Improving qpe for dual polarization hydrometeors classified as dry snow
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Improving QPE for Dual Polarization Hydrometeors Classified as Dry Snow. Aaron Reynolds WFO Buffalo. Introduction. All NWS radars have dual polarization capability. Dual Pol Expectations: Ability to determine Precip type. More info about intensity Drop/particle size AND

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Improving qpe for dual polarization hydrometeors classified as dry snow

Improving QPE for Dual Polarization Hydrometeors Classified as Dry Snow

Aaron Reynolds

WFO Buffalo


Introduction
Introduction as Dry Snow

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.


Introduction1
Introduction as Dry Snow

Freezing level

0.5 degrees

  • Radar samples “RAIN”

    • Dual Pol Quantitative Precipitation Estimate (QPE).


Introduction2
Introduction as Dry Snow

  • Radar samples “SNOW”

    • Pre dual Pol Quantitative Precipitation Estimate (QPE).

Freezing level

0.5 degrees

  • Radar samples “RAIN”

    • Dual Pol Quantitative Precipitation Estimate (QPE).


The problem
The Problem as Dry Snow

  • 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


The problem1

Non-Dual as Dry SnowPol QPE

The Problem

  • Before Dual Pol


The problem2

Non-Dual as Dry SnowPol QPE

The Problem

1.27 in, Lyndonville

1.04 in Youngstown

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!


Hypothesis
Hypothesis as Dry Snow

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.


Station selection
Station Selection as Dry Snow

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


Finding events
Finding Events. as Dry Snow

Event requirements:

Cold season months of October thru April.

Of the 13 gauges identified.

Five gauges >= 0.10 for an event.


Data collection
Data Collection as Dry Snow

  • Dry snow


Data collection1
Data Collection as Dry Snow

  • Dry snow

  • QPE


Data collection2
Data Collection as Dry Snow

Dry snow

QPE

Gauge data.


Data collection3
Data collection as Dry Snow

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”.


Quality control of data
Quality control of data as Dry Snow

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.


Methodology
Methodology as Dry Snow

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.


Results
Results as Dry Snow

  • 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.


Results1
Results as Dry Snow

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



Results3

Results by distance from radar. as Dry Snow

Results


Preliminary conclusions
Preliminary Conclusions as Dry Snow

  • 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
    Additional Research Planned as Dry Snow

    • 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?


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