1111

Short-Range QPF for Flash Flood Prediction and Small Basin Forecasts

Prediction ForecastsDavid Kitzmiller, Yu Zhang, Wanru Wu,

Shaorong Wu, Feng Ding

11

Office of Hydrologic DevelopmentNOAA National Weather Service

Silver Spring, Maryland

2 June 2010

2222

Recent performance of the High Resolution Precipitation Nowcaster (HPN) algorithm in 0-1 hour time frame

Detection of precipitation at 25mm h-1 thresholds Verification at 16 km2 grid resolution (4x4 km)

An approach to QPF in the 0-6-hour range Does blending of physical and extrapolation model precipitation

forecasts improve on either one, in the 0-6-hour time frame? HPN was targeted for FFMP application 0-6h QPF targeted primarily for RFC use, but there are

potential applications to Site Specific

In this discussion:

22

3333

Based purely on extrapolation of radar echoes Implemented in OB9.0, following implementation of High-

Resolution Precipitation Estimator (HPE) Produces forecasts of:

Rainfall rate at 15, 30, 45, and 60 minutes 1-hour rainfall total

Forecasts are computed on 4-km grid mesh, output on 1-km grid mesh

Can incorporate gauge/radar bias information from MPE See WDTB flash flood modules:http://www.wdtb.noaa.gov/buildTraining/AWIPS_OB9/index.html

HPN Extrapolation Forecastsin the 0-1 Hour Timeframe:

33

44

HPN verification study:September-October 2009

HPN was run in offline mode over the conterminous U.S., during development of 0-6h QPF algorithm

First two hours of the extrapolation forecast are from HPN algorithm

Input from NMQ radar-only precipitation rate algorithm Forecasts verified relative to subsequent NMQ radar-

only precipitation estimates 30 study hours over 15 days, 15 Sep-31 October Verified detection of ≥12.5mm and ≥ 25mm amounts Documented performance relative to persistence

forecast (initial-time rain rates)

55

Example HPN Input/Forecast/Verification

Radar Rainrate1845 UTC24 Sep 2009

NMQ Estimate1845-1945 UTC

HPN Forecast1845-1945 UTC

66

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

POD FAR CSI

HPN 25mm Persistence 25mm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

POD FAR CSI

HPN 12.5mm Persistence 12.5mm

HPN verification study: Detection of 4x4km rainfall

23.3 x 106 cases included in statistics

≥12.5mm

≥25mm

77

4-km forecasts and verification

0

5

10

15

20

25

0 5 10 15 20 25 30 35

HPN 0-1h forecast, mm

Ra

da

r Q

PE

ve

rifi

ca

tio

n

Mean obs 25th percentile obs 75th percentile obs

Poly. (75th percentile obs) Poly. (25th percentile obs) Poly. (Mean obs)

HPN verification study:Forecast vs Radar-Estimated 4x4km rainfall

22,000 grid boxes with precipitation forecasted, northeastern U.S.

75th pct

25th pct

Mean

88

HPN Verification Study:Summary

HPN consistently improves on persistence forecasts in terms of POD and FAR: 40% more detections of 12.5- and 25-mm

amounts 20% fewer false alarms

HPN QPF has little bias overall (0.9 to 1.1) For HPN QPF > 10 mm: Expected (mean)

observation is about 0.67 of the forecast amount For HPN QPF > 10 mm: 25th percentile

observation is about 0.80 of the forecast amount

9999

Original requests for development from ABRFC Designed to use a statistically-weighted combination of

QPFs from radar extrapolation and from RUC2 Extrapolation/advection model for precipitation rate fields:

Extrapolation based on recent radar echo motion for 0-2 hours Motion vector field is morphed toward RUC2 700-500 hPa wind

field forecast for 3-6 hours

Radar precipitation rate input from NMQ radar-only product (see succeeding NSSL presentation)

Model Output Statistics approach used to determine optimum blend of extrapolation and RUC QPFs

0-6 Hour QPFFrom Radar Extrapolation and RUC forecasts

99

10101010

Radar Precipitation Rates,1715 UTC, 16 May 2009

From National Mosaic and Multisensor Quantitative Precipitation Estimation system (NMQ)

Yellow: > 10mm 6-h-1

Red: > 25mm 6-h-1

Gray: > 38 mm 6-h-1

Blue: > 75 mm 6-h-1

Radar-Observed Precipitation Rates, 1715 UTC 15 May 2009

11111111

Extrapolation forecasts of rate field, 1715-2315 UTC:

12121212

Forecast products: Probability of 6-hour precipitation ≥ 0.25, 2.5, 12.5,

25, 50, 75 mm Precipitation amount forecast

Gridded forecasts, 4x4 km mesh length Issue forecasts for periods 00-06, 06-12, 12-18,

18-00 UTC (cover entire day) Forecasts use input from the hour preceding

start of valid period RUC-Satellite-Lightning equations will be applied

in radar coverage gaps Forecasts disseminated before start of valid

period

0-6h QPF Product Characteristics

1212

13131313

Regression Equation for 0-6-hPrecip Amount: Southeastern US

Precipitation = 0.52 + 0.31 RADAR QPF(0-3h)+ 0.24 RUC QPF (0-3h)+ 0.26 RUC QPF (3-6h)

+ 0.17 RADAR QPF (3-6h)

given RADAR and/or RUC QPF > 0; forecasts and predictors in mm, spatial area 4x4 km

1313

Prediction equation based on 40,000 cases: Apr-Sep 2009, Southeastern United States. Mean observed precip = 1.9 mm; R2 = 0.14

14141414

0

5

10

15

20

25

30

Amount P >2.5mm P > 25mm

RUC+Radar RUC only Radar only Operational

Regression (RUC2+Radar) Forecasts:Correlation to 6-H Rainfall, New England

(17,300 cases Apr-Sep 2009 – 18-00 UTC)

1414

Red

uct

ion

of

Var

ian

ce (

R2 )

15151515

Explained variance is small at this small spatial scale. However skill increases as accumulating area increases.

Products combining RUC2 and extrapolation QPF could match or improve on skill of current operational guidance

Radar and numerical prediction models are clearly complementary for QPF in 0-6-hour range

0-6h QPF Findings

1515

16161616

Collection of new forecast and verification data on a daily basis

Aim for 3 years’ development data Creation of probability and amount equations

for cool and warm season, and subregions of the conterminous U.S.

Create disaggregation logic to get QPFs for 1-h subintervals in 6-h period

Ongoing Work – 0-6h QPF

1616

17171717

Questions? Suggestions?Thanks to collaborators in NOAA National Severe Storms

Laboratory, Institute of Atmospheric Physics/Czech Republic Academy of Sciences

1717

1818

Supplementary Slides

1919

HPN verification study:Detection of 8x8 km rainfall

POD for individual events, 8km

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

POD for initial rainrate

PO

D f

or

HP

N

POD, 12.5mm POD, 25mm REF

All 12.5mm All 25mm

11,100 grid boxes with precipitation observed or forecasted

FAR for individual events, 8km

0.5

0.6

0.7

0.8

0.9

1

0.5 0.6 0.7 0.8 0.9 1

FAR for initial rainrate

FA

R f

or

HP

N

FAR, 12.5mm FAR, 25mm REF

All 12.5mm All 25mm