Magnetograms Of Active Regions Research Articles

The Force-freeness of the Solar Photosphere: Revisit with New Approach and Large Data Sets

Although it is generally believed that the solar photosphere is not magnetically force-free owing to its high plasma β, the estimations of force-freeness using observed magnetograms have produced disputable results. Some studies confirmed that the photosphere is largely not force-free whereas some authors argued that the photosphere is not far away from being force-free. In a previous paper of ours we demonstrated that, due to the fact that the noise levels of the transverse field in the magnetograms are much larger than those of the vertical field, wrong judgments on the force-freeness could be made: a truly force-free field could be judged as being not-force-free and a truly not-force-free field could be judged as being force-free. Here in this Letter, we propose an approach to overcome this serious problem. By reducing the spatial resolution to lower the noise level, the heavy influence of the measurement noise on the force-freeness judgment can be significantly suppressed. We first use two analytical solutions to show the success and effectiveness of this approach. Then, we apply this new approach to two large data sets of active region magnetograms, obtained with the Helioseismic and Magnetic Imager/Solar Dynamics Observatory and Spectro-Polarimeter (SP)/Hinode, respectively. Our analysis shows that the photospheric magnetic fields are actually far away from being force-free. Particularly, and most notably, the mean value of F z /F p (where F z is the net Lorentz force in the vertical direction and F p the total Lorentz force) is as low as −0.47, with more than 98% of the active regions having ∣F z /F p ∣ > 0.1 when using the SP/Hinode magnetograms of true field strength.

Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images

A solar flare is one of the most intense solar activities, and flare forecasting is necessary to avoid its destructive impact on the near-Earth space environment and technological infrastructure. Previous studies have demonstrated the importance of the photospheric magnetic field in the occurrence of flares. Therefore, most of the input data in traditional solar flare forecasting models are magnetograms of active regions. The magnetic field of the photosphere is routinely measured and observed, but the magnetic field of the corona is not. Hence, the goal of our work is to test whether precursors can be extracted from coronal multiwavelength images of active regions and to build a flare-forecasting model. Therefore, we investigated the effect of using extreme ultraviolet (EUV) images (at 94, 131, 171, 193, 211, and 335 Å) of the active region on solar flare forecasting. We generated a data set consisting of EUV images of the active regions observed by the Solar Dynamics Observatory/Atmospheric Imaging Assembly from 2010 to 2016. Based on this data set, a deep-learning method was used to extract precursors from EUV multiwavelength images. The test results of the forecasting model were discussed and analyzed, and the following conclusions were drawn. (1) Each wavelength achieved good results using the EUV multiwavelength images for flare forecasting. The 94 Å wavelength demonstrated the best result among the single-wavelength results. (2) Among the combined multiwavelength results, the best fusion results were obtained for all six wavelengths.

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Abstract Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

Comprehensive Analysis of the Geoeffective Solar Event of 21 June 2015: Effects on the Magnetosphere, Plasmasphere, and Ionosphere Systems

A full-halo coronal mass ejection (CME) left the Sun on 21 June 2015 from active region (AR) NOAA 12371. It encountered Earth on 22 June 2015 and generated a strong geomagnetic storm whose minimum Dst value was −204 nT. The CME was associated with an M2-class flare observed at 01:42 UT, located near disk center (N12 E16). Using satellite data from solar, heliospheric, and magnetospheric missions and ground-based instruments, we performed a comprehensive Sun-to-Earth analysis. In particular, we analyzed the active region evolution using ground-based and satellite instruments (Big Bear Solar Observatory (BBSO), Interface Region Imaging Spectrograph (IRIS), Hinode, Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), covering H $\upalpha$ , EUV, UV, and X-ray data); the AR magnetograms, using data from SDO/Helioseismic and Magnetic Imager (HMI); the high-energy particle data, using the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) instrument; and the Rome neutron monitor measurements to assess the effects of the interplanetary perturbation on cosmic-ray intensity. We also evaluated the 1 – 8 A soft X-ray data and the ${\sim}\, 1$ MHz type III radio burst time-integrated intensity (or fluence) of the flare in order to predict the associated solar energetic particle (SEP) event using the model developed by Laurenza et al. (Space Weather 7(4), 2009). In addition, using ground-based observations from lower to higher latitudes (International Real-time Magnetic Observatory Network (INTERMAGNET) and European Quasi-Meridional Magnetometer Array (EMMA)), we reconstructed the ionospheric current system associated with the geomagnetic sudden impulse (SI). Furthermore, Super Dual Auroral Radar Network (SuperDARN) measurements were used to image the global ionospheric polar convection during the SI and during the principal phases of the geomagnetic storm. In addition, to investigate the influence of the disturbed electric field on the low-latitude ionosphere induced by geomagnetic storms, we focused on the morphology of the crests of the equatorial ionospheric anomaly by the simultaneous use of the Global Navigation Satellite System (GNSS) receivers, ionosondes, and Langmuir probes onboard the Swarm constellation satellites. Moreover, we investigated the dynamics of the plasmasphere during the different phases of the geomagnetic storm by examining the time evolution of the radial profiles of the equatorial plasma mass density derived from field line resonances detected at the EMMA network ( $1.5 < \mathrm{L} < 6.5$ ). Finally, we present the general features of the geomagnetic response to the CME by applying innovative data analysis tools that allow us to investigate the time variation of ground-based observations of the Earth’s magnetic field during the associated geomagnetic storm.

PREDICTION OF SOLAR FLARES USING UNIQUE SIGNATURES OF MAGNETIC FIELD IMAGES

ABSTRACT Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and topology of solar magnetic fields. A new method for predicting large (M- and X-class) flares is presented, which uses machine learning methods applied to the Zernike moments (ZM) of magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for a period of six years from 2010 June 2 to 2016 August 1. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of ZMs and fed to the support vector machine classifier. ZMs have the capability to elicit unique features from any 2D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hr before their occurrence, with only 10 false negatives out of 385 flaring active region magnetograms and 21 false positives out of 179 non-flaring active region magnetograms. Our method may provide a useful tool for the prediction of solar flares, which can be employed alongside other forecasting methods.

A NEW METHOD TO QUANTIFY AND REDUCE THE NET PROJECTION ERROR IN WHOLE-SOLAR-ACTIVE-REGION PARAMETERS MEASURED FROM VECTOR MAGNETOGRAMS

ABSTRACT Projection errors limit the use of vector magnetograms of active regions (ARs) far from the disk center. In this Letter, for ARs observed up to 60° from the disk center, we demonstrate a method for measuring and reducing the projection error in the magnitude of any whole-AR parameter that is derived from a vector magnetogram that has been deprojected to the disk center. The method assumes that the center-to-limb curve of the average of the parameter’s absolute values, measured from the disk passage of a large number of ARs and normalized to each AR’s absolute value of the parameter at central meridian, gives the average fractional projection error at each radial distance from the disk center. To demonstrate the method, we use a large set of large-flux ARs and apply the method to a whole-AR parameter that is among the simplest to measure: whole-AR magnetic flux. We measure 30,845 SDO/Helioseismic and Magnetic Imager vector magnetograms covering the disk passage of 272 large-flux ARs, each having whole-AR flux &gt;1022 Mx. We obtain the center-to-limb radial-distance run of the average projection error in measured whole-AR flux from a Chebyshev fit to the radial-distance plot of the 30,845 normalized measured values. The average projection error in the measured whole-AR flux of an AR at a given radial distance is removed by multiplying the measured flux by the correction factor given by the fit. The correction is important for both the study of the evolution of ARs and for improving the accuracy of forecasts of an AR’s major flare/coronal mass ejection productivity.

Solar flare forecasting model based on automatic feature extraction

Solar flares are outbursts in the solar atmosphere resulting from sudden release of magnetic energy. The associated high energy particles and radiation threaten the safety of astronauts, reduce the lifetime of satellites, disturb the radio communications and degrade the precision of Global Positioning System. The radiation reaches the Earth about 8 min, and high energy particles take about 30 min to reach the earth after a solar flare. So solar flare forecasting is critical for providing enough time to respond to the space weather effects. Up to now, many statistical and machine learning methods are used to build a solar flare forecasting model. A machine learning based solar flare forecasting model normally requires solar physicists to design a feature extractor which can transform the observational images of active regions into physical features, and then the relationships between the features and the solar flares are discovered by the machine leaning algorithm. The priori knowledge of the solar physicists is added into the solar flare forecasting model by designing the feature extractor. For most of the machine learning methods, the hard part is what kinds of features should be extracted from the raw data. Considerable solar physicists spend a lot of time extracting the physical parameters from observational data of active regions. Deep learning method, which removes this manual step, can automatically discover useful patterns from the raw data and build a forecasting model. Instead of designing the feature extractor by solar physicists, we learn a solar flare forecasting model from magnetogram pixels by using deep learning method. We use Caffe, which is a deep learning framework developed by the Berkeley Vision and Learning Center, to build a convolutional neural network for solar flare forecasting. In order to compare the performance of proposed forecasting model with that of the forecasting model built by using traditional machine learning method, we build the other solar flare forecasting model based on the same dataset. In the traditional forecasting model, physical parameters designed by the solar physicist are extracted from the magnetogram of active regions, and then these parameters are fed to the forecasting model. We build a traditional forecasting model by multilayer neural networks. For convenience, the solar flare forecasting model built by using deep learning method is called deep model, and the solar flare forecasting model built by using the traditional multilayer neutral networks is called traditional model. Using the same testing data, the performances of the deep model and the traditional model are evaluated and compared. We find that the performance of the deep model is little better than that of the traditional model. The results confirm that the deep model can automatically learn solar flare forecasting features from magnetograms of active regions. This is our first time to automatically learn the forecasting patterns for solar flares from raw data instead of designing the physical patterns by solar physicists. The effectiveness of the deep learning method for the solar flare forecasting is validated. In the future, the deep learning method can be used to automatically discover the solar flare forecasting patterns from the vector magnetograms or the extreme ultraviolet images of active regions.

MAGNETIC NONPOTENTIALITY IN PHOTOSPHERIC ACTIVE REGIONS AS A PREDICTOR OF SOLAR FLARES

Based on several magnetic nonpotentiality parameters obtained from the vector photospheric active region magnetograms obtained with the Solar Magnetic Field Telescope at the Huairou Solar Observing Station over two solar cycles, a machine learning model has been constructed to predict the occurrence of flares in the corresponding active region within a certain time window. The Support Vector Classifier, a widely used general classifier, is applied to build and test the prediction models. Several classical verification measures are adopted to assess the quality of the predictions. We investigate different flare levels within various time windows, and thus it is possible to estimate the rough classes and erupting times of flares for particular active regions. Several combinations of predictors have been tested in the experiments. The True Skill Statistics are higher than 0.36 in 97% of cases and the Heidke Skill Scores range from 0.23 to 0.48. The predictors derived from longitudinal magnetic fields do perform well, however they are less sensitive in predicting large flares. Employing the nonpotentiality predictors from vector fields improves the performance of predicting large flares of magnitude $\geq$M5.0 and $\geq$X1.0.

A Cross-Comparison of Cotemporal Magnetograms Obtained with MDI/SOHO and SP/Hinode

We compared a set of cotemporal magnetograms of active regions obtained with the Michelson Doppler Imager (MDI) aboard SOHO and the Spectro-Polarimeter (SP) of the Solar Optical Telescope (SOT) on board Hinode. The comparison shows that even with the recent calibration of level-1.8 data, the magnetic flux density derived from the MDI data is still lower than that obtained with SP. The average ratio between current version 2008 MDI level-1.8 data and SP magnetograms is 0.71, and is 0.82 for version 2007 MDI level-1.8 data. The comparison also shows that the most recent version 2008 calibration of MDI level-1.8 data has successfully removed the center-to-limb variation, while version 2007 level-1.8 data did not, as estimated by Ulrich et al. (Solar Phys.255, 53, 2009).

Association of Mid-Infrared Solar Plages with Calcium K Line Emissions and Magnetic Structures

ABSTRACTSolar mid-IR observations in the 8–15 μm band continuum with moderate angular resolution (18″) reveal the presence of bright structures surrounding sunspots. These plage-like features present good association with calcium CaII K1v plages and active region magnetograms. We describe a new optical setup with reflecting mirrors to produce solar images on the focal-plane array of uncooled bolometers of a commercial camera preceded by germanium optics. First observations of a sunspot on 2006 September 11 show a mid-IR continuum plage exhibiting spatial distribution closely associated with CaII K1v line plage and magnetogram structures. The mid-IR continuum bright plage is about 140 K hotter than the neighboring photospheric regions, consistent with hot plasma confined by the magnetic spatial structures in and above the active region.

A Characteristic Magnetic Field Pattern Associated with All Major Solar Flares and Its Use in Flare Forecasting

Solar flares result from some electromagnetic instability that occurs within regions of relatively strong magnetic field in the Sun's atmosphere. The processes that enable and trigger these flares remain topics of intense study and debate. I analyze observations of 289 X- and M-class flares and over 2500 active region magnetograms to discover (1) that large flares, without exception, are associated with pronounced high-gradient polarity-separation lines, while (2) the free energy that emerges with these fibrils is converted into flare energy in a broad spectrum of flare magnitudes that may well be selected at random from a power-law distribution up to a maximum value. This maximum is proportional to the total unsigned flux R within ~15 Mm of strong-field, high-gradient polarity-separation lines, which are a characteristic appearance of magnetic fibrils carrying electrical currents as they emerge through the photosphere. Measurement of R is readily automated, and R can therefore be used effectively for flare forecasting. The probability for major flares to occur within 24 hr of the measurement of R approaches unity for active regions with the highest values of R around 2 × 1021 Mx. For regions with R 1019 Mx, no M- or X-class flares occur within a day.

Kuafu and the studies of CME initiation

We first briefly review the current trend in the studies of coronal mass ejections (CMEs), then summarize some recent efforts in understanding the CME initiation. Emphasis has been put on the studies of Earth-directed CMEs whose associated surface activity and large scale magnetic source have been well identified. The data analysis by combining the MDI full disc magnetograms, vector magnetograms of active regions, EUV waves and dimmings, non-thermal radio sources, and the SOHO LASCO observations has shed new light in understanding the CME magnetism. However, the current studies seem to invoke new observations in a few aspects: (1) The observations which enable us to trace CMEs from the earliest associated surface activity to its initial acceleration and key development in the low corona in the height of 1–3 R ⊙; (2) The imaging spectroscopic observations which can be used to diagnose the early plasma outflow and the line-of-sight velocity in understanding the kinematics of CMEs; (3) The accurate timing from primary magnetic energy release, manifested by chromospheric activity, non-thermal radio bursts, and EUV, X-ray and γ-ray emissions, to the CME initiation, early acceleration and propagation, and the consequences in the interplanetary space and magnetosphere. The Kuafu Mission will meet the basic requirement for the new observations in CME initiation studies and serve as a monitor of space weather of the Sun–Earth system.

The Statistical Relationship between the Photospheric Magnetic Parameters and the Flare Productivity of Active Regions

Using line-of-sight Michelson Doppler Imager (MDI) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling correlations between three parameters of magnetic fields and the flare productivity of solar active regions. These parameters are (1) the mean value of spatial magnetic gradients at strong-gradient magnetic neutral lines, ()NL; (2) the length of strong-gradient magnetic neutral lines, LGNL; and (3) the total magnetic energy, (Bz) dA, dissipated in a layer of 1 m during 1 s over the active region's area. The MDI magnetograms of active regions used for our analysis are close to the solar central meridian (within ±10°). The flare productivity of active regions was quantified by the soft X-ray flare index for different time windows from the time interval of the entire disk passage down to +1 day from the time of the analyzed magnetogram. Our results explicitly indicate positive correlations between the parameters and the overall flare productivity of active regions, and imminent flare production as well. The correlations confirm the dependence of flare productivity on the degree of nonpotentiality of active regions.

Statistics of Active Region Complexity: A Large‐Scale Fractal Dimension Survey

A quantification of the magnetic complexity of active regions using a fractal dimension measure is presented. This fully automated approach uses full-disk MDI magnetograms of active regions from a large data set (2742 days of the SOHO mission, 9342 active region images) to compare the calculated fractal dimension of each region to both its Mount Wilson classification and flare rate. Each Mount Wilson class exhibits a similar fractal dimension frequency distribution, possibly suggesting a self-similar nature of all active regions. Solar flare productivity exhibits an increase in both the frequency and GOES X-ray magnitude of flares from regions with higher fractal dimension. Specifically, a lower threshold fractal dimension of 1.2 and 1.25 exists as a necessary, but not sufficient, requirement for an active region to produce M- and X-class flares, respectively, within 24 hr of the observation.

Multifractal Analysis Of Solar Magnetograms

As solar observational techniques improve, the ne small-scale structure observed on the solar surface becomes more pronounced. Complex ligree structures of solar granulation, sunspots, photospheric magnetic and velocity elds cannot be described adequately by a single parameter (e.g., lling factor, fractal dimension, or power law index). Methods which incorpo- rate parameters that are a function of scale (multi-scale methods) to describe the complexity of a eld under study, should be involved. The multifractal ap- proach oers such a possibility. In this paper the scaling of structure functions is proposed in order to analyze multifractality. Application of the approach to SOHO/ MDI high resolution magnetograms of active regions show that the structure functions dier for all active regions studied. For a given active region, the functions may maintain their shape during several hours, however they can signican tly change during a day. Flare-quiet active regions tend to possess a lower degree of multifractality than aring active regions do. The increase in multifractality is a signal that a magnetic structure is driven to a critical state, thus gaining tangential discontinuities of various length scales.

Transequatorial Loops in the Solar Corona

Using X-ray coronal images, full-disk longitudinal magnetograms, and vector magnetograms of active regions, we study active regions connected across the solar equator. We survey the Yohkoh data set between 1991 October and 1998 December and find 87 transequatorial loop systems (TLSs). We classify these loops in four different categories and study a separation between regions, their rotation rates, and the sign of the current helicity (chirality) of the magnetic fields. We find that approximately one-third of all active regions on the Sun exhibit transequatorial loops. The fraction of TLSs is solar-cycle independent. Transequatorial loops may develop between existing active regions or between mature regions and new magnetic flux shortly after flux emergence. Observations suggest, however, that formation of TLSs is not a random process—a connection between two areas may exist well before the reconnection takes place. We find that the reconnected regions have approximately the same rotation rate and tend to appear on certain longitudes, similar to the complexes of activity. In most cases transequatorial interconnected regions have the same handedness of their magnetic field.

Coronal Heating in Active Regions as a Function of Global Magnetic Variables

A comparison of X-ray images of the Sun and full disk magnetograms shows a correlation between the locations of the brightest X-ray emission and the locations of bipolar magnetic active regions. This correspondence has led to the generally accepted idea that magnetic fields play an essential role in heating the solar corona. To quantify the relationship between magnetic fields and coronal heating, the X-ray luminosity of many different active regions is compared with several global (integrated over entire active region) magnetic quantities. The X-ray measurements were made with the SXT Telescope on the Yohkoh spacecraft; magnetic measurements were made with the Haleakala Stokes Polarimeter at the University of Hawaii's Mees Solar Observatory. The combined data set consists of 333 vector magnetograms of active regions taken between 1991 and 1995; X-ray luminosities are derived from time averages of SXT full-frame desaturated (SFD) images of the given active region taken within ±4 hours of each magnetogram. Global magnetic quantities include the total unsigned magnetic flux Φtot ≡ ∫ dA|Bz|, B2z tot ≡ ∫ dAB2z, Jtot ≡ ∫dA|Jz|, and B2⊥,tot ≡ ∫ dAB2⊥, where Jz is the vertical current density and Bz and B⊥ are the vertical and horizontal magnetic field amplitudes, respectively. The X-ray luminosity LX is highly correlated with all of the global magnetic variables, but it is best correlated with the total unsigned magnetic flux Φtot. The correlation observed between LX and the other global magnetic variables can be explained entirely by the observed relationship between those variables and Φtot. In particular, no evidence is found that coronal heating is affected by the current variable Jtot once the observed relationship between LX and Φtot is accounted for. A fit between LX and Φtot yields the relationship LX 1.2 × 1026 ergs s-1(Φtot/1022 Mx)1.19. The observed X-ray luminosities are compared with the behavior predicted by several different coronal heating theories. The Alfven wave heating model predicts a best relationship between LX and Φtot, similar to what is found, but the observed relationship implies a heating rate greater than the model can accommodate. The Nanoflare Model of Parker predicts a best relationship between LX and B2z,tot rather than Φtot, but the level of heating predicted by the model can still be compared to the observed data. The result is that for a widely used choice of the model parameters, the nanoflare model predicts 1.5 orders of magnitude more heating than is observed. The Minimum Current Corona model of Longcope predicts a qualitative variation of LX with Φtot that agrees with what is observed, but the model makes no quantitative prediction that can be tested with the data. A comparison between LX and the magnetic energy Emag in each active region leads to a timescale that is typically 1 month, or about the lifetime of an active region, placing an important observational constraint on coronal heating models. Comparing the behavior of solar active regions with nearby active stars suggests that the relationship observed between LX and Φtot may be a fundamental one that applies over a much wider range of conditions than is seen on the Sun.

A Correlation between Length of Strong-Shear Neutral Lines and Total X-ray Brightness in Active Regions

From a sample of 7 MSFC vector magnetograms of active regions and 17 Yohkoh SXT soft X-ray images of these active regions, we have found that the total X-ray brightness of an entire active region is correlated with the total length of neutral lines on which the magnetic field is both strong (>250 G) and strongly sheared (shear angle >75°) in the same active region. This correlation, if not fortuitous, is additional evidence of the importance of strong-shear strong-field neutral lines to strong heating in active regions.

Line profile steepness as an additional tool for studies of magnetic field total energy

Abstract We believe, that conventional magnetograms of active regions give no means of extracting adequate information about the magnetic field energy in the vicinity of a neutral line, especially in areas where fine-structure opposite-polarity strong fields are most close together and mixed. Magnetic broadening measurements of the magnetosensitive line profile would make up tangibly for this deficiency. To accomplish this, we suggest that the spectral line profile steepness ∂I/∂λ should be measured, considering just this parameter to be most suitable for measurement and to ensure sufficiently high sensitivity of measurements. In this paper it is shown that in the line wing it is possible to determine two parts, for which the magnetic field influence upon the line steepness is the largest and opposite in sign. This can be used to separate “nonmagnetic” sources of line broadening. The line FeII 6149.2A is of interest for measuring the profile steepness when using the spectrograph. With a measurement sensit...

The imaging vector magnetograph at Haleakala

We describe an instrument we have built and installed at Mees Solar Observatory on Haleakala, Maui, to measure polarization in narrow-band solar images. Observations in Zeemansensitive photospheric lines have been made for nearly all solar active regions since the instrument began operations in 1992. The magnetograph includes a 28-cm aperture telescope, a polarization modulator, a tunable Fabry-Perot filter, CCD cameras and control electronics. Stokes spectra of a photospheric line are obtained with 7 pm spectral resolution, 1 arc sec spatial resolution over a field 4.7 arc min square, and polarimetric precision of 0.1%. A complete vector magnetogram observation can be made every eight minutes. The flexibility of the instrument encourages diverse observations: besides active region magnetograms we have made, for example, composite vector magnetograms of the full solar disk, and Hα polarization movies of flaring regions.