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

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

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

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

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

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

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

From Kondragunta 2002

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

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