1. Aaron Reynolds WFO Buffalo
Improving QPE for Dual Polarization Hydrometeors Classified as Dry Snow
Aaron Reynolds
WFO Buffalo

2. Introduction 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.
1. National Weather Service WSR-88D was upgraded Between 2011 and 2013 with dual polarization (DP) capability. This polarimetric upgrade is consider by many to be the most significant enhancement made to the nation’s network of radars, with better information about precipitation type, intensity, and size.
2. Much work has gone into improving quantitative precipitation estimates (QPE) for rainfall, but dual polarization radar uses a modified (pre-dual polarization) radar rain relation for snow.
3. This study will focuses on the Buffalo radar and improving the current legacy Precipitation Processing System Equation (PPSE) correction factor used for the DP radar.

3. Introduction Freezing level 0.5 degrees Radar samples “RAIN”
Dual Pol Quantitative Precipitation Estimate (QPE).

4. Introduction Radar samples “SNOW” Freezing level 0.5 degrees
Pre dual Pol Quantitative Precipitation Estimate (QPE).
Freezing level
0.5 degrees
Radar samples “RAIN”
Dual Pol Quantitative Precipitation Estimate (QPE).

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
1. However, following the DP installation, forecasters at Buffalo (BUF) and Cleveland (CLE) WFO noticed a high bias in DP Quantitative Precipitation Estimates (QPE) for several cold season events. It was this high bias in DP QPE that nearly led forecasters at WFO CLE to issue flood warnings.

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

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

8. Hypothesis Difference of Dual Pol QPE – Legacy QPE
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.
1. An initial assessment of QPE also found that the polarimetric radar may overestimate QPE when the lowest radar slice samples above the melting layer (cocks et al. 2012).
2. When the DP radar algorithm classifies an eco as dry snow, it multiplies the legacy PPSE by 2.8 to derive the QPE.

9. Station Selection Mt. Morris, NY 13 gauges identified Requirements:
Knowledge of gauge type.
Track record.
Proper exposure.
Record to hundredth of an inch.
km range.
We realized early on that finding reliable hourly gauge data is one of the biggest challenges. In an effort to find reliable precipitation data, the gauge network was carefully assessed.
2. First, we looked for reliable hourly precipitation gauges within the 10 km to 100 km range of the Buffalo radar.
3. Second, gauges had to provide hourly data for all precipitation types and record precision to a hundredth of an inch.
4. Utilizing knowledge of gauge type, exposure, and track record we identified 13 gauges.
Mt. Morris, NY

10. Finding Events. Event requirements:
Cold season months of October thru April.
Of the 13 gauges identified.
Five gauges >= for an event.
1. Events were selected from cold season months between October through April.
2. Potential events were considered if at least 5 gauges received greater than .10 inches of precipitation in a day.

11. Data Collection Dry snow
1. From the subset of events, precipitation at each gauge location was checked to see if the radar classified the return as “dry snow” using the dual polarization Hybrid Hydrometeor Classification (HHC) for one continuous hour.
2. This website provided HHC, DP, QPE and gauge precipitation data for this study.

12. Data Collection Dry snow QPE
1. From the subset of events, precipitation at each gauge location was checked to see if the radar classified the return as “dry snow” using the dual polarization Hybrid Hydrometeor Classification (HHC) for one continuous hour.
2. This website provided HHC, DP, QPE and gauge precipitation data for this study.

13. Data Collection Dry snow QPE Gauge data.
1. From the subset of events, precipitation at each gauge location was checked to see if the radar classified the return as “dry snow” using the dual polarization Hybrid Hydrometeor Classification (HHC) for one continuous hour.
2. This website provided HHC, DP, QPE and gauge precipitation data for this study.

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”.
1. Brief periods of missing, undetermined, or anomalous data were common, requiring case by case judgment.
2. In general, 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.
Other factors were also considered based on the data collected. Windy surface conditions can cause unshielded precipitation gauges to significantly underestimate precipitation. In order to mitigate this impact, events with winds in excess of 4 mps were eliminated at the 9 gauges without wind shields.
Blowing snow can cause a gauge to incorrectly report precipitation at the gauge. Heated tipping bucket gauges can sometimes get clogged or not melt heavy snow fast enough to measure. These issue are usually readily evident in the raw data, and were eliminated from the data. A final check was done against questionable gauge data from nearby cooperative observers or 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
QPE from Dual pol Radar compared with measured precipitation for dry snow.
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
383
30.29
10.82
12.90
1.19
1. Results can also be looked at many ways
a. By ground precipitation type
b. By event date
C. By station

19. Results Results by precipitation type. All Rain 129 1.42 All Snow 53
Event Precipitation Type
Hourly Cases
Calculated Coefficient
All Rain
129
1.42
All Snow 53
1.53
Mixed Events (all)
201
1.00

20. Results Results by distance from radar. Close (<75 km) 119 1.0
Site
Cases
Distance (km)
Ideal Coefficient (calculated)
Close (<75 km)
119
1.0
Far (>75 km)
264
1.3

21. Preliminary Conclusions
Buffalo research supports:
-2.8 coefficient is too high.
Errors in the HCC:
-Mixed precipitation.
-All rain/snow events 1.4 would probably be more representative.
How do we handle this?
-Additional research from other locations. (Cleveland, New York, Burlington, State College, Albany and Blacksburg).
Results support Buffalo WFO initial finding!
-Cleveland 1.6
-State College 1.2
-Blacksburg 1.4
-Albany 1.9
-New York 1.5

22. Additional Research Planned
Field testing of RPG build-14 with the new coefficient began this winter at selected offices. Results expected later this year.
Any other comments or questions?