Dual‐polarization radar products for biological applications

Stepanian, Phillip M.; Horton, Kyle G.; Melnikov, Valery; Zrnić, Dúsan S.; Gauthreaux, Sidney A.

doi:10.1002/ecs2.1539

Cited by 82 publications

(126 citation statements)

References 71 publications

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“…WSR‐88D radars collect six data products. Of these, three ‘legacy’ products have been collected since the installation of the system in the early 1990s, and three dual polarization or ‘dual‐pol’ products became available when the system was upgraded during the period from 2011 to 2013 (Stepanian, ; Stepanian et al, ). The legacy data products are reflectivity factor ( Z ), radial velocity ( v r ) and spectrum width ( σ w ).…”

Section: Methodsmentioning

confidence: 99%

“…Recent advances have led to the first fully automated methods to extract biological information from weather radar data. In 2012–2013, the WSR‐88D network was upgraded to dual polarization technology, which makes it significantly easier to separate biology from precipitation in modern data (Stepanian, Horton, Melnikov, Zrnić, & Gauthreaux, ), but leaves open the problem of extracting biological information from historical data. Dokter et al () developed an algorithm to separate precipitation from biology in European C‐band radars; this was later extended to US dual polarization and S‐band data (Dokter, Desmet, et al, ; Dokter, Farnsworth, et al, ), but currently cannot fully separate precipitation from biology in historical US data.…”

Section: Introductionmentioning

confidence: 99%

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MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks

et al. 2019

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Large networks of weather radars are comprehensive instruments for studying bird migration. For example, the US WSR‐88D network covers the entire continental US and has archived data since the 1990s. The data can quantify both broad and fine‐scale bird movements to address a range of migration ecology questions. However, the problem of automatically discriminating precipitation from biology has significantly limited the ability to conduct biological analyses with historical radar data. We develop MistNet, a deep convolutional neural network to discriminate precipitation from biology in radar scans. Unlike prior machine learning approaches, MistNet makes fine‐scaled predictions and can collect biological information from radar scans that also contain precipitation. MistNet is based on neural networks for images, and includes several architecture components tailored to the unique characteristics of radar data. To avoid a massive human labelling effort, we train MistNet using abundant noisy labels obtained from dual polarization radar data. In historical and contemporary WSR‐88D data, MistNet identifies at least 95.9% of all biomass with a false discovery rate of 1.3%. Dual polarization training data and our radar‐specific architecture components are effective. By retaining biomass that co‐occurs with precipitation in a single radar scan, MistNet retains 15% more biomass than traditional whole‐scan approaches to screening. MistNet is fully automated and can be applied to datasets of millions of radar scans to produce fine‐grained predictions that enable a range of applications, from continent‐scale mapping to local analysis of airspace usage. Radar ornithology is advancing rapidly and leading to significant discoveries about continent‐scale patterns of bird movements. General‐purpose and empirically validated methods to quantify biological signals in radar data are essential to the future development of this field. MistNet can enable large‐scale, long‐term, and reproducible measurements of whole migration systems.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks

et al. 2019

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“…Weather radar with dual‐polarization capability enables the separation of signals returned from biological and meteorological scatterers and from birds and insects in the atmosphere (Zrnic and Ryzhkov ; Stepanian et al. ) and may allow the development of algorithms that can automatically detect and classify different types of biological scatterers aloft at a continent‐wide scale.…”

Section: Discussionmentioning

confidence: 99%

Detecting bird movements with L‐band avian radar and S‐band dual‐polarization Doppler weather radar

Gauthreaux

Shapiro

Mayer

et al. 2018

Remote Sens Ecol Conserv

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Marine surveillance radars with X‐band (3‐cm) and S‐band (10‐cm) wavelengths not only detect birds but also record return from insects, rain, ground objects and rough seas that often make discrimination of echoes from birds difficult. We compare the density of bird tracks recorded by an L‐band (ca. 23‐cm wavelength) avian radar at the Dallas/Fort Worth International Airport that does not detect insects and rain with the amount of mean bird reflectivity in resolution cells of a dual‐polarization S‐band Doppler weather surveillance radar. Radar data files of 1.5° scans gathered near midnight from January through May for the years 2014, 2015 and 2016 were processed by filtering radar resolution cells to retain only those with backscatter differential phase values characteristic of birds flying more or less perpendicular to the radar beam. In general, increases and decreases in the density of bird movements were correlated significantly between the two radars, but the low R2 values confirm that results obtained from one radar cannot be used to make quantitative estimates for the other. A decrease in the mean biomass index of migrating birds from January through May, and the difference in coverage volume and resolution between the two radars likely account for the low R2 values. Each type of radar has advantages and provides important information on bird movements at different spatial scales.

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“…To identify the significant medium‐ to long‐term challenges that might not be well recognized or investigated, or have great relevance for many societal issues, we used a ‘horizon scan‐approach’ (Hays et al ) and focused on challenges that can be addressed with a macrosystem‐level approach using radar technology. Our aims were to identify basic research priorities for scientists, to inform policy makers, and to increase awareness of neglected or emerging issues.…”

Section: Introductionmentioning

confidence: 99%

The grand challenges of migration ecology that radar aeroecology can help answer

et al. 2018

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Many migratory species have experienced substantial declines that resulted from rapid and massive expansions of human structures and activities, habitat alterations and climate change. Migrants are also recognized as an integral component of biodiversity and provide a multitude of services and disservices that are relevant to human agriculture, economy and health. The plethora of recently published studies reflects the need for better fundamental knowledge on migrations and for better management of their ecological and human‐relevant effects. Yet, where are we in providing answers to fundamental questions and societal challenges? Engaging a broad network of researchers worldwide, we used a horizon‐scan approach to identify the most important challenges which need to be overcome in order to gain a fuller understanding of migration ecology, and which could be addressed using radar aeroecological and macroecological approaches. The top challenges include both long‐standing and novel topics, ranging from fundamental information on migration routes and phenology, orientation and navigation strategies, and the multitude of effects migrants may have on resident communities, to societal challenges, such as protecting or preventing migrant services and disservices, and the conservation of migrants in the face of environmental changes. We outline these challenges, identify the urgency of addressing them and the primary stakeholders – researchers, policy makers and practitioners, or funders of research.

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Dual‐polarization radar products for biological applications

Cited by 82 publications

References 71 publications

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks

Detecting bird movements with L‐band avian radar and S‐band dual‐polarization Doppler weather radar

The grand challenges of migration ecology that radar aeroecology can help answer

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