5‐corotation and numerical solar wind modeling may provide the best forecast. These results also highlight that three‐dimensional solar wind structure must be accounted for when performing solar wind data assimilation. Plain Language Summary: The solar wind is a continuous outflow of electrically charged material driven by the Sun's magnetic field. Variability in solar wind conditions give rise to hazardous "space weather." One way to forecast solar wind conditions is by assuming the Sun's magnetic field is constant and therefore that same conditions at Earth will repeat every solar rotation (27 days). In reality, the Sun's field changes significantly over 27 days, particularly at solar maximum. An improved forecast may be provided by a space weather mission to L5, a gravitationally stable position 60° behind Earth in its orbit. A spacecraft at L5 would observe the solar wind expected to rotate to Earth only 4.5 days later. However, Earth's orbit is slightly misaligned with the solar rotational equator. Thus L5 and Earth are generally at different solar latitudes. While this difference is small—around 7° at most—it can still be important. We use 40 years of solar wind simulations to show that the largest forecast errors occur around the solstices and are negligible at the equinoxes. Somewhat counterintuitively, the latitudinal‐forecast error is significantly larger at solar minimum than at solar maximum. This is because the solar wind is primarily structured in latitude at solar minimum. Key Points: Heliographic latitudinal offset can have a significant effect on corotation solar wind forecasting from L5The error introduced by latitudinal offset is maximized at solar minimum and during the solsticesA combination of L5 measurements and global MHD modeling may yield the most accurate solar wind forecasts [ABSTRACT FROM AUTHOR]

We develop and test an empirical model predicting ground‐based observations of ultralow frequency (ULF, 1–20 mHz) wave power across a range of frequencies, latitudes, and MLT sectors. This is parameterized by instantaneous solar wind speed vsw, variance in proton number density var(Np), and interplanetary southward magnetic field Bz. A probabilistic model of ULF wave power will allow us to address uncertainty in radial diffusion coefficients and therefore improve diffusion modeling of radial transport in Earth's outer radiation belt. Our model can be used in two ways to reproduce wave power: by sampling from conditional probability distribution functions and by using the mean (expectation) values. We derive a method for testing the quality of the parameterization and test the ability of the model to reproduce ULF wave power time series. Sampling is a better method for reproducing power over an extended time period as it retains the same overall distribution, while mean values are better for predicting the power in a time series. The model predicts each hour in a time series better than the assumption that power persists from the preceding hour. Finally, we review other sources of diffusion coefficient uncertainty. Although this wave model is designed principally for the goal of improved radial diffusion coefficients to include in outer radiation belt diffusion‐based modeling, we anticipate that our model can also be used to investigate the occurrence of ULF waves throughout the magnetosphere and hence the physics of ULF wave generation and propagation. Plain Language Summary: We construct and test a statistical model for ground‐based ultralow frequency wave occurrence, parameterized by solar wind properties. This can be used to find magnetospheric radial diffusion coefficients that determine the transport and energization of electrons in Earth's radiation belts. Our time series prediction outperforms a time series using the assumption that wave power persists from the preceding hour. Using a probabilistic approach reproduces the true distribution of power over extended time periods and is necessary to quantify uncertainty in each step of diffusion modeling. Key Points: Determining uncertainty in wave power models is necessary to quantify uncertainty in radial diffusion coefficients for modelingOur model of ground‐based ULF wave power depends on solar wind speed, number density variance, and Bz; this outperforms hourly persistenceTotal power over extended events is best modeled probabilistically, while the wave power in a single hour is best modeled deterministically [ABSTRACT FROM AUTHOR]

Abstract: Severe space weather is driven by interplanetary coronal mass ejections (ICMEs), episodic eruptions of solar plasma, and magnetic flux that travel out through the heliosphere and can perturb the Earth's magnetosphere and ionosphere. In order for space‐weather forecasts to allow effective mitigating action, forecasts must be made as early as possible, necessitating identification of potentially “geoeffective” ICMEs close to the Sun. This presents two challenges. First, geoeffectiveness is primarily determined by the magnetic field intensity and orientation, both of which are difficult to measure close to the Sun. Second, the magnetic field evolves in transit between the Sun and the Earth, sometimes in a highly nonlinear way. Conversely, solar wind ion charge states, such as the ratio of O7+ to O6+, are fixed by the electron temperature at the coronal height where ion‐electron collisions are last possible as the ICME erupts. After this point, they are said to be “frozen in” as they do not evolve further as the ICME propagates through the solar wind. In this study we show that ion charge states, while not geoeffective in and of themselves, act as strong markers for the geoeffectiveness of the ICME. The probability of severe space weather is around 7 times higher in “hot” ICMEs than “cold” ICMEs, as defined by O7+/O6+. We suggest that coronal spectroscopy of ICMEs could complement current forecasting techniques, providing valuable additional information about potential geoeffectiveness. [ABSTRACT FROM AUTHOR]

A fraction of the magnetic flux which threads the photosphere reaches sufficient coronal altitude to be dragged out by the solar wind and form the heliospheric magnetic field (HMF). Directly measuring this 'open solar flux' (OSF) component, however, is difficult. While OSF can be extrapolated from photospheric magnetic field measurements, the most direct method is from in situ spacecraft measurements of the HMF. The difficultly is unambiguously distinguishing between HMF which connects directly back to the Sun (the OSF) and that which is locally distorted by waves, turbulence, and near-Sun reconnection. Suitable temporal filtering of the data can remove such 'noise,' but the level of filtering cannot be known a priori and varies with solar cycle, solar wind types, etc. Here we use the suprathermal electron beam, or 'strahl,' to distinguish between different HMF topologies. As strahl moves antisunward on global scales, times when strahl is observed to be moving sunward indicate that the HMF is locally inverted. By subtracting the inverted HMF, we compute the OSF without need for arbitrary filtering of the data. We find that the OSF obtained in this manner is slightly larger than the proposed 'kinematic correction' based on observed solar wind velocity structure, though in general agreement. Our new OSF estimate agrees with methods based wholly on HMF data, if the data are first used to compute approximately 1 day averages during solar minimum and approximately 3 day averages during solar maximum, stressing the point that the filter method is unreliable because the required characteristics vary. [ABSTRACT FROM AUTHOR]

Galactic cosmic rays (GCRs) are modulated by the heliospheric magnetic field (HMF) both over decadal time scales (due to long-term, global HMF variations), and over time scales of a few hours (associated with solar wind structures such as coronal mass ejections or the heliospheric current sheet, HCS). Due to the close association between the HCS, the streamer belt, and the band of slow solar wind, HCS crossings are often associated with corotating interaction regions where fast solar wind catches up and compresses slow solar wind ahead of it. However, not all HCS crossings are associated with strong compressions. In this study we categorize HCS crossings in two ways: Firstly, using the change in magnetic polarity, as either away-to-toward (AT) or toward-to-away (TA) magnetic field directions relative to the Sun and, secondly, using the strength of the associated solar wind compression, determined from the observed plasma density enhancement. For each category, we use superposed epoch analyses to show differences in both solar wind parameters and GCR flux inferred from neutron monitors. For strong-compression HCS crossings, we observe a peak in neutron counts preceding the HCS crossing, followed by a large drop after the crossing, attributable to the so-called 'snow-plough' effect. For weak-compression HCS crossings, where magnetic field polarity effects are more readily observable, we instead observe that the neutron counts have a tendency to peak in the away magnetic field sector. By splitting the data by the dominant polarity at each solar polar region, we find that the increase in GCR flux prior to the HCS crossing is primarily from strong compressions in cycles with negative north polar fields due to GCR drift effects. Finally, we report on unexpected differences in GCR behavior between TA weak compressions during opposing polarity cycles. [ABSTRACT FROM AUTHOR]

In the concluding paper of this tetralogy, we here use the different geomagnetic activity indices to reconstruct the near-Earth interplanetary magnetic field (IMF) and solar wind flow speed, as well as the open solar flux (OSF) from 1845 to the present day. The differences in how the various indices vary with near-Earth interplanetary parameters, which are here exploited to separate the effects of the IMF and solar wind speed, are shown to be statistically significant at the 93% level or above. Reconstructions are made using four combinations of different indices, compiled using different data and different algorithms, and the results are almost identical for all parameters. The correction to the aa index required is discussed by comparison with the Ap index from a more extensive network of mid-latitude stations. Data from the Helsinki magnetometer station is used to extend the aa index back to 1845 and the results confirmed by comparison with the nearby St Petersburg observatory. The optimum variations, using all available long-term geomagnetic indices, of the near-Earth IMF and solar wind speed, and of the open solar flux, are presented; all with ±2σ uncertainties computed using the Monte Carlo technique outlined in the earlier papers. The open solar flux variation derived is shown to be very similar indeed to that obtained using the method of Lockwood et al. (1999). [ABSTRACT FROM AUTHOR]

We present a new reconstruction of the interplanetary magnetic field (IMF, B) for 1846-2012 with a full analysis of errors, based on the homogeneously constructed IDV(1d) composite of geomagnetic activity presented in Part 1 (Lockwood et al., 2013a). Analysis of the dependence of the commonly used geomagnetic indices on solar wind parameters is presented which helps explain why annual means of interdiurnal range data, such as the new composite, depend only on the IMF with only a very weak influence of the solar wind flow speed. The best results are obtained using a polynomial (rather than a linear) fit of the form B = χ; . (IDV(1d)-β)α with best-fit coefficients χ = 3.469, β = 1.393 nT, and α = 0.420. The results are contrasted with the reconstruction of the IMF since 1835 by Svalgaard and Cliver (2010). [ABSTRACT FROM AUTHOR]

The images taken by the Heliospheric Imagers (HIs), part of the SECCHI imaging package onboard the pair of STEREO spacecraft, provide information on the radial and latitudinal evolution of the plasma compressed inside corotating interaction regions (CIRs). A plasma density wave imaged by the HI instrument onboard STEREO-B was found to propagate towards STEREO-A, enabling a comparison between simultaneous remote-sensing and in situ observations of its structure to be performed. In situ measurements made by STEREO-A show that the plasma density wave is associated with the passage of a CIR. The magnetic field compressed after the CIR stream interface (SI) is found to have a planar distribution. Minimum variance analysis of the magnetic field vectors shows that the SI is inclined at 54° to the orbital plane of the STEREO-A spacecraft. This inclination of the CIR SI is comparable to the inclination of the associated plasma density wave observed by HI. A small-scale magnetic cloud with a flux rope topology and radial extent of 0.08 AU is also embedded prior to the SI. The pitch-angle distribution of suprathermal electrons measured by the STEREO-A SWEA instrument shows that an open magnetic field topology in the cloud replaced the heliospheric current sheet locally. These observations confirm that HI observes CIRs in difference images when a small-scale transient is caught up in the compression region. [ABSTRACT FROM AUTHOR]

The response of galactic cosmic rays (GCRs) to an isolated enhancement of the non-axisymmetric component of the solar open magnetic field between June and November 1996 is investigated by using a combination of solar observations and numerical modelling of the interplanetary medium. The most obvious coronal hole visible from Earth at the time had little shielding effect on the flux of GCRs, as measured at Earth by neutron monitors. It is found that the evolution of the corotating interaction regions generated by a less obvious coronal hole was the principal controlling factor. Moreover, we demonstrate the imprint of the latitudinal and longitudinal evolution of that coronal hole on the variation of GCRs. The latitudinal extent of this solar minimum corotating interaction region had a determining, but local, shielding effect on GCRs, confirming previous modelling results. [ABSTRACT FROM AUTHOR]

Presents research which showed that measurements of the near-Earth interplanetary magnetic field reveal that the magnetic flux leaving the Sun has risen. Relationship between the solar wind and the Sun's magnetic flux; Influence of magnetic flux on the Earth; Possible explanations for the increase in flux; Possible impact on the Earth's environment.

Galactic cosmic ray (GCR) flux is modulated by both particle drift patterns and solar wind structures on a range of timescales. Over solar cycles, GCR flux varies as a function of the total open solar magnetic flux and the latitudinal extent of the heliospheric current sheet. Over hours, drops of a few percent in near-Earth GCR flux (Forbush decreases, FDs) are well known to be associated with the near-Earth passage of solar wind structures resulting from corotating interaction regions (CIRs) and transient coronal mass ejections (CMEs). We report on four FDs seen at ground-based neutron monitors which cannot be immediately associated with significant structures in the local solar wind. Similarly, there are significant near-Earth structures which do not produce any corresponding GCR variation. Three of the FDs are during the STEREO era, enabling in situ and remote observations from three well-separated heliospheric locations. Extremely large CMEs passed the STEREO-A spacecraft, which was behind the West limb of the Sun, approximately 2-3 days before each near-Earth FD. Solar wind simulations suggest that the CMEs combined with pre-existing CIRs, enhancing the pre-existing barriers to GCR propagation. Thus these observations provide strong evidence for the modulation of GCR flux by remote solar wind structures. [ABSTRACT FROM AUTHOR]