Preliminary AIRS NWP impact results from ECMWF

1. Preliminary AIRS NWP impact results from ECMWF Tony McNally P. Watts / G. Kelly / M.Matricardi / J.Smith

2. AIRS monitoring at ECMWF Archive departures and cloud flags/QC NASA OPERATIONAL 4D-Var system 2378 ch., all pixels NESDIS/ORA 324 ch., 1 out of 18 pixels AIRS data are passed through the operational assimilation system every day to provide real-time monitoring information (archived and displayed on WWW).

3. Pressure ranked AIRS [obs-calc] biases This is some text to hide the plot Large and air-mass dependent biases (probably systematic stratospheric temperature error in ECMWF model / AMSUA) Generally small and flat biases in the mid-troposphere / lower stratosphere Weighting function peak pressure AIRS ranked channel index Some polar biases in the surface sensitive channels (possibly related to missed cloud detection)

4. NWP impact experiments Control assimilation system (ECMWF operations): 12hr 4DVAR (T159 increments) T511 Forecast (conventional data + 3xAMSUA + 3 SSMI + 2xHIRS + 5xGEOS/MODIS + SCAT) AIRS assimilation system: 12hr 4DVAR (T159 increments) T511 Forecast (conventional data + 3xAMSUA + 3 SSMI + 2xHIRS + 5xGEOS/MODIS + SCAT + AIRS) Trial period: 10 Dec 2002 to 31 Jan 2003 (18 Oct 2002 to 18 Nov 2002 performed at low resolution)

5. AIRS data usage in 4DVAR • Input radiance data consists of sampled 324 channels from NASA / NESDIS-ORA • All channels flagged clear at a location are assimilated (subject to blacklist) • After cloud screening ‘good’ data are thinned to a horizontal spacing of 120Km • Currently we do not attempt to assimilate channels in the O3 band or 4.2 micron band • Currently we do not attempt to assimilate low level channels over land • Flat (single global number rather than varying) bias correction used for each channel • Very simple (and conservative) observation error assigned to each channel (0.6 / 1.0 / 2.0K) The initial emphasis here is on a conservative use of the AIRS data (with simple observation error models and bias correction aiding diagnosis of the results)

6. Systematic analysis incrementsin temperature Zonally averaged mean analysis increments (AN-FG) as a function of altitude (EC model level) CONTROL CONTROL + AIRS Conclusion: There do not appear to be any strong air-mass dependent biases in the AIRS radiances or the radiative transfer model used to assimilate them

7. Impact of AIRS on ECMWF assimilation system RMS analysis increment (AIRS) minus RMS analysis increment (CTRL) 500hPa temperature averaged over 10 days The assimilation of AIRS causes a clear reduction in the 4DVAR analysis increments at radiosonde locations

8. AIRS forecast impact Day-3 RMS of 500hPa geopotential forecast error averaged over 40 days (Dec 02/ Jan 03) [AIRS error] minus [CTRL error] Day-5 The assimilation of AIRS radiances shows a small but consistent positive impact on forecast quality in all areas Day-7

9. AIRS forecast impact in the Tropics

10. Summary The AIRS radiance assimilation system is currently conservatively tuned (in terms of observation errors and QC) and produces modest positive impacts in all areas. The CONTROL system (ECMWF operations) is currently performing extremely well (3xAMSUA 3xSSMI 2xHIRS 5xGEOS) and we should not expect large positive impacts from AIRS on the mean forecast skill. The dream scenario of the assimilation of AIRS data fixing up a “failed forecast” has not yet been found (lots of cloud / few busts) but we will keep looking!

11. Next steps • Finalize system for day one operational implementation of AIRS • Investigate need for air-mass dependent bias correction (CO2) • Improve cloud detection (polar areas / cross band ideas) • Add assimilation of night time 4.2 micron data • Look at reduction of observation error for key channels • Look at more use of low-level data over land / ice