The use of WSR-88D radar data at NCEP

Shun Liu1

David Parrish2, John Derber2 , Geoff DiMego2 , Wan-shu Wu2

Matthew Pyle2 , Brad Ferrier1

1IMSG/ National Centers of Environmental Prediction, Camp Springs, Maryland2NOAA/National Centers of Environmental Prediction, Camp Springs, Maryland

OUTLINE

• Radar data processing at NCEP

• High resolution forecast initialization with radar data

WSR88D-Radar Data Processing at NCEP

Problems of radar data processing in operations:

1. the relatively large volume of radar data restricting the data to be transmitted to the operational center in real time

2. the radar data decoding software and storage taking excessive computational resources

3. the quality control (QC) problems of radar data further limiting the applications of radar data for operational use

WSR88D-Radar Data Processing at NCEP

Radar data received at NCEP:

1.Digital Precipitation Arrays (DPA)2.VAD wind (velocity azimuth display)3.WSR88D Level-III (NIDS) data4.WSR88D Level 2.5 data5.WSR88D Level-II data

Flow chart of level-II radar data processing at NCEP

Radar data QC at NCEP

QC Parameters

Mean reflectivity (MRF)

refNnrefMRF /)(

max/ NNVDC vr

bmvrpsc JjIjIVSC /])(/)([

Velocity data coverage (VDC)

Along-beam perturbation velocity sign changes (VSC)

Along-beam velocity sign changes(SN)Standard deviation of radial wind (STD)

Recorded QC parameters

0 400 800 1200 1600 2000 2400

02468

10

time

MR

F (d

BZ)

203040506070

VD

C (%

)

273033363942

VS

C (%

)

0 5 10 15 20 25 300

2000

4000

6000

8000

10000

12000

14000

16000

SN(%)

SN

KFWS 200909110605

KBUF 2009090513

23%

6%

Along beam velocity sign change (SN)

Threshold to reject data

Performance of radar data QC

Observation (m/s)

anal

ysis

(m/s

)

anal

ysis

(m/s

)

Observation (m/s)

before QC after QC

With QC

Goes image

Zoom-in area

HiRes Initialization with radar data• The radial wind is directly analyzed by GSI. • The cloud analysis package developed by GSD is

modified and used to analyze reflectivity with NCEP’s forecast model background.

• Hourly cycle is used

HiRes Initialization with radar data

• After complex radial wind quality control, level II radial winds are used in GSI analysis.

• 3D reflectivity mosaic from level II data are used in cloud analysis.

• Metar and Satellite observations are used in cloud analysis to detect cloud.

• Latent heat estimated from reflectivity is used to adjust background temperature.

• After radial wind and reflectivity assimilation, wind, temperature, rain water mixing ratio, cloud water and cloud ice mixing ratio and specific humidity are upgraded.

Test case on 2011091800

03

04

05

24

Forecast start at 00z

CTL: forecast withoutRadar data assimilation

EXP: forecast with radar data assimilation

Low level

divergence

High level

CREF obs

CREF ctl

EXP-CTLat the end of dataassimilation cyclewind divergence

conv

div

div

conv

psf (mb) uv (m/s) T (k) Rh (%)

bias rms bias rms bias rms bias rms

4h fcstctl 0.06 0.96 0.07 4.69 -0.28 1.92 2.43 10.85

exp 0.32 1.25 -0.06 4.55 -0.29 1.87 3.33 10.77

10h fcstctl 0.56 1.07 0.18 4.83 -0.06 1.55 2.39 15.62

exp 0.68 1.40 -0.05 4.91 -0.07 1.53 2.47 15.86

22h fcstctl 0.25 1.21 0.40 5.22 -0.35 1.56 0.63 16.19

exp 0.33 1.25 0.39 5.09 -0.29 1.55 0.47 15.06

Conventional data verification

Forecast hour

Domain average of absolute pressure change per 3h

03 06 09

12 15 18

CREF ETS score

10 day parallel run from 20110918 --- 20111013

ctlexp

33 3630

21 24 27

Vector windAnalysisIncrement

GSI analyzed wind increment

Most of analyzed wind increments are along the radial direction.

Challenges of radar data assimilation

do we get cross-beam wind information with current 3DVAR system?

Challenges of radar data assimilation

How to get balance between wind and other model variables?

1. Examine if DFI can distribute wind increment from Vr assimilation to other model variables. T increment through DFI is small. Q increment is relative large through DFI

2. Add new constrains in GSI to get balance between wind and other variables.

Future planContinue to test current hourly data assimilation

system for HiRes initialization. Will try to extend current 2 hour assimilation window to 6 hour or change assimilation interval to half hour.

Consider including diabatic digital filter treatment.

May consider to use radar data in hybrid ensemble data assimilation system.

Improvement of radar data quality control package at NCEP is constantly needed. We will need to process TDWR, Dual-pol and Canadian radar data in near future.