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 dongjun.seo@noaa.gov

In this presentation

• An overview of multisensor precipitation estimation in NWS

– The Multisensor Precipitation Estimator (MPE)

• Features

• Algorithms

• Products

– Ongoing improvements

– Summary

ORPG/PPS

WFO RFC, WFO

Multi-Sensor PrecipitationEstimator (MPE)

WSR-88DDHR DPA

Hydro-Estimator

Rain Gauges

Lightning

NWP modeloutput

Flash Flood Monitoringand Prediction (FFMP)

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)

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

Radar Rainfall Climatology - KPBZ (Pittsburg, PA)

Warm season Cool season

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))

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

Mid-Atlantic River Forecast Center (MARFC)

Height of Lowest Unobstructed Sampling Volume Radar Coverage Map

West Gulf River Forecast Center (WGRFC)

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

PRECIPITATION MOSAIC RADAR COVERAGE MAP

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)

From Cedrone 2002

MFB and Z-R List

North-Central River Forecast Center (NCRFC)

Effect of Mean Field Bias Correction

From Seo et al. 1999

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)

Radar under-estimation (local bias > 1)

Radar over-estimation (local bias < 1)

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

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)

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

MULTISENSOR ANALYSIS ALSO FILLS MISSING AREAS

Multisensor analysis accounts for spatial variability in precipitation climatology

July PRISM climatology

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

• 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• 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)

Satellite Precip Estimate

Satellite-derived estimates fill in radar data-void areas

West Gulf River Forecast Center (WGRFC)

After BiasCorrection

From Kondragunta 2002

From Kondragunta 2002

Merging radar, rain gauge, satellite and lightning data

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

Thank you!

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