1. Slide 1 IPWG, Beijing, 13-17 October 2008 Slide 1 Assimilation of rain and cloud-affected microwave radiances at ECMWF Alan Geer, Peter Bauer, Philippe Lopez Thanks to: Deborah Salmond, Niels Bormann, Bill Bell, Chris O’Dell, Graeme Kelly

2. Slide 2 IPWG, Beijing, 13-17 October 2008 Slide 2 Rain and cloud in NWP Improved initial conditions lead to improved forecasts Variational assimilation (e.g. 4D-Var) is used to generate these initial conditions by combining a first guess forecast with observations: -Conventional: Weather stations, radiosondes, aircraft -Satellite: Infrared, microwave, scatterometer, atmospheric motion vectors Need more information on temperature, pressure, winds and humidity everywhere, but particularly in cloudy and rainy regions Need information on the cloud and rain themselves. But aren’t clouds and rain transient phenomenon? -Directly useful for short range forecasting -The presence or absence of cloud or rain can be used to help infer the temperature, pressure, wind and moisture structure of the atmosphere to benefit longer term forecasts -If comparison to the observations reveals shortcomings in the cloud and rain models, they will have to be improved

3. Slide 3 IPWG, Beijing, 13-17 October 2008 Slide 3 Assimilation of microwave imagers at ECMWF 1998 - SSM/I TCWV assimilation from 1D-Var in clear skies over oceans 2003 - Direct 4D-Var of clear-sky SSM/I 2005 - 1D+4D-Var of rainy SSM/I -Bauer et al., QJRMetS, 2006a,b,c 2007-8 - AMSR-E, TMI added in rainy and clear sky 2009? - direct assimilation of all-sky radiances in 4D-Var

4. Slide 4 IPWG, Beijing, 13-17 October 2008 Slide 4 ECMWF’s current rain and cloud assimilation approach: 1D+4D-Var Clear sky SSM/I radiances are directly assimilated in 4D- Var Cloudy and rainy SSM/I radiances have been assimilated operationally at ECMWF since 28th June 2005, over sea only, using a 1D+4D-Var method: -1D-Var retrieves T and q profiles and surface windspeed -1D-Var observation operator includes:  simplified large-scale and convective cloud schemes  Microwave radiative transfer -TCWV retrievals are assimilated in 4D-Var

5. Slide 5 IPWG, Beijing, 13-17 October 2008 Slide 5 TCWV /kgm-2 Tb departure (observation minus simulated) /K SSMI channel 19v19h22v37v37h85v85h FG7.64.57.92.04.28.84.112.0 analysis8.82.01.3-1.13.4-1.813.35.5 Tephigram – temperature and humidity Rain/snow Cloud ice / water Cloud fraction

6. Slide 6 IPWG, Beijing, 13-17 October 2008 Slide 6 Quality of 1D+4D-Var rain retrievals: near- instantaneous colocations First guessRetrieval Geer, Bauer, Lopez, QJRMetS, latest issue, 2008 SSM/I retrieval compared to mean of PR footprints within ± 7.5 minutes and 25km

7. Slide 7 IPWG, Beijing, 13-17 October 2008 Slide 7 Quality of 1D+4D-Var rain retrievals: correlation coefficients Geer, Bauer, Lopez, QJRMetS, latest issue, 2008 Rain water pathSurface rain rate First guessRetrievalFirst guessRetrieval Log(rain)0.50.640.390.54 rain0.490.830.410.73

8. Slide 8 IPWG, Beijing, 13-17 October 2008 Slide 8 RMSE against operational analyses Vector wind Relative humidity SouthTropicsNorth Forecast scores: 1D+4D-Var rainy assimilation Kelly et al., Mon. Weath. Rev., July, 2008 Limited observing system Limited observing system plus 1D+4D-Var Full observing system without 1D+4D-Var Full observing system

9. Slide 9 IPWG, Beijing, 13-17 October 2008 Slide 9 Emissive reflector biases All conical-scanning microwave imagers (TMI, SSMI, SSMIS, AMSR-E … ) incorporate a spinning reflector If the reflector is emissive: Unfortunately a common situation: -SSMIS – Bill Bell, 2008, IEEE -TMI – Frank Wentz, 2001, IEEE Reflector emissivity

10. Slide 10 IPWG, Beijing, 13-17 October 2008 Slide 10 SSM/I AMSR-E TMI First guess departure [K]

11. Slide 11 IPWG, Beijing, 13-17 October 2008 Slide 11 TMI reflector temperature estimated from first guess departure biases Estimated reflector temperature [K]

12. Slide 12 IPWG, Beijing, 13-17 October 2008 Slide 12 Modelled cloud liquid water (at SSM/I observation locations; 12hrs of data) 37v Obs – FG departure [K] (after moist physics improvements; C MAX cloud overlap) 37v Obs – FG departure [K] (after moist physics improvements; C MEAN cloud overlap) Radiative transfer biases in cloud and rain

13. Slide 13 IPWG, Beijing, 13-17 October 2008 Slide 13 Surfac e TOA Cloudy column Clear column C ma x Cloud Forecast model – 1 grid point RTTOV fast radiative transfer C ma x RTTOV-SCATT – Two independent column approximation: T b = (1 - C max ) × T b (clear) + C max × Tb(cloudy)

14. Slide 14 IPWG, Beijing, 13-17 October 2008 Slide 14 Surfac e TOA Cloudy column Clear column C mean Cloud Forecast model – 1 grid point RTTOV fast radiative transfer C ma x RTTOV-SCATT – revised version: T b = (1 - C mean ) × T b (clear) + C mean × Tb(cloudy)

15. Slide 15 IPWG, Beijing, 13-17 October 2008 Slide 15 Modelled cloud liquid water (at SSM/I observation locations; 12hrs of data) 37v Obs – FG departure [K] (after moist physics improvements; C MAX cloud overlap) 37v Obs – FG departure [K] (after moist physics improvements; C MEAN cloud overlap)

16. Slide 16 IPWG, Beijing, 13-17 October 2008 Slide 16 All-sky, direct 4D-Var assimilation In contrast to 1D+4D-Var, the full information content of the observations is assimilated: -Surface temperature and winds -Cloud and precipitation -Total column water vapour A unified assimilation: -All sky conditions (rainy, cloudy, clear) are treated in the same assimilation stream

17. Slide 17 IPWG, Beijing, 13-17 October 2008 Slide 17 RMS forecast errors: relative humidity normalised difference (all-sky 4D-Var - 33r1 control) degradation improvement 10 th Aug to 4 th Sept 2007: 18 to 26 samples verified against own analyses.

18. Slide 18 IPWG, Beijing, 13-17 October 2008 Slide 18 Departure statistics: SSM/I obs- FG mean

19. Slide 19 IPWG, Beijing, 13-17 October 2008 Slide 19 Summary 1 - issues Emissive reflectors -TMI suffers from emissive reflector bias  AMSR-E also?  Be careful when creating multi-instrument products -Recommendations for instrument builders:  Need to build non-emissive reflectors  Need for accurate measurements of reflector skin temperature Radiative transfer in rain and cloud -Move from “maximum” to “weighted average” cloud fraction -Better agreement with 10 independent column approach and with observations

20. Slide 20 IPWG, Beijing, 13-17 October 2008 Slide 20 Summary 2 - assimilation 1D+4D-Var assimilation of rain- and cloud- affected SSM/I radiances -Operational since June 2005 but only the TCWV information content is currently used -Positive impact on forecast scores for tropical moisture and winds -Impact is comparable to clear sky microwave imager assimilation. -Good quality rain retrievals (compared to PR) Direct 4D-Var assimilation of all-sky radiances (clear, cloudy, rainy) -Full information content of the observations is assimilated -Improved forecasts compared to previous system -To be made operational early 2009