Unmanned aerial vehicles (UAVs) have onboard sensors that continuously record weather data during their missions. This information is extremely valuable to both the meteorological and UAV communities with numerous benefits, which include improving weather forecast products and providing intelligence to UAV planners. The value of the data set is directly related to its accuracy. This research determined the accuracy of weather data obtained from a particular UAV, the RQ-4A Global Hawk. This was accomplished by statistical analysis and comparison with upper air data and Atmospheric Slant Path Analysis Model profiles. Recommendations are provided for the use of such valuable environmental intelligence to multiple user communities.

A similar situation exists with commercial aircraft using the Aircraft Communications Addressing and Reporting System (ACARS). Data from ACARS-equipped aircraft are compiled and quality controlled by the National Weather Service Forecast Systems Laboratory, then processed and made available to numerous agencies. Personnel use the information not only to enhance their forecast products but also as a data source for numerical weather prediction models. ACARS data are more spatially and temporally available than radiosonde data, thus having a significant impact on upper-air analysis models. Forecasters also use this near-real-time data to enhance their products such as weather warnings and advisories. ACARS information is a proven asset to the weather community as well as mission planners.

Methods analogous to the implementation and quality control of ACARS data can be applied to the information obtained from UAVs since its accuracy has been demonstrated. The research shows the utility of the UAVs to create on-demand upper-air soundings for any location worldwide. The use of this data can greatly enhance forecast models and products especially in data-sparse regions.