Ground-based microwave wind radiometry provides a method to measure
horizontal wind speeds at altitudes between 35 and 75 km as has been shown
by various previous studies. No other method is capable of continuously
delivering wind measurements in this altitude region. As opposed to lidar
systems, microwave radiometers operate autonomously and independent of
daylight and clouds.In this paper, we present the WIRA-C (Wind Radiometer for Campaigns)
instrument that observes the 142.17504 GHz rotational transition line of
ozone with a high spectral resolution using a low noise single side band
heterodyne receiver. Because the emitting molecules are drifting with the
wind, the line is Doppler shifted. Together with the pressure broadening
effect, this allows the retrieval of altitude resolved wind profiles.The novel WIRA-C instrument represents the newest development in microwave
wind radiometry and implements many improvements over its predecessor, the
WIRA instrument. The main improvements include the compact structure, lower
noise and an advanced retrieval setup. This paper describes the instrument
and the data processing with a focus on the retrieval that takes into account
a three-dimensional atmosphere and has never been used in ground-based
radiometry before. The retrieval yields profiles of horizontal wind speeds
with a 12 h time resolution and a vertical resolution of 10 km for zonal
and 10 to 15 km for meridional wind speeds. We give an error estimate that
accounts for the thermal noise on the measured spectra and additionally
estimate systematic errors using Monte Carlo methods.WIRA-C has been continuously measuring horizontal wind speeds for 1 year
at the Maïdo observatory on Réunion (21.4° S,
55.9° E). We present the time series of this campaign and compare
our measurements to model data from the European Centre for Medium-range
Weather Forecasts (ECMWF) and coincident measurements of the co-located
Rayleigh–Mie Doppler wind lidar. We find a good agreement between our
measurements and the ECMWF operational analysis for the time series, where
many features are present in both datasets. The wind profiles of the
coincident WIRA-C and lidar observations are consistent and agree within
their respective uncertainties for the lidar measurements with long
integration times.</p