Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment

Journal Article · Sat May 01 00:00:00 EDT 2021 · Monthly Weather Review

DOI:https://doi.org/10.1175/MWR-D-20-0359.1· OSTI ID:2475252

Jensen, Anders A. ^[1]; Pinto, James O. ^[1]; Bailey, Sean C. C. ^[2]; Sobash, Ryan A. ^[1]; de Boer, Gijs ^[3]; Houston, Adam L. ^[4]; Chilson, Phillip B. ^[5]; Bell, Tyler ^[5]; Romine, Glen ^[1]; Smith, Suzanne W. ^[2]; Lawrence, Dale A. ^[6]; Dixon, Cory ^[6]; Lundquist, Julie K. ^[7]; Jacob, Jamey D. ^[8]; Elston, Jack ^[9]; Waugh, Sean ^[10]; Steiner, Matthias ^[1]

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1. a National Center for Atmospheric Research, Boulder, Colorado
2. b University of Kentucky, Lexington, Kentucky
3. c Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
4. d University of Nebraska–Lincoln, Lincoln, Nebraska
5. e University of Oklahoma, Norman, Oklahoma
6. f University of Colorado Boulder, Boulder, Colorado
7. f University of Colorado Boulder, Boulder, Colorado, g National Renewable Energy Laboratory, Golden, Colorado
8. h Oklahoma State University, Stillwater, Oklahoma
9. i Black Swift Technologies, Boulder, Colorado
10. j National Severe Storms Laboratory, Norman, Oklahoma

Abstract

Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: NONE; SC0018985; AC36-08GO28308
• OSTI ID: 2475252
• Alternate ID(s): OSTI ID: 1810066
• Journal Information: Monthly Weather Review, Journal Name: Monthly Weather Review Journal Issue: 5 Vol. 149; ISSN 0027-0644
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English