Classification of flying insects in polarimetric weather radar using machine learning and aphid trap data
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.
Samuel Kwakye et al.
Status: open (until 05 Jul 2023)
RC1: 'Comment on amt-2023-69', Anonymous Referee #1, 16 May 2023
- AC1: 'Reply on RC1', Samuel Kwakye, 01 Jun 2023 reply
RC2: 'Comment on amt-2023-69', Anonymous Referee #2, 31 May 2023
- AC2: 'Reply on RC2', Samuel Kwakye, 01 Jun 2023 reply
Samuel Kwakye et al.
Samuel Kwakye et al.
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Results of classification of radar echoes are presented in the manuscript. The classification categories are rain, atmospheric biota, and plankton. The authors have applied three machine learning algorithms to distinguish these radar returns. Data from three American Nexrad radars have been used in the study.
I am confused with the authors’ approach. On one hand, it follows from the manuscript’s title and main results (i.e., Fig. 5) that this is a study of classification of radar echoes using the machine learning algorithms. This implies that the input data are some variables from the initial base radar products, i.e., reflectivity, ZDR, Doppler velocity, spectrum width, differential phase, and correlation coefficient. On the other hand, results of echo classification from the Nexrad system are used first by the authors as an input to the authors’ algorithms (lines 77-78). The Nexrad radars have the Hydrometeor Classification Algorithm (HCA) which produces numerous classes of radar returns including biological and rain echoes. Have the authors used the HCA output to preselect types of radar echoes as it follows from ll. 77-78? The HCA is based on a fuzzy logic approach. It would be of interest to compare classification results from the HCA and the authors’ approach if the latter uses the base radar data but not the HCA outputs. If my understanding is not correct, i.e., the HCA output is not used to preselect types of echoes, the manuscript should contain comparisons of the classes obtained from the authors’ algorithms and HCA. At least, the manuscript should contain comparisons of insect identifications using the HCA and authors’ algorithms. If it turns out that the HCA exhibits a better performance than the authors’ algorithms, then the latter cannot be considered as a replacement for HCA.
Classification categories considered in the manuscript are produced by the HCA in more detail. For instance, the Nexrad radars measure the rain rate, which is more valuable data than just light and heavy rain used by the authors. Also, I have never seen the term ‘plankton’ used by the authors in their classification of Nexrad echoes. The category ‘plankton’ is absent in the HCA. The authors define the plankton as clear air returns at a wind speed > 11.3 m/s (line 94). If this is clear air (is this Bragg scatter?), why the term plankton is introduced? This term implies that clear air is filled with some substances. Since the radar data from warm seasons were used in the study, such echoes are typically produced by insects as well, but not tree leaves as the authors explain.
A question can be asked about comparisons of aphids in the ground traps and insects/birds causing radar returns. The main radar return comes from heights about 1 km above the ground at the ranges indicated in the manuscript. Is there any evidence that insects near the ground and those at a height of 1 km are the same species? Nexrad data frequently shows a height dependence of reflectivity from insects, which could be an indication of different taxa. Also, could birds be present aloft and contribute to radar returns?
The manuscript contains numerous not correct radar parameters and terms, but there is no need to discuss them now because the main authors’ approach is not clear.