Short-Term Weather Forecasting for Probabilistic Wake-Vortex Prediction

Transcription

1 Short-Term Weather Forecasting for Probabilistic Wake-Vortex Prediction Frank Holzäpfel Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany Summary of WakeNet3-Europe-Workshop 10 and 11 May 2010 Institut für Physik der Atmosphäre DLR, Oberpfaffenhofen, Germany... and previous activities

2 Short-Term Weather Forecasting for Probabilistic Wake-Vortex Prediction SESAR WP 11.2 Meteorological Services challenges of weather prediction requirements, uncertainties and methods of probabilistic wake vortex prediction nowcasting methods numerical weather prediction methods deterministic predictions ensemble prediction methods feasibility and priorities thanks to: George Craig (LMU), Matthias Steiner (NCAR), M. Raschendorfer (DWD), Daniel Sacher (MeteoSolutions), Klaus Dengler (DLR), Kirstin Kober (DLR)

3 SESAR WP 11 SWP 11.1 Flight and Wing Operations Centre SWP 11.2 Meteorological Services EUMETNET Consortium 11 Meteorological Services AustroControl BelgoControl DLR NLR

4 High Level Strategy of SWP 11.2 Identify sensitivities of the flight life cycle to meteorology Understand how these sensitivities will change as ATM, aircraft, airport and meteorological systems evolve Provide proof-of-concepts Developing dedicated & innovative MET services Integrate these MET services into future ATM system in an optimal way.

5 Project Management & Coordination Consolidation of Operational Requirements Consolidation of Technical Requirements Pre Operational Validation Capabilities, integration, Feasibility & Options Integration with SWIM Technical Infrastructure Prototype(s) Development & Verification MET Transverse Support, Consultancy & Advice GIE/EIG EUMETNET, Registered Number RPM Bruxelles 5

6 dealing with probabilities is daily life... (Hagedorn 2009) Nothing is certain in this world there is nothing certain but death and taxes. (Benjamin Franklin) In many situations, decisions have to be based on probabilities the theory of probabilities is at bottom only common sense reduced to calculus. (Pierre-Simon, Marquis de Laplace) Interpretation of probabilities is sometimes not straightforward math is hard, let s go shopping. (Barbie) Appropriate presentation can help to make the right decisions solving a problem simply means representing it so as to make the solution transparent. (Herbert A. Simon) Training Course 2009 NWP-PR: How to Communicate Uncertainties 6/33

7 weather is a chaotic phenomenon... Lorenz attractor (1963), the prototype chaotic model.. (Palmer 2009) u t uu 2Ωu 1 0 p g k 0 F

8 time & space scales of atmospheric motion (The Remote Sensing Tutorial, NASA) - wind and turbulence scenarios depend on phenomena with a wide range of characteristic length and time scales - predictability may largely depend on the prevailing weather situation

9 (time-lagged ensemble Klaus Dengler, DLR) ultimate goal minimization, knowledge, and use of uncertainties of meteo parameters on average, more compact probabilistic wake vortex predictions

10 Requirements for meteo input parameters vertical profiles of: crosswind, headwind/tailwind, TKE, EDR, potential temperature in height ranges where WV develop vertical resolution: 20 m - 50 m prediction horizon: 2 min - 6 min - 20 min - 1 hour - 6 hours + appropriate probability density distributions (averages, standard deviations)

11 Prediction Skill deterministic scoring results (P2P) lateral transport: most important, easy to model, largest uncertainty RMS 5 15 / 0 RMS z / b 0 RMS y / b 0 best median factor 2-4 worst median worst median 86 m²/s 17 m 34 m

12 Probabilistic Methods Probabilistic Methods - Systematic Systematic Monte Carlo Simulation (PVM) ) ( ) ( ) TKE( ) ( ) ( ) ( ) (, ) (, ),TKE( ) (, ) (, ) ( z z z z w z v z u z z z w z z v z u...,,,,, b z y x b z y x consider uncertainties of initial, environmental, (model) parameters: difficulties: - specification of uncertainties - computation times - intrinsic wake vortex variability / deformation

13 Probabilistic Methods - Hybrid (empirical) Probabilistic Two-Phase Wake Vortex Model P2P fixed uncertainties: variation of decay parameters uncertainty allowances ( 2, u,0.8t 2 );( 2, l,1.2 T b, ) dynamic uncertainties: uncertainty allowances y 2 C q C v ( ), z ( ) y, z ( ) dt u l u l q sh sh 2 model calibration with measurement data: uncertainty allowances

14 probabilistic short-term term weather prediction methods survey - nowcasting methods (extrapolation of observed values in space and time) - persistence assumption - Lagrangian approaches - simple physical or statistical models - numerical weather prediction models (NWP) - deterministic NWP (uncertainty estimates from subgrid scale models) - ensemble prediction method - multi model, initialization, perturbation ensemble - time-lagged ensemble - spatial ensemble - combinations of the above

15 Forecast Skill of Nowcasting and NWP - Theory: theoretical limit of predictability (chaos) - Nowcasting Methods: very high initial skill followed by rapid decrease - NWP Models: skill over a longer period since dynamical processes simulated (after Kober 2009) Lin et al, 2005; Golding, 1998

16 Forecast Skill of Nowcasting and NWP WV prediction: crossover time ~ 1 hour precipitation prediction: blending of Nowcasting and NWP improves predictability NWP nowcasting blended Frech M., Holzäpfel F., J. Aircraft Kirstin Kober, DLR

17 Example: Short-term term Prediction of Wind and Temperature Profiles based on WTR data for the Wake Vortex Warning System at Frankfurt Airport measurements + fits prediction of trend + confidence interval Daniel Sacher

18 NWP: microscale (deterministic) approach deterministic numerical weather predictions, including assimilation of observations taken in the airport environment, might well capture the average state of the atmosphere subgrid scale variability of weather parameters makes the dominant contribution to the overall uncertainty currently, the turbulence parameterization of the COSMO model is being augmented: Improved EDR forecasts considering effects of horizontal shear, mountain blocking, and convection M. Raschendorfer, DWD

19 ensemble prediction (mesoscale( mesoscale) ) approaches (M. Steiner, NCAR)

20 Multi model, initialization, perturbation ensembles Example: Hurricane track & intensity prediction Hurricanes can cause huge societal impacts Focus on storm track, timing, & intensity (both precipitation & wind) Translation of hurricane track, size & intensity ensemble into probabilistic evacuation area, storm surge, damage, disruption of services, economic impact, etc. Ensemble of Hurricane Tracks M. Steiner, NCAR M. Steiner, NCAR

21 Multi model, initialization, perturbation ensembles Example: Hurricane track & intensity prediction Hurricanes can cause huge societal impacts Focus on storm track, timing, & intensity (both precipitation & wind) Translation of hurricane track, size & intensity ensemble into probabilistic evacuation area, storm surge, damage, disruption of services, currently too costly for wake vortex applications economic impact, etc. initiative to install common European-wide, high-resolution ensemble prediction systems (Chiara Marsigli, Servizio Idro-Meteo-Clima, Bologna) Ensemble of Hurricane Tracks M. Steiner, NCAR M. Steiner, NCAR

22 Example: Time-lagged ensemble K. Dengler, DLR COSMO NWP model with assimilation of SYN, TEMP, AMDAR, Radar, RASS

23 Use of Time-Lagged Ensembles for Probabilistic WV Predictions extension of P2P envelope of 3 4 runs: fast decay: 2, u, 0.8T 2, N 2 N, 2 intermediate (det.) decay: slow decay: 2,T, mean 2 2, l, 1.2T 2, N 2 N, 2 3 runs wind envelopes: u 2 u, v 2 v 4 runs uncertainty allowances: 0.2, 0 0.5b 0 y u( l), z u( l) y, z ( ) 2 C q C v q sh sh 2 dt

24 Use of Time-Lagged Ensembles for Probabilistic WV Predictions preliminary findings mean and median of TLE superior to deterministic predictions ensemble spread only partially encloses observations need to establish new skill scores (single skill score not sufficient) spread-skill correlation coefficient only slightly positive SSC = v, TLE v, TLE y y ensemble spread RMS y / b0

25 Summary nowcasting methods preferable if measurement instrumentation available added value from spatial ensembles based on measurements (Radar, Lidar, suite of instruments) possibly enhanced by 4D data analysis NWP necessary, if air volumes cannot be covered by instrumentation data assimilation increases forecast skill economical approach: time-lagged ensembles + data assimilation schemes + spatial ensembles alternative: uncertainties from subgrid scale variability of deterministic pred. blending of nowcasting and NWP bridges gap & improves prediction quality all types of NWP may benefit from improved boundary layer physics, parameterizations, and initial conditions weather prediction products can be enhanced by careful calibration

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