Impact of Assimilating Uncrewed Aircraft System Observations on River-Valley Fog Prediction

The impact of targeted Uncrewed Aircraft System (UAS) observations on the prediction of localized areas of valley fog and river-valley fog is assessed using Observing System Experiments (OSEs). Multi-rotor UAS were deployed during FOGMAP (Frequent in situ Observations Above Ground for Modeling and Advanced Prediction of fog) which took place during the summer of 2022 in Northern Kentucky. Targeted UAS missions (called intensive observations periods or IOPs) were flown to sample the spatio-temporal variability of temperature, moisture and winds in the vicinity of the Cincinnati/Northern Kentucky International Airport (CVG). A total of five IOPs were flown to target conditions prior to fog formation on nights that had an elevated likelihood of the formation of river valley fog. During each IOP, multirotor UAS were deployed to two locations in the vicinity of CVG airport to perform near-continuous profiling of the lower atmosphere throughout the night. OSEs were performed using the Ensemble Kalman Filter (EnKF) technique available within NCAR’s Data Assimilation Research Testbed (DART). A number of sensitivity experiments were performed in order to optimize the configuration of the EnKF data assimilation (DA) for the prediction of fog. One key finding was that the DA system performed better when assimilating specific humidity rather than relative humidity (RH) due to the complex covariance between RH, temperature, and moisture. Another result indicated that error variance (1 g kg^-1)² assumed to assimilate UAS specific humidity observations was too large resulting in the total error (observation error + background error) being larger than the root mean squared error of the analyses. Finally, the impact of the UAS DA was evaluated by comparing the ensemble mean analyses obtained for two sets of OSEs that were run for each IOP: one in which only conventional observations were assimilated and the other in which both conventional plus UAS observations were assimilated. The resulting analyses were evaluated using sawtooth diagnostic plots and scatter plot comparisons between the resulting ensemble mean analysis and independent observations. Finally, the impact of UAS DA on the skill of short-range ensemble predictions of fog is discussed.

Acknowledgments. This research has been conducted in response to requirements of the Federal Aviation Administration (FAA) and has been supplemented by funding provided by the National Science Foundation (NSF) to NCAR. The views expressed are those of the authors and do not necessarily represent the official policy or position of either the FAA or NSF.