Description:
The objective is to increase the spatial and temporal resolution of observations of pressure, humidity, temperature and wind direction/speed via an innovative framework of new sensors outside of normal radiosondes that pulls rawinsonde data from jets. This should expand upon the CONUS radiosonde network with data being available at multiple locations, more than twice per day and over altitudes so as to improve and vastly increase data that gets assimilated into forecast models. Project will address need for increased observations above the PBL even across oceanic regions and remote areas. The goal is to ensure reusable sensors, use of various means of communication for relaying data across vast distances
(including over oceans) to receiving stations, integration of tracking methods for use in analysis schemes and weather prediction models, and ability to measure, process and transmit turbulence information to FAA and pilots.
Project leads will work large and small corporate firms to ensure commercialization of data and address new data as a supplement to current radiosonde data. Along with dramatic increase in observations above the PBL on a spatial (horizontal and vertical) and temporal scale, the new data could replace radiosonde data from balloons with substantial cost savings. Future commercialization could be provided via a service supported by a network of servers as well as with apps that enable companies to obtain and use the new data to prevent interference from winds, humidity, icing and turbulence.
This new data will be very valuable to the airline industry as it will increase the amount of observations of wind speed, turbulence, icing, temperature and the conditions that can limit aircraft flight paths, impair shipping of products, and endanger passengers. It will also help with support to industries that depend on knowledge of the humidity and wind changes in the atmosphere that can impair optical sensors, LIDAR efficiency and ducting that can impact communication. There is also the potential for this data to greatly improve the forecasting of severe weather, winter storms and tropical storms that impact so many sectors of the economy.
