A multiple-receiver radar technique for imaging the spatial distribution of ionospheric plasma irregularities is introduced and demonstrated with equatorial electrojet data obtained at the Jicamarca Radio Observatory. The images obtained with a few seconds time resolution enable the monitoring of the temporal evolution of the irregularity structures within the radar field of view. Daytime electrojet images contain signatures of localized irregularity patches which drift in the east-west direction at about the ion-acoustic velocity.

SA, IDI, and DBS horizontal wind estimators are examined and demonstrated to share common biases under inhomogeneous flow field conditions. The biases can reach magnitudes of several tens of meters per second at mesospheric heights, contaminate gravity wave flow field measurements, cause distortions in measured flow field spectra, and possibly account for large wind measurement errors evidenced in recent experimental studies including the AIDA'89 effort. However, being a linear response to gravity wave flow field fluctuations, the biases are expected to cancel out in long‐term wind and tidal statistics compiled with radar wind data.

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Spectral and cross‐spectral signatures of lower mesospheric radar returns are investigated and modeled. Aspect sensitivity of radar echoes and the horizontal, vertical, and random components of atmospheric fluid velocity are estimated. Estimated random velocity magnitudes of ∼0.2 m/s during a specific lower mesospheric scattering event are smaller than corresponding Doppler spectral widths of ∼1 m/s. During the same event, aspect sensitivity variations as large as ∼10 dB/deg were observed. Higher mesospheric returns generally indicate stronger random velocity amplitudes and weaker aspect sensitivity variations. The extreme conditions encountered in the lower mesosphere can introduce systematic errors in wind and turbulence studies, but simple interferometric methods are suggested to cope with such problems. Data analysis in this paper includes first examples of the implementation of the recently introduced poststatistics steering (PSS) technique. A cross‐spectral model developed in the appendix should be suitable for the analysis of most middle atmospheric radar interferometer data.

Large disparities in upper mesospheric winds measured with different radar techniques have been attributed earlier to the possibility that MF radars are sensitive to the phase speed of waves, rather than to true winds. An alternative explanation attributes these disparities to biases due to horizontal gradients in the wind field. These biases are common to the Doppler beam swinging (DBS), spaced antenna (SA), and imaging Doppler interferometry (IDI) methods and arise because of spatial filtering of the wind field by the beam configuration. We develop a theoretical model for intrinsic frequency and vertical wave number spectra of DBS wind component estimates that includes the effect of spatial filtering on an ensemble of gravity waves. The model shows a strong enhancement of frequency components above a knee, at ∼1 hour intrinsic period. A similar model for the SA experiment is inherently intractable. We suggest, instead, that the DBS model should also hold for the spectra of SA wind component estimates. The knee is clearly evident in recently observed SA wind component spectra, but is less pronounced and occurs at a frequency lower than the DBS model predicts. Better agreement is expected with a model refined to include the effect of Doppler shifts due to background winds. Occurrence of the knee in observed SA wind spectra implies that MF radars are sensitive to winds rather than to phase speed of atmospheric waves.