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Instrument Siting
wind rose Generator located via wind rose
flying drone

Problem Statement

Flux sensor towers involve many pieces of equipment including observation instruments, reference instruments, data loggers, power sources, and others. For researchers, determining the optimal placement of each item requires collecting and interpreting data from many sources. This process can be technically and physically arduous.

Resolution

Dynamically-generated wind rose data visualizations

My Roles

  • UX researcher
  • UX designer
  • Feature lead
Background: A wind rose is a graphical representation of the wind patterns for a geographical location. For carbon observation, siting individual instruments as well as entire towers takes careful planning, and periodic reevaluation in the face of "disturbances" such as wildfires, crop harvesting, and species migration. Data visualization coupled with UAV imagery can reduce effort in the evaluation of potential sites. In addition, data processing and interpretation can be enhanced by reference to visualized wind data.
Process:
  • Three rounds of community interviews were conducted, in varied modalities (in person, online, via email). Each round produced refinements of needs and feasibility.
  • Interview findings informed the nature, format, JSON conversion, and hosting requirements of the windspeed data.
  • Because many wind rose users do not have sophisticated graphic capturing skills, easy image downloading as PNG or other common format was one of the criteria for the rendering tool.
  • Wind roses are now dynamically generated for all 298 AmeriFlux network towers. This has highlighted some gaps in data availability.
Data and Sketches: Below are sample data, and sketches made during interviews.
wind speed data wind rose sketches
wind roses for AmeriFlux

Resulting page

This is a sample resulting wind rose page, slightly modified for presentation here. The example shows wind roses centered on a tower site in Saskatchewan.

UAV and Satellite Data: UAV imagery[1] can greatly reduce the effort required to select sites for instruments and towers, which can be over 30 meters tall, to be able to observe the surrounding vegetative canopy. Lidar imagery and satellite data such as this MODIS image from another site also inform siting efforts.
aerial view of trees in forest Normalized Difference Vegetation Index as map
Lidar Data: Lidar data[2] can be used to generate 3D models of canopy and terrain, saving time and effort in siting decisions.
lidar data of canopy plot from lidar data
Site and tower variability: Tower sites range from the arctic such as Poker Flat in Alaska[2] to the tropics, with heights from a few meters to hundreds of meters tall such as the Sylvania Wilderness in Michigan[3]. Siting these towers and then their instruments can be challenging in even the best local weather of the year.
aerial view of trees in forest Normalized Difference Vegetation Index as map
Why this matters: Data collected is part of research that led to the identification of the source of climate change: human-sourced greenhouse gases. Based on precise field measurements from varied locations, this team concluded that radiative forcing is directly attributable to the increase of atmospheric CO₂. This confirms predictions of the greenhouse effect due to anthropogenic emissions, which are affecting the surface energy balance. See Observational Determination of Surface Radiative Forcing by CO₂ from 2000 to 2010 Nature, March 2015
Bits:
[1] Image credit: Jonathan Dandois
[2] Image credit: Gil Bohrer and Tim Morin
[3] Image credit: AmeriFlux
[4] Image credit: Jonathan Thom