Quantifying the Impact of UAS Observations on Mesoscale NWP Prediction in the Marine Boundary Layer

The Trident Warrior observational field campaign conducted off the U.S. Mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on-board for atmospheric and oceanic observation. Measurements collected from seven UAS flights over five days covering an area of approximately 3000 km² between the surface and 1550 m MSL. These UAS observations were assimilated into a three-dimensional variational data assimilation (DA) system (NAVDAS) used to generate analyses for a numerical weather prediction (NWP) model (COAMPS^®) with a coupled ocean model (NCOM). The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification.

Experiments reveal a substantial reduction of model error in forecast temperature and moisture profiles consistently throughout the five day campaign period due to the assimilation of UAS observations. The impact on local model error remains robust following the passage of a surface trough during the campaign, spanning a transition from a sub-tropical marine air mass to one of mixed maritime and continental influence. Model verification along individual UAS flight paths reveals that persistent patterns of model cold and moist biases in the marine atmospheric boundary layer (ABL) are systematically reduced by the UAS DA. The model error reduction is most significant in the vicinity of the inversion at the top of the model-estimated boundary layer (h_ABL). Investigations reveal a consistent improvement to prediction of the vertical position, strength and depth of the inversion, with the greatest improvement found in strength (model error reduction of 0.4 K and 0.7 g/kg in potential temperature and water vapor mixing ratio, respectively). It is estimated that UAS DA is responsible for a model error reduction of approximately 30 m in inversion base height and 35 m in inversion thickness.

The impact of UAS observations on atmospheric prediction in the marine boundary layer is explored further with experiments of systematic denial of data streams from the NAVDAS DA system. A hypothetical test of only UAS observations in the NAVDAS DA system suggests some limited viability of the method in data sparse environments like the open ocean. Tests removing individual measurement streams from the UAS dataset suggest some potential for optimization of the UAS assimilation methodology.