Koninklijk NederlandsMeteorologisch Instituut

Ministerie van Infrastructuur en Milieu

Scatterometer winds

Ad.Stoffelen@knmi.nl

Wind Products

atwww.knmi.nl/scatterometerscat@knmi.nl

ASCAT ASCAT

25 km 12.5 km

QSCAT

25 km

ASCAT

25 km

QSCAT

100 km

ASCAT

12.5 km

Demo ERS-2 25 km

ECMWF impact• Improved 5-day forecast of

tropical cyclones in ECMWF 4D-VAR

Isaksen & Stoffelen, 2000

RitaNo ERS Scatterometer With ERS

ECMWF Isaksen, Leidner, Hoffman, 2003 4

Surface scatterometer wind information is propagated vertically and improves the analysis Due to

flow-dependent structure functions in 4D-Var

ASCAT and QuikScat impact

• ASCAT has smaller rain effect; splash remains

Japan Meteorological Agency

KNMI Scientific Review, January 13-

14, 2004

6

• Product quality varies in TCs

TC Katrina just before landfall

KNMI SDP25 NOAA DIRTH

Thinned data• Mainly larger scales are assimilated• With good impact though

Nastrom & Gage Spectrum

• Tropospheric spectra are close to k-5/3 < 500 km

• 3D turbulence

• L/H ~ 100

• SD(log spectral density) = 0.4

(moved right an order)

AWDP@12.5k -5/3

100 km

• ASCAT contains small scales down to 25 km which verify well with buoys and k-5/3

• ECMWF contains order of magnitude too little variance at the 100-km scale over sea

• No 3D turbulent structures !• Variance deficit ~1.1 m/s over

scatterometer scales• TCs are steered by large scales

in ECMWF (lack of upscale development)

coaps.fsu.edu/scatterometry/meeting/past.php#2009_may , Stoffelen et al.

10

Comparison of SeaWinds with ECMWF and buoys

SDP at 25 km SDP at 100 kmu (m/s) v (m/s) u (m/s) v (m/s)

ECMWF 1.87 1.83 1.57 1.48Buoys 1.79 1.88 2.17 2.06

All data from January 2008

When going to coarser resolution Agreement with model increases Agreement with buoys decreases In line with spectral analysisNote that KNMI SeaWinds is smoother than ASCAT

Vogelzang et al, 2010, triple collocation

Why is ECMWF so successful

and smooth?• Optimization of the medium-range forecast skill• Smoothing is needed to control small-scale dynamic

features, i.e., to prevent upscale error growth during the forecast

• Relatively few 3D wind observations exist to initialize the ageostrophic flow

• Observations are underfitted, thus reducing spin-up effects and detrimental effects of uncertain weights due to the uncertain B matrix covariances (overfitting)

• Physical parameterizations are (really well) tuned to the smooth dynamics

• Dense grid resolves orographic forcing, i.e., improved downscale cascade over land, benefitting forecasts

( Smoothness also exists in other global NWP models)

Include small scales for short-range NWP ?

• Still relatively few 3D wind observations exist to initialize ageostrophic flow, but relatively abundant over land (radar, aircraft, in situ, .. )

• Small-scale dynamic features grow during the forecast, but forecast range is limited

• Verification metrics for short scales involve wind, precipitation rather than height/temp.

• Physical parameterizations need to be (re)tuned to improved dynamics

• Forcing may be better defined, i.e., improved upscale cascade (roughness, soil moisture, .. )

How to deal with spin-up effects and detrimental effects of uncertain weights due to the B matrix covariances (overfitting) ?

Data assimilation• o = x + o observation• b = x + b background (prior)• a = b + W(o–b) analysisx : state variable, spatial average over the “truth” field, due to limitations in the NWP modelo : random observation error, contains spatial representation error, since the (spatial) context of o is generally different from x (some o may be combinations of state variables x, e.g., limb soundings)b : random background error, contains, e.g., spatial correlations between errors of neighbouring xW : weight, depends on statistically determined “average” covariances of o in a matrix O and b in a matrix BScales < B scales in o-b=o-b are generally removed (since the analysis acts as a low pass filter)

B is essential in data assimilation

13

Small-scale data assimilation • The amplitude spectrum of small-scale

atmospheric waves can be well simulated in NWP models, but the determination of the phases of these waves will be problematic in absence of well-determined forcing (orography) or observations

• Undetermined phases at increased resolution (smaller scale x) cause– Increased NWP model error, b’ > b, i.e., small scale

errors are mixed with larger scale errors– Model errors get more variable and uncertain since small

scales tend to be coherent; coherence is of most interest– B error structures will be spatially sharper – Increased o-b, while the observation

(representativeness) errors will be reduced; observations (should) get more weight, o’ < o

– Increments would be larger When o’ > b, the analysis error will be larger! a’ > a

Challenges• Adaptive B covariances are notoriously

difficult• More (wind) observations are needed to

spatially sample small-scale B structures• Observations need to be accurate, o < b• How to prevent overfitting (uncertain b,

smaller o) due to inaccurate and high innovation weights ?

• And spin-up due to more noisy analysis (statistically determined B) ?

Separate determined from undetermined scales in data assimilation (e.g., data assimilation with (ensemble) mean b ?)

Spatial representation

• We evaluate area-mean (WVC) winds in the empirical GMFs• 25-km areal winds are less extreme than 10-minute sustained in

situ winds (e.g., from buoys)• So, extreme buoy winds should be higher than extreme

scatterometer winds• Extreme NWP winds are again lower due to lacking resolution

(over sea)

Wind scales

0

10

20

30

40

0 25 50 75 100 125 150 175 200Distance (km)

Win

d sp

eed

(m/s

)

BuoyASCATECMWF

Extreme winds

capability NOAA hurricane flights Ike: highest ASCAT speed

ever at the time (75 knots) and we were just there !

Lack of buoy data > 20 m/s ASCAT lacks H pol and

sensitivity Post-EPS too ?

ASCAT Ultra High Resolution (1)

Area of 2 by 2

Centered around

19N 129E

(NE of Philippines)

26-10-2010 00:36

12.5 km

Demo coastal ASCAT wind product available at KNMI

ASCAT Ultra High Resolution (2)

6.25 km

Sharper shear lines, divergence patterns

•Noisy

•Needs improved QC on footprint level

•MSS ?

•Rough eye as also witnessed by SFMR

•Do you want such products ?

3.125 km

ASCAT Ultra High Resolution (3)

Summary• Scatterometer winds are accurate, provide good NWP

impact and unprecedented small scales• NWP analyses lack deterministic small scales

– Global models are very smooth– Hi-res models lack skill (since too few observed inputs)

• Accurate wind observations are needed to initialize the small scales in absence of deterministic forcing, such as orography– More scatterometers ?

• Accurate characterisation of errors/resolution is needed for optimal data assimilation

• Unobserved scales should not be incorporated in the analysis, since its associated errors degrade analysis quality

NASA MISR hi-res stereo motion vector winds (SMV)

SMV observation operator• The usual:

• Taking account of height and along-track component error correlation:

• zo, uo, vo from SMV retrieval; • z, u, v analysis control variables

Further reading on SMV• Horváth, Á., and R. Davies, 2001a: Feasibility and

error analysis of cloud motion wind extraction from near-simultaneous multiangle MISR measurements. J. Atmos. Oceanic Technol., 18, 591-608.

• Horváth, Á., and R. Davies, 2001b: Simultaneous retrieval of cloud motion and height from polarorbiter multiangle measurements, Geophys. Res. Lett., 28/15, 2915-2918.

• International Winds Workshops 6-10, Horvath, Davies, Genkova, ..

• http://www-misr.jpl.nasa.gov/mission/introduction/welcome.html

ECMWF• Forecasted

Hurricanes recurve a bit too late/move too much south

• Forecasted hurricane are generally too slow

• Large speed spread

Bayesian Wind Retrieval

0 noise is uniform in 3D measurement space (~0.2 m/s only) For a given measured backscatter triplet, Bayes’ helps us to

find the most probable points on the cone surface, which are tagged with a wind vector solution

Large distances from the cone surface are unlikely due to wind (QC); also successful for QuikScat

soo

oss

oo dPPP vvvv

sv

)()()|(

0 assimilation

Main uncertainty is in the wind domain ; skew PDF in backscatter

y: 0

x: wind

0

0o 0 GMF(V)

P(V|Vb)

Po(0|0o)

Vo V

Wind assimilation

Main uncertainty is in the wind domain

y: 0

x: wind

0

0o 0 GMF(V)

P(V|Vb)

Po(0|0o)

Vo V

Scatterometer data assimilation

ob JJJ

v|ln2 0OpJ SCAT

o

Jb balanced (e.g., geostrophy)

Scatterometer wind cost

• Jo is a penalty term penalizing differences of the analysis control variables with the observations

• Scatterometer observations are not spatially correlated• Jb is a penalty term penalizing differences with a priori

NWP background field (first guess)• Jb differences should be spatially balanced according to

our knowledge of the NWP model errros• Jb determines the spatial consistency of the analysis

Po(0o|0) Pb(vb|v) Pa

DAS ambiguity removal

Assimilate ambiguities

ob JJJ

v|ln2 0OpJ SCAT

o P

N

ii

N

ii

SCATo

K

KJ

/1

1

1

P

iv

i

u

ii PvvuuK

ln2

22

Jb balance

Scatterometer wind cost

i ambiguous wind vectorsolutions provided by wind retrieval procedure (Stoffelen and Anderson, 1998)

Use probability

SeaWinds @ 25km, TC Dean, 16 Aug 2007 Without MSS With MSS

retrieval of 4 local solutions full wind vector PDF

Hourly hi-res windsSYNOP

AIREP

3D Mode-S

Data volume 15-03-2008

• 1 424 147 observations

Quality Control

Prediction of landing times Case\ Par-

ameterMinimum 

(s)Maximum 

  (s)Mean

     (s)St.Dev. 

(s)

No Wind -293 169 2,3 79,9

KNMI 1.0 -80 70 -3,8 20,5

D11 -64 56 -3,2 17,7

H11 -58 46 -3,3 17,6

M11(3) -69 55 -3,4 17,7

M11(1) -61 50 -4,9 17,4

• ModeS winds have impact

KNMI Scientific Review, January 13-

14, 2004

39

General MSS performance

Mean vector RMS difference with ECMWF FGAT (m/s) 

 

Swath region

Standard procedure

MSS NCEP

Sweet 2.48 2.23 2.85

Nadir 2.98 2.45 2.96

MSS better than 4-solution standard, in particular at nadir NCEP background for 2DVAR much worse Also better verification for MSS at 100 km at nadir

KNMI Scientific Review, January 13-

14, 2004

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IWRAP Measurement Technique• Reflectivity and Doppler profiles – four beams, two frequencies

(C and Ku), two polarizations (H and V) – simultaneously.

• High-resolution surface and volume backscatter

Range Resolution: 15, 30, 60 & 120 m

Conical Scan(60 RPM)

Range Resolution: 15, 30, 60 & 120 m

Conical Scan(60 RPM)

Range Resolution: 15, 30, 60 & 120 m

Courtesy D. EstebanJPL, NASA

Compare ASCAT to simultaneous plane 0 data

KNMI Scientific Review, January 13-14, 2004

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High Winds Ku-band Model Function

log

log

25 25

Courtesy D. EstebanJPL, NASA

KNMI Scientific Review, January 13-

14, 2004

42

Further ReferencesFurther ReferencesFor scatterometer-related papers,

documentation, and wind products at KNMI please refer to

www.knmi.nl/scatterometer

We look forward to sharing- Our scatterometer processing software- Our ASCAT and QuikScat products - Our new wind stress products- Our experience

We fund visiting scientists

E-mail: scat@KNMI.nl

KNMI Scientific Review, January 13-

14, 2004

43

Measurement Noise

0 noise is uniform in 3D ERS measurement space (~0.2 dB or 0.2 m/s)

)( os

ooP

KNMI Scientific Review, January 13-

14, 2004

44

Wind Domain Error

Wind domain noise is uniform in u and v (~1.0 m/s)

)|( SP vv

KNMI Scientific Review, January 13-14, 2004

45

Wind rather than 0 assimilation

Main uncertainty is in the wind domain

y: 0

x: wind

KNMI Scientific Review, January 13-

14, 2004

46

Wind Retrieval

0 noise is uniform in 3D ERS measurement space (~0.2 m/s) Wind domain noise is normal in u and v, the coordinates of the surface, but not so in measurement space (~1.0 m/s)

The convolution of wind and measurement space uncertainty is not uniform in the measurement space and wind dependent

soo

oss

oo dPPP vvvv

sv

)()()|(