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
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: [email protected]