1. Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan Benjamin Ming Hu Curtis Alexander Patrick Hofmann Assimilation and Modeling Branch Global Systems Division NOAA Earth System Research Lab Boulder, CO Cooperative Institute for Research in the Atmosphere Colorado State University http://rapidrefresh.noaa.gov

2. Presentation Outline 1.Background on Rapid Refresh (RAP) system 2.Data introduction and selected channels 3.Retrospective experiments  AIRS data impact  Other radiance data impact (AMSU-A, HIRS, and MHS) 4.Real-time radiance data impact in RAP (with hybrid EnKF assimilation) 5. Real-time RAP radiance data availability issues 6. Summary and future work

3. –Advanced community codes (ARW model, GSI analysis) –Key features for short-range “situational awareness” application (cloud analysis, radar DFI assimilation)  RAP guidance for aviation, severe weather, energy applications Background on Rapid Refresh NOAA/NCEP’s hourly updated model RAP version 1 -- NCEP since Spring 2012 Rapid Refresh 13-km HRRR 3-km RAP version 2 -- Planned NCEP Late 2013 –DA enhancements (Hybrid – EnKF using global ensemble) –Model enhancements (MYNN PBL, 9-layer LSM)  Improved mesoscale guidance, GSD version parent for HRRR

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 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 radiances (AMSUA, HIRS, MHS) Radar reflectivity 1 km res. Data types – counts/hr Partial cycle atmospheric fields – introduce GFS information 2x per day Fully cycle all land-sfc fields

5. - Hourly cycling of land surface model fields - 6-hour spin-up cycle for hydrometeors, surface fields RAP Hourly cycling throughout the day RAP spin-up cycle GFS model RAP spin-up cycle GFS model 00z 03z 06z 09z 12z 15z 18z 21z 00z Observation assimilation Observation assimilation Rapid Refresh Partial Cycling

6. RAP Radiance impact Testing RETROSPECTIVE REAL-TIME ModelWRF-ARW v3.2WRF-ARW v3.4 AnalysisGSI 3DVARGSI hybrid EnKF (80 member global) Cycle3-h, NO part. cycle1-h with part. cycle SatelliteIdeal (no latency)Actual latency Coverage Expts Control (conv. obs)Control (conv. obs) CNTL+ AIRS onlyCNTL + all 4 sat. CNTL+AMSUA only CNTL+HIRS only CNTL+MHS only CNTL+all 4 sat.

7. Radiance Data AIRS (Focused work) –High vertical resolution –Temperature and moisture information –Not yet operationally used in RAP, used experimentally in GSD real-time run AMSU-A (Operationally used in RAP) –Temperature information HIRS (Operationally used in RAP) –Temperature information –Moisture information (channels 10-12) MHS (Operationally used in RAP) –Moisture information

8. Radiance Channels Selected for RAP AIRS (remove high peaking channels) –Aqua: 68 channels selected from 120 GDAS channel set AMSU-A (remove high peaking and surface channels) –metop-a: channels 4-6, 8-10 –noaa_n15: channels 4-10 –noaa_n18: channels 4-8, 10 –noaa_n19: channels 4-7, 9-10 HIRS (remove high peaking and ozone channels) –noaa_n19: channels 4-8, 10-15 –metop-a: channels: 4-8, 10-15 MHS –noaa_n18: channels 1-5; –noaa_n19: channels 1-5; (not available for retro run on 2010) –metop-a: channels 1-5;

9. Retrospective Experiments AIRS impact

10. Settings for AIRS Retrospective Runs Extensive retro run for bias coefficients spin up May 8 – May 16, 2010, 3-h AIRS radiance data with bias coefficients cycled (the very first bias coefficients came from GDAS global system) For each analysis cycle, 30 successive GSI runs are completed with bias updated from the previous GSI runs. Control run (CNTL) – Conventional data only 3-h cycling run, 9 day retro run (May 8 2010 – May 16 2010) AIRS experiment one (AIRS Ex. 1) -- NO BIAS SPIN UP CNTL + AIRS radiance data (60 km thinning in GSI) Use bias coefficients from GDAS Use the 68 selected channel set for RAP AIRS experiment two (AIRS Ex. 2) – WITH BIAS SPIN UP CNTL + AIRS radiance data Use updated bias coefficients from the extensive retro run Use 68 selected channel set

11. AIRS Bias Correction Assessment After BC Before BC channel 252 (CO2 channel ~672h Pa Channel 1382 (water vapor channel ~866 hPa With BC Spin-up Without BC Spin-up

12. Number of Observations Used Water vapor Carbon dioxide Surface o Without BC spin-up * With BC spin-up Better BC  more obs used  improved BC

13. AIRS Forecast Impact (against raob, 100-1000 hPa) Normalize Errors E N = (CNTL – EXP) CNTL T emperature Relative Humidity Wind 9-day retro average Control run: conventional data only AIRS Ex. 2 (w/ BC spin-up) AIRS Ex. 1 (w/o BC spin-up) +1% -1% 0%

14. Retrospective Experiments AMSU-A impact MHS impact HIRS impact All impact (AIRS + AMSU-A + MHS + HIRS)

15. Settings for other Radiance Retrospective Runs Extensive retro run for bias coefficients spin up Control run (CNTL) – conventional data only 3-h cycling run, 9 day retro run (May 8 2010 – May 16 2010) AMSU-A experiment – WITH BIAS SPIN UP CNTL + AMSU-A radiance data MHS experiment – WITH BIAS SPIN UP CNTL + MHS radiance data HIRS experiment – WITH BIAS SPIN UP CNTL + HIRS radiance data

16. AMSU-A Forecast Impact Control run: conventional data only 9-day retro average Forecast verification against raob. for 100-1000-hPa layer over a national domain AMSU-A data from NOAA_15, 18, 19 and METOP-A T emperature Relative Humidity Wind

17. MHS Forecast Impact Control run: conventional data 9-day retro average Forecast verification against raob. for 100-1000- hPa layer over a national domain T emperature Relative Humidity Wind MHS data from NOAA_18 and METOP-A

18. HIRS Forecast Impact control run: conventional data Forecast verification against raob. for 100-1000-hPa layer over a national domain 9-day retro average HIRS4 data from NOAA_19 and METOP-A T emperature Relative Humidity Wind

19. Comparison of Radiance Impact Control run: conventional data Forecast verification against raob. for 100-1000-hPa layer over a national domain 9-day retro average T emperature Relative Humidity Wind AIRS AMSU-A HIRS MHS All

20. 24-h (2 X 12h) Precipitation Verification CSI by precip threshold (avg. over eight 24h periods) AIRS CNTL (conventional data ) MHS HIRS AMSU-A Slight improvement for heavy precipitation thresholds from radiance data MHS data have largest positive impact for heavy precipitation prediction

21. Real-time Experiments All impact (AIRS + AMSU-A + MHS + HIRS)

22. Real-Time RAP Runs Real-time RAP hybrid systems on Zeus: 1-h cycling with partial cycle real-time data Time period: May 29 – June 4 2013 RAP dev_sat_1: Control run (CNTL) conventional data only RAP dev_sat_2: radiance experiment run conventional data + radiance data (AIRS, MHS, HIRS, AMSU-A)

23. Real-Time Impact real-time control run: conventional data T emperature Relative Humidity Wind Forecast verification against raob. for 100-1000-hPa layer over a national domain 6-day real-time average

24. Data Availability Issues Estimating fraction of data used Initial examination of Regional ATOVS Retransmission Services (RARS) data

25. Estimating fraction of data used short data cutoff times combined with long data availability latency times leads to  minimal satellite data availability W = Data Window Time L = Data Latency Time C = Data Cutoff Time W = 180 min L = 60 min C = 30 min (W/2 - L + C)/W = 33% obs used after cutoff data latency cutoff time Diagram and equation following Steve Lord Sample case data window initial time 03z 02z 04z 05z06z data available 0130z 0230z 0330z0430z Obs time Fraction of data used given by:

26. Real-Time Data Availability -- RARS AMSU-A channel 3 from NOAA_18 Real-Time RAP IDEAL -- No latency/cutoff RARS feed (not used in real-time yet) 18Z May 29, 2013 Assuming +/- 1.5 h time window

27. AMSU-A RARS Data Coverage 12Z13Z14Z 15Z 16Z 17Z 18Z19Z 20Z 21Z 22Z23Z RAP used +/- 1.5 hour data window RARS +/- 1.5 hour data window RAP used +/- 1.5 hour data window RARS +/- 1.5 hour data window May 29, 2013, NOAA_19 1741

28. Summary Retrospective runs AIRS Focused -- Positive impact for short-range skill after BC spin-up; improved BC result in more data assimilated Overall (AIRS, AMSU-A, MHS, HIRS) -- Temperature: Positive impact for AIRS (largest) and MHS; negative impact from HIRS data -- Moisture: Positive impact from all; max for MHS (3.5% for 3-h fcst); descending order: MHS, AIRS, AMSU-A, HIRS, -- Wind: Positive impact from AMSU-A, AIRS; the largest impact from AMSU-A (more than 2.5% at 12-h forecast) -- Precipitation: Small positive impact at high threshold for MHS

29. Summary Real-time runs Small positive impact overall (near-neutral for temperature) Largest positive impacts for 3-h forecast: -- moisture ~ 0.8% -- wind ~0.8% -- temperature ~0.6% Real-time impact smaller than retrospective impacts (actual data vs. ideal data) Real-time Data latency/cutoff issues for rapidly updating model system

30. Future Work Investigate negative/neutral impacts for AMSU-A/HIRS data Model top problem -- Increase RAP model top and model levels (for experiment purpose) -- Blending GFS fields Real-time data latency problem -- Use RARS feed -- Partial cycle (more waiting time) Retro tests with new RAP hybrid system Tropical cyclone prediction using RAP New data/instruments -- METOP-B, CrIS/ATMS Continue retro/real-time RAP radiance assimilation testing, implement enhancements into operational RAP.