radar-derived rainfall estimation
DESCRIPTION
Radar-Derived Rainfall Estimation. Presented by D.-J. Seo 1 Hydrologic Science and Modeling Branch Hydrology Laboratory National Weather Service Presented at the NWSRFS International Workshop, Kansas City, MO, Oct 21, 2003 1 [email protected]. In this presentation. - PowerPoint PPT PresentationTRANSCRIPT
Radar-Derived Rainfall Estimation
Presented byD.-J. Seo1
Hydrologic Science and Modeling BranchHydrology Laboratory
National Weather ServicePresented at the NWSRFS International
Workshop, Kansas City, MO, Oct 21, 20031 [email protected]
In this presentation
• Introduction to weather radar
• Principles of radar rainfall estimation
• Major sources of error
• Radar-based Quantitative Precipitation Estimation (QPE) in NWS
• Ongoing improvements
• Summary
Weather radar
From Wood and Brown (2001)
Range=230 km
Reflectivity field (1x1km)
Reflectivity field
Doppler velocity field
Oklahoma City, May 3, 1999
Radar Rainfall Estimation
Z drop size6
R drop size3 fall velocity
Z = A Rb
whereZ is the reflectivity factor (mm6/m3)R is the rain rate (mm/hr)
R = A-1/b Z1/b
Major Sources of Error
• Hardware– lack of calibration– clutter– attenuation
• Microphysics– variability in raindrop size– variability in phase of hydrometeor
• Sampling Geometry– beam blockage– vertical profile of reflectivity
From Kelsh 1999
From Kelsh 1999
From Kelsh 1999
From Kelsh 1999
Bright Band
From Kelsh 1999
Radar QPE in NWS
Weather Surveillance Radar - 1988 Doppler version (WSR-88D)
WSR-88D Precipitation Processing Subsystem (PPS)
• Preprocessing– Constructs the reflectivity field from the lowest unobstructed and
uncontaminated measurements from multiple elevation angles– Removes clutter (including that from anomalous propagation
(AP)) and outliers• Rate
– Converts reflectivity to rain rate– Five Z-R relationships– Quality control checks– Capped for hail mitigation
PPS (cont.)
• Accumulation– Accumulates rainfall
• Adjustment– Applies mean field bias (based on real-time rain
gauge data)• Products
– Graphical– Digital
• For further details on PPS, see Fulton et al. (1998)
PPS Products
– Graphical (1x2 km)• 1-H Precipitation (OHP) -
every volume scan• 3-H Precipitation (THP) -
every hour• Storm Total Precipitation
(STP) - every volume scan• User Selectable storm-
total Precipitation (USP) - between 2~30 hrs, every hour
– Digital• Digital Precipitation Array
(DPA) - hourly, 4x4km2, every volume scan
• Digital Hybrid-Scan Reflectivity (DHR) - 1x1km, every volume scan
• Digital Storm-Total Precipitation (DSP) - 2x2km2, every volume scan
From Kessinger et al. 2000
Ground Clutter Removal
Radar Echo Classifier (REC)
Sep 16, 1999: Storm Total Radar-derived Accumulation from KRAX (Raleigh, NC)
From Kelsh 1999
Sep 16, 1999: Storm Total Radar-derived Accumulation from KAKQ (Wakefield, VA)
From Kelsh 1999
Accounting for Beam Blockage
Digital Terrain Model used for KFTG (Denver, CO)
Elevation angle selection map used for KFTG (Denver, CO)
From Kelsh 1999
Accounting for vertical profile of reflectivity (VPR)
Range-dependent bias Correction Algorithm (RCA)
Slant Range vs Adjustment Factor (Tilts 1 thru 3)
Vertical Profiles of Reflectivity
From Seo et al. 1999
Storm Total Rainfall - KATX (Seattle, WA), Unadjusted
From Seo et al. 1999
Storm Total Rainfall - KATX (Seattle, WA), Adjusted
From Seo et al. 1999
Before Adjustment After Adjustment
Radar-Gauge Comparison
From Vignal et al. (2000)
Accounting for VPR variability
Convective-Stratiform Separation Algorithm (CSSA)
Convective-stratiform separation
From Seo et al. (2003)
SAA snow depth estimated from Reno NV (KRGX) radar data for a heavy 12 hour Sierra Nevada snow event ending ~07UTC, 4 December 1998.
Snow Accumulation Algorithm (SAA) Developed for the ROC by the US Bureau of Reclamation, the SAA uses logic similar to the PPS to estimate snow depth and snow water equivalent.
From O’Bannon and Ding (2003)
Summary
• Radar is a critical part of the hydrologic and hydrometeorological observing and prediction system in NWS
• Radar rainfall estimates are subject to a number of significant sources of error
• Thorough understanding and systematic and explicit correction of the errors are essential to operational success
• With ongoing improvements and dual polarization in the near future, radar is expected to play an even more important role in operational hydrology and hydrometeorology
For more information, see http://www.nws.noaa.gov/oh/hrl/papers/papers.htm
Thank you!