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Biological scatterers, consisting of birds and insects, may become trapped near the circulation center of tropical cyclones, particularly if a well-developed eyewall is present. These scatterers may be observed using weather radar, where they may appear to the radar operator as areas of light precipitation. Polarimetric radar characteristics of the...
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This paper reports finely resolved radar signatures of tornadogenesis associated with a typhoon and elucidates how a small‐scale tornadic vortex emerged on the ground in relation to the parent storm structure. Approximately 5 min before producing JEF‐2 damage, a counterrotating vortex pair appeared above the rear‐flank outflow region. The vortex pa...
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... Meteorologists should be attentive to the radar biological scatterer signature near the circulation center of TCs. Furthermore, biologists can utilize the biological scatterer signature to identify potentially significant storms in terms of biological transport, which was previously challenging to assess before landfall (Van Den Broeke, 2013). In a recent study, Van Den Broeke (2022b) investigated the biological scatterer signature of 42 Atlantic-Basin TCs from 2011 to 2020. ...
... The WZ SPOL first detected weak echoes within the typhoon eye at 1436 UTC on August 9, which ended at 1731 UTC on August 9 (14 min before the typhoon's landfall). These weak echoes generally have much lower ρ hv values and much higher Z DR values compared to meteorological scatterers with intensity (Van Den Broeke, 2013, 2022bZrnić & Ryzhkov, 1998), which can be generally identified as biological scatterers. For quantitative analysis (e.g., statistics in Section 3), we defined the biological scatterers as the echoes with ρ hv values below 0.85 in the typhoon eye within a radius of 5 km from the typhoon center. ...
... According to Figure 5b, the biological echoes in Irene (2011) better exhibited the characteristics of insects (a larger proportion of scatterers were insects in Irene) with Z DR values ranging from 4.0 to 8.0 dB, in comparison with Lekima (2019). However, it does not negate that seabirds are a key source of biological scatterers in Irene (2011) which has also been reported in Van Den Broeke (2013). In Lekima (2019), the larger number of biological pixels with lower Z DR values (<4 dB) indicates a larger proportion of bird scatterers. ...
Tropical cyclones not only cause strong winds and heavy rainfall, but they can also facilitate the transport of birds and insects from tropical regions to areas along their paths. Before super Typhoon Lekima made landfall in 2019, an operational polarimetric radar in Wenzhou City observed biological scatterers in the typhoon's eye. These scatterers were likely birds and insects that were trapped in the calm center of the typhoon by strong winds and heavy rain. The polarimetric variables of these biological scatterers had specific characteristics: low reflectivity factor (ZH) values with a median of 7.5 dBZ, low cross‐correlation coefficient (ρhv) values with a median of 0.65, large differential reflectivity (ZDR) values with a median of 2.8 dB and a maximum of ∼7.8 dB, and widely‐distributed differential phase (ΦDP) values with 25th and 75th percentiles ranging from 1.8° to 36.0° and a median of 20.1°. When the edge of the eye reached the coastline, the birds and insects landed, with the biological scatterer signature changing from a circular shape to a band shape. We further compared Lekima with Atlantic Hurricane Irene which happened in 2011, and both storms had similar polarimetric characteristics contributed by both birds and insects. However, the biological echoes in Lekima better exhibited the characteristics of birds with a larger proportion of samples with ZDR lower than 5 dB as well as large ΦDP and low ρhv values. This finding could help to understand the role of typhoons in driving biological migration between oceans and/or islands and continents.
... Gauthreaux and Diehl, 2020). Biological targets tend to have lower cross-correlation ratios than meteorological targets, which have a correlation coefficient of approximately 1 (Dokter et al., 2011;Van Den Broeke, 2013;Stepanian et al., 2014). In addition, biological targets typically feature weak reflectivity values lesser than around 20-25 dBZ (Martin and Shapiro, 2007;Horn and Kunz, 2008;Dokter et al., 2011), distinct speed and direction, and seasonal and diurnal patterns. ...
Desert locusts are infamous for their ability to cause widespread devastation to crops and agricultural lands. They have been known to cause significant economic losses in many parts of the world due to their capacity to swarm and consume large quantities of vegetation. The article briefly describes the use of remote sensing for monitoring locust populations as it enables researchers to predict and prepare for potential outbreaks, as well as deploy control measures promptly and effectively. Weather surveillance radars are another powerful tool for monitoring locust populations. These radars are capable of detecting locust swarms in real-time, providing high-resolution data at exceptional spatial-temporal scales. This is particularly advantageous for detecting locust migrations, which can span hundreds or even thousands of kilometers, and can be difficult to track through other means. With the help of weather surveillance radars, it becomes possible to gain a better understanding of the behavior of locust swarms and to take appropriate measures to mitigate their impact on crops and agricultural lands. The article also delves into the basics of radars and highlights the radar network across the recession and invasion areas of desert locusts. It concludes that the installation of radars would be immensely helpful in tracking biological scatterers, including locust swarms, in near real-time. The continuous and high-resolution data provided by these radars could revolutionize our ability to monitor and control locust populations, leading to better food security and economic stability in affected regions. Overall, the article aims to spark the interest of individuals with a biological/meteorological background in the advancement of entomological polarimetric radar applications. By providing fundamental interpretations of new data products, the article helps to bridge the gap between scientific and biological research, enabling experts from different fields to collaborate and tackle the challenges posed by locust swarms and other biological scatterers.
... Martin et al. used data from the Weather Surveillance Radar-1988 Doppler (WSR88D) and X-and W-band research radars and deemed that the targets of nocturnal clear-air echoes are almost insects [4]. Further, Broeke found that biological scatterers, consisting of birds and insects, may become trapped near the circulation center of tropical cyclones [5]. Westbrook et al. used a WSR-88D radar to detect corn earworm moth migration [6]. ...
... However, there are polarimetric differences between birds and insects. Insects often have a high Z DR (up to 10 dB) and a relatively low differential phase, while birds may have a lower Z DR (1 to 3 dB) and a much larger differential phase [5]. Moreover, for both types of echoes, the cross-correlation coefficient is between 0.3 and 0.5, which is lower than the hydrometeorological signal. ...
... The products in Figure 4 show that the echo was characterized by a low , a higher than that typically observed in meteorological echoes, and a correlation coefficient lower than that observed in meteorological echoes. Generally, similar echoes have been observed in bird scattering comprising many species flying over Southern Kansas from Wichita, KS, USA [5]. Fortunately, large insects have a much stronger flight ability, and flight speed is correlated with body size in animals [43]. ...
It is commonly believed that clear-air echoes detected by weather radars are caused by atmobios migration. However, clear-air echoes are sometimes inconsistently related to the activity of living creatures. In some cases, the characteristics of radar products seem to conform to biological scattering, but the movement of echoes cannot be observed. For these reasons, we sought to expand the cause of clear-air echoes from a Chinese Doppler S-band Weather Radar (CINRAD/SA) in Beijing. Some contradictions were discovered in a case which diverged from previous conclusions. It was found that the progression and movement of clear-air echoes do not conform to the rules of biological activities. The frequency distribution of dual-wavelength ratio peaks is 21.5 dB, which is in accordance with Villars–Weisskopf’s turbulence theory. From 1 May to 20 May, the 58% dual-wavelength ratio between the S-band and the X-band was distributed between 18 dB and 24 dB. These results show that more than half of the clear-air echoes of CINRAD/SA at night were caused by turbulence in Beijing. A new model of troposcatter propagation, the reflecting-layers model, was then introduced to explain the radar observations. According to the reflecting-layers model, the echoes’ diurnal variation and reflectivity characteristics are attributed to the effects of turbulent mixing. Excessive turbulent mixing affects the generation of the reflective layer, thereby weakening the echo signal. It is necessary to re-examine the position of turbulence in clear-air echoes.
... In radar echoes, insect signatures are distinguished by predominantly low reflectivities (-10 to 6 dBZ), extremely high ZDR (3 to 14 [27]. ...
... These results are consistent with previous literature. Analysis in Van Den Broeke (2013) found that echoes attributed to birds (purple martins) had Z DR between 24 and 6 dB. In our case, the averaged Z DR (shown in Figs. ...
... Insects have lower F DP values (Fig. 4e). r HV for bird migration have been observed to have low values corresponding to tail-on viewing angles and high values for head-on angles (Stepanian and Horton 2015;Van Den Broeke 2013). This can be seen in the sinusoid-like FIG. 3. VAD analysis to reorient velocity to be relative to the target aspect for 70-km range gates. ...
The S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.
... Farnsworth et al., 2004;Gauthreaux & Belser, 1998;Russell et al., 1998). Since the WSR-88D network was upgraded to polarimetric capability from 2011 to 2013, a new set of products allows more certain distinction of biological targets (e.g. Park et al., 2009;Stepanian et al., 2016;Van Den Broeke, 2013). The primary challenge of using radar to monitor biological targets is the frequent lack of ground truth and the so-far relatively weak ability to distinguish specific groupings of organisms (e.g. ...
... The advent of polarimetric capability allows the differentiation of rain and biological targets in TCs, and bioscatter has been observed near their centers of circulation (e.g. Van Den Broeke, 2013). Including these initial observations, radar data have now been collected over 10 Atlantic Basin TC seasons. ...
... Some weak systems did not contain a clear center of circulation and were discarded. A clear center of circulation was required since bioscatter has been closely associated with the TCCC in prior observations (Van Den Broeke, 2013) and it is hypothesized (Table 1); several TCs were sampled by multiple radars at the required range. Two datasets abruptly ended when the TC destroyed the radar [Maria (2017) and Laura (2020)]. ...
Tropical cyclones (TCs) can transport birds and insects near their center of circulation. In this study, we examined the maximum altitude, area and density of the radar‐derived bioscatter signature across a set of 42 TC centers of circulation sampled from 2011 to 2020. All TC events contained at least one time when a bioscatter signature was present. More intense hurricanes with closed eyes typically had taller and denser bioscatter signatures, and sometimes larger areas dominated by bioscatter. This indicated a larger number of organisms within the circulation of more intense hurricanes, supporting the speculation that those storms were most likely to trap birds that do not want to risk flying through their eyewall thunderstorms. Larger and denser bioscatter signatures, indicating a larger number of birds, tend to occur when fall migration brings a large bird population to the Gulf and East Coasts where most storms were sampled. TC formation location was not related to bioscatter characteristics, but storms sampled in the Gulf of Mexico and Florida tended to have larger and denser bioscatter signatures. Bioscatter signatures are examined in association with 42 Atlantic tropical cyclone (TC) datasets from 2011 to 2020. In these storms, the bioscatter signature was ubiquitous, and its maximum altitude, area and density were functions of TC intensity and structure. Associated bioscatter signatures were also found to be larger and taller in mid‐ to late‐season storms, especially those in the Gulf of Mexico and over Florida.
... The results are also consistent with previous literature. Analysis in [19] found that echoes attributed to birds (Purple Martins) had Z DR between −4 and 6 dB. In our case, the averaged Z DR (shown in Figure 4a) for birds is generally low, between −2 and 4 dB. ...
... Insects have lower Φ DP values. ρ HV (Figure 3c) for bird migration have been observed to have low values corresponding to tail-on viewing angles and high values for head-on angles [18,19]. This can be seen in the sinusoid-like pattern in Figure 3c, with high values (around 0.7) between 60 • and 250 • and low values (around 0.4) otherwise. ...
... The results are also consistent with previous literature. Analysis in [19] found that echoes attributed to birds (Purple Martins) had between −4 and 6 dB. In our case, the averaged (shown in Figure 4a) for birds is generally low, between −2 and 4 dB. ...
NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate these bird and insect echoes. Multiple scans are collected for mass migration of birds (insects) and coherently averaged along their different aspects to improve the data quality. Additional features are also computed to capture the dependence of bird (insect) echoes on the observed aspect, range, and local regions of space. Next, ridge classifier and decision tree machine learning algorithms are trained on the collected data. For each method, classifiers are trained, first with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of both methods, are analyzed using metrics computed on a held-out test data set. Further case studies on roosting birds, bird migration, and insect migration cases, are conducted to investigate the performance of the classifiers when applied to new scenarios. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well on both the test data and in the case studies. This classifier is recommended for operational use on the US Next-Generation Radars (NEXRAD) in conjunction with the existing Hydrometeor Classification Algorithm (HCA). The HCA would be used first to separate biological from non-biological echoes, then the ridge classifier could be applied to categorize biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that can detect diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.
... Thunderstorm cases were drawn from 2013 to 2019, when dual-polarization was available on the WSR-88D network, and storms with varying size and intensity were included. Cases were limited to this temporal range so that cross-correlation coefficient (ρ hv ) could be used to increase confidence that a sampled area was dominated by biological scatter and that no precipitation was mixed in (e.g. Park et al. 2009;Van Den Broeke 2013). Cases had to be embedded within a clear migration signature (Fig. 1A), defined as a relatively uniform region of Z HH (generally 15-30 dBZ) fairly symmetrically surrounding the radar site and containing a clear V r signal indicating northward (southward) movement during spring (fall). ...
Seasonal bird migration occurs on large spatial scales and is influenced by many factors including weather conditions. Weather can include thunderstorms, which may force migrants to land or cause them to reroute a migration path. In this study, a sample of isolated thunderstorms was analyzed from the domains of three weather radars in the central United States to test hypotheses regarding the influence of thunderstorms on the distribution of migrants. Migrating bird density was often reduced in the wake of storms, and this wake reduction was typically more pronounced for larger, more intense and faster- moving storms, particularly in eastern Nebraska. Wind conditions more strongly influence the distribution and density of migrating birds in fall than in spring, providing evidence that migrating birds respond to environmental sig- nals more readily in the fall. This finding supports the concept that birds are more strongly obligated to cover distance in the spring and arrive in their breeding range on time. Wind conditions at the surface were generally more important to migrant density and distribution than wind conditions closer to flight level.
... Websites were used to identify the date, time, and location of a scatterer type (e.g., Table A1), and this information was used to download associated archived Level II WSR-88D data files for that event (e.g., emergence of midges at Lake Winnebago, Wisconsin; mayflies along the Mississippi River near La Crosse, Wisconsin and western portions of Lake Erie; sandhill crane (Antigone canadensis) movements along the Platte River in Nebraska). We also gathered samples of bioscatterers based on natural history documented in prior studies: waterfowl movements from known source areas [46][47][48], exodus of Mexican free-tailed bats from their daytime roosts in Texas [49,50], the departure of purple martins from overnight roost sites in the eastern and central United States [51][52][53], arriving migrant birds on the northern coast of the Gulf of Mexico [54][55][56], and departure of eared grebes in early winter from the Great Salt Lake [57][58][59][60]. In addition to samples of swarming mayflies and midges, samples of broad-scale, linear movements of insects were obtained from eleven WSR-88D stations co-located with radiosonde stations between the dates of 18 and 29 June during 2013-2017. ...
... Once dual-polarization was available, some investigators used one or more of the approaches mentioned above to identify the type of scatterer and to characterize the values of the polarimetric variables produced by that type of scatterer [36]. In most of the studies that have reported values of polarimetric variables from biological scatterers, the authors made assumptions about the identity of the type of scatterer before the analysis of the polarimetric data, but in a few cases the investigators knew the identity of the scatterer [66] or they knew about the departures of purple martins and other swallows from nighttime roosts [18,51,67,68] and the exodus of free-tailed bats from their daytime roosts [18,49,51,69]. In our study, we recorded legacy and polarimetric data from known biological scatterers and rain and generated histograms of values (Figure 1) for each type of scatterer. ...
... Once dual-polarization was available, some investigators used one or more of the approaches mentioned above to identify the type of scatterer and to characterize the values of the polarimetric variables produced by that type of scatterer [36]. In most of the studies that have reported values of polarimetric variables from biological scatterers, the authors made assumptions about the identity of the type of scatterer before the analysis of the polarimetric data, but in a few cases the investigators knew the identity of the scatterer [66] or they knew about the departures of purple martins and other swallows from nighttime roosts [18,51,67,68] and the exodus of free-tailed bats from their daytime roosts [18,49,51,69]. In our study, we recorded legacy and polarimetric data from known biological scatterers and rain and generated histograms of values (Figure 1) for each type of scatterer. ...
For radar aeroecology studies, the identification of the type of scatterer is critically important. Here, we used a random forest (RF) algorithm to develop a variety of scatterer classification models based on the backscatter values in radar resolution volumes of six radar variables (reflectivity, radial velocity, spectrum width, differential reflectivity, correlation coefficient, and differential phase) from seven types of biological scatterers and one type of meteorological scatterer (rain). Models that discriminated among fewer classes and/or aggregated similar types into more inclusive classes classified with greater accuracy and higher probability. Bioscatterers that shared similarities in phenotype tended to misclassify against one another more frequently than against more dissimilar types, with the greatest degree of misclassification occurring among vertebrates. Polarimetric variables proved critical to classification performance and individual polarimetric variables played central roles in the discrimination of specific scatterers. Not surprisingly, purposely overfit RF models (in one case study) were our highest performing. Such models have a role to play in situations where the inclusion of natural history can play an outsized role in model performance. In the future, bioscatter classification will become more nuanced, pushing machine-learning model development to increasingly rely on independent validation of scatterer types and more precise knowledge of the physical and behavioral properties of the scatterer.
... In both cases, a ringshaped pattern is evident in the reflectivity factor product, with the corresponding divergence signature in the radial velocity product that is characteristic of outward flights from a shared roost. Similar patterns of reflectivity factor and radial velocity can be seen for Purple Martin (Progne subis) flights, and are presented in Van Den Broeke (2013) and Stepanian et al. (2016). The variability of polarimetric products around the emergence ring indicates the variability of aspect viewing angles with respect to the radar site as individual headings are oriented away from the cavea polarimetric signature common to roost exodus flights of birds and bats (Van Den Broeke 2013; Stepanian et al. 2016;Mirkovic et al. 2016). ...
The high mobility of airborne organisms makes them inherently difficult to study, motivating the use of radars and radar networks as biological surveillance tools. While the utility of radar for ecological studies has been demonstrated, a number of challenges remain in expanding and optimizing their use for surveillance of birds, bats and insects. To explore these topics, a Lagrangian simulation scheme has been developed to synthesize realistic, polarimetric, pulsed Doppler radar baseband signals from modelled flocks of biological point scatterers. This radar simulation algorithm is described, and an application is presented using an agent-based model of the nocturnal emergence of a cave-dwelling colony of Brazilian free-tailed bats (Tadarida brasiliensis). Dual-polarization radar signals for an S-band weather surveillance radar are synthesized and used to develop a new extension of the spectral velocity azimuth display for polarimetric roost-ring signature analysis, demonstrating one capability of this simulation scheme. While these developments will have direct benefits for radar engineers and meteorologists, continuing investment in radar methods such as these will have cascading effects toward improving ecological models and developing new observational techniques for monitoring aerial wildlife.
