Abstract. Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying 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 framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 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, and 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 sensitivity of the Virga-Sniffer results to different settings is explored (in the Appendix). The performance of the Virga-Sniffer was assessed by comparing its results to the CloudNet target classification resulting from using the CloudNet processing chain. A total of 86 % of pixels identified as virga correspond to CloudNet target classifications of precipitation. The remaining 14 % of virga pixels correspond to CloudNet target classifications of aerosols and insects (about 10 %), cloud droplets (about 2 %), or clear sky (2 %). Some discrepancies of the virga identification and the CloudNet target classification can be attributed to temporal smoothing that was applied. Additionally, it was found that CloudNet mostly classified aerosols and insects at virga edges, which points to a misclassification caused by CloudNet internal thresholds. For the RV Meteor observations in the downstream winter trades during EUREC4A, about 42 % of all detected clouds with bases below the trade inversion were found to produce precipitation that fully evaporates before reaching the ground. A proportion of 56 % of the detected virga originated from trade wind cumuli. Virga with depths less than 0.2 km most frequently occurred from shallow clouds with depths less than 0.5 km, while virga depths larger than 1 km were mainly associated with clouds of larger depths, ranging between 0.5 and 1 km. The presented results substantiate the importance of complete low-level precipitation evaporation in the downstream winter trades. Possible applications of the Virga-Sniffer within the framework 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.

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 RV Meteor 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 by the Virga Sniffer 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 RV Meteor 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 200 m most frequently occurred from shallow clouds with depths below 500 m, while virga depths above 1 km were mainly associated with clouds of larger depths, ranging between 500 and 1000 m. 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 Atlantic 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.

<p>The dominant cloud type in the subtropical Atlantic is the trade wind cumulus with a cloud base located near the lifting condensation level (LCL) below 1 km. Other common clouds in this region with their base above 1 km are stratiform cloud layers or cloud edges near the trade wind inversion at 2-3 km. Precipitation in all these clouds mainly forms at temperatures above freezing point by collision and coalescence. Therefore, precipitation generally occurs as light rain/drizzle from stratiform cloud layers or as showers from well-developed trade wind cumuli. Precipitation underneath a cloud base is often visible as fall streaks. If the precipitation evaporates before reaching the ground, these fall streaks are called virga.</p> <p>Combined continuous long-term ground-based remote-sensing observations with vertically pointing cloud radar and ceilometer are well-suited to identify these precipitation evaporation fall streaks. Here we show the first application of a new open-source tool, the <em>Virga-Sniffer</em> which was developed within the frame of RV <em>Meteor</em> observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC<sup>4</sup>A) field experiment in Jan–Feb 2020 in the Tropical Western Atlantic. In the simplest approach, it detects virga from time-height fields of cloud radar reflectivity and time series of ceilometer cloud base height. The Virga Sniffer was applied to RV <em>Meteor</em> observations during EUREC<sup>4</sup>A and statistical results as well as an evaporation case study are presented. Spectral W-band radar data from a fall streak, identified as virga by the <em>Virga-Sniffer</em>, was used to calculate evaporative cooling rates. Sensitivity studies were performed to investigate the influence of vertical wind and relative humidity uncertainties.  Possible future applications of the <em>Virga-Sniffer</em> within the frame of EUREC<sup>4</sup>A 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.</p>