the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A technique for volumetric incoherent scatter radar analysis
- Institute for physics and technology, University of Tromsø, Tromsø, Norway
- Institute for physics and technology, University of Tromsø, Tromsø, Norway
Abstract. Volumetric measurements of the ionosphere are important for investigating spatial variations of ionospheric features, like auroral arcs and energy deposition in the ionosphere. In addition, such measurements make it possible to distinguish between variations in space and time. While spatial variations in scalar quantities such as electron density or temperature have been investigated with ISR before, spatial variation in the ion velocity, which is a vector quantity, has been hard to measure. The upcoming EISCAT3D radar will be able to do volumetric measurements of ion velocity regularly for the first time. In this article, we present a technique for relating volumetric measurements of ion velocity to neutral wind and electric field. To regularize the estimates, we use Maxwell's equations and fluid-dynamic constraints. The study shows that accurate volumetric estimates of electric field can be achieved. Electric fields can be resolved at altitudes above 120 km which is the altitude range where auroral current closure occurs. Neutral wind can be resolved at altitudes below 120 km.
Johann Stamm et al.
Status: open (extended)
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RC1: 'Comment on angeo-2022-11', Anonymous Referee #1, 03 May 2022
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The paper is expanding on a technique published by Nicolls et al. (2014) by using physical constraints to measure the electric field from ion velocities measured from ISR. The technique seems reasonable and the authors show how the method will work through simulation examples.
The authors deal with the affects of the sensor e.g. finite beam width and range extent of the measurments within equation 10. There can be a time component to this as the plasma moves through the FOV it can move to different resolution voxels. The closest cross beam voxels at 100 km along the north south track are about 5 km. This can be similar to a bluring of the data. Will this result in a major change in the algorithm? I think for the most part it will just be an adjustment to the forward model but it may not be neccesary as you're just measuring velocities and not intrinsic plasma parameters with this technique. Plus the physics based regularization might help mitigate this impact.
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AC1: 'Reply on RC1', Johann Stamm, 30 May 2022
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Thank you for the valuable feedback
The model in the article does not consider time variations in other ways than that the neutral wind is assumed to vary slowly. There could be cases where a feature in electric field or neutral wind moves with a velocity large enough to be inside several voxels within one loop of transmit beams. For example the auroral arc could move over the field of view over this time. The electric field changes would then appear blurred in the output data.
Including a time component that considers changes in the measured plasma within one integration time into the model would be possible. It could be implemented similar to the dependency on the volume.
Another way of handling rapid time-variations could be to shorten the integration time. The price to pay for avoiding motion-blurring this way is increased uncertainties. One could then apply a Kalman filter to the time domain. This is outside the scope of this study.
For Figure 11, we removed three beams completely to see how missing beams effect estimates and uncertainties. This could also be interpreted as an extreme valley in electron density. The result was that these are little affected. We expect that the technique could be applied in cases where faster scan times are used with beams in more sparse pointing directions leading to slightly larger uncertainties.
We will add to our manuscript a discussion of this topic:
The presented framework assumes that the ionosphere does not change faster than the integration time, which is 70 s for the presented example. Spatial and temporal variations occurring faster that the integration time will thus be blurred out. One way to mitigate this is to take into account in which direction the beam points at every point in time such that the model connects the time the measurement is taken to the results. Another possible mitigation procedure, is to use a shorter integration time. The latter will have increased uncertainty which may be compensated to some extent by a Kalman filter. A third option would be to use fewer beams as this needs shorter integration time. The regularization will then try to fill the gaps as best as possible as illustrated in the example above.
In general we can state that an improved time-resolution requires measurements in fewer pointing-directions, that is either covering a smaller volume with a compact set of beams or a more sparse set of beams covering a large volume.
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AC1: 'Reply on RC1', Johann Stamm, 30 May 2022
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Johann Stamm et al.
Johann Stamm et al.
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