We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
The Virga-Sniffer - a new tool to identify precipitation evaporation using ground-based remote-sensing observations.
- Authors
Kalesse-Los, Heike; Kötsche, Anton; Foth, Andreas; Röttenbacher, Johannes; Vogl, Teresa; Witthuhn, Jonas
- Abstract
Combined continuous long-term ground-based remote-sensing observations with vertically pointing cloud radar and ceilometer are well-suited to identify precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer which was developed within the frame of RVMeteor observations during the ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte (EUREC4A) field experiment in Jan-Feb 2020 in the Tropical Western Atlantic. The Virga-Sniffer Python package is highly modular and configurable and can be applied to multilayer cloud situations. In the simplest approach, it detects virga from time-height fields of cloud radar reflectivity and time series of ceilometer cloud base height. In addition, optional parameters like lifting condensation level, a surface rain flag as well as time-height fields of cloud radar mean Doppler velocity can be added to refine virga event identifications. The netcdf-output files consist of Boolean flags of virga- and cloud detection, as well as base- and top heights and depth for the detected clouds and virga. The performance of the Virga-Sniffer was assessed by comparing its results to the Cloudnet target classification resulting from using the CloudnetPy processing chain. 88% of the pixel identified as virga correspond to Cloudnet classifications of precipitation. The remaining 12% of virga pixel correspond to Cloudnet-classifications of aerosols and insects (about 7%), cloud droplets (3%), or clear-sky (about 1%). Some discrepancies of the virga identification and the Cloudnet target classification can be attributed to applied temporal smoothing. Additionally, it was found that Cloudnet mostly classified "aerosols and insects" at virga edges which points to a misclassification caused by CloudnetPy internal thresholds. For the RVMeteor observations during EUREC4A, about 50% of all detected clouds with bases below the trade inversion were found to produce precipitation that evaporates before reaching the ground. The most important virga-producing clouds were either anvils of convective cells or stratocumulus clouds. 36% of the detected virga originated from trade wind cumuli. Small virga with depths below 200m most frequently occurred from shallow clouds with depths below 500m, while virga depths above 1 km were mainly associated with clouds of larger depths, ranging between 500 and 1000m. Virga depth showed no strong dependency on column-integrated liquid water path. The presented results substantiate the importance of low-level precipitation evaporation in the lower winter trades. Possible applications of the Virga-Sniffer within the frame of EUREC4A include detailed studies of precipitation evaporation with a focus on cold pools or cloud organization, or distinguishing moist processes based on water vapor isotopic observations. However, we envision extended use of the Virga-Sniffer for other cloud regimes or scientific foci as well.
- Subjects
FRONTS (Meteorology); CLOUD droplets; TRADE winds; DOPPLER radar; AUTUMN; STRATOCUMULUS clouds
- Publication
Atmospheric Measurement Techniques Discussions, 2022, p1
- ISSN
1867-8610
- Publication type
Article
- DOI
10.5194/amt-2022-252