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Multi-Sensor Precipitation 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.

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multi sensor precipitation estimation

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 [email protected]

in this presentation
In this presentation
  • An overview of multisensor precipitation estimation in NWS
    • The Multisensor Precipitation Estimator (MPE)
      • Features
      • Algorithms
      • Products
    • Ongoing improvements
    • Summary
slide3
DPA

DHR

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

multi sensor precipitation estimator mpe
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)
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)

Stage III versus MPE
delineation of effective coverage of radar
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
mosaicking of data from multiple radars
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))

slide9
Mid-Atlantic River Forecast Center (MARFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

slide10
West Gulf River Forecast Center (WGRFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

slide11
Southeast River Forecast Center (SERFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

slide12
PRECIPITATION MOSAIC

RADAR COVERAGE MAP

mean field bias mfb correction
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)
slide15
MFB and Z-R List

North-Central River Forecast Center (NCRFC)

local bias lb correction
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)
slide18
Radar under-estimation (local bias > 1)

Radar over-estimation (local bias < 1)

multi sensor analysis
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)
slide23
Multisensor analysis accounts for spatial variability in precipitation climatology

July PRISM climatology

mpe products
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
human input via graphical user interface
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)
ongoing improvements
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)

slide27
Satellite-derived estimates fill in radar data-void areas

West Gulf River Forecast Center (WGRFC)

summary
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
slide31
Thank you!

For more information, see http://www.nws.noaa.gov/oh/hrl/papers/papers.htm

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