1. Assimilation of AIRS SFOV Profiles in the Rapid Refresh Rapid Refresh domain Haidao Lin Ming Hu Steve Weygandt Stan Benjamin Assimilation and Modeling Branch Global Systems Division Cooperative Institute for Research in the Atmosphere Colorado 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 of Wisconsin

2. 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

3. 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

4. Rapid Refresh Hourly Update Cycle 1-hr fcst 1-hr fcst 1-hr fcst 11 12 13 Time (UTC) Analysis Fields 3DVAR Obs 3DVAR Obs Back- ground Fields Rawinsonde (12h) 150 NOAA profilers 35 VAD winds ~130 PBL profilers / RASS ~25 Aircraft (V,T) 3500 – 10,000 TAMDAR 200 – 3000 METAR surface 2000 -2500 Mesonet (T,Td) ~8000 Mesonet (V) ~4000 Buoy / ship 200-400 GOES cloud winds 4000-8000 METAR cloud/vis/wx ~1800 GOES cloud-top P,T 10 km res. satellite radiance ~5,000 Radar reflectivity 1 km res. Data types – counts/hr Partial cycle atmospheric fields – introduce GFS information 2x per day Fully cycle all LSM fields

5. 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)

6. Evaluate Rawinsonde Denial Impact RMS error impact Raob denial retro run Benj. et al. MWR 2010 6-h fcst T0.06 K0.05 K 12-h fcst T0.11 K0.15 K 6-h fcst RH0.77%1.25 % 12-h fcst RH 1.11%1.75% 6-h fcst wind 0.13 m/s0.1 m/s 12-h fcst wind 0.17 m/s0.18 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 Error Mean diff 0.17 m/s 12-h fcst wind

7. 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

8. 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 12z 06z 09z 18z 21z

9. 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 AIRS RAOB Temperature Mixing Ratio AIRS RAOB Temperature AIRS - RAOB

10. 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 Temp bias Mixing Ratio bias RH bias Temp RMS Mixing Ratio RMS RH RMS old obs new obs old obs new obs

11. 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) CNTLFULL T 0508 06Z 1.5 2 2.5 200 400 600 800 1000

12. RMS Stats from SFOV T Assim. Expt. Similar negative impacts from SFOV T on forecast V, Q Time series of 6-h fcst T RMS error (100-1000 mb mean) Vertical profile of 6-h fcst T RMS error Time series of 12-h fcst 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

13. 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

14. 100 km No thinning 45 km 200 km 60 km Horizontal Thinning Analysis Difference (A-A) at 578 mb 0508 09Z

15. No Vertical 25 mb 50 mb100 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)

16. 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

17. 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 obs new obs Raob comparison

18. 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