Ice water content from radar reflectivity factor and temperature
Quality Control of Canadian Radar Reflectivity Data
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Valliappa Lakshmanan, Jian Zhang, Carrie Langston University of Oklahoma & National Severe Storms Laboratory, Norman OK, USA Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227 Operates in real-time, on virtual volumes so that elevations are cleaned tilt-by-tilt. Excellent preprocessing filters Speckle removal Entropy check to remove radar test patterns Sun-strobe check to remove radial contamination Neural network trained to classify range gates Based on texture features computed from reflectivity, 3D volumetric features of reflectivity, velocity and spectrum width Objective and data- driven technique Post-processing based on region growing provides high (99.9%) accuracy Change to 88D Algorithm (to handle Canadian Data) Reason for change (characteristic of Canadian data) Train reflectivity-only neural network on truthed 88D data Velocity data not collected at same time as reflectivity Use tilt at physical height (3-5km) instead of next higher tilt when computing 3D features Several scans at low tilts (0.3, 0.5) subject to AP errors Yes, you can! Download the software from http://www.wdssii.org/ 88D version New version Raw data We modified the WDSS-II Quality Control Neural Network (QCNN) so that it would be able to QC Canadian radar reflectivity data What we did Why Canadian data? So that we can include Canadian data into our 4- dimensional real-time reflectivity mosaics. These mosaics are used by both severe weather algorithms and by precipitation estimation algorithms at NSSL. Why Adapt QCNN? The changes we made to QCNN and reason for change Can I try QCNN on my radar data? 88D version New version Raw data 88D version New version Raw data Quality Control of Canadian Radar Reflectivity Data XDR June 6, ‘07 XDR June 6, ‘07 WGJ Oct 1, ‘07 Please do stop me if you see me in the hallway! I’d love to address any questions or comments.
description
Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227. Quality Control of Canadian Radar Reflectivity Data. Valliappa Lakshmanan, Jian Zhang, Carrie Langston University of Oklahoma & National Severe Storms Laboratory, Norman OK, USA. What we did. - PowerPoint PPT Presentation
Transcript of Quality Control of Canadian Radar Reflectivity Data
Valliappa Lakshmanan, Jian Zhang, Carrie LangstonUniversity of Oklahoma & National Severe Storms Laboratory, Norman OK, USA
Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227
Operates in real-time, on virtual volumes so that elevations are cleaned tilt-by-tilt.
Excellent preprocessing filtersSpeckle removalEntropy check to remove radar
test patternsSun-strobe check to remove
radial contamination
Neural network trained to classify range gatesBased on texture features
computed from reflectivity, 3D volumetric features of reflectivity, velocity and spectrum width
Objective and data-driven technique
Post-processing based on region growing provides high (99.9%) accuracy
Change to 88D Algorithm(to handle Canadian Data)
Reason for change(characteristic of Canadian data)
Train reflectivity-only neural network on truthed 88D data
Velocity data not collected at same time as reflectivity
Use tilt at physical height (3-5km) instead of next higher tilt when computing 3D features
Several scans at low tilts (0.3, 0.5) subject to AP errors
Yes, you can! Download the software from http://www.wdssii.org/
88D version New versionRaw data
We modified the WDSS-II Quality Control Neural Network (QCNN) so that it would be able to QC Canadian radar reflectivity data
What we did
Why Canadian data?
So that we can include Canadian data into our 4-dimensional real-time reflectivity mosaics. These mosaics are used by both severe weather algorithms and by precipitation estimation algorithms at NSSL.
Why Adapt QCNN?
The changes we made to QCNN and reason for change
Can I try QCNN on my radar data?
88D version New versionRaw data
88D version New versionRaw data
Quality Control of Canadian Radar Reflectivity Data
XDR
June 6, ‘07
XDR
June 6, ‘07
WGJ
Oct 1, ‘07
Please do stop me if you see me in the hallway! I’d love to address any questions or comments.
