NOAA Picks Black Swift sUAS for Nighttime Firefighting Efforts

The National Oceanic and Atmospheric Administration (NOAA) has selected the Black Swift Technologies (BST) small Unmanned Aircraft System (sUAS), SuperSwift™ as the sUAS Observation System (sUASOS) for wildfire measurements in support of NOAA’s FIREX field mission and their fire weather forecasting initiative.

With a legacy of delivering highly accurate sUAS that are Modular by Nature, Accurate by Design™, Black Swift Technologies continues to be the sUAS platform of choice for scientific atmospheric monitoring and discovery.

BST will deliver to NOAA a tightly integrated system consisting of an airframe, avionics, and multiple sensors capable of research-quality measurements of CO2, CO, aerosol, RH, p, and T in wildfire plumes, and multispectral high-resolution maps of wildfires.

The SuperSwift sensor suite and remote-sensing flights will provide invaluable in situ nighttime measurements of wildfire plumes and remote measurements of wildfire properties. (Image Credit: Dan Lack)
The SuperSwift sensor suite and remote-sensing flights will provide invaluable in situ nighttime measurements of wildfire plumes and remote measurements of wildfire properties. Rendering of how the SuperSwift sUAS will be used to monitor nighttime wildfires. Rendering of how the SuperSwift sUAS will be used to monitor nighttime wildfires. (Image Credit: Dan Lack)

The sUAS which will be operated by the University of Colorado’s Integrated Remote & In Situ Sensing Program (IRISS) in close collaboration with NOAA.

Brian Argrow, Chair, Ann & H.J. Smead Aerospace Engineering Sciences; Director, Integrated Remote & In Situ Sensing Program (IRISS)

“One of the purposes of IRISS is to work with the science community to develop and deploy platforms which make primarily in situ measurements,” says Brian Argrow, Chair, Ann and H.J. Smead Aerospace Engineering Sciences; Director, Integrated Remote & In Situ Sensing Program (IRISS).

“This naturally lead us to partnerships with NOAA on the science perspective, and to Black Swift Technologies for their sUAS technology and expertise.

“It’s a partnership that looks like a three-legged stool with the science interest of NOAA, the technology and engineering expertise of IRISS, and the unique sUAS platform designed by Black Swift Technologies, as the corresponding legs.”

The FireFOX sUAS is based on BST’s commercially available SuperSwift™ airframe and SwiftCore™ Flight Management System—which have been proven in the field to provide a cost-effective, powerful, and easy-to-operate—the SuperSwift™ is specifically engineered to meet the demands of high altitude flights through strong winds and damaging airborne particulates typical of nomadic scientific field campaigns in harsh environments.

(Learn More about Black Swift Technologies (BST), courtesy of the company and YouTube)

“While there are many sUAS manufacturers for agencies like NOAA to consider, most are simply not suitable for scientific atmospheric measurements,” states Jack Elston, Ph.D., CEO of Black Swift Technologies.

Jack Elston, Ph.D., CEO of Black Swift Technologies
Jack Elston, Ph.D., CEO of Black Swift Technologies

“The SuperSwift™ addresses NOAA’s requirements for endurance and operational radius (> 2 hours, and between 30 to 60km) sufficient for fire observations, its payload capability (up to 5 pounds), and its unique forward-facing payload bay, ideal for atmospheric sampling and for easy instrument package swapping.”

The ultimate goal of NightFOX is to perform nighttime in situ measurements of wildfire plumes and remote measurements of wildfire properties, and use of the measured data for fire weather forecast improvement.

Due to safety concerns, manned aircraft flights are limited to daytime operations due to dangers associated with nighttime operations.

Ground observations using a mobile laboratory provide detailed chemical information on fire plumes, but lack information on plume spatial (vertical and horizontal) distribution to put the point measurements in context.

UAS observations are the only technology capable of this task.

SuperSwift small Unmanned Aircraft SystemSuperSwift sUAS featuring its unique forward-located, spacious, interchangeable nose-cone payload bay. (Image of SuperSwift sUAS in flight)
SuperSwift sUAS featuring its unique forward-located, spacious, interchangeable nose-cone payload bay. (Image of SuperSwift sUAS in flight) 

sUAS observations can indeed provide useful information for firefighting efforts by accurately detecting fire perimeter and identifying fire hotspots, but have not attempted to make measurements relevant to studying fire emissions or incorporate observations into fire forecast models.

“Our proposed work, if successful, will significantly advance the integration of UAS-based observations of wildfires into fire-weather modeling and forecasting,” says Dr. Ru-Shan Gao, Principal Investigator, Chemical Sciences Division, Earth Systems Research Laboratory, NOAA.

“The collected data will also provide otherwise missing data for studying the impact of North American wildfires on the atmosphere and human health, and ultimately supporting better land management decisions and practices, thus contributing to NOAA OAR’s core mission “(to) advance understanding and prediction of the Earth System to enhance society’s ability to make effective decisions.”

(Learn More about FIREX – Fire Influence on Regional and Global Environments Experiment’s. Setting up the Fire Influence on Regional and Global Environments Experiment (FIREX) Fire Lab 2016 intensive, which was a coordinated effort at the Fire Sciences Lab in Missoula, Montana. Courtesy of CIRESvideos and YouTube)

Fully Integrated Solution

The existence of a sUAS capable of carrying the necessary instruments routinely through harsh environments adds an invaluable contribution to the calibration and validation of data collected from ground- and satellite-based methods.

The innovations of the SuperSwift™, including the total sensor suite, can be utilized for scientific research by federal and state public agencies and other state-funded laboratories to collect data on coherent atmospheric structures such as smog, volcano plumes, wildfire smoke, chemical fires, forest humidity, studying oil and gas field flares for calibration/validation of satellite measurements, etc.

The Ultimate Goal

“NOAA is interested in a UAS observational system (UASOS) that can use be used for fire-related measurements, and so in a sense what we want to know is when and where does the fire flow and ultimately what kind of fire and air quality will result regionally,” notes Dr. Gao.

Dr. Ru-Shan Gao, Research Physicist at NOAA

“We want to monitor the fire and incorporate the remote and in situ measurements into a fire forecast model so ultimately we’ll be able to do better fire forecasts that will help firefighters better fight the fire and keep human and property losses to a minimum.”

Black Swift Technologies LLC, develops custom small Unmanned Aircraft System (sUAS) solutions leveraging the SwiftCore™ Flight Management System (FMS) consisting of the SwiftPilot™ autopilot system, the SwiftTab™ tablet-based user interface, the SwiftStation™ ground station, and application specific sensor integrations.

The SwiftCore™ FMS is designed to be modular, robust, and simple to operate allowing users to focus on data products.

In addition to the SwiftCore™ FMS, Black Swift Technologies has unique capabilities to develop and deploy advanced small unmanned aircraft systems (UAS) due the team’s combined expertise in the design, implementation, and analysis of advanced “smart” control systems, expertise in legal and safe flight operations in the United States airspace, and their practical experience and knowledge from thousands of hours of UAS flight operations in demanding conditions.

The SwiftCore™ FMS enables advanced control systems.

These “smart” control systems provide industry leading sensor-based control of the sUAS that minimizes operator workload while improving the quality of the observed data by autonomously modifying the flight path based on sensor inputs.