1. Physically-Based Inversion of Cloud & Precip Pete Weston, 3rd Joint JCSDA-ECMWF Workshop on Assimilating Satellite Observations of Clouds and Precipitation into NWP Models 2nd December 2015

2. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work Acknowledgements: Ed Pavelin, Pete Francis, Bob Tubbs, Bill Bell, Stefano Migliorini

3. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work

4. 1D-Var grey cloud analysis CF CTP Initially selected IASI channel Jacobians Retrieve cloud parameters in 1D-Var using RTTOV: Single level “grey” cloud Choose channels with minimal sensitivity below cloud top Pass these channels to 4D- Var

5. 1D-Var grey cloud analysis In many cases, 1D-Var cloud model is unrealistic: Not (generally) single-level grey cloud Cloud is generally multi-level, 3D Leads to biases below cloud top Solution is to remove channels most likely to be poorly modelled: Reject all channels peaking below retrieved cloud top 10% of weighting function area allowed below cloud top Channel selection carried out for each sounding

6. Example cloudy weighting functions (  B i /  T j ) Mid-level cloud Use 26 of 94 channels Low cloud Use 67 of 94 channels Retrieved CTP

7. Simulated 1D-Var analysis errors: Mid- level cloud cases (Pavelin et al., 2008) CTP Retrieval Background Retrieval Background

8. Comparison of data volumes Clear observations Cloudy observations

9. Forecast Impact Results from trial of AIRS and IASI cloudy v AIRS and IASI clear Strong positive impact Mostly from use of additional data Double impact of clear IASI

10. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work

11. UKV introduction Convective scale model over UK Resolution: ~1.5 km to ~4 km 70 levels (top ~40 km) Forecast range: T+36 6 hourly LBCs from global model Data assimilation methods used: 3D-Var with 3 hour window 3km resolution analysis Observations assimilated: Conventional, Doppler radar winds, surface cloud, SEVIRI CSRs, MHS, IASI, AMVs, Scatterometer winds, ground based GNSS, Geo cloud

12. SEVIRI observations Classified as clear or flagged as cloudy in Autosat pre-processing If clear then radiance is made available for direct assimilation If cloudy then further processing required... SEVIRI radiance and cloud mask passed to OPS

13. Retrieved cloud from SEVIRI Minimum residual method used for cloud first guess 1D-Var used to retrieve CTP and ECA Stable layers method used for low cloud and to reduce uncertainty Outputs are CTP and ECA at the cloud top

14. Retrieved cloud from SEVIRI CTP values are turned into a layer of cloud between 1 and 4 NWP layers thick with zero Effective Cloud Amount (ECA) above Layer thickness used is based on a model climatology as a function of model level The state variable which is modified is the specific total water (q T ) at and above the cloud top

15. SEVIRI channel 5 above low cloud One final use of SEVIRI data is the assimilation of channel 5 (6.2µm) above low cloud This was introduced to remove areas of extremely low humidity in the upper levels of the model There were previously no observations that could constrain the humidity in that region

16. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work

17. Future strategy At the Met Office we are currently working on assimilating cloud information from radiances in a number of different approaches: IR cloudy error model in a variational scheme: Larger observation error used for cloudy radiances Concentrating on humidity sensitive channels Initial results are promising with positive impacts on low- level water vapour Cloud incrementing operator in a variational scheme (see Stefano Migliorini’s talk later today) Direct cloudy IR radiance assimilation in an ensemble scheme (see next section)

18. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work

19. Initial experiments (Pavelin, 2015) Use Ensemble Kalman Filter method using: MOGREPS-UK ensemble 12 members every 6 hours 2.2km resolution Simulated imagery output Brightness temperatures derived from MOGREPS-UK Using RTTOV multiple scattering parameterisation 3 SEVIRI channels for analysis: 6.2  m (wv), 7.3  m (wv), 10.8  m (window) No Kalman filter cycling, only one linear update cycle

20. Observations Ens. std. dev. (“B”) Analysis Fit (A-O) Generally very good fit to obs. Some small-scale errors Interpolation artefacts? Scale mismatches? Ensemble mean Example: 5 June 2014 - SEVIRI 10.8  m BT

21. Analysis increments at 660 hPa T CFCIWCLW q

22. Increments Analysis TqCLWCIWCF Single gridpoint example

23. Assimilation of cloudy radiances into an EnKF Promising initial results from several non-cycling cases Next step was to test assimilation of cloudy radiances in a cycling system My colleague Jonathan Flowerdew has developed a prototype convective scale EnKF system assimilating conventional observations only Plan was to use this system to assimilate cloudy SEVIRI observations

24. Assimilation of cloudy radiances into an EnKF I used a deterministic serial filter avoiding costly matrix inversions and the need to perturb observations Ensemble spread is too small so we use relaxation to prior perturbations to increase spread The horizontal and vertical localisation follows Flowerdew (2015) 2.2km resolution over the UK Assimilation window of an hour

25. Updating H(x f ) Serial filters normally update x f for observation j before calculating H j+1 (x f ) Our separate OPS calculates all H(x f i ) at the start using the original background state We can work around this by updating the observation priors just like any other part of the state (Anderson, 2003) As a bonus, this naturally gives the innovation variance for each observation after assimilating all prior observations – an independent measure of analysis error 123456 Observation number ff aa Initial background error Final analysis error Diagnostic averages these errors Slide: Jonathan Flowerdew

26. Observation processing configuration 12km thinning to reduce effects of spatially correlated errors 15 min temporal thinning (use closest image to the end of hour window) SEVIRI channels at 6.2µm (water vapour) & 10.8µm (window) Relaxed QC (80K gross check) 3K obs errors (implying that any cloud errors >3K are in the model) Existing SEVIRI CSR bias correction Add qcf and qcl to state vector Observation type specific horizontal localisation (124km for SEVIRI) and channel specific vertical localisation parameters (broader for channel 9 due to sensitivity to cloud throughout column)

27. Results (SEVIRI ch 5 & 9 v ch 5 v control)

28. Mean Normalised Innovations Suite IDDescript.SondeSurfaceAircraftSEVIRI ag759Ctrl1.19410.95981.0820N/A ag842Ch 5 & 91.28000.97181.18112.2235 ah038Ch 51.19990.96111.09250.5837 ah029Tuned loc1.31240.97781.19452.3541 ah361Broad loc1.61791.07321.37202.4750 SEVIRI channel 9 performing badly – too small observation error or vertical localisation problem? Tuned localisation performing worse!? – spin down effects or tuner results are dominated by large innovations e.g. high cloud? Broader vertical localisation (mi-ah361) performs much worse – problem with vertical localisation (‘overshooting’ increments?)

29. Verification Suite ID Descript.UKV index Cloud amount Cloud base height ag759CtrlN/A ag842Ch 5 & 9-2.19%-0.045 ah038Ch 5+0.97%+0.004+0.029 ah029Tuned loc0.00%-0.058+0.046 ah361Broad loc-6.45%-0.049-0.101 SEVIRI channel 9 still performing badly SEVIRI channel 5 performing well Tuned localisation performing better in VER Broader vertical localisation much worse

30. Future work Experiment with shorter cycle lengths e.g. 15/30 minutes Should help with ‘spin-down’ effects Tuning observation errors More channels: From SEVIRI initially and then extend to hyperspectral IR Optimise thinning and other observation configuration details Look in more detail at RTTOV configuration Optimise vertical localisation including: Dynamic central pressure and widths, properly accounting for sensitivity to clouds at different heights

31. Clear sky ch 9 Jacobian Low cloud ch 9 Jacobian High cloud ch 9 Jacobian Vertical localisation function

32. Contents What we do now 1D-Var grey cloud analysis Retrieved cloud from SEVIRI Future strategy Assimilation of cloudy radiances into an EnKF Conclusions and future work

33. All of the current/future approaches represent a shift from our currently operational schemes which: Avoid cloud altogether by only assimilating observations or channels unaffected by cloud Retrieve cloud parameters first and then assimilate these or derived products (e.g. GeoCloud, AMVs) To schemes which: Assimilate cloud information from radiances directly Obviously there are still many challenges in the new approaches but initial results are promising By the next workshop in 5 years we may have cracked it...

34. References Anderson, J. L. 2003. A local least squares framework for ensemble filtering. Monthly Weather Review, 131(4), 634-642. Flowerdew, J. 2015. Towards a theory of optimal localisation. Tellus A, 67. doi:http://dx.doi.org/10.3402/tellusa.v67.25257http://dx.doi.org/10.3402/tellusa.v67.25257 Pavelin, English and Eyre, 2008, Q. J. Roy. Met. Soc. Pavelin, E. 2015. Towards the assimilation of cloud from IR sounders: A simple ensemble Kalman filter. SA Tech Memo 29.

35. Thanks for listening Any questions?

36. Ensemble Kalman Filter (EnKF) Kalman Filter: Variational equivalent (4DEnVar): Model-space EnKF: Observation-space EnKF: Image: Adam Clayton Slide: Jonathan Flowerdew