Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles Atmospheric Measurement Techniques Year : 2023

Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy


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 • LW C b 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 clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor.
Fichier principal
Vignette du fichier
amt-16-1211-2023.pdf (8.62 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Licence : CC BY - Attribution

Dates and versions

insu-03550994 , version 1 (01-02-2022)
insu-03550994 , version 2 (09-03-2023)





Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frédéric Burnet, et al.. Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy. Atmospheric Measurement Techniques, 2023, 16 (5), pp.1211-1237. ⟨10.5194/amt-16-1211-2023⟩. ⟨insu-03550994v2⟩
108 View
22 Download



Gmail Facebook X LinkedIn More