Assimilation of Scatterometer Winds Manager NWP SAF at KNMI Manager OSI SAF at KNMI PI European OSCAT Cal/Val project Leader KNMI Satellite Winds Group

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

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

Inversion Bayesian approach: 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 The statistical error in finding this point is small and equivalent to a vector error of 0.5 m/s in wind p(z M |z S )  exp{ - ½(z M - z S ) 2 /noise(z) } p(z M |z S )  exp{ - ½(z M - z S ) 2 /noise(z) } p(z S ) = constant; p(  o S ) ≠ constant p(z S ) = constant; p(  o S ) ≠ constant Stoffelen and Portabella, 2006

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

Azimuthal diversity MLE Wind direction (  ) Local minima Solution bands  0   180  MSS  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

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

Local minimaMSS NWP model

Local minima MSS

NOAA 25 km Improved cold front Better Around rain 50 km Plots !

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 cost function J by varying the control variables representing the atmospheric state, e.g., u j, the wind components of wind vector v j, At every observation point prior knowledge is available on the observed state from a sort-range forecast, called NWP background J B is a penalty term penalizing differences of, e.g., u j with the NWP background (subscript B)  B denotes the expected background wind component error J B differences should be spatially balanced according to our knowledge of the NWP model errros So, J B determines the spatial consistency of the analysis (i.e., a low pass filter) Lorenc, Q.J.R.Meteorol.Soc., 1988

Wind error model 13 Error distributions: p(v SCAT |v B ) = p(v SCAT |v True ) p(v True |v B ) 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 Stoffelen, Q.J.R.Meteorol.Soc., 1998 p([u,v] SCAT |v B ) p([V,  ] SCAT |v B )

5% Measurement Noise 14    noise is uniform in measurement space (~5 % or 0.5 m/s VRMS)  Wind retrieval provides very accurate    S given    O, so well-defined p(v S |    O )

NWP SAF Workshop | 14 April 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 Vogelzang et al., 2011

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

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

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

19 Scatterometer data assimilation J O is a penalty term penalizing differences of the analysis control variables with the observations Choices: Direct assimilation of  0 O Complex error PDFs Assimilate p(v S |  0 O ), 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 Stoffelen & Anderson, Q.J.R.Meteorol.Soc., 1997 p(v S |  0 O )

20 Direct assimilation of  0 O  Main uncertainty is in the wind domain y:  0 x: wind Stoffelen, PhD thesis,1998    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

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

22 Assimilate ambiguities Scatterometer wind cost ambiguous wind vector  solutions u i,v i provided by wind retrieval procedure and complemented by estimated observation wind error,  u =  v Stoffelen and Anderson, 1998  Derive probability P i from MLE info

23 Prob v ٧Retains essential wind solution PDF information along the valley of solutions that generally exists ٧Provides very good approximation to p(v |  0 O ) Assimilate solution “valley” p(v S |v)MSS Portabella and Stoffelen, 2004

v 24 Prob [a.u.] NWP Scatterometer Observation from MSS Scatterometer input Representation error X Portabella and Stoffelen, 2004 ٧Provides very good approximation to p(v |  0 O )

Assimilation of ambiguous winds Potentially provides multiple minima in 3D/4D-Var Problem is very limited for ASCAT 2DVAR tests show <1% of wrong selection May be linearized by selecting one solution at a time (inner loop) 25 v true = (0,3.5) ms -1 v 2 = -v 1  u/v,O = 2 ms -1 p 2 = p 1 =.5  u/v,B = 2 ms -1 = (0,3.25) ms -1 Monte Carlo simulation, Stoffelen & Anderson, 1997

26 Assimilation of unambiguous winds 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 NWP backgroundScatterometer wind Analysis Prob [a.u.]

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

28 Another example ASCAT has smaller rain effect Japan Meteorological Agency

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

30 Underpredicted surge Delfzijl 31/10/’6 18Z1/11/’06 4Z

31 NWP 100 km Storm near HIRLAM misses wave; SeaWinds should be beneficial!

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

33 Missed wave train in QuikScat

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:

35

Geographical statistics for QuikSCAT, July 2009

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

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