All-sky assimilation of microwave imagers and sounders at ECMWF

1. All-sky assimilation of microwave imagers and sounders at ECMWF Alan Geer, Peter Bauer, Philippe Lopez, Niels Bormann, Deborah Salmond With thanks to: Bill Bell, Sabatino Di Michele, Anne Fouilloux, Carla Cardinali, Dick Dee, Jan Haseler, Tony McNally, Angela Benedetti, Elias Holm EUMETSAT fellowship talks, 8th Dec 2010

2. All-sky 4D-Var SSM/I - example SSM/I Channel 37v observations and FG departures FG departure (1st July 2009) EUMETSAT fellowship talks, 8th Dec 2010

3. Outline • Overview of 5 years… • Evolution of cloud- and rain-affected microwave radiance assimilation at ECMWF • Common themes • Underlying everything: model doesn’t put clouds and rain in exactly the right place • Mostly under control: Nonlinearity, non-Gaussianity • Still a problem: model biases, data assimilation methods • Recent work • Assimilation of all-sky microwave sounder radiances EUMETSAT fellowship talks, 8th Dec 2010

4. Development of cloudy/rainy assimilation EUMETSAT fellowship talks, 8th Dec 2010

5. 1D+4D-Var (cloud and rain) Direct radiance assimilation (clear) Radiances (cloudy + rainy) Assimilated variable 1D-Var retrieval Radiances (clear) TCWV Assimilated variable Model variables at observation time Wind, mass, humidity, cloud & precipitation Wind, mass, humidity, cloud & precipitation 4D-Var including atmospheric model Control variables at beginning of window Wind, mass, humidity Wind, mass, humidity EUMETSAT fellowship talks, 8th Dec 2010

6. All-sky 4D-Var Radiances (all-sky) Assimilated variable Scattering radiative transfer model Model variables at observation time Wind, mass, humidity, cloud & precipitation Wind, mass, humidity, cloud & precipitation 4D-Var including atmospheric model Control variables at beginning of window Wind, mass, humidity Wind, mass, humidity EUMETSAT fellowship talks, 8th Dec 2010

7. Rainy microwave assimilation at ECMWF • 2000-2005: Much research on: • Linearised moist physics operators • Fast scattering radiative transfer • Assimilation approaches, e.g. 1D+4D-Var • Operational implementation: • June 2005 – 1D+4D-Var assimilation of SSM/I • June 2008 – addition of TMI and AMSR-E • March 2009 – all-sky 4D-Var (SSM/I and AMSR-E) • November 2010 – new error model with much greater weight given to observations. TMI and SSMIS added. • Research • Microwave sounders, e.g. AMSU-A • Land surfaces EUMETSAT fellowship talks, 8th Dec 2010

8. The underlying difficulty in all cloudy / rainy data assimilation – sampling EUMETSAT fellowship talks, 8th Dec 2010

9. Mislocation between observations and model Observed TB [K] First guess TB [K] 220km FG Departure [K] (observation minus FG) EUMETSAT fellowship talks, 8th Dec 2010

10. ‘All-sky’ approach Observations or FG Cloudy (59%): correct sampling Observations cloudy (47%): biased sampling All-sky (100%) Clear-clear Cloud-cloud Obs Cloud- FG clear Obs Clear- FG cloud EUMETSAT fellowship talks, 8th Dec 2010

11. Asymmetric and symmetric sampling as a function of observed cloud Mean channel 19v departures [K] as a function of mean of observed and forecast cloud as a function of forecast cloud Cloud amount derived from 37GHz radiances ECMWF-JCSDA workshop, June 2010

12. Where asymmetry caught us out • Bias correction • 1D+4D-Var sampling Experimental assimilation of MODIS optical depths 1D Large rain bias in 1D-Var moist physics operators 4D PR EUMETSAT fellowship talks, 8th Dec 2010

13. Observation errors as a function of cloud amount ‘Symmetric’ observation error Standard deviation of AMSR-E channel 24hdepartures [K] • Mean cloud amount • derived from 37GHz radiances EUMETSAT fellowship talks, 8th Dec 2010

14. Symmetric approach underlies everything in All-sky assimilation: • All-sky approach (rather than separate clear and cloudy streams) • Bias correction as a function of cloud – not needed • Observation errors that vary with mean cloud amount (operational November 2010) • Quality control EUMETSAT fellowship talks, 8th Dec 2010

15. Issues ‘under control’? EUMETSAT fellowship talks, 8th Dec 2010

16. Non-Gaussianity Departures symmetric error Departures 4.3K Gaussian EUMETSAT fellowship talks, 8th Dec 2010

17. All-sky 4D-Var departures: Quality Control Normalised by σSYM SSM/I 37v departure EUMETSAT fellowship talks, 8th Dec 2010

18. Incremental 4D-Var handles nonlinearitySingle observation test in tropical convective rain TL hypothesis invalid EUMETSAT fellowship talks, 10 Dec 2009

19. Outstanding issues? EUMETSAT fellowship talks, 8th Dec 2010

20. Model bias – e.g. cold sectors FG departure (1st July 2009) “Cloud water fraction” Weighted mean cloud fraction * LWP / (LWP+IWP) EUMETSAT fellowship talks, 8th Dec 2010

21. Model bias – e.g. excess snow AMSU-A channel 5, global, 1st February 2010 Rescale snow amount by: Number per bin 1× 0.05× 0.2× FG departure [K] EUMETSAT fellowship talks, 8th Dec 2010

22. Wind increments being spread from boundary layer into free troposphere, probably by the background error structure functions. Effect also occurs with AMV winds Tropical wind increments Change in RMS forecast error (versus own analysis) due to increased weight of all-sky observations: 850hPa vector wind at T+12 (August to October 2010) +0.4 ms-1 Maritime boundary layer cloud regions 0.0 -0.4 EUMETSAT fellowship talks, 8th Dec 2010

23. Inter-channel observation error correlation (from Desroziers technique) FG departure correlation EUMETSAT fellowship talks, 8th Dec 2010

24. Summary 1 • Microwave imagers SSMIS, TMI and AMSR-E operationally assimilated in 4D-Var in all-sky conditions • Main constraint on oceanic lower tropospheric humidity in ECMWF analyses • Analysed cloud and precipitation are modified to fit the observations • Symmetric observation errors represent the model’s inability to put cloud and rain in exactly the right place • Issues under control: • Non-linearity • Non-Gaussianity • Quality control EUMETSAT fellowship talks, 8th Dec 2010

25. Summary 2 - problems / future work • Assimilation methods • Background errors need to be more flow dependent (or use Ensemble KF) – e.g. big increments above maritime boundary layer • Cloud control variable • Observation errors are currently representing forecast model errors • Cloud and precipitation in models • Improve moist physics – e.g. cold sectors, 5 times too much snow in deep convection • Radiative transfer modelling • Scattering from frozen hydrometeors • Surface emissivity, both land and ocean • All-sky assimilation over land EUMETSAT fellowship talks, 8th Dec 2010

26. Current work: All-sky AMSU-A assimilation EUMETSAT fellowship talks, 8th Dec 2010

27. AMSU-A clear-sky weighting functions Channel Transmittance to space 7 0% 6 0% 5 5% 4 19% 3 56% 2 92% EUMETSAT fellowship talks, 8th Dec 2010

28. Channel 4 FG departures [K]Mean 1 – 8 February 2010, Metop-A, with QC & BC Simulations ignore cloud +1.5 Simulations use cloudy radiative transfer -1.5 EUMETSAT fellowship talks, 8th Dec 2010

29. Channel 5 FG departures [K]Mean 1 – 8 February 2010, Metop-A, with BC, but no QC Simulations ignore cloud +1.5 Simulations use cloudy radiative transfer -1.5 EUMETSAT fellowship talks, 8th Dec 2010

30. What does all-sky bring to AMSU-A? • Channel 6, 7, & 8 (UTLS) • no decrease in FG departure std. dev. • 2% increase in coverage, compared to clear-sky. • Channel 5 (mid troposphere) • 0.01K decrease in FG departure std. dev. • 2% increase in coverage, compared to clear-sky. • Channels 3 & 4 (lower troposphere) • Not operationally assimilated (partly historical) • HIRS, IASI, AIRS have similar channels but these are rarely cloud free • This is new temperature sounding information. • Channels 1 & 2 (24 & 36 GHz water vapour and cloud) • Information content is the same as AMSR-E, SSM/I, SSMIS etc. EUMETSAT fellowship talks, 8th Dec 2010

31. Scientific and technical upgrades for AMSU-A EUMETSAT fellowship talks, 8th Dec 2010

32. Grid-point approach needs modifcation Conical scanner (e.g. SSM/I) Swath scanner (e.g. AMSU-A) Observations with constant zenith angle Observations with varying zenith angle All need simulation One simulation per gridbox EUMETSAT fellowship talks, 8th Dec 2010

33. FG departure standard deviation by scan positionMetop AMSU-A channel 4 Standard deviation of FG departure [K] RMS of observation error Scan position EUMETSAT fellowship talks, 8th Dec 2010

34. Nearest gridpoint not appropriate Metop AMSU-A channel 5, 00Z, 1st February 2010 TB from T profile interpolated to observation – TB at nearest gridpoint [K] Std. dev. 0.07K Obs. error 0.35K Need to use an interpolated temperature profile EUMETSAT fellowship talks, 8th Dec 2010

35. Results from AMSU-A channel 4 assimilation EUMETSAT fellowship talks, 8th Dec 2010

36. Wind forecast scores:adding AMSU-A ch. 4 Degradation Minor Improvement? EUMETSAT fellowship talks, 8th Dec 2010

37. AMSU-A fits: channels 5 – 14Tropics + all-sky ch. 4 control EUMETSAT fellowship talks, 8th Dec 2010

38. AMSU-A fits: channels 5 – 14Midlatitudes + all-sky ch. 4 control EUMETSAT fellowship talks, 8th Dec 2010

39. All-sky AMSU-A – summary 1 • Technical challenges: • Swath scanning rather than conical scanning • Observation error varies with scan position • Different horizontal resolutions • Different sensitivity to low-level cloud • Time-saving “one simulation per gridpoint” approach is no longer possible • Nearest gridpoint approximation is not valid for temperature channels EUMETSAT fellowship talks, 8th Dec 2010

40. All-sky AMSU-A – summary 2 • Usefulness of all-sky • Little benefit for channel 5 (mid-troposphere) and upwards • Revisit when we can better simulate deep convection and ice/snow scattering • Channels 3 and 4 – best hope • Channels 1 and 2 – duplicate information content of imagers • Adding AMSU-A channel 4 via the all-sky route • Forecast scores: • Neutral in long range • Poor in short range at high latitudes – cold sector cloud bias? • Observation fits: • good in tropics in short range EUMETSAT fellowship talks, 8th Dec 2010