Quantitative Precipitation

Estimation by WSR-88D Radar

Dan Berkowitz

Applications Branch

Radar Operations Center

WSR-88D Radar QPE

•Where we were•Where we are now•Where we are going

Steps in Radar Rainfall Estimation

1. Reflectivity is sampled for an unblocked volume in space by scanning at selected elevation angles.

2. Ground clutter contamination is reduced or eliminated.3. An equation relating reflectivity to rainfall rate is

applied.4. Accumulations are made from each volume scan and

added to the previous amounts.5. Adjustments can be made to account for a bias –

overestimate or underestimate compared with rain gauges.

6. Products are created and distributed to users.

History of Major Changes in PPS

• 1991: First system deployment (using sectorized occultation data)

• 1998: Terrain-based occultation data and additional rainfall rate relationships

• 2004: Build 5 software with Enhanced Preprocessing (EPRE) and new (high vertical resolution) volume coverage pattern (VCP)

Objectives in Hybrid Scan Construction

Original (Sectorized) Hybrid Scan -- The objective is to utilize reflectivity

measurements from as close to 1-km altitude above radar level as possible while minimizing the likelihood of ground clutter and data loss due to terrain blockages (i.e., 1 km beam clearance).

Terrain-based Hybrid Scan -- The objective is to use reflectivity from the

lowest unblocked and “uncontaminated” elevation angle (i.e., 1 meter beam clearance).

Construction of Hybrid Scan Reflectivity

• Generic hybrid scan construction from bottom four slices and USGS DEM terrain– Rings at 11, 19, and 27 nm

• Terrain-based hybrid scan construction from bottom four slices and NIMA DTED terrain data– Elimination of most ring discontinuities

• Beam blockage algorithm (BBA) starting in 2004 using NIMA and SRTM DTED data

Terrain-based Hybrid Scan (NE cross-section at Eureka, CA)

Original Hybrid ScanOriginal Hybrid Scan

Terrain-based Hybrid ScanTerrain-based Hybrid Scan

Terrain and Blockage Map Quality• From 1991-1998 USGS (NAD 27) “native DEM” data –

Resolution was 3 arc-seconds, roughly 90 meters (1:250,000-scale), but with poor geolocation of some radar sites and with vertical accuracy inadequate for blockage determination (called “occultation data”). The algorithm for map generation was poor.

• Starting 1998 NIMA DTED-1 (NAD 83) – These were better and more complete data than USGS DEM data. An improved blockage map generation algorithm was used.

• Starting 2005 SRTM-1 DTED-2 (WGS 84 datum) – These data have a resolution of 1 arc-second, roughly 30 meters (1:24,000-scale) to be used within 50 km of each radar where it is available (i.e., not for sites above 60° N latitude)

Resolution of Digital Terrain Elevation Data

(blue boxes = 3 arc-sec; red boxes = 1 arc-sec)

Clutter Mitigation

• Clutter suppression of ground return in base data using near zero velocity (notch widths)

• Ignoring AP-contaminated lowest elevation in PPS by use of the “tilt test”

• Tilt test replaced in 2004 by Radar Echo Classifier AP Detection Algorithm (REC-APDA) on a bin-by-bin basis (Build 5 software)

• Clutter suppression of ground return in base data using Gaussian Model Adaptive Processing (GMAP) with ORDA

Ground Returns (Ground Clutter)

• NP = Normal Propagation ground returns (identified in a clutter bypass map)

• AP = Anomalous Propagation ground returns (typically causing “contamination” of RPG products and often handled by operator-selected clutter suppression regions)

(Note that at least one volume scan will pass before an operator sees AP ground returns and “rectifies” them with clutter regions. A much greater period of time is likely to pass before an operator returns to bypass map only.)

Ground Clutter Mitigation – Legacy RDA

RDA

ORPG

Clutter filtering: NP Clutter

Bypass Map and/or clutter regions (low, medium, or high velocity notch width

suppression)

Zh, V, W

(often with AP ground

returns)

ProductsI & Q

1. Radar Echo Classifier (REC) AP Detection Algorithm

2. Enhanced Precip. Preprocessing

(EPRE)

3. Hybrid Scan Refl. (HSR) used in PPS,

SAA, & RCM

Ground Clutter Mitigation – Initial ORDA

ORDA

ORPG

Clutter filtering

(GMAP): NP Clutter Map and/or

clutter regions

Zh, V, W ProductsI & Q

1. Radar Echo Classifier (REC) AP Detection Algorithm

2. Enhanced Precip. Preprocessing

(EPRE)

3. Hybrid Scan Refl. (HSR) used in PPS,

SAA, & RCM

(often with AP ground

returns)

Ground Clutter Mitigation – Proposed ORDA-based AP Detection

ORDA

ORPG

1. NP Clutter filtering (GMAP) Zh, V, W

ProductsI & Q

1. Radar Echo Classifier (REC) AP Detection Algorithm

3. Enhanced Precip. Preprocessing

(EPRE)

4. Hybrid Scan Refl. (HSR) used in PPS,

SAA, & RCM

2. REC Precip. Detection Algorithm

2. Clutter Filter

Decision Support

(for AP)

3. AP Clutter filtering (GMAP)

(Nearly all APground returnis removed, as needed, in real time.)

CFDS Methodology and Example• CFDS will automate GMAP application in AP conditions

– Fuzzy logic used to discriminate AP clutter from precipitation– GMAP applied only to gates with AP clutter

• Precipitation is not filtered and, therefore, is not biased (unless weather spectrum is (narrow and centered at 0m/s velocity)• Clutter filtering is done in real time.

• Example: KJIM squall line (assume NP clutter is AP clutter)Reflectivity – No GMAP Velocity – No GMAP

NP Clutter

Precipitation w/ velocitiesnear 0 m/s

Clutter Flag determined by CFDS

CFDS Specifies Where GMAP is Applied• Clutter flag specifies GMAP application• Near-zero precipitation return is not clutter filtered and no bias

is introduced• NP clutter is removed and underlying signal recovered

Reflectivity – CFDS turns GMAP on/off Velocity – CFDS turns GMAP on/off

What if GMAP is Applied Everywhere?• Example shown for comparison purposes only

– Shows the bias that is introduced when precipitation is clutter filtered

• CFDS will automate the clutter filter application decision and remove the human from this decision loop– Result: much improved moment estimates and data quality

Reflectivity – GMAP applied at all gates Velocity – GMAP applied at all gates

Ground Clutter Mitigation – Dual Polarization (Proposed)

ORDA ORPG

Zh, Zv, ZDR, V, W, ρhv, KDP, ΦDP Products

I & Q

1. NP Clutter filtering (GMAP)

2. CFDS includes dual pol. variables (for AP)

3. AP Clutter filtering (GMAP)

1. Hydrometeor Classification

Algorithm (HCA)

2. Enhanced Precip.

Preprocessing (EPRE v2)

3. Hybrid Scan DP moments used in

Dual Pol. PPS, SAA, & RCM

(AP ground returnis removed in realtime.)

VCP11 VCP12 – starting 2004

Near or Deep Convection Volume Coverage Patterns

PPS Adaptable Parametersin Enhanced Preprocessing (EPRE)

PPS Adaptable Parametersin Enhanced Preprocessing (EPRE)

• CLUTTHRESH (refer to CLR product)

• RAINZ (dBZ threshold)RAINA (area threshold)

• NEXZONE (number of exclusion zones)– Wind farms– Highways– Plumes

Supplementary Precipitation Data (SPD)

VOLUME COVERAGE PATTERN = 21 MODE = A TIME CONT: PASSED GAGE BIAS APPLIED - NO BIAS ESTIMATE - 0.34 EFFECTIVE # G/R PAIRS - 11.38 MEMORY SPAN (HOURS) - 5.00 DATE/TIME LAST BIAS UPDATE - 06/24/05 20:26 TOTAL NO. OF BLOCKAGE BINS REJECTED - 0 CLUTTER BINS REJECTED - 41281 FINAL BINS SMOOTHED - 0 HYBRID SCAN PERCENT BINS FILLED - 99.86 HIGHEST ELEV. USED (DEG) - 9.90

TOTAL RAIN AREA (KM**2) - 15210.94 MISSING PERIOD: 06/24/05 16:06 06/24/05 17:27

Human Computer Interface (HCI) for Build 8 RPG

Build 8 Precipitation Status Window

Converting Reflectivity to Rainfall Rate

• Default WSR-88D Convective Relationship(Z=300R1.4)

• Additional Relationships (late 1990s) Optimized for Different Weather Situations – Tropical Convective (Rosenfeld) Z=250R1.2

– General Stratiform (Marshall-Palmer) Z=200R1.6

– Winter/Cool Stratiform & Orographic – East (Z=130R2.0)

– Winter/Cool Stratiform & Orographic – West (Z=75R2.0)

Maximum Precip. Rate Allowed (MXPRA) a.k.a. “hail cap”

Climate/regime MXPRA Value

Arid - High Plains Spring 75 mm/hr (~3 in/hr)

Central Plains Spring, High Plains Summer 100 mm/hr (~4 in/hr)

Central Plains Summer, Gulf Coast Spring 125 mm/hr (~5 in/hr)

Tropical - Gulf Coast Summer 150 mm/hr (~6 in/hr)

G/R Mean Field Bias Table (in SPD product)

MEMORY SPAN

(HOURS)

EFFECTIVE NO.

G-R PAIRS

AVG. GAGE

VALUE (MM)

AVG. RADAR

VALUE (MM)

MEAN FIELD

BIAS

0.001 1.000 1.016 1.372 0.740

1.000 1.769 0.695 1.260 0.552

2.000 3.144 0.693 1.729 0.401

3.001 5.558 0.831 2.370 0.350

4.998 11.377 1.026 3.053 0.336

10.004 22.779 1.210 3.576 0.338

168.006 338.968 2.584 4.296 0.601

LAST BIAS UPDATE TIME: 06/24/05 20:26 BIAS APPLIED ? NO

Legacy PPS with Sectorized HSR

2004: Build 5 PPS with Terrain-Based HSR (unlimited angles)

Evolution of Radar Rainfall Rate Calculation

Horizontal Polarization:• R(Zh) for all drop size

distributions (with adaptable coefficient and exponent for specific weather regimes)

Dual Polarization:• R(Zh) okay for reference to past but

underestimates in small droplet rain (low ZDR) and overestimates in large drop rainfall (high ZDR); highly vulnerable to hail contamination

• R(KDP) okay for cool season stratiform (esp. with bright band) but not good for long ranges or very light rain

• R(KDP,ZDR) okay for areal rainfall estimation and high rainfall rates

• R(Z,ZDR) okay for low rainfall rates R(Z,KDP,ZDR) “synthetic algorithm”

good for warm season convection and best overall

Dual Polarization Weather Radar (by end of this decade)

• Combined radar moments will lead to a Hydrometeor Classification Algorithm (HCA) to distinguish hail, heavy rain, snow, birds, insects, etc.

• Rate relationship depends upon HCA.

R(Z) R(KDP)R(Z, ZDR) R(KDP, ZDR)

R(Z, KDP, ZDR)

Z ZDR DP

53 dBZ “cap”

Linear units

KDP

Threshold at 0.9 hv

Filter out AP, biological, unknown, and no echo from HCA

Linear units

R(Z)

R(Z,ZDR)

R(KDP, ZDR)

R(KDP) R(Z)

R(KDP-C)

R(KDP-M)

R(KDP)

R(KDP-MS)

Compare

Smooth, filter

Uncertainties in Radar Precip. Estimation (abbreviated list)

Complete or partial beam blockage • Beam propagation path – height of sample• Mixed precipitation types (hail, rain, and/or snow)• Beam not filled with precipitation• Evaporation near the ground Detection of non-meteorological targets

(ground, birds, bugs, etc.)• Displacement of radar sample relative to a

ground location (rain gauge) due to wind shear Methods of integrating radar with rain gauge

and satellite data

Radar Only Gauge Only

CNRFC 24-Hour Precipitation 17 Dec 2002

Radar Only Gauge Bias-Corrected Radar

CNRFC 24-Hour Precipitation 17 Dec 2002

Satellite Hydroestimator (mm)Gauge Bias-Corrected Hydroestimator

CNRFC 24-Hour Precipitation 17 Dec 2002

Summary

• Blockages and ground clutter contamination are reduced.

• EPRE provides flexibility to use new VCPs.• Multi-sensor mosaics enable River Forecast

Centers and other radar data users to get better areal rainfall estimates.

• Changes in the near future will reduce uncertainties and help discriminate meteorological from non-meteorological targets.

Dealing With Uncertainty

“As we know, there are known knowns. There are things we know we know.

We also know there are known unknowns. That is to say, we know there are some things we do not know.

But there are also unknown unknowns, the ones we don’t know we don’t know.”

-- Donald Rumsfeld (2004)

Questions?

Snow Accumulation Algorithm Products

• OSW, #144: One-Hour Snow Accumulation (Water Equiv.)

• OSD, #145: One-Hour Snow Accumulation (Depth)• SSW, #146: Storm Total Snow Accumulation (Water

Equiv.)• SSD, #147: Storm Total Snow Accumulation (Depth)• USW, #150: User Selectable Snow Accumulation (Water

Equiv.)• USD, #151: User Selectable Snow Accumulation (Depth)