Focal Area: In this work we seek to improve surface-layer parameterizations of heat, moisture, and momentum exchange for use in numerical weather prediction (NWP) models . We will use data from a variety of sources and locations as input into a physics-guided neural network-driven spatiotemporal sequence forecasting method to develop a new approach for calculating these land-atmosphere interactions.

The scientific community is beginning to see how our environment reacts to changes on an unprecedented time and space scale with the utilization of small Unmanned Aircraft Systems or sUAS. These new observation platforms can be utilized for flood forecasting, local weather forecasting, monitor wildlife, improve hurricane forecasts and this the tip of the iceberg. This technology is a new tool that will allow the scientific community to observe the environment on time and space scales that are unprecedented. This particular talk will primarily address the future of these observing platforms as it relates to advancing the atmospheric sciences. UAS’s are rapidly becoming the new technology that can acquire low-level environment information more frequently, in support of higher-resolution model forecasts of severe thunderstorm and tornado potential, improvement in Environmental Model Prediction, provide environmental information to provide better support the spread of wildfires and smoke, as well as wildfire imagery for Incident Command and more complete/accurate storm damage surveys. One of the end goals would be to have a nationwide network of sUAS providing near-continuous observations of thermodynamic parameters, NDVI, surface sensible heat and wind speed and direction. Most of these observations are being done on a regular basis and some will be attainable in the future as technology progresses and National Airspace becomes more accessible.

Unique data from seven flights of the Coyote small unmanned aircraft system (sUAS) were collected in Hurricanes Maria (2017) and Michael (2018). Using NOAA's P-3 reconnaissance aircraft as a deployment vehicle, the sUAS collected high-frequency (>1 Hz) measurements in the turbulent boundary layer of hurricane eyewalls, including measurements of wind speed, wind direction, pressure, temperature, moisture, and sea surface temperature, which are valuable for advancing knowledge of hurricane structure and the process of hurricane intensification. This study presents an overview of the sUAS system and preliminary analyses that were enabled by these unique data. Among the most notable results are measurements of turbulence kinetic energy and momentum flux for the first time at low levels (<150 m) in a hurricane eyewall. At higher altitudes and lower wind speeds, where data were collected from previous flights of the NOAA P-3, the Coyote sUAS momentum flux values are encouragingly similar, thus demonstrating the ability of an sUAS to measure important turbulence properties in hurricane boundary layers. Analyses from a large-eddy simulation (LES) are used to place the Coyote measurements into context of the complicated high-wind eyewall region. Thermodynamic data are also used to evaluate the operational HWRF model, showing a cool, dry, and thermodynamically unstable bias near the surface. Preliminary data assimilation experiments also show how sUAS data can be used to improve analyses of storm structure. These results highlight the potential of sUAS operations in hurricanes and suggest opportunities for future work using these promising new observing platforms.

(NOAA/AOML/Hurricane Research Division), George H. Bryan, Ronald Dobosy, Jun A. Zhang, Gijs de Boer, Altug Aksoy, Joshua B. Wadler, Evan A. Kalina, Brittany A. Dahl, Kelly Ryan, Jonathan Neuhaus, Ed Dumas, Frank D. Marks, Aaron M. Farber, Terry Hock, and Xiaomin Chen. Published in BAMS online February 2020. For the full citable article see DOI:10.1175/BAMS-D-19-0169.1. Launched into the Hurricane Observations from Small Unmanned Aircraft

PreviousNext No AccessNear-Surface Asia Pacific Conference, Waikoloa, Hawaii, 7-10 July 2015Aircraft-based thermal imaging and remote sensing to assess initial conditions leading to thermal convection and thunderstorm developmentAuthors: Steve Brooks*Tilden MeyersEd DumasBruce BakerChris VogelWill PendergrassRick EckmanShuyan LiuSteve Brooks*The University of Tennessee Space InstituteSearch for more papers by this author, Tilden MeyersNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion DivisionSearch for more papers by this author, Ed DumasNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion DivisionSearch for more papers by this author, Bruce BakerNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion DivisionSearch for more papers by this author, Chris VogelNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion DivisionSearch for more papers by this author, Will PendergrassNational Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion DivisionSearch for more papers by this author, Rick EckmanNational Oceanic and Atmospheric Administration, Field Research DivisionSearch for more papers by this author, and Shuyan LiuNational Oceanic and Atmospheric Administration, Field Research DivisionSearch for more papers by this authorhttps://doi.org/10.1190/nsapc2015-041 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract During the summer months of 2014 we conducted aircraft-based thermal imaging and surface temperature measurements over an area (~25 km2) centered over the Auburn University's Tennessee Valley Research and Extension Center near Belle Mina, Alabama. The purpose of this research was to assess the surface and meteorological conditions that initiate the atmospheric convection that potentially develops thunderstorms, and increase the severity of storms advected into the local area. Further, these measurements will be compared to current weather models and used to improve the determination of initial convection conditions that favor the formation of severe storms. Keywords: imaging, remote sensing, weatherPermalink: https://doi.org/10.1190/nsapc2015-041FiguresReferencesRelatedDetails Near-Surface Asia Pacific Conference, Waikoloa, Hawaii, 7-10 July 2015ISSN (online):2159-6832Copyright: 2015 Pages: 501 publication data© 2015 Published in electronic format with permission by the Society of Exploration Geophysicists, Australian Society of Exploration Geophysicists, Chinese Geophysical Society, Korean Society of Earth and Exploration Geophysicists, and Society of Exploration Geophysicists of JapanPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Jul 2015 CITATION INFORMATION Steve Brooks*, Tilden Meyers, Ed Dumas, Bruce Baker, Chris Vogel, Will Pendergrass, Rick Eckman, and Shuyan Liu, (2015), "Aircraft-based thermal imaging and remote sensing to assess initial conditions leading to thermal convection and thunderstorm development," SEG Global Meeting Abstracts : 156-157. https://doi.org/10.1190/nsapc2015-041 Plain-Language Summary Keywordsimagingremote sensingweatherPDF DownloadLoading ...

The utility of aircraft-based flux data is hindered in heterogeneous landscapes by the averaging length required for proper flux calculations. In regions where the scale of heterogeneity is smaller than the traditional 3-5 km averaging length, it has been problematic to relate measured flux signals to an individual land use. This paper introduces the "Flux Fragment" method (FFM) which is based on traditional eddy-covariance flux techniques but uses a conditional sampling scheme to better segregate flux signals by surface type with aircraft-based data. Flux measurements from a low-flying aircraft and from towers were obtained as part of a campaign in the 'patchy' agricultural landscape of the Midwestern United States. The fluxes from maize and soybean were computed and compared to the tower-based flux signals from both land uses.The FFM-derived fluxes of CO2 clearly discern the maize and soybean signals in all midday flights and display the same diurnal patterns as observed using the flux towers. The quantitative match between airborne and fixed measures of CO2 exchange is good, but displays significant discrepancies. The discrepancies arise, we argue, from the natural variation in flux within the same land-use class over the landscape, a variation invisible to a single tower. Spatially averaged fluxes from the FFM complement towers' temporal coverage to provide an improved basis for scaling local fluxes to regional estimates based on total areas of each land use. (C) 2007 Elsevier B.V. All rights reserved.