bioRad: biological analysis and visualization of weather radar data

Talk at the 2nd International Radar Aeroecology Conference in Zhengzhou, China - September 22, 2019.

Weather surveillance radars are increasingly used for monitoring the movements and abundances of animals in the airspace. However, analysis of weather radar data remains a specialised task that can be technically challenging. Major hurdles are the difficulty of accessing and visualising radar data on a software platform familiar to ecologists and biologists, processing the low‐level data into products that are biologically meaningful, and summarizing these results in standardized measures. To overcome these hurdles, we developed the open source R package bioRad (described in Dokter et al. 2019, https://doi.org/10.1111/ecog.04028), which provides a toolbox for accessing, visualizing and analyzing weather radar data for biological studies. In this talk we will cover how bioRad provides functionality to access low‐level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics. We will also describe how the package introduces weather radar equivalents for familiar measures used in the field of migration ecology, facilitates the use and spread of interoperable data standards, and integrates with a research infrastructure for weather radar aeroecology in Europe and the United States.

Transcript

1. bioRad biological analysis and visualization of weather radar data IRAC 2019, 22 Sep 2019, Zhengzhou Peter Desmet
2. Hi!
3. ENRAM
4. Bird migration on weather radars
5. Bird migration on weather radars
6. Bird migration on weather radars •  Reflectivity factor: how many animals?
7. Bird migration on weather radars •  Reflectivity factor: how many animals? •  Radial velocity: where are they going?
8. How to •  Read and inspect weather radar data •  Extract biological information: vertical profiling •  Analyze and integrate vertical profile data •  Standardize measures to compare studies
9. bioRad
10. bioRad •  R package for analysis and visualization of biological signals in weather radar data •  Open source •  Available on CRAN and GitHub adokter.github.io/bioRad
11. Dokter et al. 2019 Ecography
12. Authors Adriaan Dokter* Peter Desmet* Jurriaan Spaaks Stijn Van Hoey Lourens Veen Liesbeth Verlinden Cecilia Nilsson* Günther Haase Hidde Leijnse Andrew Farnsworth* Willem Bouten Judy Shamoun-Baranes*
13. Functionality
14. Installation # install bioRad from CRAN install.packages("bioRad") # load bioRad library("bioRad")
15. pvol Read polar volume data pvol <- read_pvolfile("KBRO20170514_055831_pvol.h5") radar file
16. pvol scan Read polar volume data pvol <- read_pvolfile("KBRO20170514_055831_pvol.h5") radar file
17. pvol scan param: VRADH radial velocity param: DBZH reflectivity factor Read polar volume data pvol <- read_pvolfile("KBRO20170514_055831_pvol.h5") radar file
18. pvol scan param: VRADH radial velocity param: DBZH reflectivity factor scan param: VRADH radial velocity param: DBZH reflectivity factor Read polar volume data pvol <- read_pvolfile("KBRO20170514_055831_pvol.h5") radar file
19. pvol scan param: VRADH radial velocity param: DBZH reflectivity factor scan param: VRADH radial velocity param: DBZH reflectivity factor Read polar volume data pvol <- read_pvolfile("KBRO20170514_055831_pvol.h5") scan param: VRADH radial velocity param: DBZH reflectivity factor radar file
20. Inspect scan scan <- get_scan(pvol, 1.5) # use get_elevation_angles(pvol) to know angles pvol scan param: VRADH radial velocity param: DBZH reflectivity factor scan param: VRADH radial velocity param: DBZH reflectivity factor scan param: VRADH radial velocity param: DBZH reflectivity factor
21. Inspect scan scan <- get_scan(pvol, 1.5) # use get_elevation_angles(pvol) to know angles pvol scan param: VRADH radial velocity param: DBZH reflectivity factor
22. Plot scan plot(scan, 
 param = "DBZH") pvol scan param: VRADH radial velocity param: DBZH reflectivity factor
23. x y Project scan ppi <- project_as_ppi(scan, range_max = 120000)
 # ppi: plan position indicator pvol scan param: VRADH radial velocity param: DBZH reflectivity factor
24. Project scan plot(ppi, 
 param = "DBZH") pvol scan param: VRADH radial velocity param: DBZH reflectivity factor
25. Project scan plot(ppi, 
 param = "VRADH") pvol scan param: VRADH radial velocity param: DBZH reflectivity factor
26. Vertical profiling calculate_vp(
 pvolfile = "KBRO20170514_055831_pvol.h5", 
 vpfile = "KBRO20170514_055831_vp.h5"
 ) # Makes use of vol2bird algorithm radar file
27. Vertical profiling calculate_vp(
 pvolfile = "KBRO20170514_055831_pvol.h5", 
 vpfile = "KBRO20170514_055831_vp.h5"
 ) # Makes use of vol2bird algorithm radar file vp: vertical profile Vertical profile (class vp) radar: KBRO source: RAD:KBRO,PLC:BROWNSVILLE,state:TX nominal time: 2017-05-14 05:58:32 generated by: vol2bird 0.3.18
28. Plot vertical profile vp <- read_vpfiles( "KBRO20170514_055831_vp.h 5") plot(vp, quantity = "dens")
29. Plot vertical profile plot(vp, quantity = "ff") # ff: ground speed
30. Time series of vertical profiles vpts <- bind_into_vpts(vp, vp, vp) # read file with: read_vpts("vpts_file.txt") # regularize with: regularize_vpts() vp: vertical profile
31. Time series of vertical profiles vpts <- bind_into_vpts(vp, vp, vp) # read file with: read_vpts("vpts_file.txt") # regularize with: regularize_vpts() vp: vertical profile vp: vertical profile vp: vertical profile vp: vertical profile
32. Time series of vertical profiles vpts <- bind_into_vpts(vp, vp, vp) # read file with: read_vpts("vpts_file.txt") # regularize with: regularize_vpts() vp: vertical profile Irregular time series of vertical 
 profiles (class vpts) radar: KBRO # profiles: 95 time range (UTC): 2017-05-14 00:09:00 – 2017-05-14 13:25:00 time step (s): min: 300 max: 1800 vpts: vertical profile time series vp: vertical profile vp: vertical profile vp: vertical profile
33. Time series of vertical profiles plot(vpts)
34. Integrated vertical profiles vpi <- integrate_profile(vpi) vpts: vertical profile time series
35. Integrated vertical profiles vpi <- integrate_profile(vpi) vpi: integrated vertical profile vpts: vertical profile time series
36. Integrated vertical profiles plot(vpts, 
 quantity = "mtr") # mtr: migration traffic rate (birds/km/hour)
37. Documentation
38. Help ?plot.vpts
39. adokter.github.io/bioRad
40. adokter.github.io/bioRad
41. bioRad software note
42. Supporting open reproducible science
43. Common measures
44. Common measures
45. Common measures RCS = 11 cm2 Dokter et al. 2011 J. R. Soc. Interface
46. Data standards •  Supported pvol formats: ODIM (Europe), NEXRAD (US), Vaisala Iris (e.g. Canada) radar file pvol: polar volume
47. Data standards •  Supported pvol formats: ODIM (Europe), NEXRAD (US), Vaisala Iris (e.g. Canada) •  Export vp format of vol2bird: ODIM bird profile radar file pvol: polar volume vp: vertical profile
48. vp: vertical profile vp: vertical profile Data standards •  Supported pvol formats: ODIM (Europe), NEXRAD (US), Vaisala Iris (e.g. Canada) •  Export vp format of vol2bird: ODIM bird profile •  Export vpts format: tabular format under discussion radar file pvol: polar volume vp: vertical profile vpts: vertical profile time series
49. Open source
50. Collaborative effort
51. Roadmap •  Range bias correction of PPIs •  Inclusion of the MistNet segmentation model (Lin et al. 2019 Methods Ecol. Evol.) •  Unit tests and continuous integration •  Submission for rOpenSci code review •  …
52. bioRad is currently used for projects in Europe, US, Canada and Colombia… … but we hope it can become the go-to tool in R for all biological studies using weather radar data.
53. Thank you! Desmet et al. (2019) Biological analysis and visualization of weather radar data. Presentation. http://bit.ly/biorad-irac2019 adokter.github.io/bioRad oscibio.inbo.be