Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: Observing System Experiments Article Swipe
Anders A. Jensen
,
James O. Pinto
,
Sean Bailey
,
Ryan A. Sobash
,
Glen S. Romine
,
Gijs de Boer
,
Adam L. Houston
,
Suzanne Smith
,
Dale Lawrence
,
Cory Dixon
,
Julie K. Lundquist
,
Jamey Jacob
,
Jack Elston
,
Sean Waugh
,
David Brus
,
Matthias Steiner
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1175/mwr-d-22-0090.1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1175/mwr-d-22-0090.1
Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling 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 (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/mwr-d-22-0090.1
- OA Status
- green
- Cited By
- 13
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285042625
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285042625Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1175/mwr-d-22-0090.1Digital Object Identifier
- Title
-
Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: Observing System ExperimentsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-07-11Full publication date if available
- Authors
-
Anders A. Jensen, James O. Pinto, Sean Bailey, Ryan A. Sobash, Glen S. Romine, Gijs de Boer, Adam L. Houston, Suzanne Smith, Dale Lawrence, Cory Dixon, Julie K. Lundquist, Jamey Jacob, Jack Elston, Sean Waugh, David Brus, Matthias SteinerList of authors in order
- Landing page
-
https://doi.org/10.1175/mwr-d-22-0090.1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1905793Direct OA link when available
- Concepts
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Terrain, Environmental science, Lapse rate, Meteorology, Outflow, Convection, Atmospheric sciences, Climatology, Geology, Geography, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 3, 2022: 6Per-year citation counts (last 5 years)
- References (count)
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58Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.regimes | 100 |
| abstract_inverted_index.results | 90 |
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| abstract_inverted_index.surface | 45, 54, 88, 106, 233 |
| abstract_inverted_index.thermal | 121 |
| abstract_inverted_index.weather | 258 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Aircraft | 15 |
| abstract_inverted_index.Research | 30 |
| abstract_inverted_index.Uncrewed | 1 |
| abstract_inverted_index.addition | 43, 86 |
| abstract_inverted_index.aircraft | 2 |
| abstract_inverted_index.analyses | 249 |
| abstract_inverted_index.campaign | 20 |
| abstract_inverted_index.captured | 227 |
| abstract_inverted_index.compared | 48 |
| abstract_inverted_index.improved | 117, 151 |
| abstract_inverted_index.increase | 94, 191 |
| abstract_inverted_index.northern | 125 |
| abstract_inverted_index.observed | 73 |
| abstract_inverted_index.obtained | 51, 103, 237 |
| abstract_inverted_index.outflows | 184, 220 |
| abstract_inverted_index.produced | 128 |
| abstract_inverted_index.reducing | 159 |
| abstract_inverted_index.stronger | 129 |
| abstract_inverted_index.targeted | 39 |
| abstract_inverted_index.terrain. | 169 |
| abstract_inverted_index.upvalley | 130 |
| abstract_inverted_index.Mountains | 213 |
| abstract_inverted_index.Profiling | 10 |
| abstract_inverted_index.addition, | 198 |
| abstract_inverted_index.capturing | 203 |
| abstract_inverted_index.different | 256 |
| abstract_inverted_index.foothills | 181 |
| abstract_inverted_index.low-level | 192 |
| abstract_inverted_index.observing | 58 |
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| abstract_inverted_index.triggered | 215 |
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| abstract_inverted_index.importance | 200 |
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| abstract_inverted_index.simulation | 230 |
| abstract_inverted_index.subsequent | 222 |
| abstract_inverted_index.ultimately | 188 |
| abstract_inverted_index.Atmospheric | 9 |
| abstract_inverted_index.Forecasting | 32 |
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| abstract_inverted_index.convergence | 193 |
| abstract_inverted_index.environment | 157 |
| abstract_inverted_index.experiments | 60 |
| abstract_inverted_index.predictions | 252 |
| abstract_inverted_index.surrounding | 168 |
| abstract_inverted_index.(LAPSE-RATE) | 18 |
| abstract_inverted_index.Colorado’s | 75 |
| abstract_inverted_index.Observations | 236 |
| abstract_inverted_index.assimilating | 38, 53, 105 |
| abstract_inverted_index.assimilation | 81 |
| abstract_inverted_index.observations | 5, 41, 46, 55, 84, 89, 107, 116, 150, 234 |
| abstract_inverted_index.Elevation–A | 13 |
| abstract_inverted_index.configuration | 26 |
| abstract_inverted_index.observations. | 143 |
| abstract_inverted_index.pre-convective | 156 |
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| abstract_inverted_index.terrain-driven | 64, 111 |
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| abstract_inverted_index.mesogamma-scale | 257 |
| abstract_inverted_index.Remotely-Piloted | 14 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 16 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.83699102 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |