Monitoring flying insects with Doppler cloud radar and in situ precipitation sensors

Flying insect biomass declined by 76 % from 1989-2016 in Germany (Hallmann et al., 2017). However, insects are vital to food security and a functioning eco system. In the atmosphere, flying insects are continuously observed by cloud and weather radars, albeit usually as unwanted clutter. Techniques to filter out insects in radar data can in turn be used to extract the insect signal for further examination. In this study, the supervised machine learning model VOODOO (Schimmel et al., 2022), developed to detect cloud liquid beyond lidar attenuation by its characteristic signatures in the Doppler spectra, is modified and used to detect flying insects in Doppler cloud radar data based on their characteristic spectral signatures. The main cloud radar data set contains 15 months of continuous observations in Leipzig, Germany (Dec 2020 to Mar 2022). In addition, multiple months of field experiment data from Punta Arenas, Chile (2018), Lindenberg, Germany (2020), and the SAIL campaign in Colorado, USA (2022/23), are available for investigation.

Close to the ground, flying insects are detected by the Video In Situ Snowfallsensor VISSS (Maahn et al., 2023). Even though the VISSS was developed for observing snowfall by recording shadow images of snow particles while precipitating, we will show the potential of the instrument for observing flying insects and quantifying their concentration. VISSS data are available from Ny-Ålesund, Norway, since 2021 and from Hyytiälä, Finland, in 2021/22 and again from winter 2023 onwards.

 

Hallmann CA, Sorg M, Jongejans E, Siepel H, Hofland N, Schwan H, et al. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12(10): e0185809. https://doi.org/10.1371/journal.pone.0185809

Maahn, M., Moisseev, D., Steinke, I., Maherndl, N., and Shupe, M. D.: Introducing the Video In Situ Snowfall Sensor (VISSS), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-655, 2023.

Schimmel, W., Kalesse-Los, H., Maahn, M., Vogl, T., Foth, A., Garfias, P. S., and Seifert, P.: Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks, Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, 2022.