The use of radar data to improve rainfall estimation across the Tennessee River Valley Transitioning from the rain gauge Patrick N. Gatlin, W. Petersen, L. Carey Earth Systems Science Center/ University of Alabama in Huntsville, Huntsville, Alabama S. Jacks, M. McGee and R. Myers Tennessee Valley Authority, Knoxville, Tennessee

Motivation Reduction of TVA gauge network Reduction of TVA gauge network Radar rainfall estimation using ARMOR dual- polarimetric radar Radar rainfall estimation using ARMOR dual- polarimetric radar Use of UAH infrastructure to “tune” current NEXRAD radars in Tennessee Valley Use of UAH infrastructure to “tune” current NEXRAD radars in Tennessee Valley Prepare dual-pol rainfall algorithms for deployment with NEXRAD upgrade Prepare dual-pol rainfall algorithms for deployment with NEXRAD upgrade Support NASA Global Precipitation Measurement Mission Support NASA Global Precipitation Measurement Mission

Advanced Radar for Meteorological and Operational Research Jointly owned by UAH and WHNT Jointly owned by UAH and WHNT Location: HSV, Huntsville, AL Location: HSV, Huntsville, AL C-band Doppler C-band Doppler SIGMET RVP8 and RCP8 SIGMET RVP8 and RCP8 Dual-polarization Dual-polarization Transmits simultaneous H and V Transmits simultaneous H and V Recieves H and V Recieves H and V Variables obtained: P, Z, V, W, Variables obtained: P, Z, V, W,  ZDR = 10 log (Z h / Z v ),  ρ hv = correlation between Z h & Z v,  Φ DP = Φ h – Φ v  K DP T. Schuur Conventional Doppler Radar Dual-Polarimetric Doppler Radar Variables Z h, V, W Additional variables ZDR, Φ DP, ρ hv, K DP

Rainfall Mapping with ARMOR H, V return power tells us about drop shape H, V return power tells us about drop shape Larger rain drops tend to be oblate spheroids Larger rain drops tend to be oblate spheroids Smaller drops spherical Smaller drops spherical Can delineate regions of hail from rain and stratiform vs. convective Can delineate regions of hail from rain and stratiform vs. convective Specific differential attenuation (K DP ) is good estimator of rainfall Specific differential attenuation (K DP ) is good estimator of rainfall  Improved rainfall algorithms adapted from Beard and Chuang (1987)

ICE PRESENT? NO YES K DP  0.3 and Z H  35? R = R(K DP ) YE S NONO Z H  BAD? YE S R = R(Z H RAIN ) R=BAD NONO K DP  0.3 , Z H  35.0 dBZ Z DR  0.5 dB? YE S R > 50 mm/hr, dBZ > 50,or Z, ZDR corr. too large ? ZH > 30 dBZ, Z DR  0.5 dB? R = R(Z H,Z DR ) R = R(Z H ) ARMOR RAIN RATE ALGORITHM (1) R(K DP,Z DR ) (2) R(K DP ) (3) R(Z H,Z DR ) R = R(Z H ) GOOD DATA? YES NO R=BAD KDP ≥ 0.5? KDP< 0.5? YE S R = R(K DP ) YE S R =R(K DP,Z DR ) YE S R =R(Z H,Z DR ) no NONO YE S NONO UAH Rainfall algorithm Proprietary information, Walter A. Petersen, University of Alabama Huntsville 1-hr Accumulation 6-hr (N-hr) Accumulation

NEXRAD radar network dual-polarimetric upgrade scheduled for : 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 network Gauges are sole rainfall input into streamflow model

Replacement of gauges with radar Radar and gauges used as separate rainfall inputs into streamflow model

Rainfall Products Development 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 comparisonRadar-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 Approach

How do we get improved precipitation estimates: UAH Infrastructure

Quantitative Comparison: Calibration Bias Corrected Pre-Cal correctionRecent 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 Verification Rain gauge onlyRadar only Forecast using Radar input more closely matches observed streamflow Observed (red) Forecast Observed (red) Forecast

Future Work Create hourly basin rainfall maps for Tennessee River Valley from NEXRAD Create hourly basin rainfall maps for Tennessee River Valley from NEXRAD Optimize radar rainfall estimation using UAH Infrastructure (ARMOR, MAX, MIPS, etc.) Optimize radar rainfall estimation using UAH Infrastructure (ARMOR, MAX, MIPS, etc.) Replacement of rain gauge with radar rainfall estimates as input into TVA streamflow model Replacement of rain gauge with radar rainfall estimates as input into TVA streamflow model Contact Info Patrick Gatlin Earth Systems Science Center/ UAH phone: (256)