Preprints
https://doi.org/10.5194/amt-2023-69
https://doi.org/10.5194/amt-2023-69
11 Apr 2023
 | 11 Apr 2023
Status: this preprint was under review for the journal AMT. A final paper is not foreseen.

Classification of flying insects in polarimetric weather radar using machine learning and aphid trap data

Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas

Abstract. Over the past decades, studies have observed strong declines in biomass and the abundance of flying insects. However, there are many locations where no surveys of insect biomass or abundance are available. Weather radars are known to provide quantitative estimates of flying insect biomass and abundance, and can therefore be used to fill knowledge gaps in space and time. In this study, we go beyond previous studies by combining a machine-learning approach with ground- truth observations from an aphid trap network. In this study, radar echoes from Level-II (Base) data of three Next Generation Weather Radar (NEXRAD) stations in the U.S. are classified using machine learning approaches. Weekly aphid counts from suction traps at Manhattan (Kansas), Morris (Illinois), and Rosemount (Minnesota) are used as validation data. Variability and distribution of the radar signals of four scatterer classes (insects, light rain, heavy rain, and plankton) are assessed. Probability density functions (PDF) of reflectivities of insects and plankton were found to be distinct from those of light- and heavy rain. Furthermore, the PDF of radar variables of the insect scatter class was also characterized by a broad distribution of spectrum width, cross-correlation ratio, and a broad range of differential reflectivity values. Decision trees, random forests, and support vector machine models were generated to distinguish three combinations of scatterers. A random forest classifier is found to be the best-performing model.

This preprint has been withdrawn.

Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-69', Anonymous Referee #1, 16 May 2023
    • AC1: 'Reply on RC1', Samuel Kwakye, 01 Jun 2023
      • RC3: 'Reply on AC1', Anonymous Referee #1, 28 Jun 2023
        • AC3: 'Reply on RC3', Samuel Kwakye, 28 Nov 2023
  • RC2: 'Comment on amt-2023-69', Anonymous Referee #2, 31 May 2023
    • AC2: 'Reply on RC2', Samuel Kwakye, 01 Jun 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-69', Anonymous Referee #1, 16 May 2023
    • AC1: 'Reply on RC1', Samuel Kwakye, 01 Jun 2023
      • RC3: 'Reply on AC1', Anonymous Referee #1, 28 Jun 2023
        • AC3: 'Reply on RC3', Samuel Kwakye, 28 Nov 2023
  • RC2: 'Comment on amt-2023-69', Anonymous Referee #2, 31 May 2023
    • AC2: 'Reply on RC2', Samuel Kwakye, 01 Jun 2023
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas

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This preprint has been withdrawn.

Short summary
Insect numbers in the atmosphere can be calculated using polarimetric weather radar but they have to be identified and separated from other echoes, especially weather phenomena. Here, the separation is demonstrated using three machine-learning algorithms and insect count data from suction traps and the nature of radar measurements of different radar echoes is revealed. Random forest is the best separating algorithm and insect echoes radar measurements are distinct.