> • Doppler radar measurements: Contact: Lukas Strauss ([email protected]) University of Vienna Department of Meteorology and Geophysics http://img.univie.ac.at/en/research/tm Research partially supported by the Austrian Science Fund, FWF, through the project P24726–N27 STABLEST – Stable Boundary Layer Separation and Turbulence. References • Chen, A., G. Leptoukh, S. Kempler, C. Lynnes, A. Savtchenko, D. Nadeau, and J. Farley, 2009: Visualization of A-Train vertical profiles using Google Earth. Computers & Geosciences, 35, 419-427. doi: 10.1016/j.cageo.2008.08.006. • Erickson, T., 2011: pyKML Package. https://pythonhosted.org/pykml/ • Grubišić, V., and Coauthors, 2008: The Terrain-Induced Rotor Experiment: A field campaign overview including observational highlights. BAMS, 89, 1513-1533. • Huynh, G., Y. Wang, and C. Williamson, 2012: Visualization of Wind Data on Google Earth for the three- dimensional Wind Field (3DWF) Model. US Army Research Laboratory. Technical Report ARL-TR-6138. • Huynh, G., C. Williamson, and Y. Wang, 2013: Isosurface Display of 3-D Scalar Fields from a Meteorological Model on Google Earth. US Army Research Laboratory. ARL-TR-6509. • Mayr, G. J., and L. Armi, 2010: The influence of downstream diurnal heating on the descent of flow across the Sierras. J. Appl. Meteor. Climatol., 49, 1906–1912, doi: 10.1175/2010JAMC2516.1. Poster Session 1 P1.47 31 August 2015 Multi-instrument measurements in complex terrain • Large, three-dimensional data sets are obtained from the deployment of meteorological instruments (e.g., surface stations, scanning lidars, aircraft) in complex terrain. • Their analysis is increasingly demanding and asks for adequate tools. • This work explores the potential of Google Earth for the visualization and quantitative scientific analysis of meteorological data in complex terrain. Why Google Earth? • Google Earth maps the Earth by superimposing images from satellite imagery, aerial photography and geographic information systems onto a 3D virtual globe. • Time-dependent, geo-referenced user data can be easily visualized, using point, line and polygon objects together with image overlays. • For this work, Python with its pyKML Package (Erickson 2011) has been used to encode observational data in Keyhole Markup Language (KML) text files for display in Google Earth. Examples from the Terrain-induced Rotor Experiment (T-REX) • The objective of T-REX (Sierra Nevada, CA, 2006) was to study the interaction between mountain waves, rotors, and the boundary layer (Grubišić et al. 2008). • Comprehensive measurements by ground-based and airborne in situ and remote sensors were made in and over Owens Valley. Picture by Andreas Dörnbrack DLR Lidar DRI AWS UW King Air Sierra Nevada Owens Valley Inyo Mnts. Surface measurements • Wind data from surface weather stations are displayed as triangles coloured using pot. temperature. Aircraft measurements • Measurements of the three wind components on research aircraft are shown as cones. Vertical velocity is colour-coded (red up, blue down). (Inspired by Mayr and Armi (2010) and Huynh et al. (2012).) • Additional measurements (e.g., turbulence, θ, RH) can be saved as meta data with each 3D vector. Remote-sensing measurements • The 3D visualization of cross-sections from scanning remote sensors reveals possible influences of the surrounding 3D terrain (mountain high valleys, passes etc.) on the flow. (Inspired by Chen et al. (2009).) Composite analysis of a T-REX flow separation event • The power of Google Earth lies in the composite analysis of data from several instruments. • The separation of downslope flow from a nocturnal valley inversion (T-REX, 0800-1330 UTC 16 April 2006) is made evident from the combined surface, wind profiler, lidar and sounding data. > Numerical model output Conclusions • Preliminary cross-sections from a Weather Research and Forecasting (WRF) model simulation have been produced. The visualization can be used for a comparison of model results with observations. • Attempts to display model data using vertical wind profiles, terrain overlays, and isosurfaces exist (e.g., Huynh et al. 2013), however, their implementation is rather involved. • Specialized software applications such as NCAR’s VAPOR or IDV seem superior to Google Earth for their fast model-data processing and optimized rendering. • Google Earth offers unprecedented 3D visualization capabilities for data collected in complex terrain. • The tool reveals three-dimensional flow features that are likely to be missed otherwise. + New types of data can be easily added, using programming languages with support for KML scripting. + Data from very different sensors can be displayed and studied together. ~ Displaying data from numerical models is possible, but software applications designed just for that purpose are generally more powerful and require less programming by the user. Feel free to ask for snippets of Python/pyKML code ! DLR Lidar scans at 1100 UTC 16 April Looking up-valley DLR Lidar scans at 1100 UTC 16 April Looking towards the east Sunlight ON Sunlight OFF Cap cloud and wave cloud 9 April 2006 Lee wave and cap cloud 2 March 2006 Composite aircraft data Dual-Doppler radar measurements (Wyoming Cloud Radar) Doppler lidar observations of a transient rotor event Radar data m s -1 m s -1 Vertical velocity Cross-section-parallel wind WRF model output at 1100 UTC 16 April WRF model output at 1100 UTC 16 April 1700 LST 5 March 2006 1700 LST 5 March 2006 Sierra Nevada Owens Valley Inyo Mnts. Sierra Nevada Owens Valley Picture by Greg McCurdy Picture by Vanda Grubišić Google Maps Google Maps Google Earth map data: Google, Image Landsat, Data SIO, NOAA, U.S. Navy, NGA, GEBCO, Data LDEO-Columbia, NSF Using Google Earth for visualization of meteorological data in complex terrain Lukas Strauss 1 , Stefano Serafin 1 , and Vanda Grubišić 1,2 1 Department of Meteorology and Geophysics, University of Vienna, Vienna (Austria) 2 Earth Observing Laboratory, National Center for Atmospheric Research, Boulder (Colorado)
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• Doppler radar measurements:
Contact: Lukas Strauss ([email protected]) University of Vienna Department of Meteorology and Geophysics http://img.univie.ac.at/en/research/tm
Research partially supported by the Austrian Science Fund, FWF, through the project P24726–N27 STABLEST – Stable Boundary Layer Separation and Turbulence.
References • Chen, A., G. Leptoukh, S. Kempler, C. Lynnes, A. Savtchenko, D. Nadeau, and J. Farley, 2009: Visualization of
A-Train vertical profiles using Google Earth. Computers & Geosciences, 35, 419-427. doi: 10.1016/j.cageo.2008.08.006.
• Erickson, T., 2011: pyKML Package. https://pythonhosted.org/pykml/ • Grubišić, V., and Coauthors, 2008: The Terrain-Induced Rotor Experiment: A field campaign overview
including observational highlights. BAMS, 89, 1513-1533.
• Huynh, G., Y. Wang, and C. Williamson, 2012: Visualization of Wind Data on Google Earth for the three-dimensional Wind Field (3DWF) Model. US Army Research Laboratory. Technical Report ARL-TR-6138.
• Huynh, G., C. Williamson, and Y. Wang, 2013: Isosurface Display of 3-D Scalar Fields from a Meteorological Model on Google Earth. US Army Research Laboratory. ARL-TR-6509.
• Mayr, G. J., and L. Armi, 2010: The influence of downstream diurnal heating on the descent of flow across the Sierras. J. Appl. Meteor. Climatol., 49, 1906–1912, doi: 10.1175/2010JAMC2516.1.
Poster Session 1 P1.47
31 August 2015
Multi-instrument measurements in complex terrain
• Large, three-dimensional data sets are obtained from the deployment of meteorological instruments (e.g., surface stations, scanning lidars, aircraft) in complex terrain.
• Their analysis is increasingly demanding and asks for adequate tools. • This work explores the potential of Google Earth for the visualization and quantitative
scientific analysis of meteorological data in complex terrain.
Why Google Earth?
• Google Earth maps the Earth by superimposing images from satellite imagery, aerial photography and geographic information systems onto a 3D virtual globe.
• Time-dependent, geo-referenced user data can be easily visualized, using point, line and polygon objects together with image overlays.
• For this work, Python with its pyKML Package (Erickson 2011) has been used to encode observational data in Keyhole Markup Language (KML) text files for display in Google Earth.
Examples from the Terrain-induced Rotor Experiment (T-REX)
• The objective of T-REX (Sierra Nevada, CA, 2006) was to study the interaction between mountain waves, rotors, and the boundary layer (Grubišić et al. 2008).
• Comprehensive measurements by ground-based and airborne in situ and remote sensors were made in and over Owens Valley.
Picture by Andreas Dörnbrack
DLR Lidar DRI AWS UW King Air
Sierra Nevada
Owens Valley
Inyo Mnts.
Surface measurements
• Wind data from surface weather stations are displayed as triangles coloured using pot. temperature.
Aircraft measurements
• Measurements of the three wind components on research aircraft are shown as cones. Vertical velocity is colour-coded (red up, blue down). (Inspired by Mayr and Armi (2010) and Huynh et al. (2012).)
• Additional measurements (e.g., turbulence, θ, RH) can be saved as meta data with each 3D vector.
Remote-sensing measurements
• The 3D visualization of cross-sections from scanning remote sensors reveals possible influences of the surrounding 3D terrain (mountain high valleys, passes etc.) on the flow. (Inspired by Chen et al. (2009).)
Composite analysis of a T-REX flow separation event
• The power of Google Earth lies in the composite analysis of data from several instruments. • The separation of downslope flow from a nocturnal valley inversion (T-REX, 0800-1330 UTC 16 April
2006) is made evident from the combined surface, wind profiler, lidar and sounding data.
>
Numerical model output
Conclusions
• Preliminary cross-sections from a Weather Research and Forecasting (WRF) model simulation have been produced. The visualization can be used for a comparison of model results with observations.
• Attempts to display model data using vertical wind profiles, terrain overlays, and isosurfaces exist (e.g., Huynh et al. 2013), however, their implementation is rather involved.
• Specialized software applications such as NCAR’s VAPOR or IDV seem superior to Google Earth for their fast model-data processing and optimized rendering.
• Google Earth offers unprecedented 3D visualization capabilities for data collected in complex terrain. • The tool reveals three-dimensional flow features that are likely to be missed otherwise.
+ New types of data can be easily added, using programming languages with support for KML scripting. + Data from very different sensors can be displayed and studied together. ~ Displaying data from numerical models is possible, but software applications designed just for that purpose are generally more powerful and require less programming by the user.
Feel free to ask for snippets of
Python/pyKML code !
DLR Lidar scans at 1100 UTC 16 April Looking up-valley
DLR Lidar scans at 1100 UTC 16 April Looking towards the east
Sunlight ON Sunlight OFF
Cap cloud and wave cloud 9 April 2006
Lee wave and cap cloud 2 March 2006
Composite aircraft data
Dual-Doppler radar measurements (Wyoming Cloud Radar)
Doppler lidar observations of a transient rotor event
Radar data
m s-1 m s-1
Vertical velocity Cross-section-parallel wind
WRF model output at 1100 UTC 16 April WRF model output at 1100 UTC 16 April
1700 LST 5 March 2006 1700 LST 5 March 2006
Sierra Nevada
Owens Valley
Inyo Mnts.
Sierra Nevada
Owens Valley
Picture by Greg McCurdy
Picture by Vanda Grubišić
Google Maps
Google Maps
Google Earth map data: Google, Image Landsat, Data SIO, NOAA, U.S. Navy, NGA, GEBCO, Data LDEO-Columbia, NSF
Using Google Earth for visualization of meteorological data in complex terrain
Lukas Strauss1, Stefano Serafin1, and Vanda Grubišić1,2
1 Department of Meteorology and Geophysics, University of Vienna, Vienna (Austria) 2 Earth Observing Laboratory, National Center for Atmospheric Research, Boulder (Colorado)
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