Affiliation: * The Norwegian Meteorological institute, Oslo, ** SINTEF ICT> > Applied Mathematics, Trondheim, Norway 8/15/14

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1 An operational system for forecasting of turbulence in the vicinity of airports in Norway WWOSC 2014, Montréal, Canada Knut Helge Midtbo*, Jakob Suild*, Karstein Soerli** and Joern Kristiansen* Affiliation: * The Norwegian Meteorological institute, Oslo, ** SINTEF ICT> > Applied Mathematics, Trondheim, Norway 8/15/14

Karstein Soerli** and Joern Kristiansen* Affiliation: * The Norwegian

2 OUTLINE: BACKGROUND / MODELS USED IN THE FORECASTING SYSTEM END USER PRODUCTS ROUTINE VALIDATION (WIND) FDR (FLIGHT RECORDER DATA) IN VALIDATION TESTS (TURBULENCE) -> POWER SPECTRA METHOD -> SIMPLER (RMS) METHOD REMARKS AND PLANS

(FLIGHT RECORDER DATA) IN VALIDATION TESTS (TURBULENCE)

3 Parameterization of wind in NWP Forecasting grid cell averages of the variable wind leeds to the need for linking the mean flow (wind) to the unresolved flow. 3

4 MODEL TOOLS IN OUR MULTIMODEL NEST: ECMWF NWP MODEL degrees resolution HARMONIE NWP MODEL WITH 2.5 km resolution SIMRA CFD MODEL m resolution Acronyms with comment (to remind me to tell you): NWP = Numerical Weather Prediction «full» physics CFD = Computational Fluid Dynamics dry physics Implication: > > SIMRA will not handle wet convection 4

5 km resolution SIMRA CFD MODEL 100-200 m resolution Acronyms with comment (to remind me

5 SIMRA is using a k-epsilon (k-ε) closure for the turbulence with three constants. SIMRA computes TKE and EDR. Square root of TKE given as a turbulence indicator More than 3 m/s is considered severe. TKE = Turbulent Kinetic Energy 8/15/14 EDR = Eddy Dissipation

Square root of TKE given as a turbulence indicator More than 3

Brønnøysund, Værnes, Ørsta-Volda, Sandane, Førde Fagernes 1 WINDFARM: Bessaker

6 20 AIRPORTS: Longyearbyen Mehamn, Honningsvåg, Hammerfest, Hasvik, Tromsø, Bardufoss, Evenes, Narvik, Svolvær, Leknes, Mo i Rana, Mosjøen, Sandnessjøen, Brønnøysund, Værnes, Ørsta-Volda, Sandane, Førde Fagernes 1 WINDFARM: Bessaker Wind farm HARMONIE 2.5 km grid AROME physics Using ECMWF degrees on lateral boundaries

7 Wellknown problem: The mountains around airports force the flow Severe problems frequently occurs First example from Hammerfest north in Norway Status in 2005: Documents describing turbulence for each airport Multimodel in use for investigations 7 Project using multimodel proposed by us

Norway Status in 2005: Documents describing turbulence for each airport

8 First example HAMMERFEST CASE 1st of May Strong SE. The decoupled lower layer is forced by the mountains. The arrow on the DWD surface map points at Hammerfest (surface map by Deutsche Wetter Dienst)

9 First example HAMMERFEST CASE 1st of May The airport is known for tough conditions. Turbulence often all the way to touchdown! Frequent restrictions caused by wind and turbulence. This aircraft landed hard indeed. Fortunately no people injured. The aircraft was scrapped. Our project proposal eventually got support in later The project is since then funded by AVINOR AS (Avinor AS is a state-owned limited company and operates most of the civil airports in Norway)

This aircraft landed hard indeed. Fortunately no people injured. The aircraft was scrapped.

10 SECOND EXAMPLE: VÆRNES AIRPORT, TRONDHEIM 10

11 Værnes airport, Trondheim View by Google earth 11

12 END USER PRODUCT FUNNEL SHAPED SURFACE ON A 2D MAP FOR VÆRNES AIRPORT, TRONDHEIM (The runway and the lines defined by the Instrument Landing System (ILS) is in black, Grey is model topograhy intersecting this surface)

defined by the Instrument Landing System (ILS) is in

13 END USER PRODUCT VERTICAL CROSS SECTION FOR VÆRNES AIRPORT, TRONDHEIM (The runway and the lines defined by the Instrument Landing System (ILS) is in black)

14 STATUS OF THE SYSTEM PILOT REPORTS Pilot reports are positive. They want the product for turbulence awareness and for generell high resolution wind forecasting in the area. ROUTINE VALIDATION (WIND) 14 SYNOP reports used at all airports. Gain in quality compared to HARMONIE 2.5 km using SIMRA m at some airports. AMDAR reports of wind are used for comapring SIMRA and the other models. Only avialable for three larger airports The winter error inside SIMRA area measured by AMDAR during taking off or landing is 3 m/s. Even though wind is a condition for turbulence WE WOULD LIKE to validate turbulence against measurements

ROUTINE VALIDATION (WIND) 14 SYNOP reports used at all airports. Gain in quality compared to HARMONIE 2.5 km using SIMRA 100-200 m at some airports.

15 ABOUT OUR 8 Hz Flight Recorder Data (FDR) FDR DATA PROVIDED BY Norwegian Air Shuttle ASA, trading as Norwegian, the ninthlargest airline in Europe in terms of passenger numbers FDR DATA PREPARED BY Anne Rowlinson, Flight Data Services, Fareham, UK DATA downloaded from BOING aircrafts. FDR DATA USED BY US (results from 49 landings at Værnes) -> FOR OBJECTIVE VALIDATION OF SIMRA 15 -> FOR CASE STUDIES

Rowlinson, Flight Data Services, Fareham, UK DATA downloaded from BOING 737-800 aircrafts.

16 OUR DATA PLATFORM LOOKS LIKE THIS Note Vilhelm Bjerknes on the tail!

17 VÆRNES CASE A «CLEVER NAVIGATION» 15th of December UTC

18 VÆRNES Case A «CLEVER NAVIGATION» 15th of December UTC

19 VÆRNES Case B «The severe case» 15th of December UTC

20 VÆRNES Case B «The severe case» 15th of December UTC

21 VÆRNES Case B «The severe case» Vertical g Rough case consistent with other measures 21

22 POWER SPECTRUM METHOD Spectral method for estimating EDR Code developed by Line Båserud and Joachim Reuder (University of Bergen)

23 POWER SPECTRUM METHOD VÆRNES Case B «The severe case» Computations based on data 30 seconds around peak g 23

24 POWER SPECTRUM METHOD We have used sliding tiem windows along the flight track and selected the maximum for each track Scatter reveals low scores 24

25 RMS METHOD We refer to the preferred aircraft turbulence hazard metric (Bowles and Buck 2009; Hamilton and Proctor 2006; Proctor and Hamilton 2005) is a five-second root mean square of the aircraft s normal load accelerations. (NASA Langley Research Center) According to NASA researchers 0.4 to 0.6 is severe VÆRNES Case B «The severe case» Sliding variance 5 seconds sampling (40 data points). 25

26 RMS METHOD Scatter reveals somewhat better scores 26

27 DEFINITION OF SCORES 27

28 RMS METHOD Scatter reveals somewhat better scores 28

29 RMS METHOD Scatter reveals somewhat better scores No skill for threshold of 4 m/s 29

30 REMARKS My case studies of all the 49 landings supports the pilot view. For those events for which we have a turbulence estimate the guidance from the products is of value. Based on the results on the limited data sets Further test the RMS method Keep concern about both hits and false alarms The scale of the turbulence makes it resonable to seek for improvments in the line of the following tags PLANS Go to presenting distributions instead of one value ->Neighbourhood methods in both space and time ->Tempting to use Ensembles of SIMRA runs 30

31 Thank you!

Knut Helge Midtbø, Mariken Homleid and Viel Ødegaard (P.O. Box 43, N-0313 OSLO, NORWAY)

Knut Helge Midtbø, Mariken Homleid and Viel Ødegaard (P.O. Box 43, N-0313 OSLO, NORWAY) Verification of wind and turbulence forecasts for the airports Mehamn, Honningsvåg, Hammerfest, Hasvik, Tromsø, Bardufoss, Evenes, Narvik, Svolvær, Leknes, Mo i Rana, Mosjøen, Sandnessjøen, Brønnøysund,