The United States experiences over 1,000 tornado’s per year; causing an average of $5 billion in damage, 80 fatalities, and over 1,500 injuries. The most destructive and deadly tornados occur in supercells – rotating thunderstorms with a well-defined circulation called a mesocyclone. Collection and processing of in situ data pertaining to thermodynamic conditions in tornadic supercell thunderstorms is critical to enhancing severe weather forecasting and mitigating the destructive effects of these tornados.
Project Storm leverages the CU Boulder’s proven expertise and extensive capability in nomadic, integrated collection of real-time supercell data using unmanned aircraft systems (UAS) to better predict tornadogenesis and dramatically increase Tornado Warning lead-time. CU Boulder collaborates with an established network of government, academic, and industry partners to improve measurement and collection of supercell data. The CU Boulder Earth Lab works closely with IRISS to process and distribute datasets for improved tornado forecasting techniques.
Goals of Project
- Deployment to verify CONOPS for autonomous, integrated, multi-UAS collection of In Situ Tornadic Supercell data
- Collaboration with NOAA/NSSL and University partners to increase understanding of the Rear-Flank Downdraft/ Forward-Flank Downdraft (RFD/FFD)-Tornado connection in Supercells
- Data assimilation strategy with the goal to double current supercell tornado-warning lead time (15 minutes to 30 minutes) through improved warning accuracy and reduced false alarms
- Leverage the Mobile Research Collaboratory (MRC) and CU Boulder Earth Lab to enable in-field deployment of high-performance computing in support of real-time UAS path-planning for targeted storm observations, and data management
Brian Argrow, Professor, Aerospace Engineering Sciences (AES), Director, Integrated Remote & In Situ Sensing (IRISS), Research & Engineering Center for Unmanned Vehicles (RECUV), Aerospace Engineering Sciences (AES) | email@example.com | 303-492-5312
Researchers & Collaborators
Brian Argrow, Professor, Aerospace Engineering Sciences (AES), University of Colorado Boulder
Mission-derived unmanned aircraft systems design, high-speed & hypersonic aerodynamics, dense gas dynamics, rarefied gas dynamics
Eric Frew, Associate Professor, Aerospace Engineering Sciences (AES), University of Colorado Boulder
Networked heterogeneous unmanned aircraft systems, optimal distributed sensing by mobile robots, controlled mobility in ad-hoc sensor networks, miniature self-deploying systems, guidance and control of unmanned aircraft in complex atmospheric phenomena
Adam Houston, Assistant Professor, Earth and Atmospheric Sciences, University of Nebraska Lincoln
Atmospheric convection, mesoscale processes, severe weather, instrumentation, climate diagnostics
Chris Weiss, Associate Professor, Atmospheric Science, Texas Tech University
Severe storm dynamics and tornadogenesis, the initiation and sustenance of deep moist convection (particularly as related to High Plains drylines), radar meteorology
Eric Rasmussen, PhD, Meteorologist, National Severe Storms Laboratory
Mesoscale meteorology, severe convective storms, forecasting of storms, tornadogenesis
Josh Wurman, PhD, President, Center for Severe Weather Research
Tornadogenesis, tornado structure, hurricane boundary layers and surface wind damage, radar technology
Karen Kosiba, PhD, Researcher, Center for Severe Weather Research
Characterization of low-level wind structure in tornadoes, supercell storm dynamics, quantifying boundary layer winds in hurricanes
Research and Engineering Center for Unmanned Vehicles (RECUV)
RECUV is a university, government, and industry partnership dedicated to development and application of Unmanned Aircraft Systems (UAS). Facilities include the Mobile Research Collaboratory (MRC), an indoor flying-robot lab, a systems-integration lab, ground-based LIDAR capability, and a fixed-wing and rotary-wing unmanned aircraft fleet integrated with a variety of sensors. RECUV also possesses the most expansive university network of FAA Certificates of Authorization (COAs), allowing UAS operation and research over more than 100,000 square miles of space in Colorado, Oklahoma, Kansas, Texas, Nebraska, Wyoming, and Alaska.
The Tempest UAS Airframe was designed by UASUSA in collaboration with CU Boulder’s RECUV for the National Science Foundation (NSF) Tornado Research Project VORTEX-2, and was integral to the 2014 Longmont, Colorado Start-up that develops UAS technology to apply to many of society’s greatest commercial, environmental, and scientific challenges for governments, businesses, and non-governmental organizations.
Black Swift Technologies
Black Swift is a Boulder-based engineering firm, founded by CU Boulder Alumni, specializing in high capability small unmanned aircraft systems. Black Swift leverages a history of collaboration with CU-Boulder in UAS atmospheric research. With an array of in-house developed plug-and-play technologies, Black Swift provides a broad range of UAS support including Avionics and Ground Support, Customized User Interface, Airframe Selection and Integration, and Flight Management Software.
Center for Unmanned Aircraft Systems (C-UAS)
Unmanned Aircraft System and Severe Storms Research Group (USSRG)
University of Nebraska – Lincoln Earth & Atmospheric Sciences (UNL)
Texas Tech University National Wind Institute (NWI)
NOAA National Severe Storms Laboratory (NSSL)
Center for Severe Weather Research (CSWR)
University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies (CIMMS)
Severe-storm Target Observation and Robotic Monitoring (STORM)
(Frew, Argrow, Houston, Weiss, Isler, Song)
- Addresses development of Autonomous, Self-deploying, Aerial Robot Systems (SDARS) that will enable new in-situ atmospheric science applications through targeted observation.
- Primary emphasis of work is on improvement of accuracy and lead time of tornado warnings
- Leverages multiple robotic sensors and distributed computing nodes including fixed-wing unmanned aircraft, deployable Lagrangian drifters, mobile Doppler radar, mobile command and control systems, distributed computation nodes, net-centric middleware, and autonomous decision-making architecture
- Draws on team expertise in robotics, unmanned systems, networked control, wireless communications, active sensing and atmospheric science
- Combines autonomous airborne sensors with environmental models dispersed over multiple communication and computation channels to enable collection of data essential to examining and predicting weather phenomena
Energy-Aware Aerial Systems for Persistent Sampling and Surveillance
(Frew, Argrow, Houston, Weiss)
- Goal of this work is to create energy-aware, airborne, dynamic data-driven application systems for persistent sensing in complex atmospheric conditions.
- Exploits wind energy through proactive modeling and planning for autonomous soaring.
- Combines new onboard and remote real-time, wind sensing capabilities; online models for planning that use machine learning techniques for onboard data and dynamic atmospheric models that assimilate Doppler radar data; and a hierarchical guidance and control framework with algorithms that can adapt to environmental, sensing, and computational resources.
- Deployment to Lubbock, TX validated algorithms behavior and full system performance.
Verification of the Origins of Rotation in Tornados Experiment 2 (VORTEX-2)
(NOAA, NSF, and 100 scientists and researchers from around the globe)
- NSF/NOAA-funded tornado research project to explore how, when, and why tornados form
- Incorporated 100 scientists from government and universities around the world to collect weather measurements around and under supercell thunderstorms
- Teams employed 10 mobile radars and over 70 measurement instruments, all equipped with cutting-edge communication and computer technologies
- Partners roamed more than 10,000 miles across the southern and central plains in 2009-2010 and collected data on 11 supercells, including 1 tornadic supercell
- Results from VORTEX-2 data collection will continue to enhance cloud models and storm forecasting well into the future