Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
Creators
- 1. Haystack Observatory, Massachusetts Institute of Technology, USA
- 2. Leibniz Institute of Atmospheric Physics at the University of Rostock, Germany
- 3. UiT Arctic University of Norway, Norway
Description
This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.
The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).
Files
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Additional details
References
- Vierinen, J., Chau, J. L., Asokan, H. C., Urco, J. M., Clahsen, M., Avsarkisov, V., Marino, R., & Volz, R. (2019). Observing mesospheric turbulence with specular meteor radars: A novel method for estimating second order statistics of wind velocity. Earth and Space Science. https://doi.org/10.1029/2019EA000570
- Volz, R., Chau, J. L., Erickson, P. J., Vierinen, J. P., Urco, J. M., and Clahsen, M. (submitted). Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations.