Abstract
Abstract. Cloud Doppler radars are increasingly used to study cloud and precipitation microphysical processes. Typical bulk cloud properties such as liquid or ice content are usually derived using the first three standard moments of the radar Doppler spectrum. Recent studies demonstrated the value of higher moments for the reduction of retrieval uncertainties and for providing additional insights into microphysical processes. Large effort has been undertaken, e.g., within the Atmospheric Radiation Measurement (ARM) program to ensure high quality of radar Doppler spectra. However, a systematic approach concerning the accuracy of higher moment estimates and sensitivity to basic radar system settings, such as spectral resolution, integration time and beam width, are still missing. In this study, we present an approach on how to optimize radar settings for radar Doppler spectra moments in the specific context of drizzle detection. The process of drizzle development has shown to be particularly sensitive to higher radar moments such as skewness. We collected radar raw data (I/Q time series) from consecutive zenith-pointing observations for two liquid cloud cases observed at the cloud observatory JOYCE in Germany. The I/Q data allowed us to process Doppler spectra and derive their moments using different spectral resolutions and integration times during identical time intervals. This enabled us to study the sensitivity of the spatiotemporal structure of the derived moments to the different radar settings. The observed signatures were further investigated using a radar Doppler forward model which allowed us to compare observed and simulated sensitivities and also to study the impact of additional hardware-dependent parameters such as antenna beam width. For the observed cloud with drizzle onset we found that longer integration times mainly modify spectral width (Sw) and skewness (Sk), leaving other moments mostly unaffected. An integration time of 2 s seems to be an optimal compromise: both observations and simulations revealed that a 10 s integration time – as it is widely used for European cloud radars – leads to a significant turbulence-induced increase of Sw and reduction of Sk compared to 2 s integration time. This can lead to significantly different microphysical interpretations with respect to drizzle water content and effective radius. A change from 2 s to even shorter integration times (0. 4 s) has much smaller effects on Sw and Sk. We also find that spectral resolution has a small impact on the moment estimations, and thus on the microphysical interpretation of the drizzle signal. Even the coarsest spectral resolution studied, 0. 08 ms−1, seems to be appropriate for calculation moments of drizzling clouds. Moreover, simulations provided additional insight into the microphysical interpretation of the skewness signatures observed: in low (high)-turbulence conditions, only drizzle larger than 20 µm (40 µm) can generate Sk values above the Sk noise level (in our case 0.4). Higher Sk values are also obtained in simulations when smaller beam widths are adopted.
Highlights
Millimeter wavelength radars are a key component of ground-based remote sensing because of their ability to detect and penetrate most cloud types, providing rangeresolved cloud structure
One question we aimed to address with this study is whether this is relevant only for specific case studies or whether such discrepancies in radar settings might have implications on the derived radar moment statistics, which may affect the quality of evaluations of drizzle parameterizations in numerical models
Larger effects of different integration times are found for Vd, between the 10 s and the two shorter integration times
Summary
Millimeter wavelength (cloud) radars are a key component of ground-based remote sensing because of their ability to detect and penetrate most cloud types, providing rangeresolved cloud structure. Doppler spectra are not directly used but several moments are derived from them: the first two moments (equivalent radar reflectivity factor Ze, mean Doppler velocity Vd) are most widely exploited while microphysical studies increasingly make use of higher moments such as spectral width (Sw), skewness (Sk) and kurtosis (e.g. Kollias et al, 2011a; Luke and Kollias, 2013; Maahn et al, 2015; Maahn and Löhnert, 2017). Kollias et al (2011a) showed the added value of higher radar moments like skewness and kurtosis for drizzle studies using forward simulations of radar Doppler spectra. They found that, in particular, the combined signatures of reflectivity and skewness are very sensitive to early drizzle formation. In a follow-up study, Luke and Kollias (2013) developed a retrieval of drizzle particle size distribution based on the deconvolution of cloud and drizzle peak in regions where drizzle presence was identified by positive skewness
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
