Using Spatiotemporal Relational Random Forests to Predict Convectively Induced Turbulence Also know as: U.S.R.R.F.P.C.I.T or Purscrift Dr. Amy McGovern (OU) Jon Trueblood (Dordt College) Timothy Sliwinski (Florida State Univ.)

Motivation Turbulence is a major hazard for aviation – Delays in flight – Structural damage to aircraft – Injuries to passengers – Frightening experiences – Fatalities Better understanding of turbulence allows for better avoidance of these hazards

Convectively-Induced Turbulence (CIT) Turbulence associated with a thunderstorm, yet occurs outside of clouds Current FAA guidelines for CIT: – Don’t attempt to fly under a thunderstorm – Avoid severe storms by at least 20 miles – Clear the top of known severe thunderstorms by at least 1000 feet for each 10 kt of wind speed at the cloud top – Be warned of thunderstorm tops in excess of 35,000 ft

CIT Prediction – Current prediction methods: Graphical Turbulence Guidance (GTG) – Combination of turbulence diagnostic quantities derived from 3-D forecast grids – Grid is too coarse NEXRAD Turbulence Detection Algorithm (NTDA) – Provides detection within clouds – No out of cloud CIT There is hope! SRRFs coupled with numerical weather prediction model data

Data Sources In-situ Data United Airlines flights (currently March 10, 2010 to March 31, 2010) Collects EDR (eddy dissipation rate) Co-Located Data from Weather Research and Forecasting (WRF) Model – 123 different variables – Interpolated to aircraft’s position Misc grid data – lightning, reflectivity (2D and 3D), GOES satellite (2D), EDR (3D)

Method Keep all related data – Within 40 nautical miles – Above 15,000 feet – Decide on thresholds to distinguish objects Create objects – Rain, convection, hail, lightning, vertically integrated liquid (VIL), clouds, aircraft, EDR Decide what relations you want.. Allow these to vary temporally Make the computer to do the rest!

Schema SCHEEEEEEEEEMAAAAAAAAA

How do SRRF’s work? Imagine a beautiful mountain landscape – Now imagine a bucket of data – SRRF’s take out a predetermined amount of instances with replacement 1/3 will actually not be chosen, used for verification, error estimates, and variable importance – Begin by randomly choosing 3 instances from the set Use information entropy Split instances accordingly – Continue until satisfied – Repeat with a multitude of trees to create – a forest!! – Test datasets on forests to get votes on turbulence and find out which variables are most important for turbulence <INSERT IMAGINARY BEAUTIFUL MOUNTAIN LANDSCAPE HERE>

Current Status Rewriting code from scratch – Using bits and pieces from previous code supplied by Tim Supinie

Questions?!?!

Sources and Image Credits Sources: Williams, et al. A Hybrid Machine Learning and Fuzzy Logic Approach to CIT Diagnostic Development. (Currently Unpublished) Image Credits: Aircraft Induced Turbulence (title slide): - Aircraft Turbulence Damage (Motivation slide): - damage2.jpg Turbulence and thunderstorms (CIT slide): - GTG2 Product Output (CIT Prediction slide): - Random Forests Diagram (“How do SRRF’s work?” slide): - Anonymous Hacker (Current Status slide): - Air Pocket Cartoon (Questions slide): - This material is based upon work supported by the National Science Foundation under Grant No. IIS/REU/ Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.