Code Resources

A brief tutorial on how to access CLAMPS data and start visualizing them is available online, courtesy of Dr. Tyler Bell. You can find it on his Github account under CLAMPS-Demos.

For folks that are new to coding or new to Python, there are many resources online to help get started. People affiliated with the University of Oklahoma have access to LinkedIn Learning, which offers some specific options:

Several BLISS team members host their own code on their own Github accounts. Check out the People page and look for the small Github logo under a person’s name to find their Github.

As the number of Doppler lidars the BLISS team works with grows, Dr. Joshua Gebauer has been taking his own tools and compiling the tools of some other scientists and gathering them in a Lidar Toolbox which you can find on his Github.

Given CLAMPS unique combination of instruments, Dr. Smith collaborated with Dr. Jacob Carlin (CIWRO/NSSL) to create an algorithm that can estimate the boundary-layer hieight from the sensors onboard. You can find the algorithm on the NOAA OAR Observations github.

Dr. Jeremy Gibbs has developed a tool which operates on the 1D Stochastic Burgers Equation, allowing insight into turbulence without having to generalize to the fully-3D case and thus remaining very computationally cheap.

TROPoe The Tropospheric Remotely Observed Profiling via Optimal Estimation is an optical estimation based retreival method that was formerly known as AERIoe (as in the AERI instrument). BLISS team scientists Dr.

WINDoe is an optimal estimation algorithm to retrieve wind profiles from common wind profiling instrumentation such as Doppler lidar, radar wind profilers, and UAS. The advantage of WINDoe over traditional wind retrieval techniques is that information from the prior helps to constrain regions of the profile where observations have large uncertainty or the wind is under-determined from the observatins available.