Multi-static SMR networks

The SIMONe concept was developed by IAP in collaboration with MIT Haystack (US) and UiT The Arctic University of Norway. It was successfully tested during a one-week observational campaign carried out in Germany in November of 2018, and later implemented by commissioning systems in Germany, Norway, South America and, most recently, in the US.

Multi-static meteor radar networks can be implemented in SIMO (Single Input Multiple Output), MISO (Multiple Input Single Output) or MIMO (Multiple Input Multiple Output) configurations. The SIMONe approach, in particular, facilitates the implementation of MISO and MIMO configurations. Examples of MISO networks are SIMONe Argentina and the two SIMONe systems in Peru, while SIMONe Germany and Norway constitute the first, and so far only, MIMO meteor radar networks in the world (Poblet et al., 2023).

Currently, the IAP operates five multi-static meteor radar networks: MMARIA/SIMONe Germany (which combines SIMONe Germany with the pulsed SMR located in Collm), MMARIA/SIMONe Norway (composed of SIMONe Norway and the pulsed SMRs located in Trømso and Alta, northern Norway), SIMONe Peru (Jicamarca), SIMONe Peru 2 (Piura) and SIMONe Argentina. SIMONe New Mexico is owned and operated by the Air Force Research Laboratory (US).

Multi-static meteor radars like SIMONe are not only capable of detecting considerably higher amounts of meteors but, more importantly, they can observe a given volume from different viewing angles. This improvement in the viewing angle diversity allows for the estimation of new parameters, such as horizontal divergence, relative vorticity and correlation functions of the Doppler velocities. The first two parameters are helpful in determining the relative importance of gravity waves and stratified turbulence. However, the correlation functions of the Doppler velocities can be used to explore the wavenumber spectra of the first two parameters and thus determine how the energy cascades from mesoscale to kilometre-scales in the mesopause region. Furthermore, these correlation functions have the potential to routinely characterize mesoscale turbulence dissipation rates at MLT altitudes (Vierinen et al., 2024).

In recent years, Physics Informed Machine Learning (PIML) has emerged as a promising approach to solving complex and high-dimensional problems by incorporating the principles of physics into observational data. The increasingly larger SIMONe datasets represent a unique opportunity to implement these new approaches. The first tests carried out at IAP have resulted in unprecedented 3D wind fields. This opens the door to new exciting discoveries. Stay tuned.