Improving QPE for Dual Polarization Hydrometeors Classified as Dry

Snow

Aaron ReynoldsWFO Buffalo

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

0.5 degrees

Freezing level

Radar samples “RAIN” Dual Pol Quantitative Precipitation Estimate

(QPE).

Introduction

0.5 degrees

Freezing level

Radar samples “RAIN” Dual Pol Quantitative Precipitation Estimate

(QPE).

Radar samples “SNOW” Pre dual Pol Quantitative Precipitation

Estimate (QPE).

Introduction

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

Non-Dual Pol QPE

The Problem

Before Dual Pol

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

1.27 in, Lyndonville

1.11 in, Chili

1.11 in, Chili

Overestimation of QPE!

Huge overestimation of 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.

Hypothesis

Difference of Dual Pol QPE – Legacy QPE

Station Selection13 gauges identified

Requirements:Knowledge of gauge type. Track record.Proper exposure.Record to hundredth of an inch. 10 -100 km range.

Mt. Morris, NY

Finding Events.Event requirements:

Cold season months of October thru April.Of the 13 gauges identified.

Five gauges >= 0.10 for an event.

Data Collection Dry snow

Data Collection Dry snow

QPE

Data Collection Dry snow

QPE

Gauge data.

Data collectionBrief 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”.

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.

MethodologyCalculations 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.

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.

ResultsHourly 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

QPE from Dual pol Radar compared with measured precipitation for dry snow.

Results

Event Precipitation Type

Hourly Cases Calculated Coefficient

All Rain 129 1.42

All Snow 53 1.53

Mixed Events (all) 201 1.00

Results by precipitation type.

ResultsSite Cases Distance

(km)Ideal Coefficient (calculated)

Close (<75 km) 119 1.0

Far (>75 km) 264 1.3

Results by distance from radar.

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

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?