On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

Autor: Clark Amerault, Daniel A. Geiszler, David D. Flagg, Teddy Holt, Jason E. Nachamkin, Tracy Haack, James D. Doyle, Daniel P. Eleuterio, Jonathan R. Moskaitis, Daniel P. Tyndall
Rok vydání: 2018
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Zdroj: Monthly Weather Review. 146:599-622
ISSN: 1520-0493
0027-0644
DOI: 10.1175/mwr-d-17-0028.1
Popis: 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. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal 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 bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.
Databáze: OpenAIRE
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