Absolute calibration method for frequency-modulated continuous wave (FMCW) cloud radars based on corner reflectors

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.

W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

“…The uncertainty due to shape parameters of the cloud droplet size distribution was assessed to be 6.1 dB. Although this value seems large considering that the standard deviation of innovation errors was reduced to less than 5dB with the MRP method, the use of a two-moment microphysical scheme, such as LIMA (Vié et al, 2016), which is currently being tested for operational use, promises to reduce this error by a prognostic evolution of the droplet number concentration. Future methods of OE retrieval with cloud radar could also include the droplet number concentration and size distribution parameters in the set of variables to be retrieved.…”

“…The forward model may contain errors as a result of the hypotheses needed to simulate the observations, such as assumptions on the cloud droplet size distribution in the context of radar reflectivity. Observation errors are due to calibration uncertainties (Toledo et al, 2020;De Angelis et al, 2017), instrumental drifts, and random noise.…”

An Optimal Estimation Algorithm for the Retrieval of Fog and Low Cloud Thermodynamic and Micro-physical Properties

“…The observation error covariance matrix is assumed to be diagonal with observation error values for each channel provided in table 1. For the cloud radar errors, instrumental error of 2 dB was assumed from work on the calibration of the BASTA cloud radar (Toledo et al, 2020). As discussed in Bell et al (2021), the primary component of forward model errors in the radar simulator comes from the hypothesis made on the assumed cloud droplet size distribution and was found to be approximately of 3 dB (Bell et al, 2021).…”

“…Though the exact reason for this is not perfectly known, it could come partially from temperature dependencies of certain components of the radar (Toledo et al, 2020), from the CDP underestimating the LWC (through missing droplets or the mis-sizing of droplets for example) or other unaccounted-for effects. It should be noted that the maximum droplet size observable by the CDP is 50 µm.…”

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

“…In order to apply the mm-wave radar in practical applications, the relevant data features must firstly be extracted from the reflected signal [ 22 , 23 , 24 , 25 , 26 ]. Hence, data processing methods for extracting more detailed features from the reflected signal, such as the features of the distance [ 27 ], velocity [ 28 , 29 ], Radar Cross Section (RCS) value [ 30 ] and angle [ 31 ], are widely investigated. Radar point cloud data not only contains almost all the aforementioned features but also can directly indicate the spatial locations of the targets, and they are receiving more attention.…”