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Absolute Calibration method for FMCW Cloud Radars

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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 SIRTA atmospheric observatory, located in Palaiseau, France, in the framework of the ACTRIS-2 research and innovation program. The experimental setup includes 10 cm and 20 cm triangular trihedral targets installed at the top of 10 m 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, incomplete antenna overlap, temperature variations inside the radar, clutter and experimental setup misalignment. Setup misalignment is a source of bias previously undocumented in the literature, that can have an impact on the order of tenths of dB in calibration retrievals of W band Radars. A detailed analysis enabled the design of a calibration methodology which can reach a cloud radar calibration uncertainty of 0.3 dB based on the equipment used in the experiment. Among different sources of uncertainty, the two largest terms are due to signal-to-clutter ratio and radar-to-target alignment. 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 ten times smaller than for the latter. 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 mid-tropospheric 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.
Title: Absolute Calibration method for FMCW Cloud Radars
Description:
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 SIRTA atmospheric observatory, located in Palaiseau, France, in the framework of the ACTRIS-2 research and innovation program.
The experimental setup includes 10 cm and 20 cm triangular trihedral targets installed at the top of 10 m 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, incomplete antenna overlap, temperature variations inside the radar, clutter and experimental setup misalignment.
Setup misalignment is a source of bias previously undocumented in the literature, that can have an impact on the order of tenths of dB in calibration retrievals of W band Radars.
A detailed analysis enabled the design of a calibration methodology which can reach a cloud radar calibration uncertainty of 0.
3 dB based on the equipment used in the experiment.
Among different sources of uncertainty, the two largest terms are due to signal-to-clutter ratio and radar-to-target alignment.
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 ten times smaller than for the latter.
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 mid-tropospheric 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.

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