# Presentation: 707917

## Preliminary AIRS NWP impact results from ECMWF

- Tony McNally
- P. Watts / G. Kelly / M.Matricardi / J.Smith

## AIRS monitoring at ECMWF 	

- NASA

- NESDIS/ORA

- Archive
- departures
- and cloud
- flags/QC

- OPERATIONAL
- 4D-Var
- system

**2378 ch., all pixels**

**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).

## 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

- Some polar biases in the surface sensitive channels (possibly related to missed cloud detection)

- AIRS ranked channel index

- Weighting function peak pressure

## 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)

## 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)

## Systematic analysis incrementsin temperature

- CONTROL

- CONTROL + AIRS

- Zonally averaged mean analysis increments (AN-FG) as a function of altitude (EC model level)

- 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

## 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

## AIRS forecast impact

- RMS of 500hPa geopotential forecast error averaged over 40 days (Dec 02/ Jan 03)
- [AIRS error] minus [CTRL error]

- Day-3

- Day-5

- Day-7

- The assimilation of AIRS radiances shows a small but consistent positive impact on forecast quality in all areas

## AIRS forecast impact in the Tropics

## 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!

## 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