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National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.

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Presentation on theme: "National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale."— Presentation transcript:

1 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies and NOAA/National Severe Storms Laboratory, Norman, OK Kevin Scharfenberg University 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

2 National Weather Association 31 st Annual Meeting 18 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

3 National Weather Association 31 st Annual Meeting 18 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

4 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Precip. Estimation Just as there are empirical relationships between rainfall rate & (horizontal) reflectivity… Just as there are empirical relationships between rainfall rate & (horizontal) reflectivity… R(Z h ) R = (0.171 Z h ) 0.714

5 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Precip. Estimation Just as there are empirical relationships between rainfall rate and reflectivity… …there are also empirical relationships between rainfall rate and dual-pol variables R(K DP ) R(Z h ) R = (0.171 Z h ) R = 44 |K DP | sign(K DP )

6 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Rainfall Estimation Dual-pol rainfall estimators provide the most improvement in heavy rain (hail?) –Use R(K DP ) in heavy rain –Use R(K DP, Z DR ) in moderate rain –Use R(Z h, Z DR ) 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(K DP ) in heavy rain –Use R(K DP, Z DR ) in moderate rain –Use R(Z h, Z DR ) in light rain We call it the “synthetic” dual-pol QPE algorithm

7 National Weather Association 31 st Annual Meeting 18 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(K DP ) from km range –Use R(Z h ) 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(K DP ) from km range –Use R(Z h ) beyond 200 km We call it the “combined” dual-pol QPE algorithm

8 National Weather Association 31 st Annual Meeting 18 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

9 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Case Study Potential flash flood warning? Potential flash flood warning?

10 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Case Study Potential flash flood warning? Potential flash flood warning? R(Z h ) Z=300R 1.4

11 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Case Study Potential flash flood warning? Dual-pol combined Potential flash flood warning? Potential flash flood warning? Dual-pol combined

12 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Case Study Decision: No flash flood warning issued Result: No significant flash flooding was reported Decision: No flash flood warning issued Result: No significant flash flooding was reported

13 National Weather Association 31 st Annual Meeting 18 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

14 National Weather Association 31 st Annual Meeting 18 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!

15 National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Questions? Questions? Thank you for listening!


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