Abstract. This paper describes a novel, airborne pod-based millimeter (mm) wavelength radar. Its frequency of operation is 94 GHz (3 mm wavelength). The radar has been designed to fly on the NCAR Gulfstream V HIAPER aircraft; however, it could be deployed on other similarly equipped aircraft. The pod-based configuration occupies minimum cabin space and maximizes scan coverage. The radar system is capable of collecting observations in a staring mode between zenith and nadir or in a scanning mode. Standard pulse-pair estimates of moments and raw time series of backscattered signals are recorded. The radar system design and characteristics as well as techniques for calibrating reflectivity and correcting Doppler velocity for aircraft attitude and motion are described. The radar can alternatively be deployed in a ground-based configuration, housed in the 20 ft shipping container it shares with the High Spectral Resolution Lidar (HSRL). The radar was tested both on the ground and in flight. Preliminary measurements of Doppler and polarization measurements were collected and examples are presented.

The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters off Panama and Costa Rica (2019). An extensive set of quality assurance and quality control procedures were developed and applied to all collected data. Engineering measurements yielded calibration characteristics for the antenna, reflector, and radome, which were applied during flight, to produce the radar moments in real-time. Temperature changes in the instrument during flight affect the receiver gains, leading to some bias. Post project, we estimate the temperature-induced gain errors and apply gain corrections to improve the quality of the data. The reflectivity calibration is monitored by comparing sea surface cross-section measurements against theoretically calculated model values. These comparisons indicate that the HCR is calibrated to within 1–2 dB of the theory. A radar echo classification algorithm was developed to identify “cloud echo” and distinguish it from artifacts. Model reanalysis data and digital terrain elevation data were interpolated to the time-range grid of the radar data, to provide an environmental reference.

The 94-GHz airborne HIAPER Cloud Radar (HCR) has now been deployed in three major field campaigns. NCAR has developed an extensive set of quality assurance and quality control procedures which are applied to all collected data.Engineering measurements performed both in the laboratory and in an antenna measurement chamber yielded calibration characteristics for the antenna, reflector, and radome. These calibration results are applied during flight, to produce the radar moments available in real-time. However, temperature changes in the instrument during flight affect the receiver gains, leading to some bias in the calibration values applied in real time. Post project, we estimate the temperature -induced gain errors and apply gain corrections to improve the quality of the final data set. In addition, the reflectivity calibration is monitored by comparing sea surface cross section measurements against theoretically -calculated model values. These comparisons confirm that HCR is calibrated to within 1 dB of the theory. A radar echo classification algorithm was developed to identify "cloud echo" and distinguish it from artifacts such as the echo from the surface, transmitter leakage , and a number of other categories.Model reanalysis data and digital terrain eleva tion data were interpolated to the radar time-range grid of the radar data, to provide an environmental reference. These fields were used for the sea surface calibration and also are made available as an aid for scientific research.

This paper presents experimental results of an initial and in-situ mutual coupling calibration techniques of an active phased array antenna. The antenna is a dual-polarized 64-element C-band subarray panel equipped with an RF beamformer that enables mutual coupling measurements. Both techniques were implemented, tested and validated using a custom anechoic chamber and a radar backend. The implementation of a radar backend enables the in-situ validation of both methods. A conventional near-field calibration procedure, also known as park and probe, was used as a reference, in order to validate the proposed method. To quantify the calibration effectiveness of the mutual coupling-based techniques, the results are translated into phase and amplitude errors. It is found that the amplitude estimation of the tested initial technique is affected by edge effects, resulting in errors larger than 1 dB, whereas the phase estimation is less sensitive, yielding an overall root mean square error (RMSE) of 2.5 •. In contrast, the proposed in-situ technique is not affected by edge effects, and its estimation RMSE is less than 0.12 dB in amplitude and less than 0.75 • in phase. Mutual coupling-based calibration techniques are demonstrated to be very versatile, as they provide calibration methods for environments outside an anechoic chamber, and they also enable immediate feedback on the health of the system. INDEX TERMS Active phased array, calibration, component failure, mutual coupling, park and probe.

A technique for correcting radar radial velocity Vr in airborne, nadir-pointing radar data using the surface of Earth as a reference is proposed and tested. Operating airborne Doppler radars requires correcting the radial velocity for platform motion. This can be accomplished with accurate beam-pointing and platform motion measurements. However, there are often residual pointing errors due to drift in inertial navigation systems (INS) and/or errors in platform-relative pointing. The technique proposed here takes advantage of the fact that the surface is stationary and the mean of the measured Vr at the surface [Formula: see text] should be 0 m s−1. Therefore, if a good estimate of the mean [Formula: see text] is made, it can be subtracted from the measured Vr to correct for errors due to residual pointing errors. The [Formula: see text] data contain many independent deviations from 0 m s−1 due to various causes, including measurement variance and large deviations due to surface features. These deviations must be filtered out of [Formula: see text] before the surface reference can be applied to correct the Vr data. A two-step filtering process was developed and tested. The first step removes large deviations in [Formula: see text] and the second step removes the measurement noise. The technique was examined using data from three field campaigns and was found to improve the quality of Vr in all cases. The Vr bias was removed and the variance was substantially reduced. The approach is generally applicable to nadir-pointing airborne radar data.