1. Page 1© Crown copyright 2006 The Analysis of Water Vapour in Met Office NWP Models Bill Bell (SSMI, SSMIS in global NWP) Amy Doherty (AMSU-B, scattering RT at 183GHz) Tim Hewison (groundbased MWR for regional NWP) Wettzell MWR meeting, October 9 -11 th 2006

2. Page 2© Crown copyright 2006 Outline Background NWP models & variational assimilation. Fast RT model – RTTOV. Satellite radiance observations The accuracy of forecast water vapour fields ? Issues for MW observations of WV The Assimilation of SSMI / SSMIS Radiances Fundamental limitations of TCWV / 22GHz observations – no profile information RT Model issues (22GHz line parameters) Assimilation of 183 GHz radiances (AMSU-B) Forward modelling in the presence of ice cloud Ground based MWR (nowcasting) 1DVar Instrument Retrieval performance ( accuracy and resolution ) Pros and Cons Summary and Conclusions

3. Page 3© Crown copyright 2006 Met Office NWP models Global 40 km N320L50 640x481x50 63 km top North Atlantic & European 12 km 720x432x38 38 km top Old UK 12 km Retired New UK 4 km 288x320x38 38 km top The main application for MW radiances is defining the initial conditions from which the forecast model runs

4. Page 4© Crown copyright 2006 Variational Assimilation ‘The Analysis Problem’:  Determine most probable state vector, x a, given  Observation, y  Background state x b (prior knowledge of atmospheric state)  Error characteristics of each (assumed Gaussian)  Minimise cost function: J(x a )= (y-H(x a )) T R -1 (y-H(x a )) + (x a - x b ) T B -1 (x a - x b )  Requires:  Observations, y  Background, x b  Background Error Covariance Matrix, B  Observation Error Covariance Matrix, R  Observation Operator, H(x)  Jacobian of H(x), H(x)=  x H(x)=  y/  x If y is a radiance observation H is a RT model

5. Page 5© Crown copyright 2006 Four-dimensional variational assimilation (4D-Var) Observation Time Temperature Background x b Analysis Slide Courtesy of Amos Lawless/Sue Ballard OBSERVATIONS DISTRIBUTED IN TIME

6. Page 6© Crown copyright 2006 Fast Radiative Transfer Model: RTTOV  Fast MW and IR RT model – primarily designed for TOA T b calculations  Predictor based scheme based on LbL calculations : OD i =f(T i,q i, {c 1 i, c 2 i,c 3 i,…}) for each layer i {c 1 i, c 2 i,c 3 i,…} determined from diverse profile dataset  MW RT based on Liebe MPM 92 for O 2, MPM 89 for H 2 O and WV continuum  Each call ~1msec  http://www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/index.html

7. Page 7© Crown copyright 2006 N N15 N16 N18 N AQUA F13 N F16 ATOVS (T & q)AIRS (T & q) SSMI (WS) SSMIS (T, q, WS) Satellite Radiances Assimilated 06 12 18 00

8. Page 8© Crown copyright 2006 Accuracy of temperature & humidity forecasts: T+24 hours Southern Hemisphere T RH BIAS RMS NWP models represent T fields better than RH fields Pressure / hPa

9. Page 9© Crown copyright 2006 Forecast Accuracy vs Range : RH at 1000hPa

10. Page 10© Crown copyright 2006 Radiance Processing  1DVar ( QC & intelligent thinning of obs) :  Analyse skin temperature  Check convergence  Detect cloud and select channels for 4D Var  No of obs per 6 hour window :  ATOVS (AMSU) : 500,000 obs (3 satellites)  SSMIS : 680,000 observations ( 1 satellite )  Preprocessing time : ~5 minutes on NEC SX-8  4DVar (analyse global atmospheric state):  Uses QC’d observations (conventional and satellite data)  13,000 ATOVS obs, 4,000 SSMIS, 3,000 AIRS (+ other satellite and conventional obs)  Run time : < 10 minutes

11. Page 11© Crown copyright 2006 Issues for MW radiance assimilation  Better treatment of surface/near surface channels  Better treatment of cloud and precipitation affected radiances, including scattering effects  Lack of information in the vertical (eg SSMI, AMSU-B)  Calibration & Biases (SSMIS/SSMIS)

12. SSMI & SSMIS

13. Page 13© Crown copyright 2006 SSMIS: Instrument and scan geometry Special Sensor Microwave Imager/Sounder (SSMIS)

14. Page 14© Crown copyright 2006 Background: SSMIS / SSMI Channels SSMIS = SSMI channels + 13 T sounding (O 2 line) chs + 3 q sounding (H 2 O line) chs + 150 GHz channel ε surf = f (WS, pol) … so T B → WS (& TCWV & LWP)

15. Page 15© Crown copyright 2006 SSMI 40N 20N 0N 20S 40S AIRS More vertical structure in AIRS increments

16. Page 16© Crown copyright 2006 Moist Static Energy SSMI AIRS ATOVS SSMI adds large ‘energy’ increments

17. Page 17© Crown copyright 2006 RT Errors - 22GHz linewidth parameter SSMI DATA MPM 89 HITRAN 92

18. Cloud Affected Radiances Scattering at 183 GHz

19. Page 19© Crown copyright 2006 Validation of Scattering RT models: RTTOV / ARTS comparisons AMSU Channel 20 (183 ± 7 GHz) ObservationARTS simulationRTTOV simulation Brightness Temperature (K)

20. Page 20© Crown copyright 2006 Publicly available RTMS RTTOV8.7  Simple two stream scattering solution (Eddington)  Fast geometric optics ocean surface emissivity model  Marshall-Palmer/Modified Gamma Drop Size Distribution  Ice particle diameter up to 100 microns, snow 100-20000 microns  Density of ice particles 0.9 g/cm 3  Density of snow particles 0.1 g/cm 3  Permittivity dependent on ice/water/air mixture of hydrometeors (Maxwell-Garnet mixing formula) http://www.metoffice.com/research/interproj/nwpsaf/rtm ARTS  Multi-Stream Radiative Transfer  Flexible but slow  Constant ocean/land emissivity  Gamma Drop Size Distribution  Fixed Effective Radius  Cloud ice water inputs only http://www.sat.uni-bremen.de/arts/

21. Page 21© Crown copyright 2006 Ice Cloud The problem: Too many degrees of freedom Available: T IWC Required: Size distribution Density shape Parameterisations and approximations are required to constrain the unknowns  A forecast model can give temperature and ice (and liquid) water profiles which can be input to the RTM. Few forecast models give ice microphysics as diagnostic output. Many parameterisations for density and size distribution exist in the literature. Relating these to other known quantities (such as T and IWC) is a promising way forward. For speed, spherical ice particles are usually assumed in NWP. Errors from this assumption are small (~15%) compared to possible errors from size distribution uncertainties (~40%)

22. Ground Based MW radiometry

23. Page 23© Crown copyright 2006 Radiometrics TP/WVP-3000 Microwave Radiometer  7 Channels: 51-59 GHz  O 2 band - temp. profile  5 Channels: 22-30 GHz  H 2 O line - humidity, cloud  Pressure, temp., RH sensors  Dew Blower & Rain Sensor  Infrared Radiometer  Cloud base temperature  Automatic Calibration  black body, noise diode  Zenith and Elevation Scans  Observation Cycle: ~1 min Radiometrics MP3000 Microwave Radiometer at Camborne

24. Page 24© Crown copyright 2006 1D-Var  Determine most probable state vector, x a, given  Observation, y  Background state x b (prior knowledge of atmospheric state)  Error characteristics of each (assumed Gaussian)  Minimise cost function: J(x a )= (y-H(x a )) T R -1 (y-H(x a )) + (x a - x b ) T B -1 (x a - x b )  Requires:  Observations, y (MW radiometer)  Background, x b  Background Error Covariance Matrix, B  Observation Error Covariance Matrix, R  Observation Operator, H(x)  Jacobian of H(x), H(x)=  x H(x)=  y/  x

25. Page 25© Crown copyright 2006 Background and State Vector, x  Need background, x b to resolve ill-posed problem  Use T+3 to T+9 Forecast  Independent of validation  28 lowest levels  Concentrated near surface  T(z), q(z), L(z)  Fix profile above 14km  Choice of State Vector: x=[T 1,..T 28, lnq t1,.. lnq t28 ] 17 Sites of archived profiles from UK Mesoscale model UMG3 Models levels Met Office UK Mesoscale Model

26. Page 26© Crown copyright 2006 Error Analysis of Retrieved Profiles  Gaussian linear case: Analysis error of optimal estimation retrieval: A = (H T R -1 H + B -1 ) -1  Compare with B:  T: A < B for z<5km,  T<1 K q: A < B for z<3km,  lnq<0.4  For q, A depends on state  Using surface sensors only – A < B for z<500m  A ~ sondes for z<1km Background Error, B, (black) and Analysis Error, A, using Radiometer (red), Only surface sensors (green), Radiosonde (blue).

27. Page 27© Crown copyright 2006 Vertical Resolution  Gain Matrix, K = BH T (HBH T +R) -1  Averaging Kernel Matrix = KH  Vertical Resolution of Analysis,  z.diag((KH) -1 )  ~2x larger than other def n s  T profile resolution increases with height ~2z  lnq profile resolution =  (x)  Some q resolution for z<1km, but IWV above Vertical resolution of analysis temperature and humidity (lnq t ) profiles calculated as the inverse of the trace of the averaging kernel matrix [Purser and Huang, 1993] (US Std Atm)

28. Page 28© Crown copyright 2006 Example retrievals  100 synthetic observations, y o based on real sonde, x t and NWP background, x b  Forecast inversion too low  Overestimated the humidity x2  83% converged in ~9 iterations on average  Retrievals closely clustered  Robust to observation noise  Retrievals closer to x t than x b  Thins the cloud  B makes it impossible for retrieval to move inversion Retrievals (red), Background (black), Radiosonde (blue). Left panel shows temperature profiles. Right panel shows profiles of humidity (lnq) and liquid water (lnq l ) and specific humidity at saturation (dotted)

29. Page 29© Crown copyright 2006 Ground based MW radiometry: Conclusions  Pros  Optimal method to integrate observations with background  Provides estimate of error in retrieval  Shows impact from MWR below ~4km – most <1km  Cons  Fundamentally poor vertical resolution of passive profilers  Convergence problems for very non-linear problems  Difficult when background is wrong (shifting patterns)  Future Work  Add ceilometer cloud base/cloud radar tops/GPS IWV to y  Integrate with Wind Profiler SNR – e.g. Boundary Layer top  How to exploit high time resolution? 4D-VAR? Variability?

30. Page 30© Crown copyright 2006 Summary & Conclusions  MW radiances are an important component of operational NWP systems, it is now normal to assimilate these directly as radiances, rather than using retrievals  Variational assimilation (1d, 3d or 4d) is an optimal way of combining background and observational information to define an atmospheric state  Fast RT models are important in achieving this  Challenges presented by MW radiance measurements include : dealing with cloud and precipitation, limited vertical resolution, biases (eg RT biases)  Ground based MWR is being assessed for nowcasting and assimilation applications. Column water estimates are accurate, but vertical resolution is poor (~1km for q)