Koninklijk Nederlands Meteorologisch Instituut Ministerie van Infrastructuur en Milieu

1. Koninklijk Nederlands Meteorologisch Instituut Ministerie van Infrastructuur en Milieu Scatterometer winds Ad.Stoffelen@knmi.nl

2. OSCAT Wind Productsatwww.knmi.nl/scatterometerscat@knmi.nl QSCAT QSCAT 25 km 100 km ASCAT ASCAT Demo ERS-2 25 km 12.5 km 25 km ASCAT ASCAT 12.5 km 25 km

3. ECMWF impact • Improved 5-day forecast of tropical cyclones in ECMWF 4D-VAR Isaksen & Stoffelen, 2000 No ERS Scatterometer With ERS Rita

4. Surface scatterometer wind information is propagated vertically and improves the analysis Due to flow-dependent structure functions in 4D-Var

5. ASCAT and QuikScat impact Japan Meteorological Agency • ASCAT has smaller rain effect; splash remains

6. Product quality varies in TCs TC Katrina just before landfallKNMI SDP25 NOAA DIRTH 6 KNMI Scientific Review, January 13-14, 2004

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

8. 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)

9. 100 km k -5/3 AWDP@12.5 • 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 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 10

11. 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)

12. 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) ?

13. Data assimilation • o = x + do observation • b = x + db background (prior) • a = b + W(o–b) analysis x : state variable, spatial average over the “truth” field, due to limitations in the NWP model do : 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) db : random background error, contains, e.g., spatial correlations between errors of neighbouring x W : weight, depends on statistically determined “average” covariances of do in a matrix O and db in a matrix B Scales < B scales in o-b= do-db are generally removed (since the analysis acts as a low pass filter) • B is essential in data assimilation

14. 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, db’ > db, 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, do’ < do • Increments would be larger • When do’ > db, the analysis error will be larger! da’ > da

15. Challenges • Adaptive B covariances are notoriously difficult • More (wind) observations are needed to spatially sample small-scale B structures • Observations need to be accurate, do < db • How to prevent overfitting (uncertain db, smaller do) 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 ?)

16. 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)

17. 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 ?

18. ASCAT Ultra High Resolution (1) • Area of 2 by 2 • Centered around • 19N 129E • (NE of Philippines) • 26-10-2010  00:36 • 12.5 km • Demo coastal ASCAT wind product available at KNMI

19. ASCAT Ultra High Resolution (2) • 6.25 km • Sharper shear lines, divergence patterns

20. ASCAT Ultra High Resolution (3) • Noisy • Needs improved QC on footprint level • MSS ? • Rough eye as also witnessed by SFMR • Do you want such products ? • 3.125 km

21. 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

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

24. 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

25. 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

26. Thanks !

27. ECMWF • Forecasted Hurricanes recurve a bit too late/move too much south • Forecasted hurricane are generally too slow • Large speed spread

28. Bayesian Wind Retrieval • s0 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

29. s0 y: s0 x: wind s0o s0 = GMF(V) Po(s0|s0o) P(V|Vb) V Vo s0 assimilation • Main uncertainty is in the wind domain ; skew PDF in backscatter

30. s0 y: s0 x: wind s0o s0 = GMF(V) Po(s0|s0o) P(V|Vb) V Vo Wind assimilation • Main uncertainty is in the wind domain

31. Scatterometer data assimilation 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

32. DAS ambiguity removal Po(s0o|s0) Pb(vb|v) Pa

33. Assimilate ambiguities Jb balance Scatterometer wind cost i ambiguous wind vectorsolutions provided by wind retrieval procedure (Stoffelen and Anderson, 1998) Use probability

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

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

36. Data volume 15-03-2008 • 1 424 147 observations

37. Quality Control

38. Prediction of landing times • ModeS winds have impact

39. 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 13-14, 2004

40. 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 Compare ASCAT to simultaneous plane s0 data Courtesy D. EstebanJPL, NASA KNMI Scientific Review, January 13-14, 2004

41. High Winds Ku-band Model Function log log 25 25 Courtesy D. EstebanJPL, NASA KNMI Scientific Review, January 13-14, 2004

42. Further References For 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 Thank you! KNMI Scientific Review, January 13-14, 2004

43. Measurement Noise • s0 noise is uniform in 3D ERS measurement space (~0.2 dB or 0.2 m/s) KNMI Scientific Review, January 13-14, 2004

44. Wind Domain Error • Wind domain noise is uniform in u and v (~1.0 m/s) KNMI Scientific Review, January 13-14, 2004

45. y: s0 x: wind Wind rather than s0 assimilation • Main uncertainty is in the wind domain KNMI Scientific Review, January 13-14, 2004

46. Wind Retrieval • s0 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 KNMI Scientific Review, January 13-14, 2004