6/30/2005 Q2 Workshop, Norman, OK

MS QPE Discussion Group Report

Thomas Adams

Eyal Amitai

Beth Clarke

Norman Donaldson

Henry Fuelberg

Robert Gergens

Steve HunterWitek Krajewski William LawrenceKevin LowBrian NelsonMichael Perryman

Stephen Pilney

Bob RabinBob SandboGreg StoryBen WeigerJian Zhang

Topics

• Main sensors for QPE

• Advantages/deficiencies of each sensor

• Outstanding issues/challenges for operational MS QPE

• New and emerging science and technology solutions for improving MS QPE

• Recommendations/action items w.r.t. MS QPE

Main Sensors for QPE

• Rain gauge– Various networks operated by different agencies

• Radar– WSR-88D

– Dual-pol

– TDWR, CASA/gap-filling radars

Main Sensors for QPE (cont.)

• Satellite

– GOES (imager and sounder)

– Polar-orbiting satellite data

• Additional data sources*– Surface observations– Sounding– NWP models

Main Sensors for QPE (cont.)

• Potential new sensors– Rain attenuations in telecommunication signals – GPS

Pros/Cons of Each Sensor: Radar• Pros:

– High spatial resolution

– High temporal resolution

– Relatively direct measure of precipitation

• Cons:– Beam blockage/areal coverage– Vertical profile of reflectivity / extrapolation to surface

precipitation

• Bright band

• Overshooting

• Sub-cloud evaporation

• Z-R and Z-S uncertainties (drop size distribution variations in the atmosphere, snow measurements)

– Non-meteorological scatters contaminating precipitation estimates/AP

Pros/Cons of Each Sensor: Gage

• Pros:– Direct measure of precipitation

• Cons:– Poor spatial resolution/non-uniform distribution

– Latency in real-time data transfer

– Problems with quality of measurements• Frozen hydrometeors

• Local wind effect

• Ground truth?

Pros/Cons of Each Sensor: Satellite

• Pros:– Good spatial coverage

– Relatively high spatial/temporal resolution (GOES imager)

• Cons:– Indirect measurements of precip

– Difficulty with non-precipitation clouds

– Poor spatial/temporal resolution (MW from polar-orbiting satellite)

New Sci/Tech Solutions for Improving MS QPE

• Scientific:

– Range correction/VPR

– Precipitation typing

– Satellite-radar algorithms (MPE, NMQ, GMSRA, etc)

– Satellite IR-MW algorithms (SCaMPR, etc)

– Satellite IR-NWP model algorithms (Hydro Estimator, etc)

– Improved data QC (REC, NMQ, WDSS-II QCNN)

• Technology:

– More data sources

– Fast access (e.g., level-II)

– More powerful computers

Outstanding Issues with Existing Operational MS QPE

• Radar:

– Overestimation in 1) hail and 2) brightband

– Underestimate for warm rain

– Inaccurate snow water equivalent estimates

– Problem in mountainous regions

• Satellite:

– Overestimate in precip areal coverage

– Underestimate for warm rain

– Inaccurate snow water equivalent estimates

• Gauge QC

– Critical for bias adjustment and QPE evaluation

– Investigate existing gage QC algorithms/concepts

(Mountain mapper, OK mesonet)

– Develop an automated gage QC for Q2

• Getting more gage data available in real-time and more frequently

– NCEP/OHD

Recommendations/Action Items w.r.t. MS QPE

• Radar– Make necessary adjustments to radar QPE (e.g., range

correction) before applying gage bias correction

– Local vs. regional radar/gage bias adjustment

• Accurate local gage adjustment can be achieved with a good

gage QC

• Satellite

– Useful in regions without radar and gage coverage

– A necessary component for MS QPE

Recommendations/Action Items w.r.t. MS QPE

Recommendations/Action Items w.r.t. MS QPE

• Improving snow estimates– SNOTEL/Snow Pillows

– SAA (Snow Accumulation Algorithm) and PAA (Precipitation

Accumulation Algorithm) by the Bureau of Reclamation

– NOHRSC (National Operational Hydrologic Remote Sensing

Center) expertise

– Rain/snow delineation

– Satellite MW?

Recommendations/Action Items w.r.t. MS QPE

• Verification/Evaluation -- very important!

– Evaluations using independent, good quality gages

• E.g., various micronets, OK mesonet?

– Verifications against strategically planned gage network (e.g., something similar to the piconet?)

– Hydro model/stream gages

– Investigate/quantify uncertainties of each sensor’s QPE

individually and collectively

– Define uncertainties of MS QPE

Recommendations/Action Items w.r.t. MS QPE

• To combine efforts/expertise from multiple agencies to

obtain best possible, cost-effective MS QPE

• To make the best possible MS QPE available to the

operational communities (e.g., RFCs) more quickly

• Continuing collaborative research and development