Multi sensor precipitation estimation
Download
1 / 31

Multi-Sensor Precipitation Estimation - PowerPoint PPT Presentation


  • 342 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Multi-Sensor Precipitation Estimation' - palmer


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Multi sensor precipitation estimation l.jpg

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 l.jpg
In this presentation

  • An overview of multisensor precipitation estimation in NWS

    • The Multisensor Precipitation Estimator (MPE)

      • Features

      • Algorithms

      • Products

    • Ongoing improvements

    • Summary


Slide3 l.jpg

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 l.jpg
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 l.jpg

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 l.jpg
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 l.jpg
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 l.jpg

Mid-Atlantic River Forecast Center (MARFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map


Slide10 l.jpg

West Gulf River Forecast Center (WGRFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map


Slide11 l.jpg

Southeast River Forecast Center (SERFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map


Slide12 l.jpg

PRECIPITATION MOSAIC

RADAR COVERAGE MAP


Mean field bias mfb correction l.jpg
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 l.jpg

MFB and Z-R List

North-Central River Forecast Center (NCRFC)



Local bias lb correction l.jpg
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 l.jpg

Radar under-estimation (local bias > 1)

Radar over-estimation (local bias < 1)



Multi sensor analysis l.jpg
Multi-Sensor Analysis rainfall

  • 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 l.jpg

Multisensor analysis accounts for spatial variability in precipitation climatology

July PRISM climatology


Mpe products l.jpg
MPE products precipitation climatology

  • 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 l.jpg
Human Input via Graphical User Interface precipitation climatology

  • 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 l.jpg
Ongoing improvements precipitation climatology

  • 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 l.jpg

Satellite-derived estimates fill in radar data-void areas precipitation climatology

West Gulf River Forecast Center (WGRFC)


Slide28 l.jpg

From Kondragunta 2002 precipitation climatology


Slide29 l.jpg

Merging radar, rain gauge, satellite and lightning data precipitation climatology

From Kondragunta 2002


Summary l.jpg
Summary precipitation climatology

  • 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 l.jpg

Thank you! precipitation climatology

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


ad