Assimilation of AIRS SFOV Profiles in the Rapid Refresh

Rapid Refresh domainHaidao LinMing Hu

Steve WeygandtStan Benjamin

Assimilation and Modeling Branch

Global Systems DivisionCooperative 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 Li

CIMMS, University ofWisconsin

Presentation Outline

1. Background on Rapid Refresh system2. Retrospective experiment design and

data impact benchmarking3. AIRS SFOV data coverage and assessment4. Initial AIRS SFOV data assimilation experiment5. Impact of different assimilation settings6. Test with new improved SFOV retrievals7. 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 retro1-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 K12-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 raobNearby AIRS SFOV retrieval ( time / space gap: 1 h 21 min. / 6 km)

Less vertical detail in SFOV Some T Differences > 3K

AIRSRAOB

Temperature

Mixing RatioAIRS

RAOB

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 SFOVFULL 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 SFOVFULL T – std. obs + SFOV T – 50-1000 mb PART T – std. obs + SFOV T – 100-800 mbMID 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 SFOVSTD 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 SFOVOLD 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 data5. Evaluate sensitivity to retrieval vs. radiance data