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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. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of the cloud. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z = a · LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power-law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z-LWC relation for a continuous cloud system is complicated. Additional information such as Liquid water path (LWP) from a co-located microwave radiometer is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of warm clouds using radar-microwave radiometer synergy in a variational framework is presented. This method also accounts for attenuation due to cloud droplets and retrieves a suitable scaling factor (lna) of the profile in addition to the LWC. The optimal estimation techniques incorporate a priori information of desired variables, and the forward model converts these variables into observation parameters. In this algorithm formulation, the measure of uncertainty in observations, forward model and, a priori acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information. The algorithm is then implemented to various cloud and fog cases at SIRTA observatory to estimate LWC and the scaling factor. The scaling factor changes for each cloud profile, and the range of lna are consistent with suggested values in literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (South-West of France, October 2019 to March 2020), in-situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud-fog heterogeneity was playing a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for scaling factor are proposed for fog and cloud. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of scaling factor as a priori information to estimate the LWC of warm cloud. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of leaf area index (LAI) and surface soil moisture (SSM) on satellite-derived radar backscatter (sigma0) observations over southwestern France. Observations from the Advanced Scatterometer (ASCAT) are compared to simulated sigma0 values produced by the Water Cloud Model (WCM) coupled to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM). The LAI and SSM variables used by the WCM are derived from satellite observations and from ISBA simulations, respectively. Values of four parameters of the WCM are calibrated. The ability of the WCM to simulate ASCAT sigma0 observations in contrasting land cover conditions is explored. The response of observed and simulated sigma0 to LAI and SSM is analyzed across seasons. Two ways of representing vegetation in the WCM are compared. The impact of the Klaus storm on the ASCAT observations over the Les Landes forest in January 2009 is examined. The difference in sigma0 between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm and hardly any seasonality during four years after the storm. From 2013 onward, a regeneration phase is visible, with the return of seasonality in the forest vs. non-forest sigma0 difference. The ability of the WCM to represent this phenomenon is investigated. It is shown that the WCM can be used as an observation operator for the assimilation of ASCAT sigma0 observations into the ISBA LSM. [ABSTRACT FROM AUTHOR]