Assimilation of AIRS SFOV Profiles in the Rapid Refresh
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Transcript of Assimilation of AIRS SFOV Profiles in the Rapid Refresh
Assimilation of AIRS SFOV Profiles in the Rapid Refresh
Rapid Refresh domainHaidao LinMing Hu
Steve WeygandtStan Benjamin
Assimilation and Modeling Branch
Global Systems Division
Cooperative Institute for Research in the AtmosphereColorado State Univerisity
AIRS 500-mb retrieved temperature,
grey-scaled RR cloud-top analysis
http://rapidrefresh.noaa.gov
Collaborators:
Tim Schmit, Jun Li, Jinlong LiCIMMS, University of
Wisconsin
Presentation Outline
1. Background on Rapid Refresh system
2. Retrospective experiment design and data impact benchmarking
3. AIRS SFOV data coverage and assessment
4. Initial AIRS SFOV data assimilation experiment
5. Impact of different assimilation settings
6. Test with new improved SFOV retrievals
7. Ongoing work and future plans
1. Background on Rapid Refresh
Rapid Refresh13
RUC-13
– Advanced community codes (ARW and GSI)– Retain key features from RUC analysis / model system
(hourly cycle, cloud analysis, radar DFI assimilation)– Domain expansion consistent fields
over all of N. America for aviation / other hazards (convection, icing, turbulence, ceiling, visibility, etc.)
Status /implementation- Two real-time cycles running at GSD- Frozen test version running at EMC- NCEP operational implementation
planned for 4Q 2011 (Aug./Sept.)
RUC Rapid Refresh transition
Rapid Refresh Hourly Update Cycle
1-hrfcst
1-hrfcst
1-hrfcst
11 12 13Time (UTC)
AnalysisFields
3DVAR
Obs
3DVAR
Obs
Back-groundFields
Rawinsonde (12h) 150NOAA profilers 35VAD winds ~130PBL profilers / RASS ~25
Aircraft (V,T) 3500 – 10,000TAMDAR 200 – 3000METAR surface 2000 -2500Mesonet (T,Td) ~8000Mesonet (V) ~4000Buoy / ship 200-400GOES cloud winds 4000-8000METAR cloud/vis/wx ~1800
GOES cloud-top P,T 10 km res.satellite radiance ~5,000Radar reflectivity 1 km res.
Data types – counts/hr
Partial cycle atmospheric fields – introduce GFS information 2x per dayFully cycle all LSM fields
2. Experiment Design / Benchmarking
• 9 day retrospective period (May 8-16, 2010)
• Initial tests with 3-h cycle, no partial cycling
• Comparison of 3-h retro cycle with R/T RUC
1-hourly R/T RUC
3-hourly RR retro
1-hourly RR retro(partial cycle)
3-h RR retro: -- worse than 1-h RR -- similar to R/T RUC
12-h fcst wind RMS Error (100-1000 mb mean)
Evaluate Rawinsonde Denial Impact
RMS errorimpact
Raob denial retro run
Benj. et al. MWR 2010
6-h fcst T 0.06 K 0.05 K
12-h fcst T 0.11 K 0.15 K
6-h fcst RH 0.77% 1.25 %
12-h fcst RH 1.11% 1.75%
6-h fcst wind 0.13 m/s 0.1 m/s
12-h fcst wind
0.17 m/s 0.18 m/s
RAOB -- conventional obs (with raobs) + radiance (no AIRS)
NO-RAOB -- conventional obs (no raobs) + radiance (no AIRS)
(No AIRS SFOV for either)
From Benjamin et al. MWR 2010 3-h 6-h 12-h
Raob denial results closely match previous study
RAOB
NO-RAOB
RMS ErrorMean diff0.17 m/s
12-h fcst wind
3. AIRS SFOV Data Assessment
• Launched in May 2002 on NASA Earth Observing System (EOS) polar-orbiting Aqua platform
• Twice daily, global coverage• 13.5 km horizontal resolution (Aumann et al. 2003)• 2378 spectral channels (3.7-15.4 µm) • Single Field of View (SFOV) soundings are derived
using CIMSS physical retrieval algorithm (Li et al. 2000)
• Clear sky only soundings
AIRS SFOV Data Coverage • 1.5-h time window (+/- 1.5 h)• 3-h cycle, data available on 06Z, 09Z,12Z,18Z, 21Z
– Append available AIRS sounding data RR prebbufr observation files
12z06z 09z
18z 21z
Compare AIRS SFOV with Raobs
Salem, Oregon 12z 8 May raob
Nearby AIRS SFOV retrieval ( time / space gap: 1 h 21 min. / 6 km)
Less vertical detail in SFOV Some T Differences > 3K
AIRSRAOB
Temperature
Mixing Ratio
AIRSRAOB
Temperature
AIRS - RAOB
Compare AIRS SFOV with Raobs• 54 matched raob profiles during 0508—0516 period• Conditions for matched profiles : 3-h time window, less than 15 km
horizontal distance under clear-sky
Tempbias
Mixing Ratiobias
RHbias
TempRMS
Mixing RatioRMS
RHRMS
old obsnew obs
old obsnew obs
4. Initial Assimilation Expt: T only
Temperature error variance
• Use complete soundings -- 13.5-km horizontal resolution
-- All available vertical levels (50-1000
mb)
• Use supplied T error variance
Std. obs + SFOV TStandard obs
500 mb T Analysis
Increments
Data coverage (500 mb temperature)
CNTL FULL T
0508 06Z
1.5 2 2.5
200
400
600
800
1000
RMS Stats from SFOV T Assim. Expt.
Similar negative impacts from SFOV T on forecast V, Q
Time series of 6-hfcst T RMS error(100-1000 mb mean) Vertical profile of
6-h fcst T RMS error
Time series of 12-hfcst T RMS error(100-1000 mb mean)
Vertical profile of
12-h fcst T RMS error
CNTL – std. observations No AIRS SFOV
FULL T – std. obs + SFOV T – 50-1000 mb
5. Variations in SFOV Coverage (T only)
Temperature error variance
200
400
600
800
1000
CNTL – std. observations - No AIRS SFOV
FULL T – std. obs + SFOV T – 50-1000 mb
PART T – std. obs + SFOV T – 100-800 mb
MID T – std. obs + SFOV T – 400-800 mb
Impact of reduced vertical coverage
6-h fcst T RMS Error
6-h fcst V RMS Error
6-h fcst T RMS Error
6-h fcst V RMS Error
1.5 2 2.5
100 km
No thinning 45 km
200 km60 km
Horizontal Thinning Analysis Difference (A-A) at 578 mb
0508 09Z
No Vertical
25 mb
50 mb 100 mb 200 mb
Analysis with AIRS – analysis without AIRS from single GSI runs on 20100508 09Z
All AIRS data in 60 km
Vertical Thinning Analysis Difference (A-A)
CNTL – std. observations, No AIRS SFOV
STD Err – standard temperature error variance, (400-800 mb)
DBL Err – 2X standard temperature error variance (400-800 mb)
THINNING – 60-km horiz., 50 mb vert., 2X std. error (400-800 mb)
Impact of assumed Obs error variance, data thinning
Other Variations in SFOV Assim. (T only)
6-h fcst T RMS Error
6-h fcst V RMS Error
CNTL – std. observations, No AIRS SFOV
OLD T Data-2X std. error, 60-km horiz,
50 mb vert., (400-800mb)
New T Data – 60-km horiz, 50 mb vert.,
2X std. error (400-800 mb)
Other Variations in SFOV Assim. (T only)6. Tests with New Improved SFOV
6-h fcst T RMS Error
6-h fcst V RMS Error
Temp RMS
old obsnew obs
Raob comparison
7. Ongoing and Near Future Work
1. Complete basic assimilation experiments-- temperature, moisture, combined-- vertical extent, data thinning, observation error
2. Use more selective data QC information-- detailed QC mark from CIMSS (esp. vertical)-- cloud edges, ocean only, night-time only
3. Possible bias correction (data analysis needed)
4. Possible use of 3x3 retrieval data
5. Evaluate sensitivity to retrieval vs. radiance data