WSR-88D PRECIPITATION ESTIMATION FOR HYDROLOGIC APPLICATIONS DENNIS A. MILLER

Enhancements to PPS

• Build 10 (Nov. 1998) – Terrain Following Hybrid Scan – Graphical Hybrid Scan – Adaptable parameters appended to DPA • Open Systems RPG – Range Correction – Mean Field Bias Correction

Radar Precipitation Estimation Stage II and III Processing

WFO HDP 4 km res.

RFC Stage II Rain gages Stage II WHFS/FFMP Stage III

Stage II

• Processing for individual radars • 4 km resolution on HRAP grid • 131 x 131 array

Stage II

• Mean field bias adjustment • multisensor gage/radar merging • gage only analysis

Stage II processing

• Generally run once per hour at H+15 mins for each radar using hourly rainfall ending at H+00 min • Updated every hour to incorporate late arriving gage data by (H+1:15,H+2:15 etc)

Automated QC of HDP Data

• Removal of HRAP bin data that are consistently bad (e.g. Mountain blockage or ground clutter contamination) • Removal of bin data contaminated by anomolous propagation (AP) though use of GOES IR satellite and surface temperature data • Removal of outlier bin data (R > threshold)

Mean Field Bias Adjustment

• Attempts to account for uniform errors over the entire field such as radar calibration, improper Z-R relationship • Bias is a function of current and previous hours bias • Memory span parameter indicates how many hours to look into the past when determining the current bias

Mean Field Bias Adjustment

B k* G(u i ,k) R(u i ,k) N j K L is the estimated bias for hour K is the i-th gage rainfall at location u i at hour j is the i-th radar rainfall at location u i at hour j is the number of radar-gage pairs at hour j is the current hour is the lag in hours (memory span)

R 1

Single Optimal Estimation

HRAP GRID R 0 = “TRUE” value R 2 R 0 R 3

Problem

: Estimate R 0 (i.e. R e ) Given R 1 ,R 2 ,R 3 R 0 = f(R 1 ,R 2 ,R 3 ) =w 1 R 1 + w 2 R 2 + w 3 R 3 =  w i R i Where w i’s are unknown

Find W i’s so that

E [(R e -R O ) 2 ]  

i



0

for i = 1,2,3 solve for w i’s do for all HRAP Gridpoints

Stage II Multisensor Rainfall Field Generation B 1) Start with 1 hour radar accumulations (HDP) which may contain mean and local biases A A Cross Section A Cross Section B BIAS 2) Remove mean field bias R = Bias*R 3) Merge Gage and Radar Observations Re=w 1 G 1 + w 2 G 2 +w 3 G 3+ w 4 R B

Stage II

Stage III

• Mosaics Stage II multisensor rainfall estimates on to larger HRAP grid • Interactive Quality Control • Can be used as main input into hydrologic models through (MAPX)

Stage III Mosaic

• In areas that where more than one radar overlaps forecaster has choice: – mean value of overlapping bins – maximum value of overlapping bins • If multisensor field is not available for a given area, the gage only field is used

Stage III interactive features

• Display geographic overlays • Time Lapse • Zoom • Display and Edit Gages • Add pseudo gages • Delete AP • Re-run Stage II and re-mosaic

Important Adaptable Parameters

• Memory Span (1-1000) – controls responsiveness of bias adjustment • Indicator Cross Correlation Coefficient (0-1) – controls how good radar verses gage is at indicating where it is raining • Conditional Cross Correlation Coefficient (0-1) – controls how good radar verses gage is at indicating amount of rainfall

Case Study

• Site: ABRFC • Study impact of varying adaptable parameters • Vary ICC (0-1) • Vary CCC(0-1) • Compare with 24 hour co-op gages • Compare forecast with observed hydrograph

Raw unadjusted Radar Estimate Analysis of 24 hour co-op reports

Bias Corrected Radar

Multisensor

WATTS RADAR ONLY WITH NO BIAS ADJUSTMENT

WATTS GAGE ONLY

WATTS RADAR ONLY WITH BIAS ADJUSTMENT

WATTS MULTISENSOR ESTIMATE

RFC-WIDE Multisensor Precipitation Estimation

• Mosaic of data from lowest available height • Radar Climatology used to define blocked areas • Optimal Estimation to fill missing areas using available gages and surrounding good radar data • Satellite and Model Data to delineate clear air AP • No radar data taken from above freezing level used • PRISM data used to scale estimates in missing areas

FCX frequency of rainfall

FCX Coverage

PBZ Total Rainfall Summer Months

Summer Coverage

PBZ Total Rainfall Winter Months

Winter coverage

HEIGHT OF COVERAGE RADAR COVERAGE MAP

RADAR COVERAGE MAP PRECIPITATION MOSAIC

BIAS ADJUSTMENT

MULTISENSOR ESTIMATION FILLS MISSING AREAS

MULTISENSOR ESTIMATION FILLS MISSING AREAS

HURRICANE FLOYD RAINFALL

SUMMARY AND CONCLUSIONS P RFC-wide Multisensor Precipitation Estimation optimizes the usefulness of several different types of sensors while minimizing the impact of range degradation and beam blockage in radar data.

P RFC-wide will produce the best possible gridded precipitation estimate using multiple sensors.

P RFC-wide estimates which have been QC’ed at River Forecast Centers will be the input for a national Precipitation Estimate and will provide the ground truth for verification of gridded Quantitative Precipitation Forecasts.