National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg...
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Transcript of National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg...
![Page 1: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/1.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Kevin ScharfenbergUniversity of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies
and NOAA/National Severe Storms Laboratory, Norman, OK
Kevin ScharfenbergUniversity of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies
and NOAA/National Severe Storms Laboratory, Norman, OK
Dual-pol Radar in Operational Forecasting:an overview
Dual-pol Radar in Operational Forecasting:an overview
![Page 2: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/2.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Dual-pol Review
• Fewer flash flood warning false alarms
• Reliable hail detection
• High resolution precip typing
in winter storms
• Much cleaner data displays
• Fewer flash flood warning false alarms
• Reliable hail detection
• High resolution precip typing
in winter storms
• Much cleaner data displays
![Page 3: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/3.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Dual-pol Review
• Pre-processor algorithm
• Hydrometeor classification algorithm
• Rain accumulation algorithms– Biggest improvements in
heavy rain, near radar
• Pre-processor algorithm
• Hydrometeor classification algorithm
• Rain accumulation algorithms– Biggest improvements in
heavy rain, near radar
![Page 4: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/4.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Precip. Estimation
Just as there areempirical relationshipsbetween rainfall rate &(horizontal) reflectivity…
Just as there areempirical relationshipsbetween rainfall rate &(horizontal) reflectivity…
R(Zh)
R = (0.171 Zh)0.714
![Page 5: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/5.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Precip. Estimation
Just as there areempirical relationshipsbetween rainfall rateand reflectivity…
…there are alsoempirical relationshipsbetween rainfall rateand dual-pol variables
R(KDP)R(Zh)
R = (0.171 Zh)0.714 R = 44 |KDP|0.822 sign(KDP)
![Page 6: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/6.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Rainfall Estimation
Dual-pol rainfall estimators provide the most improvement in heavy rain (hail?)– Use R(KDP) in heavy rain
– Use R(KDP, ZDR) in moderate rain
– Use R(Zh, ZDR) in light rain
We call it the “synthetic” dual-pol QPE algorithm
Dual-pol rainfall estimators provide the most improvement in heavy rain (hail?)– Use R(KDP) in heavy rain
– Use R(KDP, ZDR) in moderate rain
– Use R(Zh, ZDR) in light rain
We call it the “synthetic” dual-pol QPE algorithm
![Page 7: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/7.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Rainfall Estimation
Dual-pol rainfall estimators provide the most improvement near the radar– Use R(synthetic) within 120 km
– Use R(KDP) from 120-200 km range
– Use R(Zh) beyond 200 km
We call it the “combined” dual-pol QPE algorithm
Dual-pol rainfall estimators provide the most improvement near the radar– Use R(synthetic) within 120 km
– Use R(KDP) from 120-200 km range
– Use R(Zh) beyond 200 km
We call it the “combined” dual-pol QPE algorithm
![Page 8: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/8.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Case Study
• 14 May 2003 – very early morning
• “Training” high-precip. supercell storms
• Flash flood guidance:– 2.6 inches in 1 hours– 3.0 inches in 3 hours– 3.8 inches in 6 hours
• 14 May 2003 – very early morning
• “Training” high-precip. supercell storms
• Flash flood guidance:– 2.6 inches in 1 hours– 3.0 inches in 3 hours– 3.8 inches in 6 hours
![Page 9: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/9.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Case Study
Potential flash flood warning?Potential flash flood warning?
![Page 10: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/10.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Case Study
Potential flash flood warning?Potential flash flood warning?
R(Zh) Z=300R1.4R(Zh) Z=300R1.4
![Page 11: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/11.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Case Study
Potential flash flood warning?
Dual-pol combined
Potential flash flood warning?Potential flash flood warning?
Dual-pol combinedDual-pol combined
![Page 12: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/12.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Case Study
Decision: No flashflood warning issued
Result: No significantflash floodingwas reported
Decision: No flashflood warning issued
Result: No significantflash floodingwas reported
![Page 13: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/13.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Conclusions
• Major improvement in this case due to hail contamination in R(Z) product
• Dual-pol also helps with:– Bright-band contamination– Attenuation and partial beam blockage– Filtering out non-precipitation echoes
• Most of the improvement is near the radar – All algorithms perform poorly beyond 200 km
• Major improvement in this case due to hail contamination in R(Z) product
• Dual-pol also helps with:– Bright-band contamination– Attenuation and partial beam blockage– Filtering out non-precipitation echoes
• Most of the improvement is near the radar – All algorithms perform poorly beyond 200 km
![Page 14: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/14.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Discussion
• Dual-pol algorithms are still in their infancy
• 100s of potential rainfall algorithms– Multiple radar 3D merger of base data– Add in more hydrometeor classification info.– Integrate with other data sources and run
precip. estimation ensembles!
• Little work so far on snow accumulation estimation!
• Dual-pol algorithms are still in their infancy
• 100s of potential rainfall algorithms– Multiple radar 3D merger of base data– Add in more hydrometeor classification info.– Integrate with other data sources and run
precip. estimation ensembles!
• Little work so far on snow accumulation estimation!
![Page 15: National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.](https://reader030.fdocuments.in/reader030/viewer/2022032702/56649cd95503460f949a30f9/html5/thumbnails/15.jpg)
National Weather Association 31st Annual Meeting18 October 2006 Cleveland, Ohio
Questions?
Questions?
Thank you for listening!Thank you for listening!