1. Aaron Reynolds WFO Buffalo

2.  All NWS radars have dual polarization capability.  Dual Pol Expectations:  Ability to determine Precip type.  More info about intensity  Drop/particle size AND  Better Precipitation estimates...for RAIN  However...a NON-dual polarization equation is used for snow. Introduction

3. 0.5 degrees Freezing level Radar samples “RAIN”  dual Pol QPE. Introduction

4. 0.5 degrees Freezing level Radar samples “RAIN”  dual Pol QPE. Radar samples “SNOW”  Pre dual Pol QPE. Introduction

5. The Problem WFO CLE found: High QPE bias Primarily cool season Above freezing level Based on DP QPE only – would have led to issuance of flood warnings

6. Non-Dual Pol QPE The Problem  Before Dual Pol

7. Non-Dual Pol QPE Dual Pol QPE The Problem  Before Dual Pol  After Dual Pol  Both show overestimates, but Dual Pol is MUCH worse (higher)  What happened? 1.04 in Youngstown 1.04 in, Youngstown 1.27 in, Lyndonville 1.11 in, Chili

8.  Overestimate of QPE when the lowest radar slice samples above the melting layer (Cocks et al. 2012).  Radar classified areas above the melting layer as “dry snow’”.  Multiplied by 2.8 to derive QPE. Hypothesis

9. Station Selection  13 gauges identified  Requirements:  Knowledge of gauge type.  Track record.  Proper exposure.  Record to hundredth of an inch.  10 -100 km range. Mt. Morris, NY

10. Finding Events.  Event requirements:  Cold season months of October thru April.  Five gauges >= 0.10 for an event.

11. Data Collection  Dry snow

12. Data Collection  Dry snow  QPE

13. Data Collection  Dry snow  QPE  Gauge data.

14. Data collection  Brief periods of missing, or anomalous data were common which required case by case judgment.  Data requirements:  90% of the hour had to be “Dry snow”.

15. Quality control of data  Preliminary cases were further screened for accuracy, keeping in mind gauge limitations in certain environments.  Data quality requirements:  Wind >= 4 m/s 9 gauges w/o shield.  Heated tipping bucket issues.  Final check of data from cooperative observers and COCORAHs measurements.

16. Methodology Calculations A total of 383 hourly cases were identified, from 17 event days. To calculate the dry snow coefficient we divided the dual-pol QPE by 2.8 to get a raw radar estimate. This raw value was then compared to the actual gauge measurement, to calculate the ideal coefficient for that event.

17. Results For all of the 383 cases, the average dry snow coefficient was 1.19. This was calculated from the sum of all dual-pol QPE compared to the sum of measured precipitation.

18. Results Hourly Cases DP Radar QPE using 2.8 dry snow coefficient [inches] Legacy PPSE with dry snow coefficient removed [inches] Measured Precipitation [inches] Calculated Coefficient 38330.2910.8212.901.19 QPE from Dual pol Radar compared with measured precipitation for dry snow.

19. Results Event Precipitation TypeHourly CasesCalculated Coefficient All Rain1291.42 All Snow531.53 Mixed Events (all)2011.00 Results by precipitation type.

20. Results SiteCasesDistance (km)Ideal Coefficient (calculated) Close (<75 km)1191.0 Far (>75 km)2641.3 Results by distance from radar.

21. Preliminary Conclusions This research supports: -2.8 coefficient is too high. The mean coefficient: -[1.19] may not be the ultimate answer. Errors in the HCC: -Mixed precipitation. -All rain/snow events 1.5 would probably be most representative. How do we handle this? -Additional research from other locations.

22. Additional Research Planned Field test beginning this winter to test different coefficients. Several office will be participating.  Any other comments or questions?