Quantifying range‐ and topographical biases in weather surveillance radar measures of migratory bird activity

Abstract Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. Here, we assess these biases with relation to the Cheyenne, WY Next Generation Weather Radar (NEXRAD) site, one of the large‐scale radars in a network of 160 weather surveillance stations based in the United States. We compared local density measures collected using a mobile, vertically looking radar with reflectivity from the NEXRAD station in the corresponding area. Both mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated ( r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage. Range‐corrected NEXRAD reflectivity was a stronger predictor of observed mobile radar densities than uncorrected reflectivity at the mean nightly scale, suggesting that current range correction methods are somewhat effective at correcting for this bias. At the within night temporal scale, corrected and uncorrected reflectivity models performed similarly up to 65 km, but beyond this distance, uncorrected reflectivity became a stronger predictor than range‐corrected reflectivity, suggesting range limitations to these corrections. Together, our findings further validate weather radar as an ornithological tool, but also highlight and quantify potential sampling biases.

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Publication Details

Journal
Remote Sensing in Ecology and Conservation
Published
2024-12-12
DOI
https://doi.org/10.1002/rse2.423
Citations
3
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.62
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article

Quantifying range‐ and topographical biases in weather surveillance radar measures of migratory bird activity

Ali Khalighifar, Kyle G. Horton, Abigail B. Feuka, Birgen Haest et al.
3 citations
Remote Sensing in Ecology and Conservation
Remote Sensing and LiDAR Applications
0.62
article

Quantifying range‐ and topographical biases in weather surveillance radar measures of migratory bird activity

Ali Khalighifar, Kyle G. Horton, Abigail B. Feuka, Birgen Haest, Aitao Liu, Miguel F. Jimenez, Annika L. Abbott
article en
3 citations

Abstract

Abstract Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. Here, we assess these biases with relation to the Cheyenne, WY Next Generation Weather Radar (NEXRAD) site, one of the large‐scale radars in a network of 160 weather surveillance stations based in the United States. We compared local density measures collected using a mobile, vertically looking radar with reflectivity from the NEXRAD station in the corresponding area. Both mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated ( r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage. Range‐corrected NEXRAD reflectivity was a stronger predictor of observed mobile radar densities than uncorrected reflectivity at the mean nightly scale, suggesting that current range correction methods are somewhat effective at correcting for this bias. At the within night temporal scale, corrected and uncorrected reflectivity models performed similarly up to 65 km, but beyond this distance, uncorrected reflectivity became a stronger predictor than range‐corrected reflectivity, suggesting range limitations to these corrections. Together, our findings further validate weather radar as an ornithological tool, but also highlight and quantify potential sampling biases.

Remote Sensing in Ecology and ConservationVol. 11(3)
Swiss Ornithological Institute (CH), Colorado State University (US)
Openalex Percentile: Top 34%
Remote Sensing and LiDAR Applications
0.62
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