The use of radar data to The use of radar data to improve rainfall improve rainfall

estimation across the estimation across the Tennessee River Valley Tennessee River Valley

Transitioning from the rain Transitioning from the rain gaugegauge

Patrick N. Gatlin, W. Petersen, L. Patrick N. Gatlin, W. Petersen, L. CareyCarey

Earth Systems Science Center/ Earth Systems Science Center/ University of Alabama in Huntsville, University of Alabama in Huntsville,

Huntsville, AlabamaHuntsville, Alabama

S. Jacks, M. McGee and R. MyersS. Jacks, M. McGee and R. MyersTennessee Valley Authority, Knoxville, Tennessee Valley Authority, Knoxville,

TennesseeTennessee

MotivationMotivation

Reduction of TVA gauge networkReduction of TVA gauge network Radar rainfall estimation using ARMOR Radar rainfall estimation using ARMOR

dual-polarimetric radardual-polarimetric radar Use of UAH infrastructure to “tune” Use of UAH infrastructure to “tune”

current NEXRAD radars in Tennessee current NEXRAD radars in Tennessee ValleyValley

Prepare dual-pol rainfall algorithms for Prepare dual-pol rainfall algorithms for deployment with NEXRAD upgradedeployment with NEXRAD upgrade

Support NASA Global Precipitation Support NASA Global Precipitation Measurement MissionMeasurement Mission

AAdvanced dvanced RRadar for adar for MMeteorological eteorological and and OOperational perational RResearchesearch

Jointly owned by UAH and Jointly owned by UAH and WHNTWHNT

Location: HSV, Huntsville, ALLocation: HSV, Huntsville, AL C-band DopplerC-band Doppler SIGMET RVP8 and RCP8SIGMET RVP8 and RCP8 Dual-polarizationDual-polarization

Transmits simultaneous H and V Transmits simultaneous H and V Recieves H and VRecieves H and V

Variables obtained: P, Z, V, W, Variables obtained: P, Z, V, W, ZDR = 10 log (ZZDR = 10 log (Zhh / Z / Zvv),),

ρρhvhv = correlation between = correlation between ZZhh & Z & Zvv ,,

ΦΦDPDP = = ΦΦh h – – ΦΦvv K KDPDP

T. Schuur

T. Schuur

Conventional Doppler Radar

Dual-Polarimetric Doppler Radar

VariablesVariablesZZhh, V, W, V, W

Additional variablesAdditional variablesZDR, ZDR, ΦΦDPDP, , ρρhvhv, K, KDPDP

Rainfall Mapping with Rainfall Mapping with ARMORARMOR

H, V return power tells H, V return power tells us about drop shapeus about drop shape Larger rain drops tend to Larger rain drops tend to

be oblate spheroidsbe oblate spheroids Smaller drops sphericalSmaller drops spherical

Can delineate regions of Can delineate regions of hail from rain and hail from rain and stratiform vs. convectivestratiform vs. convective

Specific differential Specific differential attenuation (Kattenuation (KDPDP) is good ) is good estimator of rainfallestimator of rainfall

Improved rainfall Improved rainfall algorithmsalgorithms

adapted from Beard and Chuang (1987)

ICE PRESENT?

NO

YES

KDP 0.3 and ZH 35? R = R(KDP)YES

NO

ZH BAD? YE

S

R = R(ZHRAIN)

R=BADNO

KDP 0.3, ZH 35.0 dBZZDR 0.5 dB?

YES

R > 50 mm/hr, dBZ > 50 ,or Z, ZDR corr. too large ?

ZH > 30 dBZ, ZDR 0.5 dB?

R = R(ZH,ZDR)

R = R(ZH)

ARMORRAIN RATE

ALGORITHM

(1) R(KDP,ZDR)(2) R(KDP)(3) R(ZH,ZDR)

R = R(ZH)GOOD DATA? YES

NO

R=BAD

KDP ≥ 0.5?

KDP< 0.5?

YES

R = R(KDP)

YES

R =R(KDP,ZDR)

YES

R =R(ZH,ZDR)

no

no

NO

YES

NO

UAH Rainfall algorithm

Proprietary information, Walter A. Petersen, University of Alabama Huntsville

1-hrAccumulation

6-hr (N-hr)Accumulation

24 Hour Rain Totals July 6, 2007

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Rain Gauge (inches)

Rad

ar (

inch

es)

• NEXRAD radar network dual-polarimetric upgrade scheduled for 2009-2011: improved precipitation estimation a primary driver.

• Can rain estimates using the new radar technology (i.e., dual-polarimetric) replace a significant % of the TVA rain gauge network?

• Demonstration project with UAH ARMOR radar in advance of NEXRAD dual-pol upgrade

• ARMOR rain rate estimator, NO gauge input

• 1-24 hour rain estimates over basin scales

• Real time data and web-products

• Facilitate/reintroduce radar precipitation estimation tailored to TVA needs

• Future customer specific extensions (e.g., National Weather Service products, site specific terrain corrections etc.

E.g., Summer season precipitation event

Radar rainfall estimates compare favorably to individual rain gauge totals……………

BUT much of the heaviest precipitation missed the rain gauges altogether (this is typical)!

Non-uniform nature of the rain field presents problems for rain gauges- but not for radars!

Moving away from “point” measurements: Radar Applications for TVA

Walter A. Petersen, University of Alabama Huntsville

Current TVA gauge Current TVA gauge networknetwork

Gauges are sole rainfall input into streamflow model

Replacement of gauges Replacement of gauges with radarwith radar

Radar and gauges used as separate rainfall inputs into streamflow model

Rainfall Products Rainfall Products DevelopmentDevelopmenthttp://www.nsstc.uah.edu/ARMOR/webimage/6-Hour Rainfall Accumulation Algorithm and

Product development

• Centered on ARMOR radar in Huntsville

• TVA Basins and 25 km range rings indicated with white contours.

• TVA gauge locations indicated as points

• Creation of simple numeric table summarizing basin mean rainfall statistics (area mean, maximum, minimum and standard deviation of 1 km pixels in each basin).

• ASCII or netCDF Data files available on demand (can modify formats and integration times as needed)

• ASCII now distributed to TVA automatically

Individual Rain Gauge-Radar comparison Radar-TVA Basin area-means comparison

• Bias ~ 20% (and uniform- good!) Random error 30-35%

• Difference in “basin-means” methodology a likely factor

• E.g. radar samples the whole basin, rain gauges sample a point (and the network is coarse) and then the point estimates are up-scaled to create a basin mean

Quantitative Comparison of Radar and Rain Gauge ApproachQuantitative Comparison of Radar and Rain Gauge Approach

How do we get improved precipitation estimates: UAH Infrastructure

Quantitative Comparison: Calibration Bias CorrectedQuantitative Comparison: Calibration Bias Corrected

Pre-Cal correction Recent event after correction

Here the radar calibration is done using an internal consistency algorithm developed using dual-polarimetric variables. Bias reduced to < 0.1 %

Bias = 19% Bias < < 1%

Streamflow Forecast Streamflow Forecast VerificationVerification

Rain gauge only Radar only

Forecast using Radar input more closely matches observed streamflow

Observed (red)

Forecast

Observed (red)

Forecast

Future WorkFuture Work Create hourly basin rainfall maps for Create hourly basin rainfall maps for

Tennessee River Valley from Tennessee River Valley from NEXRADNEXRAD

Optimize radar rainfall estimation Optimize radar rainfall estimation using UAH Infrastructure (ARMOR, using UAH Infrastructure (ARMOR, MAX, MIPS, etc.)MAX, MIPS, etc.)

Replacement of rain gauge with Replacement of rain gauge with radar rainfall estimates as input into radar rainfall estimates as input into TVA streamflow modelTVA streamflow model

Contact Info

Patrick Gatlin

Earth Systems Science Center/ UAH

phone: (256)-961-7910

e-mail: gatlin@nsstc.uah.edu