multi-sensor precipitation estimation presented by d.-j. seo 1 hydrologic science and modeling...
TRANSCRIPT
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Multi-Sensor Precipitation Estimation
Presented by
D.-J. Seo1
Hydrologic Science and Modeling Branch
Hydrology Laboratory
National Weather Service
Presented at the NWSRFS International Workshop, Kansas City, MO, Oct 21, 2003
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In this presentation
• An overview of multisensor precipitation estimation in NWS
– The Multisensor Precipitation Estimator (MPE)
• Features
• Algorithms
• Products
– Ongoing improvements
– Summary
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ORPG/PPS
WFO RFC, WFO
Multi-Sensor PrecipitationEstimator (MPE)
WSR-88DDHR DPA
Hydro-Estimator
Rain Gauges
Lightning
NWP modeloutput
Flash Flood Monitoringand Prediction (FFMP)
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Multi-Sensor Precipitation Estimator (MPE)
• Replaces Stage II/III• Based on;
– A decade of operational experience with NEXRAD and Stage II/III
– New science– Existing and planned data availability from
NEXRAD to AWIPS and within AWIPS– ‘Multi-scale’ accuracy requirements (WFO,
RFC, NCEP, external users)
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Stage III versus MPE
• No delineation of effective coverage of radar
• Radar-by-radar precipitation analysis
• Mosaicking without explicit considerations of radar sampling geometry
• Delineation of effective coverage of radar
• Mosaicking based on radar sampling geometry
• Precipitation analysis over the entire service area
• Improved mean-field bias correction
• Local bias correction (new)
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Delineation of Effective Coverage of Radar
• Identifies the areal extent where radar can
‘see’ precipitation consistently
• Based on multi-year climatology of the
Digital Precipitation Array (DPA) product
(hourly, 4x4km2)
• RadClim - software for data processing and
interactive delineation of effective coverage
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Radar Rainfall Climatology - KPBZ (Pittsburg, PA)
Warm season Cool season
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Mosaicking of Data from Multiple Radars
• In areas of coverage overlap, use the radar rainfall estimate from the lowest unobstructed1 and uncontaminated2 sampling volume
1 free of significant beam blockage2 free of ground clutter (including that due to anomalous propagation (AP))
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Height of Lowest Unobstructed Sampling Volume Radar Coverage Map
Mid-Atlantic River Forecast Center (MARFC)
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Height of Lowest Unobstructed Sampling Volume Radar Coverage Map
West Gulf River Forecast Center (WGRFC)
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Southeast River Forecast Center (SERFC)Height of Lowest Unobstructed Sampling Volume Radar Coverage Map
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PRECIPITATION MOSAIC RADAR COVERAGE MAP
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Mean-Field Bias (MFB) Correction
• Based on (near) real-time hourly rain gauge data
• Equivalent to adjusting the multiplicative constant in the Z-R relationship for each radar; Z = A(t) Rb
• Accounts for lack of radar hardware calibration
• Designed to work under varying conditions of rain gauge network density and posting delays in rain gauge data
• For details, see Seo et al. (1999)
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From Cedrone 2002
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MFB and Z-R List
North-Central River Forecast Center (NCRFC)
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Effect of Mean Field Bias Correction
From Seo et al. 1999
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Local Bias (LB) Correction
• Bin-by-bin (4x4km2) application of mean field bias correction
• Reduces systematic errors over smaller areas
• Equivalent to changing the multiplicative constant in the Z-R relationship at every bin in real time; Z = A(x,y,t) Rb
• More effective in gauge-rich areas
• For details, see Seo and Breidenbach (2000)
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Radar under-estimation (local bias > 1)
Radar over-estimation (local bias < 1)
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Local bias-corrected rainfall = local bias x raw radar rainfall
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Multi-Sensor Analysis
• Objective merging of rain gauge and bias-corrected radar data via optimal estimation (Seo 1996)
• Reduces small scale errors
• Accounts for spatial variability in precipitation climatology via the PRISM data (Daly 1996)
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Multi-Sensor Analysis
A
B
BIAS
1) Start with 1 hour radaraccumulations (HDP) which maycontain mean and local biases
2) Remove mean field bias
3) Merge Gage and Radar Observations
R = Bias*R
Re=w1G1 + w2G2 +w3G3+w4R
A
A
B
B
Cross Section
Cross Section
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MULTISENSOR ANALYSIS ALSO FILLS MISSING AREAS
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Multisensor analysis accounts for spatial variability in precipitation climatology
July PRISM climatology
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MPE products
• All products are hourly and on the HRAP grid (4x4km2)
• RMOSAIC - mosaic of raw radar rainfall• BMOSAIC - mosaic of mean field bias-
adjusted radar rainfall• GMOSAIC - gauge-only analysis• MMOSAIC - multi-sensor analysis of
BMOSAIC and rain gauge data• LMOSAIC - local bias-adjusted RMOSAIC
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Human Input via Graphical User Interface
• Through HMAP-MPE (a part of HydroView)• Allows interactive
– quality control of raw data, analysis, and products
– adjustment, draw-in and deletion of precipitation amounts and areas
– manual reruns (i.e. reanalysis)• For details on HMAP-MPE, see Lawrence et al.
(2003)
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Ongoing improvements• Quality-control of rain gauge data (Kondragunta
2002)– automation– multisensor-based
• local bias correction of satellite-derived precipitation estimates1 (Kondragunta et al. 2003)
• Objective integration of bias-corrected satellite-derived estimates into multisensor analysis
1 Hydro-estimator (formerly Auto-estimator) product from NESDIS (Vicente et al. 1998)
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Satellite Precip Estimate
Satellite-derived estimates fill in radar data-void areas
West Gulf River Forecast Center (WGRFC)
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After BiasCorrection
From Kondragunta 2002
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From Kondragunta 2002
Merging radar, rain gauge, satellite and lightning data
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Summary
• Multisensor estimation is essential to quantitative use of remotely sensed precipitation estimates in hydrological applications
• Built on the experience with NEXRAD and Stage II/III and new science, the Multisensor Precipitation Estimator (MPE) offers an integrated and versatile platform and a robust scientific algorithm suite for multisensor precipitation estimation using radar, rain gauge and satellite data
• Ongoing improvements includes multisensor-based quality control of rain gauge data and objective merging of satellite-derived precipitation estimates with radar and rain gauge data
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Thank you!
For more information, see http://www.nws.noaa.gov/oh/hrl/papers/papers.htm