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The Impact of Unmanned Aircraft System Observations on Convection Initiation Along a Boundary in Numerical Weather Prediction.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
The Impact of Unmanned Aircraft System Observations on Convection Initiation Along a Boundary in Numerical Weather Prediction./
作者:
Trask, Taylor Nicole.
面頁冊數:
1 online resource (162 pages)
附註:
Source: Masters Abstracts International, Volume: 84-12.
Contained By:
Masters Abstracts International84-12.
標題:
Aerospace engineering. -
電子資源:
click for full text (PQDT)
ISBN:
9798379691738
The Impact of Unmanned Aircraft System Observations on Convection Initiation Along a Boundary in Numerical Weather Prediction.
Trask, Taylor Nicole.
The Impact of Unmanned Aircraft System Observations on Convection Initiation Along a Boundary in Numerical Weather Prediction.
- 1 online resource (162 pages)
Source: Masters Abstracts International, Volume: 84-12.
Thesis (M.S.)--The University of North Dakota, 2023.
Includes bibliographical references
Executing meteorological research experiments that utilize Small Unmanned Aircraft Systems (sUASs) is difficult due to regulatory limitations, and knowledge regarding weather impacts is limited. To overcome these challenges, an Observing System Simulation Experiment (OSSE) is used herein as a relatively inexpensive method to evaluate how these platforms could hypothetically improve the development, progression, and characteristics of simulated meteorological phenomena in Numerical Weather Prediction (NWP).This OSSE is part of a case study of an event that occurred in southwestern Oklahoma in May 2016 to examine how sUAS observations impact Convection Initiation (CI) along a boundary in NWP. Synthetic observations of dew point, temperature, wind speed and wind direction were collected by a simulated sUAS, and were ingested into the Weather Research and Forecasting (WRF) model via WRF Data Assimilation (WRFDA). The Three-Dimensional Variational Data Assimilation (3DVAR) technique was used and four sensitivity tests were conducted. These sensitivity tests included how the type of flight pattern, sampling frequency, background error covariance length scale, and assimilated observations impacted convection initiation along a dry line. Results showed that the type of flight pattern, background error covariance length scale, and type of observations assimilated significantly impacted CI and dry line characteristics.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379691738Subjects--Topical Terms:
686400
Aerospace engineering.
Subjects--Index Terms:
Three-Dimensional Variational Data AssimilationIndex Terms--Genre/Form:
554714
Electronic books.
The Impact of Unmanned Aircraft System Observations on Convection Initiation Along a Boundary in Numerical Weather Prediction.
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Executing meteorological research experiments that utilize Small Unmanned Aircraft Systems (sUASs) is difficult due to regulatory limitations, and knowledge regarding weather impacts is limited. To overcome these challenges, an Observing System Simulation Experiment (OSSE) is used herein as a relatively inexpensive method to evaluate how these platforms could hypothetically improve the development, progression, and characteristics of simulated meteorological phenomena in Numerical Weather Prediction (NWP).This OSSE is part of a case study of an event that occurred in southwestern Oklahoma in May 2016 to examine how sUAS observations impact Convection Initiation (CI) along a boundary in NWP. Synthetic observations of dew point, temperature, wind speed and wind direction were collected by a simulated sUAS, and were ingested into the Weather Research and Forecasting (WRF) model via WRF Data Assimilation (WRFDA). The Three-Dimensional Variational Data Assimilation (3DVAR) technique was used and four sensitivity tests were conducted. These sensitivity tests included how the type of flight pattern, sampling frequency, background error covariance length scale, and assimilated observations impacted convection initiation along a dry line. Results showed that the type of flight pattern, background error covariance length scale, and type of observations assimilated significantly impacted CI and dry line characteristics.
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