1. 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 1

2. In this presentation
An overview of multisensor precipitation estimation in NWS
The Multisensor Precipitation Estimator (MPE)
Features
Algorithms
Products
Ongoing improvements
Summary

3. WFO RFC, WFO DHR DPA WSR-88D ORPG/PPS Hydro-Estimator Rain Gauges
Flash Flood Monitoring
and Prediction (FFMP)
Multi-Sensor Precipitation
Estimator (MPE)
Lightning
NWP
model
output
WFO
RFC, WFO

4. 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)

5. 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)

6. 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

7. Radar Rainfall Climatology - KPBZ (Pittsburg, PA)
Warm season
Cool season

8. 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 blockage
2 free of ground clutter (including that due to
anomalous propagation (AP))

9. Mid-Atlantic River Forecast Center (MARFC)
Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

10. West Gulf River Forecast Center (WGRFC)
Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

11. Southeast River Forecast Center (SERFC)
Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

12. PRECIPITATION MOSAIC
RADAR COVERAGE MAP

13. 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)

14. From Cedrone 2002

15. MFB and Z-R List
North-Central River Forecast Center (NCRFC)

16. Effect of Mean Field Bias Correction
From Seo et al. 1999

17. 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)

18. Radar under-estimation (local bias > 1)
Radar over-estimation (local bias < 1)

19. Local bias-corrected rainfall = local bias x raw radar rainfall

20. 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)

21. Multi-Sensor Analysis

22. MULTISENSOR ANALYSIS ALSO FILLS MISSING AREAS

23. Multisensor analysis accounts for spatial variability in precipitation climatology
July PRISM climatology

24. 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

25. 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)

26. 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)

27. Satellite-derived estimates fill in radar data-void areas
West Gulf River Forecast Center (WGRFC)

28. From Kondragunta 2002

29. Merging radar, rain gauge, satellite and lightning data
From Kondragunta 2002

30. 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

31. Thank you!
For more information, see