Koninklijk Nederlands Meteorologisch Instituut Ministerie van Infrastructuur en Milieu Scatterometer winds

Wind Products at scatterometer scatterometer 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 Rita No ERS Scatterometer With ERS

ECMWF Isaksen, Leidner, Hoffman, 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 , Product quality varies in TCs TC Katrina just before landfall KNMI SDP25NOAA 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)

k -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 kmSDP at 100 km  u (m/s) v (m/s) u (m/s) v (m/s) ECMWF Buoys All data from January 2008 When going to coarser resolution  Agreement with model increases  Agreement with buoys decreases  In line with spectral analysis Note 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 + oobservation b = x + bbackground (prior) a = b + W(o–b)analysis x : state variable, spatial average over the “truth” field, due to limitations in the NWP model o : 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 x W : weight, depends on statistically determined “average” covariances of o in a matrix O and b in a matrix B Scales < 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)

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 19  N 129  E (NE of Philippines)  00: 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 ? 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: z o, u o, v o 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, Horváth, Á., and R. Davies, 2001b: Simultaneous retrieval of cloud motion and height from polarorbiter multiangle measurements, Geophys. Res. Lett., 28/15, International Winds Workshops 6-10, Horvath, Davies, Genkova,.. misr.jpl.nasa.gov/mission/introduction/welcom e.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

 0 assimilation  Main uncertainty is in the wind domain ; skew PDF in backscatter y:  0 x: wind 00 0o0o  0  GMF(V) P(V|V b ) Po(0|0o)Po(0|0o) VoVo V

Wind assimilation  Main uncertainty is in the wind domain y:  0 x: wind 00 0o0o  0  GMF(V) P(V|V b ) Po(0|0o)Po(0|0o) VoVo V

Scatterometer data assimilation J b 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)Po(0o|0) P b (v b |v)PaPa DAS ambiguity removal

Assimilate ambiguities J b balance Scatterometer wind cost i ambiguous wind vector  solutions provided by wind retrieval procedure (Stoffelen and Anderson, 1998) Use probability

25km, TC Dean, 16 Aug 2007 Without MSS With MSS retrieval of 4 local solutions full wind vector PDF

Hourly hi-res winds SYNOP AIREP 3D Mode-S

Data volume observations

Quality Control

Prediction of landing times Case\ Par- ameter Minimum (s) Maximum (s) Mean (s) St.Dev. (s) No Wind ,379,9 KNMI ,820,5 D ,217,7 H ,317,6 M11(3) ,417,7 M11(1) ,917,4 ModeS winds have impact

KNMI Scientific Review, January , General MSS performance Mean vector RMS difference with ECMWF FGAT (m/s)  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 , 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 Courtesy D. Esteban JPL, NASA Compare ASCAT to simultaneous plane  0 data

KNMI Scientific Review, January 13-14, High Winds Ku-band Model Function log 25 Courtesy D. Esteban JPL, NASA

KNMI Scientific Review, January , Further References For scatterometer-related papers, documentation, and wind products at KNMI please refer to 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

KNMI Scientific Review, January , Measurement Noise  0 noise is uniform in 3D ERS measurement space (~0.2 dB or 0.2 m/s)

KNMI Scientific Review, January , Wind Domain Error Wind domain noise is uniform in u and v (~1.0 m/s)

KNMI Scientific Review, January 13-14, Wind rather than  0 assimilation  Main uncertainty is in the wind domain y:  0 x: wind

KNMI Scientific Review, January , 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