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

Abstract

Abstract. Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. 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 (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables 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 because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln a) changes for each cloud profile, and the range of ln a is consistent with suggested values in the 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 (southwest 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 played 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 the scaling factor are proposed for fog and clouds. 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 the 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.

Vertical Profiles of Liquid Water Content in fog layers during the SOFOG3D experiment

“…Using neural net- work inversion, it provides vertical temperature and humidity profiles up to 2.5 km with a vertical resolution of 25 m up to 100 m high and 30 m above, as well as the liquid water path (LWP) over the whole layer. The synergy between both instruments was investigated for IOP 11 (Vishwakarma et al, 2023) and IOP 14 (Bell et al, 2022), by combining the LWP retrieved from the radiometer and the reflectivity from the BASTA radar in order to better estimate the vertical profile of LWC within the fog layer. However, we analyse here all the data collected during the SOFOG3D experiment, and we then use independant retrieval.…”

“…The observation strategy combined vertical profiles derived from remote sensing instruments (microwave radiometer (MWR), Doppler cloud radar and Doppler lidars) and balloon-borne in-situ measurements of fog microphysics and thermodynamics. Bell et al (2022) and Vishwakarma et al (2023) combined cloud radar reflectivity with temperature and humidity profiles and LWP retrieved from MWR, to better estimate the vertical profile of LWC in the fog layer. They demonstrated that LWC retrieval is highly sensitive to the prescribed droplet concentration, and that agreement with in situ data is highly dependent on cloud-fog heterogeneity.…”

“…The vertical profile dataset is extended with constant-height sections at various altitudes within fog layers, which were performed for TKE measurements. Reflectivity and Doppler velocity along the sight of water drops, measured by the BASTA cloud radar, also provide valuable information on the dynamical and microphysical properties of the fog layer (Vishwakarma et al, 2023;Dione et al, 2023). A scanning unit was deployed around 1 km away from the vertical pointing unit of the Charbonière site, which allowed for the first time a volume sampling of a fog layer.…”

A Comparative Investigation of Light Scattering and Digital Holographic Imaging to Measure Liquid Phase Cloud Droplets

“…Ground-based and spaceborne remote sensing techniques have been developed considerably over the past few decades. In remote sensing methods, power spectrum data measured by satellites and radars is used to invert to obtain cloud microphysics parameters [21][22][23][24][25][26][27][28][29][30]. Remote sensing methods have the advantage of detecting high-altitude clouds from a long distance [31,32].…”