Post on 27-Mar-2015
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