Evaluation of OVATION Prime as a forecast model for visible aurorae

Transcription

1 SPACE WEATHER, VOL. 10,, doi: /2011sw000746, 2012 Evaluation of OVATION Prime as a forecast model for visible aurorae Janet L. Machol, 1,2 Janet C. Green, 1 Robert J. Redmon, 1 Rodney A. Viereck, 3 and Patrick T. Newell 4 Received 28 October 2011; revised 10 January 2012; accepted 11 January 2012; published 9 March [1] This study evaluates the ability of the OVATION Prime auroral precipitation model to provide operational forecasts of the visible aurora. An operational implementation would primarily provide the general public with some guidance for viewing the aurora. We evaluate the likelihood that if aurorae are predicted to be visible at a location, they will be seen there within the hour. Nighttime model forecasts were validated with Polar Ultraviolet Imager data for Kp 3 and for the years 1997 and The overall forecasts for a visible aurora to occur or to not occur were correct 77% of the time. The most important prediction for public auroral viewing is that the visible aurora will occur, and these forecasts were correct 86% of the time. Citation: Machol, J. L., J. C. Green, R. J. Redmon, R. A. Viereck, and P. T. Newell (2012), Evaluation of OVATION Prime as a forecast model for visible aurorae, Space Weather, 10,, doi: /2011sw Introduction [2] There have been a number of auroral precipitation models developed in order to better understand the interactions between the geomagnetic field, the solar wind, and the ionosphere, and concomitant impacts on communications [e.g., Evans, 1987; Brautigam et al., 1991; Hardy et al., 1991; Lui et al., 2003; Zhang and Paxton, 2008; Newell et al., 2010b]. For this study, we are interested in predictions of where aurorae can be viewed from Earth. These forecasts, of what one might call the visible aurora, are primarily of interest to people searching for a chance to view the spectacular auroral display. Here we evaluate the ability of the OVATION Prime model [Newell et al., 2010a] to provide an operational forecast of the visible aurora by comparing the model predictions with images from the Ultraviolet Imager (UVI) aboard the NASA Polar spacecraft. [3] The OVATION Prime model was developed using energetic particle measurements from the polar-orbiting Defense Meteorological Satellite Program (DMSP) satellites and considers four types of aurorae: two types of discrete electron aurorae (monoenergetic and broadband) and two types of diffuse aurorae (electron and ion) [Newell et al., 2010a]. The often spectacular discrete aurorae are thin (100 to several 1000 m) vertical sheets and rayed arcs 1 National Geophysical Data Center, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA. 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA. 3 Space Weather Prediction Center, National Weather Service, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA. 4 Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA. which generally run in the east west direction and which may dance about with changing intensities, colors and shapes during active conditions [Prölss, 2004]. Discrete aurorae are produced by accelerated particles: the monoenergetic discrete aurora is due to acceleration by quasistatic electric fields, while the broadband discrete aurora is produced by dispersive Alfvén waves and has the largest response to geomagnetic activity [Newell et al., 2009]. In contrast, diffuse aurorae are broad (100 to 1000 km wide) patches which sometimes pulse slowly at about 0.1 Hz [Jones, 1974]. Although the diffuse electron aurora contains the majority of the auroral energy flux [Newell et al., 2009], its lack of structure and intensity generally makes it less apparent and less visually intriguing than the discrete aurora. The diffuse electron aurora is due to plasma sheet particles in the equatorial magnetosphere that have been scattered into the loss cone (pitch angle diffusion) mostly by chorus waves [Thorne et al., 2010]. The ion diffuse aurora is weak and so it is difficult to detect visually [Jones, 1974]. On the nightside it is primarily due to pitch angle scattering in the equatorial current sheet [Sergeev et al., 1983]. [4] Since most of the visible wavelengths in aurorae are generated through combinations of primary and secondary collisional processes and chemical reactions [Prölss, 2004], the emission rates at various visible wavelengths are dependent on oxygen-nitrogen ratios and/or electron energy spectra. The violet and blues lines are dominated by emissions from ionized molecular nitrogen (N 2 + ), the green line at nm and the red lines near 630 nm by emissions from atomic oxygen (O), and the red lines above 640 nm by emissions from molecular nitrogen (N 2 ). The auroral emissions are also height dependent because Copyright 2012 by the American Geophysical Union 1of8

2 particles with higher energies can penetrate deeper into the atmosphere. The green lines are generally emitted at lower altitudes than the oxygen red lines. [5] During an auroral substorm, the visibility of the aurora increases as the aurora brightens and expands both in latitude and longitude [Prölss, 2004]. The onset location of auroral activity is usually near 22:30 MLT (magnetic local time) and 67 MLAT (magnetic latitude) and depends on the season and the interplanetary magnetic field (IMF) [Liou et al., 2001a]. During a substorm, the aurora becomes more dynamic with fluctuating shapes, colors and intensities. [6] This validation seeks to determine if the aurora will be seen at a particular location within an hour of the time the OVATION Prime model predicts activity. A 1 h window is chosen for the comparison because it is the timescale for large changes in the aurora. Because the model is statistical, it averages short-timescale fluctuations of the aurora which occur especially during high-activity periods. This study focuses on the northern hemisphere, since the aurora is much more accessible for viewing there. Similarly, the equatorward edge of the aurora is more critical than the poleward side, since that region is more accessible to auroral observers. Since the aurora is most visible near midnight (when observers might wish to sleep if no aurora is visible), this study notes that it is especially important that the model produce few false positive predictions, i.e., those when the aurora is predicted, but is not actually visible. [7] Because there is much interest in viewing the aurora, a number of nowcasts and forecasts of the aurora are available to the public. Some of these are based directly or indirectly on the Kp index which is a weighted average of K indices from a network of geomagnetic observatories. The K index for an observatory, which ranges from 0 to 9, is a logarithmic value related to the maximum fluctuation during a 3 h interval of the horizontal magnetic field component after removing the quiet day baseline. Estimated Kp values and Kp values forecasted out to 3 days are available from several space weather forecast centers. For a number of years, the University of Alaska has provided auroral forecasts out to 3 days [Lummerzheim, 2007] using magnetic field parameters obtained from the Hakamada Akasofu Fry (version 2) kinematic solar wind model [Fry et al., 2007]. The National Oceanic and Atmospheric Administration Space Weather Prediction Center has for many years presented nowcasts of the aurora based on a model developed by Evans [1987] which uses particle measurements from recent transits by polar-orbiting satellites. The nowcasts are based on a set of statistical maps of the aurora as a function of hemispheric precipitating particle energy flux created from particle measurements from polar-orbiting satellites. Other simple nowcasts and forecasts have also been created from similar maps correlated to the Kp index. Newell et al. [2010b] performed a quantitative comparison of hemispheric power levels derived from Polar UVI global images with those obtained from four auroral precipitation models including OVATION Prime. The models all performed similarly; the different models accounted for 46% 56% of the variance in the image data (for statistics based on the squared correlation coefficient). The authors commented that further major improvement in models would require the ability to predict substorm development. [8] For consideration as an operational forecast of the visible aurora, the OVATION Prime model has a number of advantages. First, it is has been well validated [Newell et al., 2010b]. Second, the model is a function of the solar wind parameters, rather than derived indices such as Kp. Since solar wind parameters are measured up to an hour before the solarwindimpactsearth,themodelcanbeusedinapredictive mode. In the future, when heliospheric solar wind models improve, the model solar wind parameters can be used to give a predictive capability that may extend to days. Third, since the model is not based on a discrete set of maps, the model produces a high-resolution forecast map that varies smoothly in time. Finally, since the OVATION Prime model provides separate forecasts of the different auroral components, forecasts can be made which emphasize the components of more interest to people seeking to view aurorae. 2. Evaluation Technique [9] This validation compared the OVATION Prime model output with Polar UVI images. The UVI data set was chosen for the validation because it is very accessible and covers an extended time period with good spatial and temporal resolution. The auroral boundaries for both the model and the images were defined with fixed thresholds, and then the maximum extents of the model and measured aurorae during hour-long intervals were compared over a 2 year period to determine the accuracy of the model predictions OVATION Prime Model [10] The OVATION Prime model [Newell et al., 2010a] is derived from electron and proton flux measurements from the SSJ4 detectors on the DMSP satellites. The current version of the model was developed with 22 years ( ) of data rather than the 11 years referenced in the paper and employed a new noise reduction filter (P. Newell, personal communication, 2011). There were two to four DMSP satellites in orbit at any time throughout this period. The model predicts four auroral components; these are an ion aurora and three types of electron aurora: monoenergetic, broadband, and diffuse [Newell et al., 2009]. The model grid resolution is 15 min MLT 0.25 MLAT over for either the northern or the southern hemisphere, and the output units are energy flux (erg cm 2 s 1 ). Data are binned with a function proportional to a solar wind coupling function (dayside merging rate), df MP /dt, that has been shown to predict a wide array of magnetospheric indices including Kp under a variety of conditions [Newell et al., 2007]. The solar wind coupling function is dф MP =dt ¼ n 4=3 B 2=3 T sin 8=3 ðq=2þ ð1þ 2of8

3 Figure 1. (a) UVI observations during a substorm on 22 November 1997 at 14:41 UT. (b) Energy flux map for the total aurora generated by the OVATION Prime model at the same time. Axes for both images are MLAT and MLT. UVI image was generated at nsstc.nasa.gov/uvi/ost/uvi.html, and the model image was generated at jhuapl.edu/aurora/ovation_prime/prime_display.html. where v is the bulk wind speed, B T is the field magnitude, and q is the IMF angle [Newell et al., 2007].Thisfunctionis used to make a linear fit of the auroral power for each type of aurora for each season: auroral powerðmlat; MLT; aurora type; seasonþ ¼ a þ bdф MP =dt at each grid location where the locations are given in Altitude Adjusted Corrected Geomagnetic Coordinates (AACGM). A probability is also generated for each type of electron aurora. An example of an energy flux map for the total aurora generated by the OVATION Prime model is shown in Figure 1 for the case of a substorm. [11] For this comparison, we applied weighting factors of 1 for the three electron auroral types and 0 for the proton aurora. The proton aurora was not included because its light levels are very low. For the solar wind measurements needed by the OVATION Prime model, we used hourly averages which were obtained from the OMNI2 data set created by the National Space Science Data Center and had been propagated to the Earth s bow shock nose. [12] Due to the orientation of the DMSP sun-synchronous orbits, the satellites do not sample the geomagnetic postmidnight region well. Sotirelis and Newell [2000] found that during higher auroral activity levels there is a wedgeshaped region of minimal data expanding from 1:30 MLT and 60 MLAT to lower MLATs. The result is that the model predictions in this region are too low during high activity; this is apparent in Figure 1b. As a correction, below 60 MLAT, we linearly interpolated the forecasts along MLT from 1:00 to 5:00 MLT. The interpolation greatly improves the forecasts in the postmidnight lower-latitude region for high Kp. Interpolation has minimal impact for low Kp, when the aurora is neither seen nor predicted at lower latitudes Ultraviolet Imager Data [13] For this validation we compared the model with ultraviolet images of the Earth taken by Polar UVI. The Polar satellite was launched in 1996 and had an elliptical ð2þ orbit with an apogee of 9 R E, a perigee of 2 R E, an initial inclination of 86, and a period of 17.5 h [Torr et al., 1995]. The images have an 8 field of view. Due to the precession of the satellite orbit plane, UVI viewed the northern hemisphere from 1996 to 2003, with the best coverage in the years 1997 and 1998, the time range chosen for this study. There is adequate auroral activity in this time frame to achieve good statistics; during the years 1997 and 1998, there were 1100 substorms [Liou, 2010] and Kp ranged up to 8.7. Figure 1 shows the UVI image and the simultaneous model forecast for one time during a substorm in [14] The UVI images were obtained from the online data service at the UVI mission website ( nasa.gov/uvi/ost/uvi.html) and were already background subtracted, flat field and line of sight corrected, and calibrated to photon flux units. The image resolution was pixels and at least 75% of each image was of Earth with off-earth pixels set to zero. The UVI output is brightness at each pixel in units of kilorayleigh (kr), where 1R=10 6 photons cm 2 s 1 for the column being viewed and 1 kr is about the luminosity of the Milky Way [Prölss, 2004]. The UVI one-count level corresponds to a minimum detectable energy flux of about 0.2 erg cm 2 s 1 or a brightness of 22 R [Brittnacher et al., 1997]. The data used in the model comparison had 10 min spacing and were filtered for the long-wavelength bands ( nm) of the N 2 Lyman-Birge-Hopfield (LBHL) emissions. [15] The UVI images are impacted by platform wobble and dayglow. The image resolution is about km, but platform wobble degrades it by a factor of ten in one direction [Liou et al., 2001b] and we did not correct for this. Aurorae occur infrequently at the highest latitudes, but the UVI images contain dayglow at high latitudes which cannot be distinguished from aurorae. As a result, in comparisons of the model and UVI data, dayglow in the images make the forecasts appear to falsely predict a lack of aurora at high latitudes more than 50% of the time. To prevent this, we only used UVI pixels with little or no dayglow, by only including data with a solar zenith angle greater than 102 ; at an altitude of 110 km, the height at 3of8

4 Figure 2. (a c) Examples of comparisons between the UVI observations (solid lines) and the OVATION Prime model (dashed lines) for MLTs between 20:00 and 4:00 for the same UTC time (22 November 1997 at 14:41 UT) as Figure 1. Each plot is scaled so that the dotted line represents the auroral boundary thresholds for both the observations (0.25 kr) and the model (1 erg cm 2 s 1 ). (d) Auroral boundaries for the observations (solid lines) and the model (dashed lines) during a 1 h period (14:00 15:00 UT) encompassing the time in Figures 2a 2c. which much of the dayglow is produced, the sun is below the horizon for solar zenith angles greater than 102. [16] The UVI images are provided in the Apex coordinate system and the OVATION Prime output is in AACGM, but since the deviation between the two coordinate systems [Laundal, 2010] is much smaller than our comparison grid spacing, we did not convert the UVI coordinates to AACGM. [17] Limitations of the imager and Polar spacecraft prevent it from accurately observing low-intensity aurorae [e.g., Newell and Gjerloev, 2011]. The UVI images were in the level of the noise during quiet times, and so in this study we did comparisons only for Kp 3. It is most important that the OVATION Prime model is validated to work well at medium and high Kp values, because, the model is already inherently more accurate for low and medium Kp where more data was available for the model development Comparison Technique [18] For the comparison of the model with the UVI images to be meaningful, it is essential that the UVI images are a good proxy for the nighttime visible aurora. Cummer et al. [2000] compared images taken during a substorm by the Polar Visible Imaging System (VIS) and UVI, and filtered for the nm (green) and LBHL wavelengths, respectively. They found that the emissions were generally similar although the ultraviolet aurora extended further equatorward than the visible aurora. Quantitatively we show that validation with the UVI images is well founded by demonstrating that the visible aurora and the aurora at LBHL wavelengths are each approximately proportional to the particle energy flux, and hence they must be approximately proportional to each other. [19] For the visible aurora, we focus on the nm green line which frequently dominates the visible aurora because it is near the maximum sensitivity of the eye [Kivelson and Russell, 1995]. The visibility threshold at this wavelength for a dark-adapted eye is about 1 kr [Kivelson and Russell, 1995]. Rees et al. [1988] showed that in aurorae, the strength of the nm line depends roughly linearly on the precipitating particle flux and only weakly on the precipitating particle spectra. They modeled Maxwellian distributions of incoming electrons with a fixed energy flux of 1 erg cm 2 s 1 and characteristic energies ranging from 0.06 to 80 kev, and found that the corresponding count rates varied by about a factor of two at nm. [20] The brightness of the ultraviolet aurora at LBHL wavelengths is also closely proportional to the total electron energy flux. Modeling of the auroral vertical column 4of8

5 Table 1. Truth Table, With Four Options for Forecast Success, Applied at Each Grid Point Aurora Observed Aurora Forecasted Yes No Yes A B No C D brightness for the LBHL filter by Germany et al. [1998] found that these emissions vary by only about 10% with changes in the electron energy spectra and that the conversion factor from energy flux to brightness is about 0.12 R per erg cm 2 s 1. Baker et al. [2000] found a similar value of 0.13 kr for the optimum fixed threshold for the poleward boundary in UVI data, where we assume a factor of to convert from photon flux to surface brightness [Brittnacher et al., 1997]. In some specific comparisons of DMSP data with UVI photon irradiance, Carbary et al. [2004] also showed that the energy flux is proportional to the irradiance. Given that brightnesses of both the ultraviolet and visible aurorae are roughly proportional to the incoming electron flux, we conclude that it is reasonable to use the UVI images as a proxy for the visible aurora. [21] This study depended on defining fixed thresholds for the boundaries of the visible aurora for both the model and the UVI images. For the OVATION Prime model, the threshold was chosen as 1 erg cm 2 s 1 which is considered to be a reasonable threshold for the visible aurora [Kivelson and Russell, 1995]. For the UVI data, the threshold to define the auroral boundaries was a coarse estimate because it depends on a simplified conversion of energy flux to column photon flux. For our validation, we chose a threshold of 0.25 kr for the UVI auroral boundaries because this value provided a good correlation between the model and the image data. This brightness value corresponds to an energy flux of about 2 erg cm 2 s 1. Any lower choice for the threshold is in the wings of the aurora and results in a lot of noise in the boundaries. [22] The model was run for the time periods where the UVI data fit the criteria listed in section 2.2. The UVI data set and model output were averaged to grid point resolutions of 1 h MLT (from 19:00 to 6:00) and 2 MLAT (from 50 to 88 ). Figures 2a 2c show examples of comparisons of model output and UVI observations for three MLTs during a substorm. For each UTC hour, the maximum extents of the aurora in MLAT in both the model and the UVI data at each MLT were then determined using the fixed thresholds (Figure 2d). [23] The statistics for the success of the hourly forecasts were compiled. At all of the grid points the model (forecast) was compared with the observations (truth) and the results were accumulated in four arrays: (A) true positive, (B) false positive, (C) false negative, and (D) true negative (Table 1). Here a positive forecasts means that the visible aurora was forecasted, a negative forecast means that no visible aurora was forecasted, and true and false refer to whether the forecast agreed with the observations or not. The arrays were a function of MLT and MLAT and the arrays were normalized at each grid point by the number of data-model comparisons made at that point. [24] Various statistical values can be calculated from these arrays. For this study, it is most important to give correct positive predictions so that people who make an effort to observe the aurora (in the middle of the night) will be very likely to see it. Some statistics of interest are: detection rate ¼ A= ða þ CÞ ð3þ false alarm rate ðfarþ ¼ B= ða þ BÞ ð4þ aurora predicted and observed ð1 FARÞ ¼ A= ða þ BÞ ð5þ no aurora predicted; but aurora observed ¼ C= ðc þ DÞ ð6þ overall accuracy ¼ ða þ DÞ= ða þ B þ C þ DÞ ð7þ The choice of UVI threshold as discussed in section 2.3 impacts these statistics; raising this threshold results in fewer false negatives (C) but more false positives (B). 3. Results [25] The OVATION Prime model was compared with 2194 UVI images during the years 1997 and 1998 for Kp 3 and nighttime MLTs between 19:00 and 6:00 and the results were accumulated in the A-D arrays. Maps of the arrays at all of the MLAT and MLT grid points are shown in Figure 3. The OVATION Prime model was found to do a good job of predicting the visible aurora. The overall accuracy is 77%, a value obtained by averaging equation 7 for all of the grid points shown in Figure 3. Most importantly, when the aurora is predicted, the aurora is observed 86% of the time (true positives, equation 5), corresponding to a false alarm rate of 14% (equation 4). The fraction of forecasts where no aurora was predicted, but where the aurora was actually seen (false negatives, equation 6), is 26% and the detection rate (equation 3) is 58%. Both of these statistics are dependent on the false negatives, and most of the false negatives are on the poleward boundary of the aurora (Figure 3c) which is of less importance for auroral viewing (since there are few people in that region). Increasing the threshold on the model does not improve the forecast accuracy since the model has a sharp equatorward edge and sometimes a different profile (as a function of latitude) from the observations (e.g., see Figures 2a 2c). 4. Operational Use and Summary [26] For operational use, several improvements could be made to the forecast. The four types of aurora could be weighted relative to their contributions to the visible 5of8

6 Figure 3. Comparison of OVATION Prime model with UVI images for 2194 times during with Kp 3. The maps show the fractions of data at each location that are (a) true positive, (b) false positive, (c) false negative, and (d) true negative; these are maps of the A, B, C, and D of Table 1. Positive and negative refer to whether an aurora was forecast to be seen or not at a location. The comparison of the forecast to the observation at a location determines the true or false designation. aurora. Although the diffuse electron aurora contributes the largest fraction of the energy to the aurora, it is much less visible than the other types of electron aurora and so should be given a significantly reduced weighting factor in the visible aurora forecast. The ion aurora is seldom visible and so should not be included for operational forecasts. The model forecast represents where the aurora would be likely to be seen overhead, but because the sheets of the discrete aurora can be observed at an angle, the region where the aurora can be viewed could be expanded to include aurorae might seen at an angle. Forecasts could be extended by as much as 1200 km (11 degrees latitude) which is the distance to an aurora with an altitude of 110 km which is visible on the horizon. Actual angular viewing of an aurora is based on a number of factors including the true altitude of the aurora, intervening topography, the brightness of the aurora, the color of the aurora (since human vision is more sensitive to green than red), and atmospheric viewing conditions. [27] Atmospheric viewing conditions that impact auroral viewing include city lights, sunlight, moonlight, dayglow, and clouds. Sunlight, dayglow and moonlight could all be calculated. A map of cloud fractions would show where viewing is likely to be unimpeded by clouds. For the angular viewing predictions, height-resolved cloud maps would be a further improvement. It should be noted, though, that high-resolution cloud maps and forecasts, especially at high latitudes, are still under development. To be useful to the viewing public, the auroral forecasts should be presented in geographic coordinates. Viewing conditions could be incorporated as overlays in graphical presentations or their probabilities could be convolved with those of the auroral forecasts to provide a net forecast for the visibility of the aurora. [28] Other improvements might be made to the forecasts in the low-latitude regions and the region where interpolation is done. The OVATION Prime model forecasts down to 50 MLAT because there are poor DMSP sampling statistics below that latitude. As can be seen in Table 2, this latitude limit impacts the auroral forecasts Table 2. Lowest Nighttime Equatorward Auroral Boundaries as a Function of Kp a Kp MLAT a Values were derived from equations given by Gussenhoven et al. [1983]. Slightly higher (by 1.4 MLAT) boundaries are obtained using the formulas presented by Carbary [2005]. 6of8

7 only for the highest Kp, where Kp > 8. If angular viewing is included in the forecasts, then the forecasts could go lower. For operational use, a plan should be devised for how to present forecasts below 50 MLAT. In the postmidnight region, a very simple interpolation greatly improved the forecasts there. For operational use, the interpolation could perhaps be improved by more carefully considering the DMSP sampling statistics in that region. [29] Currently, we run the OVATION Prime model with solar wind data obtained from the ACE satellite at the first Lagrange (L1) point, and so the model forecasts aurorae about 45 min in advance. Longer-range auroral predictions could be made using solar wind parameters that have been propagated from the sun. Comprehensive space weather prediction models such as the Wang- Sheeley-Arge Enlil Cone model [Pizzo et al., 2011] can provide 1 to 4 day forecasts of the arrival of the leading edge of Earth-directed interplanetary coronal mass ejections (ICMEs). Currently, the model includes the effects of the spiral interplanetary magnetic field, and work is in progress with this model to also provide specific predictions of the magnitude and direction of the swept-up magnetic structure ahead of CMEs as well as of the duration of ICME impacts at Earth. A further challenge is to model the internal magnetic structure within propagating CMEs, which are unrelated to the magnetic fields preceding the CMEs [Liu et al., 2010]. In the future, as models such as this become able to provide longer-range estimates of the solar wind parameters, these values can be utilized by OVATION Prime to provide longer-range auroral predictions. [30] In conclusion, a comparison of the OVATION Prime precipitation model with Polar UVI images has shown that the model can provide good forecasts of the visible aurora for the general public. The model has good spatial resolution and an overall accuracy of 77%. For the important case of when the aurora is predicted, the likelihood of seeing the aurora is 86%, corresponding to a false alarm rate of 14%. For operational use, a number of additions can be made to the model output so that it is more useful for the general public. [31] Acknowledgments. We thank the National Space Science and Technology Center for providing the Polar UVI data and the National Space Science Data Center at NASA Goddard Space Flight Center for providing the OMNI solar wind data. We thank the reviewers for their useful comments. References Baker, J. B., C. R. Clauer, A. J. Ridley, V. O. Papitashvili, M. J. Brittnacher, and P. T. 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