Regional Data Impact Studies at NCAR And The JCSDA

WMO Observation Impact Meeting,

Geneva, Switzerland,

March 27th 2008

Dale Barker, T. Auligne, M. Demirtas, H. C. Lin, Z. Liu, S. Rizvi, H. Shao,

Q. Xiao, and X. Zhang

National Center for Atmospheric Research, Boulder, Colorado, USA

WRF DA Research To Operations (NCAR/AFWA)

• NCAR/AFWA DA Program initiated in August 2006.• NCAR responsible for WRF-Var development and initial testing.• JCSDA provides Community Radiative Transfer Model (CRTM), etc.

• WRF Community contributions include radar, radiance (RTTOVS), GPS, etc.

• Data Assimilation Testbed Center (DATC) performs rigorous testing prior to ops.

AFWA: Pre-operational testing, implementation

NCAR (DTC, DATC): Extended period testing

NCAR/MMM (WRF-Var, ARW)

JCSDA(CRTM, GSI)

WRFCommunityR&D

Testbeds

Operations

1) WRF-Var Overview

2) Antarctica: COSMIC/AMSU/AIRS/MODIS Impact.

3) AFWA: AMSU Impacts

4) Korea: Radar Impacts.

5) Summary

Outline of Talk

WRF-Var Data Assimilation Overview

• Goal: Community WRF DA system for regional/global, research/operations, and deterministic/probabilistic applications.

• Techniques: 3D-Var, 4D-Var (regional), Hybrid Variational/Ensemble DA.

• Models: WRF, MM5, KMA global.

• Support: MMM Division, NCAR.

• Observations: Conv.+Sat.+Radar

AFWA Theaters:

GPS Radio Occultation (B. Kuo):

2. Antarctica: COSMIC, AMSU, AIRS, and MODIS

Impacts

DATC Antarctica Testbed

Testbed Configuration (from MMM/AMPS):

• Model: WRF-ARW, WRF-Var (version 2.2).

• Namelists: 60km (165x217), 31 vertical levels, 240s timestep.

• Period: October 2006.

• Purpose: Impact of DA cycling, model top, COSMIC.

Sonde Coverage

COSMIC Coverage

(24hrs)

Hui Shao, DATC

DATC-COSMIC Testbeds

Antarctic Mesoscale Prediction System (AMPS)

Taiwan Civil Aeronautics Administration (CAA)

Air Force Weather Agency (AFWA)

Number of radiosonde and COSMIC soundings within one time window

T (K)

Assimilation of COSMIC refractivity:

NOGPS WGPS WGPS_ BE

Bias

RMSE

36hr Forecasts of Temperature vs

Sondes

Direct impacts

• Increases the RMSE in the stratosphere

• Reduces the RMSE in the troposphere

Forecast Impact

* Verified in the domain south of 60S

Assimilation of COSMIC refractivity:

• Can reduce wind biases

• Reduces the RMSE of wind forecast

U (m/s)

NOGPS WGPS WGPS_ BE

36hr Forecasts of Wind Speed (U) vs

Sondes

Indirect impacts

Bias

RMSE

* Verified in the domain south of 60S

RMSE Difference of u, v, T and q: WGPS-NOGPS (Negative Values <=> Positive Impacts of COSMIC data)

ANL F12 F24 F48 F72

u (m/s)

v (m/s)

T (K)

q (g/kg)

-1.2

0.2

-0.4

-0.4

1.6

0.5-2.0

0.3

Positive impacts

Experiment Description Background Error Covariance

Top Pressure of Assimilated COSMIC Data

Model Top

Vertical Level Number

Damping Scheme

NOGPS 6 hourly cycling run without assimilating COSMIC data

WGPS 6 hourly cycling run with assimilating COSMIC data

1

WGPS_damp3 Same as WGPS , except uses damp_opt=3 in WRF

50mb

3

WGPS_250mb Same as WGPS , except assimilates COSMIC data below 250mb .

NMC method using forecasts for May 2004

250mb

50mb 31

NODA_10mb 12 hourly cold -start run of WRF

NOGPS_10mb Same as NOGPS , except for model top at 10mb

WGPS_10mb Same as WGPS , except for model top at 10mb and assimilation of

COSMIC up to 10mb

NMC method using forecasts

from NODA_10mb for Oct 2006

10mb

10mb

57

1

Sensitivity Study of Stratospheric COSMIC Data Assimilation

NOGPS WGPS WGPS_250mb WGPS_damp3 WGPS_10mb

WGPS_250mb vs WGPS & WGPS_250mb vs NOGPS:Assimilation of COSMIC data only in troposphere sustains positive impacts in troposphere and decreases the RMSE of T forecasts in stratosphere as shown in WGPS. WGPS_damp3 vs WGPS:The enhanced damping at the model top only marginally changes the RMSE of T(U) forecasts.

WGPS_10mb vs WGPS:Moving the model top to 10mb decreases the RMSE of U and T forecasts in the stratosphere.

RMSE of 36hr Forecasts wrt Sondes

Bias and RMSE of 36hr Forecasts of T wrt Sondes

Assimilation of COSMIC data: • Reduces the bias of T forecasts in the lower-middle troposphere and stratosphere• Decreases the RMSE of T forecasts below 70mb

NOGPS_10mb

WGPS_10mb

WRF-Var Radiance Assimilation (Liu et al. 2009)• BUFR 1b radiance ingest.

• RTM interface: RTTOV8_5 or CRTM

• NESDIS microwave surface emissivity model

• Range of monitoring diagnostics.

• Quality Control for HIRS, AMSU, AIRS, SSMI/S.

• Bias Correction (Adaptive or Variational)

• Variational observation error tuning

• Parallel: MPI

• Flexible design to easily add new satellite sensors

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DMSP(SSMI/S)

Aqua (AMSU, AIRS)

NOAA (HIRS, AMSU)

OMB and OMA for NOAA-15 CH7OMB Before Bias Correction

OMB After Bias Correction

* QC has been applied to the data after BC

OMA

AMSUA Impact: 36hr Forecast Score vs. RS

• Horiz. resolution = 60km

• 57 Levels, Model top = 10hPa

• Full cycling

• NOAA 15/16/18 AMSU-A channels 4 to 9

• Radiance over ocean only

• Static Bias Correction (Harris and Kelly, 2001):

4 predictors

• Thinning 120km

• QC = thresholds on innovations

AIRS innovations: Channel Selection

TSurface O3T Q

AIRS T JacobiansAIRS T Jacobians

ModelTop Ozone

Solarcontamination

ModelTop

RTTOVRTTOV

CRTMCRTM

AIRS innovations: QC & Thinning

• Pixel-level QC– Reject limb observations

– Reject pixels over land and sea-ice

• NESDIS Cloud detection– LW window channel > 271K

– Thresholds on model SST minus SST from 4 AIRS LW channels

• Channel-level QC– Gross check (innovations <15 K)

– First-guess check (innovations < 3o). Error factor tuned from objective method (Desrozier and Ivanov, 2001)

• Imager AIRS/VIS-NIRDay only (cloud coverage within AIRS pixel <5%)

Thinning (120km)345 active data

Thinning (120km)345 active data

Warmest FoV696 active data

Warmest FoV696 active data

• Thinning

AIRS Impact: 36hr Fcst. Score vs. Sondes

Whole Domain High Latitudes (> 60S)

McMurdo

Pegasus North

Black Is.

Western Ross Sea / Ross Is. grids

McMurdo Region & AWS sites

2.2-km

6.6-km, 2.2-km grids

•

Gill

Minna Bluff

Mt. DiscoveryMt. Morning

Transantarctic Mtns.

Impact Of High-Resolution Cycling 2300 UTC 15 May— Hr 23

DA: With MODIS

25 25

ms-1ms-1

L

Von Karman vortex

•

DA: With reduced MODIS

DA - Conventional

CTRL - WRF with GFS ICs

34

Sfc

Win

ds

(ms-1

)

SL

P (

hPa)

Pegasus North Winds

Win

d S

pee

d (

ms-1

)

OBS:WRF:

Hr from 00 UTC 15 May

Win

d S

pee

d (

ms-1

)

Win

d S

pee

d (

ms-1

)W

ind

Sp

eed

(m

s-1)

Hr from 00 UTC 15 May

NoDA

DA - Reduced MODIS Conventional

DA - With MODIS

20

20

35

20

35

35 35

20

35.3

35.3 35.3

35.3 36.6

24.6

31.529.3

Record ends

Record ends Record ends

Record ends

3. AFWA: AMSU Impacts

24hr Forecast Verification Vs. Obs for AFWA Testbed

Conclusions:

1. Regional DA adds significant value (even without radiances).

2. Update-cycling (GFS first guess at 00/12 UTC) superior to full-cycling.

No Data Assimilation

“Update” Cycling

Full-cycling

Meral Demirtas, DATC

East Asia Domain (T46)

Land Use Category

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• 162*212*42L, 15km• model top: 50mb• Full cycling exp. for a month

• 1 ~ 30 July 2007• GTS+AMSU

• NOAA-15/16, AMSU-A/B from AFWA• AMSU-A: channels 5~9 (T sensitive)• AMSU-B: channels 3~5 (Q sensitive)• Radiance used only over water • thinned to 120km• +-2h time window• Bias Correction (H&K, 2001)

• Compare to GTS exp.• Only use GTS data from AFWA

• 48h forecast, 4 times each day• 00Z, 006, 12Z, 18Z

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Obs used in assimilation (from AFWA operational datafeed)

Vs. Profiler VSlightly positive impactBeyond 24h

Vs. Profiler USlightly negative impactwithin 24h

Vs. Sound TNeutral

Vs. Sound QNeutral/Slightly negative

Impact Of AMSU Radiances in T46 (Liu et al. 2009)Verification against assimilated obs

Vs. SATEM ThicknessPositive impact

Vs. GPS RefractivityPostive impact

Vs. AIRS retrieval TSlightly positive impact

Vs. AIRS retrieval QSlightly positive impact beyond 24h

Impact decreasesWith forecast range

LBC takes controlFor long range FC

Impact Of AMSU Radiances in T46Verification against unassimilated obs

Atlantic Domain (T8)

Land Use Category

• 361*325*57L, 15km• Quite compute-demanding for WRF forecast

• model top: 10mb• Full cycling exp. for 6 days

• 15 ~ 20 August 2007• GTS: assimilate NCAR conventional obs

• Select similar data type used by AFWA• No SSM/I retrieval

• GTS+AMSU+MHS (use NCEP BUFR rad.)• NOAA-15/16/18, AMSU-A, ch. 5~10• NOAA-15/16/17, AMSU-B, ch. 3~5• NOAA-18, MHS (similar to AMSU-B)• Radiance used only over water • thinned to 120km• +-2h time window• Bias Correction (H&K, 2001)

• 48h forecast twice each day• 00Z, 12Z

• Might not optimal to use all sensors/satellites at the first try, but I want to test the robustness of the system with all Microwave sensors which can be assimilated in WRF-Var now.

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T8: 48h forecast error vs. sound

4. Korea: Radar Impacts

Radar Assimilation In WRF-Var

• Quality Control:• Complex, vital……

• Radial Velocity Assimilation:• Vertical velocity increments diagnosed.

• 3D radial velocity observations assimilated.

• Reflectivity Assimilation:• Total water control variable (qt=qv+qc+qr).

• Background error statistics for qt currently based on water-vapor (qv).

• 3D-Var: Moist physics scheme included in observation operator.

• 4D-Var: Awaiting inclusion of microphysics scheme in linear model.

Flowchart for radar data preprocessing

Coordinate conversion/Coordinate conversion/interpolationinterpolation (SPRINT, CEDRIC)

RKSGVr, dBZ

RGDK, RJNIVr, dBZ (r,, )

Vr, dBZ (Lat, Lon, Z )

• Resolution : 0.02o x 0.02o x 0.5km • Domain : 5o x 5o x 10km

Composite map Composite map • Reflectivity: Maximum value• Radial velocity: in order

Additional QCAdditional QC

Velocity DealiasingVelocity Dealiasing

Thinning Thinning

• dBZ >= 10• > 5 levels

3-hr forecast as a

reference wind

• every 3 grid points ~ 6 km

Write out for 3dVarWrite out for 3dVar

Obs (03Z, 31/08) No Radar

Radar RV Radar RV+RF

Typhoon Rusa Test Case 3hr Precip:

Korean Radar Data Assimilation in WRF-Var

Threshold = 5 mm

0.50.550.6

0.650.7

0.750.8

0.85

3103 3106 3109 3112

TIME (Date/hr)

TS SCORE

Without Radar

With RF

With RV

With RV and RF

Threshold = 10 mm

0.5

0.6

0.7

0.8

0.9

3103 3106 3109 3112

TS SCORE

Typhoon Rusa 3hr Precip. Verification:

2004082600 ~ 2004092812

Threshold = 5.0mm

TIME

3 6 9 12 15 18 21 240.0

0.2

0.4

0.6

0.8

1.0

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

KMA Pre-operational Verification:(no radar: blue, with radar: red)

Bias

Th

reat

Sco

re

KMA/WRF Testbed

Testbed Configuration (NCAR/KMA project 2007):

• Model:WRF-ARW, WRF-Var (version 2.2).

• Domains: 10km (574x514) RDAPS, 3.3km (428x388) HiNWP.

• Period: Summer 2007 Changma Season (July 1 - August 10th).

• DA: 3D-Var. RDAPS - 6-hrly cycling. HiNWP- 3hrly cycling.

10km res. “RDAPS” 3.3km res. “HiNWP” Nest

Korean 41 day Changma/Baiu Season Testbed:24hr Forecast Verification: Bias

10km Domain (+ve cycling impact) 3.3km Domain (+ve radar RV impact)

Barker et al., In preparation

Airborne Doppler Radar Assimilation for Hurricane Jeanne (Xiao et al 2007)

NOAA 43 Flight TrackSurface Pressure analysis

Hurricane Jeanne Forecast SkillTrack Error Maximum Wind

– CRTL = No data assimilation.– GTS = Conventional observations only.– RV43 = Radar winds only.– RV43/GTS = Radar winds + GTS.– 24hr forecast errors shown.

1) WRF-Var: 3D-Var robust, 4D-Var/EnKF initial tests.

2) Antarctica: Encouraging results from COSMIC, AMSU, AIRS.

3) AFWA: Neutral/positive impacts of AMSU in E. Asia/Tropics.

4) Korea: +ve impact in 3D-Var, mainly from radial velocities.

5) Current foci: Bias correction, cloud detection, new applications.

Summary