Maximizing the Impact of High-Resolution Winds in Meteorology Challenges

1. Koninklijk Nederlands Meteorologisch Instituut Ministerie van Verkeer en Waterstaat The challenge of exploiting high-resolution winds NCEP seminar 19 June 2009 Ad.Stoffelen@knmi.nl

2. Wind Topics • Need for NWP winds • Doppler Wind Lidar, DWL, Scatterometer • Impact of winds • Challenges

3. Need for Space Winds • Wind determines small-scale dynamics and evolution • Wind determines tropical circulation • Over the ocean where storms develop and sparse 3D meteorological observations are present; reduce errors over the ocean • Coastal and marine warnings for wind, waves, surges • Forcing of ocean models, fluxes • Circulation component in climate

4. mature systems developing systems boundary layer storms, fronts orographic circulations planetary waves low pressure systems Wind determinesweather evolution Bus? Slow Development Rossby limiet voor 45 N (of 45 Z) Mist Cloud layer Rain colomn 10 V [m] 100 1000 10.000 Fast Temperature and pressure forweather evolution 10 100 1000 10.000 H [km] Shower Front Storm Climate zone World

5. Wind profiles large upper air impact, but • Inhomogeous coverage • Usefulness for Netherlands limited

6. Impact of Space Winds • Areas without other 3D wind sensing, above sea, tropics, Southern Hemisphere • On small scales • Extreme weather; hurricanes, storms, waves, surges • Tropical cyclones • Wind profiles provide effective impact

7. Delfzijl 31-10-2006 • Surge of 4,8 m; > 0.5 m underpredicted • ECMWF too low winds; HiRLAM direction wrong as verified by QuikScat

8. ERS scatterometer observes wave train • HiRLAM model (and other NWP models) miss the wave train (too smooth) • The MSG clouds are aligned with the wave train, but in themselves provide little dynamical information • Next day a forecast bust occurred for cloud and precipitation in England and the Netherlands HiRLAM ERS ERS QC

9. 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 Gebruik van scatterometers ECMWF analysis vs ENVISAT altimeter wind

10. ASCAT advantage for tropical storms Japan Meteorological Agency • ASCAT has smaller rain effect; splash remains

11. Impact of DLR 2 m DWL ECMWF T511, two weeks 3000 DWL observations 0.005% of all used observations Better winds than Sonde and AIREP Weissman et al, Aeolus Workshop First assimilation of real Doppler lidar observations Average 48 - 96 h forecast error reduction over Europe ~3% Many OSSEs +ve

12. Analysis improvement at forecast initial time of ’99 Christmas storm Martin (26 Dec 1999 12:00 UTC) for the Tandem-Aeolus scenario Tandem-Aeolus impact on analyses Single-time SOSE; 6 hours DWL obs. SOSE – cycling; 84 hours DWL obs.

13. EPS storm probability forecast • Three times more storm members in DWL (30%) than in noDWL (10%) over France and Gulf of Biscay • DWL storm locations are better situated than noDWL

14. 12.5 km AWDP 1 25 km

15. 100 km k -5/3 AWDP@12.5 • Nastrom and Gage (1987) establish climate spectra • ASCAT contains small scales down to 25 km which verify well with buoys and climate • No noise floor • k-1.9 • ECMWF contains order of magnitude too little variance at the 100-km scale coaps.fsu.edu/scatterometry/meeting/past.php#2009_may , Stoffelen et al.

16. 6-hourly ECMWF update • ECMWF analysis increments modest wrt spatial deficit (1.2 m2s-2) • Most mesoscale scatterometer information remains unexploited • Can more beneficial impact be achieved ? How ?

17. ECMWF versus hi-res SPARC radiosondes • ECMWF 1.5-2 km resolut’n • SD:2 m/s • Shear 3 times too low even • Physics tuned to poor vertical shearstructure

18. Model resolution cell • spatial scales below the MRC are not well resolved by the model • ECMWF model: MRC ~250km • unresolved wind variability: UKMO 1992: unresolved wind variability: 3.95 m2s-2 computational grids of global NWP models have increased substantially over the last 15 years, but the horizontal scales that are resolved by these models have increased to a much lesser extent

19. Why is ECMWF so successful and smooth? • Optimization of the 5-day 500 hPa anomaly skill score • 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 ageostrophic flow • Physical parameterizations are (really well) tuned to smooth dynamics • Dense grid resolves orographic forcing, i.e., improved downscale cascade without compromising forecasts • Observations are underfitted, thus reducing spin-up effects and detrimental effects of uncertain weights due to the uncertain B matrix covariances

20. 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 scale involve wind, precipitation rather than height • Physical parameterizations need to be retuned 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)

22. Hi-res NWP for Tropical Cyclones • Hi-res NWP from (HWRF) looks very realistic • But, structures do not verify in detail • TC dynamics does not follow real dynamics • Few observations, forcing unclear, • Track and strength forecasts are poor w.r.t. other low-res NWP models coaps.fsu.edu/scatterometry/meeting/past.php#2009_may , Brennan et al.

23. Challenges • 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 high resolution cause • Increased NWP model error • Model errors get more variable and uncertain since small scales tend to be coherent; coherence is of most interest • Adaptive B covariances are notoriously difficult • B error structures get spatially much sharper, • More (wind) observations are needed to spatially sample these B structures • Increased O-B, while the observation (representativeness) errors will be reduced • Observations get much more weight • Increments will be larger in well-observed areas • How to prevent overfitting (uncertain B) and spin-up (statistical B) ?

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

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

26. Quality Control

27. Prediction of landing times • ModeS winds have impact

28. Radial velocity Doppler data- when it rains - De Bilt Den Helder

29. Summary • Surface winds have good impact for extreme weather forecasts • In nowcasting • In NWP • Wind (profile)s show good simulated and real impacts • NWP analyses lack deterministic small scales • Global models are very smooth • Hi-res models lack skill (since no good observed inputs) • Wind observations are needed to initialise the small scales in absence of deterministic forcing • Using these remains challenging

30. ECMWF model