Abstract
Abstract. This article presents a new cloud radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA) atmospheric observatory, located in Palaiseau, France, in the framework of the Aerosol Clouds Trace gases Research InfraStructure version 2 (ACTRIS-2) research and innovation program. The experimental setup includes 10 and 20 cm triangular trihedral targets installed at the top of 10 and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, temperature variations inside the radar, frequency-dependent losses in the receiver's intermediate frequency (IF), clutter and experimental setup misalignment. Setup misalignment is a source of bias, previously undocumented in the literature, that can have an impact of the order of tenths of a decibel in calibration retrievals of W-band radars. A detailed analysis enabled the quantification of the importance of each uncertainty source to the final cloud radar calibration uncertainty. The dominant uncertainty source comes from the uncharacterized reference target which reached 2 dB. Additionally, the analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is 10 times smaller than for the latter. Following the proposed methodology, it is possible to reduce the added contribution from all uncertainty terms, excluding the target characterization, down to 0.4 dB. Therefore, this procedure should enable the achievement of calibration uncertainties under 1 dB when characterized reflectors are available. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated, the cloud radar provides valid reflectivity values when sampling midtropospheric clouds. Thus, we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.
Highlights
Clouds remain, to this day, one of the major sources of uncertainty in future climate predictions (Boucher et al, 2013; Myhre et al, 2013; Mülmenstädt and Feingold, 2018)
Since the term σ 0 is much larger than all other uncertainty sources, we calculate a partial calibration uncertainty including all but this term to simplify the comparison of uncertainty contributions between different experimental setups
This study presents a cloud radar calibration method that is based on a cloud radar power signal backscattered from a reference reflector
Summary
To this day, one of the major sources of uncertainty in future climate predictions (Boucher et al, 2013; Myhre et al, 2013; Mülmenstädt and Feingold, 2018). This arises partly from the wide range of scales involved in cloud systems, where a knowledge of cloud microphysics, cloud–aerosol interactions, is critical for predicting. Toledo et al.: Absolute calibration method for FMCW cloud radars large-scale phenomena such as cloud radiative forcing or precipitation To address this and other related issues, the Aerosol Clouds Trace gases Research InfraStructure (ACTRIS) is establishing a state-of-the-art ground-based observation network (Pappalardo, 2018). The Centre for Cloud Remote Sensing (CCRES) is in charge of creating and defining calibration and quality assurance protocols for the observation of cloud properties across the complete network
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