1. Assimilation of AIRS Radiance Data within the Rapid Refresh Rapid Refresh domain Haidao Lin Steve Weygandt Ming Hu Stan Benjamin Patrick Hofmann Curtis Alexander 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.Background on Atmospheric Infrared Sounder (AIRS) data 3.AIRS radiance assimilation in RAP  Bias correction  Channel selection  RAP retrospective runs and forecast verification  HRRR case runs initialized from RAP 4.Real-time RAP data availability issues 5.Summary and future work

3. Background on Rapid Refresh NOAA/NCEP’s hourly updated model 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 -RAP guidance for aviation, severe weather, energy applications High-Resolution Rapid Refresh (HRRR) - 3-km nested domain for storm predictions - New 15-hour forecast each hour -- Real-time experimental runs at ESRL RUC  Rapid Refresh -- May 1, 2012 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 Benchmarking / Retro Configuration 9 day retro period (8-16 May 2010) Use 3-h cycle, no partial cycling Benchmark against R/T and perform raob denial 3-h RAP retro cycle results as expected -- 1-h RAP slightly better -- 3-h RAP similar to R/T RUC 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 RH1.11%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 denial results closely match previous OSE study 1-hourly R/T RUC 3-hourly RAP retro 1-hourly RAP retro (partial cycle) 12-h fcst wind RMS Error (100-1000 mb mean) Assimilate all standard observations

7. AIRS Data Launched 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) 281-channel subset is available for operational assimilation AIRS Brightness Temperature (BT) simulated from Community Radiative Transfer Model (CRTM)

8. AIRS Radiance Coverage in RAP 3 h time window (+/- 1.5 h), in 3-h cycle RAP retro run 00Z 03Z 06Z 09Z 12Z 15Z 18Z21Z 08 May 2010 Brightness Temperature (BT) from AIRS channel 791

9. Radiance Assimilation for RAP Challenges for regional, rapid updating radiance assimilation Bias correction -- Cycled predictive bias correction in GSI -- Spin-up period, complicated by non-uniform data coverage Channel Selection Many channels sense at levels near RAP model top (10 hPa) Use of these high peaking channels can degrade forecast Jacobian / adjoint analysis to select channels for exclusion Data availability issues for real-time use Rapid updating regional models: short data cut-off, small domain Above combined with large data latency  little data availability Complicates bias correction, partial cycle assimilation options

10. Observation Operator (CRTM) Air mass bias Angle bias are the coefficients of predictors (updated at every cycle) = predictors mean constant scan angle cloud liquid water (for microwave) square of T lapse rate T lapse rate Bias parameter background error covariance matrix Variational Satellite Bias Correction in GSI

11. BT O-B Difference AIRS channel 261 (CO2 channel) with PWF height around 840 hPa Diff. before and after application of bias correction during retrospective cycle, after 2-week spin-up Mean BT diff without BC Mean BT diff with BC Histogram 0.0 0.0

12. Two month time series bias coefficients AIRS channel 261 (CO2 channel, PWF ~ 840 hPa) How long a period to adequately spin up bias- correction predictor coefficients? Highly variable for different predictors and channels Some can take two months or more Problems due to big differences in data coverage for successive cycles (in contrast to global models)

13. The CRTM K-matrix model (Jacobian model) computes the radiance derivatives with respect to the input-state variables, such as temperature and gas concentration Forward model TL model AD model K-matrix model is the input K-matrix radiance input variable and is the transpose of the ith row of the H matrix: Setting for (i=1,….,m), the matrix returned from the K-matrix model contains the Jacobians The matrix H contains the jacobian element Channel Selection Because of Low Model Top Jacobian calculation in CRTM to find problem channels

14. Spurious warming from low model top warm Sample RAP Temperature Analysis Increment and Jacobian cool Background (B) and analysis (A) temperature Temperature increment (A-B) Temp Jacobian from standard profile AIRS channel 22 T Jacobian for this profile

15. Temperature and Moisture Jacobians Standard profile (0.01 hPa top)RAP profile (10 hPa top) Artificial sensitivity due to low model top in RAP dBT/dT (K/K) Artificial sensitivity due to low model top in RAP (dBT/dq) * q (K) Temperature Moisture

16. Adjoint Sensitivity Channel Selection The brightness temperature sensitivity for channel j The total contribution above the top of the model (10 hPa for RAP) is Channels with larger than 0.06 K were discarded More details in McCarty et al. 2009 Threshold 0.06 K is conservative and tunable Channel arranged by PWF Height Removed Channels 68 selected channels Removed channels

17. Settings for Retrospective Runs Previous two-week warm up retro run April 23 – May 7, 2010 3-h AIRS radiance data with bias coefficients cycled (the very first bias coefficients were set to be zeroes) Control run (CNTL) – NO AIRS RADIANCE DATA 3-h cycle run, 9 day retro run (May 8 2010 – May 16 2010) Conventional data AIRS experiment one (AIRS Ex. 1) -- NO CHANNEL SELECTION CNTL + AIRS radiance data (60 km thinning in GSI) Use updated bias coefficients from warm up retro run, cycle the bias Use the 120 GDAS channel set AIRS experiment two (AIRS Ex. 2) – CHANNEL SELECTION CNTL + AIRS radiance data (60 km thinning in GSI) Use updated bias coefficients from warm up retro run, cycle the bias Use the 68 selected channel set based on adjoint analysis

18. Mean BT Differences & RMS Errors before and after Assimilation Mean Difference RMS Results from Ex. 2 * Background * Assimilated

19. BT Differences & RMS Errors before and after Assimilation Vertically Arranged by PWF Height Results from Ex. 2 * Background * Assimilated Mean Difference RMS

20. 6-h Forecast RMS Error (against raob ） AIRS Ex. 2 (selected 68 channels) CNTL AIRS Ex. 1 (default 120 channels Temperature Relative Humidity Wind

21. AIRS Radiance Assimilation Summary Assimilation of AIRS radiance data in RAP produces small positive impact for winds, temperature, relative humidity and heavy precipitation Use of Jacobian / adjoint sensitivity test to eliminating channels with maximum sensitivity near RAP model top (10 hPa) improves forecasts Lengthy spin-up of GSI variational bias correction needed for some channels and predictors (issues with limited data coverage) Slightly improved longer lead time reflectivity forecast from several case HRRR runs Key data availability challenges for real-time use of data in rapidly updating, regional models

22. Future Work Improve radiance bias correction in RAP context Investigate the cloud contamination issues Re-scripting RAP partial cycles to increase the data cutoff time to include more real-time AIRS data (and other polar-orbiting satellite data) Increase RAP model top Incorporate AIRS radiance data into operational hourly updated Rapid Refresh at NCEP

24. 24-h (2 X 12h) CPC Precipitation Verification CSI by precip threshold (avg. over eight 24h periods) Slight improvement for heavy precipitation thresholds from AIRS radiance data AIRS Ex. 2 (selected 68 channels) CNTL (no AIRS) AIRS Ex. 1 (default 120 channels

25. HRRR Radar Reflectivity Verification 3 case HRRR runs Initialization time from RAP: 21Z May 10, 2010 06Z May 13, 2010 09Z May 13, 2010 (with good airs coverage) AIRS Ex. 2 (selected 68 channels) CNTL 25 dBZ 3-km CONUS 30 dBZ 3-km CONUS | | | | | | | 0-h 2-h 4-h 6-h 8-h 10-h 12-h | | | | | | | 0-h 2-h 4-h 6-h 8-h 10-h 12-h 3 case HRRR runs averaged

26. AIRS Data Coverage in RAP June 18 2012 Real time +/- 3 hour data window Real time +/- 1.5 hour data window 00Z 01Z 02Z 03Z 04Z 05Z Ideal +/- 3 hour data window Ideal +/- 1.5 hour data window

27. Satellite Data Availability Issues For Rapid Refresh models: short data cutoff times combined with long data availability latency times lead to minimal satellite data availability for model assimilation W = Data Window Time L = Data Latency Time C = Data Cutoff Time W = 180 min L = 60 min C = 30 min % of data used = (W/2 - L + C)/W % of data used = (180/2 - 60 + 30)/180 = 60/180  33% obs used after cutoff data latency cutoff time Diagram and equation following Steve Lord Sample values data window initial time 03z 02z 04z 05z06z data available 0130z 0230z 0330z0430z Obs time

28. Satellite Data Availability Issues Worst case for RAP model: W = Data Window Time C = Data Cutoff Time L = Data Latency Time W = 90 min L = 80 min C = 25 min % of data used = (90/2 - 80 + 25)/90 = -10/180  0% NOTE: Data latency time is variable, based on proximity of satellite to download station W = 90 min L = 80 min C = 180 min % of data used = (90/2 - 80 + 180)/90 = 145/90  100% Assimilation in partial cycle: Delay cycles 3-4 hrs  longer cutoff NO data used 00z 03z 06z 09z RAP spin-up cycle ALL data used % of data used = (W/2 - L + C)/W