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Assimilation of Scatterometer Winds

Assimilation of Scatterometer Winds. Ad.Stoffelen@KNMI.nl Manager NWP SAF at KNMI Manager OSI SAF at KNMI PI European OSCAT Cal/Val project Leader KNMI Satellite Winds Group www.knmi.nl/scatterometer. 2. Level 2 Wind Processing. INPUT. INPUT. OUTPUT. OUTPUT. Ambiguity. Ambiguity.

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Assimilation of Scatterometer Winds

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  1. Assimilation of Scatterometer Winds Ad.Stoffelen@KNMI.nl Manager NWP SAF at KNMI Manager OSI SAF at KNMI PI European OSCAT Cal/Val project Leader KNMI Satellite Winds Group www.knmi.nl/scatterometer

  2. 2. Level 2 Wind Processing INPUT INPUT OUTPUT OUTPUT Ambiguity Ambiguity Wind Wind Inversion Inversion Observations Observations Removal Removal Field Field Quality Quality Quality Control Control Monitor

  3. Geophysical Model Function A geophysical model function (GMF) relates ocean surface wind speed and direction to the backscatter cross section measurements. : wind speed ø: wind direction w.r.t. beam view :incidence angle p:polarization λ: microwave wavelength

  4. Inversion • Bayesian approach: • Find closest point on 3D or 4D manifold • The statistical error in finding this point is small and equivalent to a vector error of 0.5 m/s in wind • p(zM |zS )  exp{ - ½(zM - zS)2/noise(z)} • p(zS ) = constant; p(s oS ) ≠constant Stoffelen and Portabella, 2006

  5. Ambiguity removal • Scatterometer inversion produces a set of wind (direction) solutions or ambiguities • Ambiguity removal is performed with spatial filters

  6.  0  180 Local minima MLE Solution bands Wind direction (f) Azimuthal diversity • Accounting for local minima, erratic winds are produced • MSS accounts for lack of azimuthal diversity • A relative weight (probability) is derived for every solution • Suitable with a variational filter MSS

  7. Meteorological balance (2D-VAR) Spatial filter: • Mass conservation • Continuity equation  0U = 0 • Vertical motion < horizontal motion • Parameters: • Background error (variance) • Correlation length • Rotation vs divergence Cost function:

  8. Local minima MSS NWP model

  9. MSS Local minima

  10. NOAA MSS @ 25 km 50 km Plots ! Improved coldfront Better Around rain

  11. Remarks • Scatterometer wind retrieval skill depends on viewing geometry • Measurement error characterization is essential, notably for QC and AR • Effective QC is very important for DA • Rain screening is especially relevant for Ku-band • Variational AR accounts for full wind PDF

  12. Data assimilation • The analysis minimizes the costfunction J by varying the controlvariables representing theatmospheric state, e.g., uj , the wind components of wind vector vj, • At every observation point prior knowledge is available on the observed state from a sort-range forecast, called NWP background • JB is a penalty term penalizing differences of, e.g., uj with the NWP background (subscript B) • sB denotes the expected background wind component error • JB differences should be spatially balanced according to our knowledge of the NWP model errros • So, JB determines the spatial consistency of the analysis (i.e., a low pass filter) Lorenc, Q.J.R.Meteorol.Soc., 1988 12

  13. Wind error model p([u,v]SCAT|vB) p([V,f]SCAT|vB) • Error distributions: p(vSCAT|vB) = p(vSCAT|vTrue) p(vTrue|vB) • Combined NWP background and scatterometer error distribution looks like a normal distribution in wind components with rather constant width as a function of wind speed • In speed it is a skew distribution • In direction the width of the distribution depends on speed and the distribution is periodic • Wind component error model clearly simplest 13 Stoffelen, Q.J.R.Meteorol.Soc., 1998

  14. Measurement Noise 5% • s0 noise is uniform in measurement space (~5 % or 0.5 m/s VRMS) • Wind retrieval provides very accurate s0S given s0O , so well-defined p(vS | s0O) 14

  15. Observation error The analysis control variables follow the NWP model spectrum (model balance) Measured scales not represented by the NWP model state are attributed as observation representation error The scatterometer wind vector representation error is about 1.5 m/s In triple collocation scatterometer wind errors on NWP scale are estimated at about 1 m/s vector RMS 15 Vogelzang et al., 2011 NWP SAF Workshop | 14 April 2011

  16. Scatterometer input NWP Scatterometer Observation Representation error p(vS |v) v X v Prob [a.u.] 16

  17. Rotating beam (SeaWinds, OSCAT: mid swath) true • Fixed antennas (ASCAT: inner swath) • Broad MLE minima and closeby multiple ambiguous solutions are complicating scatterometer wind assimilation 17

  18. Scatterometer Data Assimilation Posteriori Wind Probability given a set of measurements Wind domain uncertaintyDu, Dv ~ 1.5 m/s Measurement space noise D ~ 5% (0.2 m/s)  0S = GMF(vS, .. ) Geophysical solution manifold ERS/ASCAT: Manifold in 3D measurement space SeaWinds/NSCAT: Manifold in 4D measurement space Stoffelen&Portabella, 2006

  19. Scatterometer data assimilation • JO is a penalty term penalizingdifferences of the analysis control variables with the observations • Choices: • Direct assimilation of s 0O • Complex error PDFs • Assimilate p(vS | s 0O), like in MSS and 2DVAR • Needs p information • Assimilate ambiguities • Reduces wind solution space to max 4 points • Assimilate selected solution • Reduces wind solution space to one point p(vS | s 0O) Stoffelen & Anderson, Q.J.R.Meteorol.Soc., 1997 19

  20. Direct assimilation of s 0O • s0 noise is narrow leading to accurate wind retrieval • Observation and background wind noise are relatively large leading to complex and skew error PDFs in measurement space • Not compatible with BLUE, higher order statistics needed • Wind assimilation appears simplest y: s0 x: wind • Main uncertainty is in the wind domain 20 Stoffelen, PhD thesis,1998

  21. Assimilate ambiguities v Prob Prob p(vS |v) Ambiguities • Reduces wind solution space to max 4 points (delta functions); solution wind PDF information is lost 21

  22. Assimilate ambiguities Scatterometer wind cost ambiguous wind vectorsolutions ui ,vi provided by wind retrieval procedure and complemented by estimated observation wind error, eu = evStoffelen and Anderson, 1998 • Derive probability Pi from MLE info 22

  23. Assimilate solution “valley” v Prob Prob p(vS |v) MSS • Retains essential wind solution PDF information along the valley of solutions that generally exists • Provides very good approximation to p(v | s 0O) 23 Portabella and Stoffelen, 2004

  24. Scatterometer input NWP Scatterometer Observation from MSS Representation error v • Provides very good approximation to p(v | s 0O) X Prob [a.u.] 24 Portabella and Stoffelen, 2004

  25. Assimilation of ambiguous winds Potentially provides multiple minima in3D/4D-Var Problem is very limitedfor ASCAT 2DVAR tests show <1% of wrong selection May be linearized byselecting one solutionat a time (inner loop) vtrue = (0,3.5) ms-1v2 = -v1 eu/v,O = 2 ms-1 p2 = p1 = .5 eu/v,B = 2 ms-1 <vA> = (0,3.25) ms-1 Monte Carlo simulation, Stoffelen & Anderson, 1997 25

  26. Assimilation of unambiguous winds Prob NWP background Scatterometer wind Analysis [a.u.] 26 • AR by 2DVAR well tested and independent of B • Broad B structure functions provide best AR skill • Assimilation of scatterometer wind product is straightforward • Few spatially correlated outliers due to AR errors, but mainly in dynamic weather

  27. Example Improved 5-day forecasts of tropical cyclone in ECMWF 4D-VAR No ERS Scatterometer With ERS Rita Isaksen & Stoffelen, 2000 27

  28. Another example ASCAT has smaller rain effect Japan Meteorological Agency 28

  29. Gebruik van scatterometers Assimilation ASCAT winds ECMWF from 12/6/’07 Beneficial for U10 analysis Operational okt/nov 2007 (added to QuikScat&ERS) Hans Hersbach & Saleh Abdalla, ECMWF ECMWF analysis vs ENVISAT altimeter wind 29

  30. Underpredicted surge Delfzijl 31/10/’6 18Z 1/11/’06 4Z 30

  31. NWP Impact @ 100 km 29 10 2002 Storm near HIRLAM misses wave; SeaWinds should be beneficial! 31

  32. NWP models miss wave; Next day forecast bust ERS-2 scatterometer wave train; missed by HiRLAM 32

  33. Missed wave train in QuikScat 33

  34. Conclusions ASCAT on board MetOp provides accurate daily global ocean surface winds at high spatial resolution NWP models lack such high resolution MetOp-B due for launch in 2012 probably providing a tandem ASCAT Further information: www.nwpsaf.orgscat@knmi.nl www.osi-saf.org www.knmi.nl/scatterometer 34

  35. Thank you ! 35

  36. Geographical statistics for QuikSCAT, July 2009

  37. Geographical statistics for ASCAT, July 2009 • Rain flag removes stronger winds • for QuikSCAT • There are some regional differences

  38. Lack of cross-isobar flow in NWP • Large effect warm advection • Small effect cold advection QuikSCAT vs model wind dir Stratify w.r.t. Northerly, Southerly wind direction. (Dec 2000 – Feb 2001) • Similar results for NCEP Hans Hersbach, ECMWF (2005) WISE 2004, Reading

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