Does the stratosphere provide predictability for month-ahead wind power in Europe?

Transcription

1 ICEM 2017 Bari 27 June 2017 Does the stratosphere provide predictability for month-ahead wind power in Europe? Remo Beerli, Heini Wernli and Christian Grams Institute for Atmospheric and Climate Science ETH Zurich, Switzerland

2 The North Atlantic Oscillation (NAO) and its implications for surface winds Variation of pressure gradient between Iceland and Azores Correlation between the NAO and monthly mean surface wind speed NAO is the leading mode of atmospheric variability in the Euro- Atlantic sector NAO is strongly correlated to surface winds in Europe Zubiate et al

3 The stratosphere: A predictor for the NAO on monthly timescales Weak Vortex events Stratosphere NAO- Troposphere Time lag Strong Vortex events Stratosphere NAO+ Troposphere Lag Time lag Baldwin and Dunkerton (2001) 3

4 The stratosphere: A predictor for the NAO on monthly timescales Weak Vortex events Stratosphere NAO- Troposphere Time lag Strong Vortex events Stratosphere NAO+ Troposphere Time lag Baldwin and Dunkerton (2001) What does this mean for wind power? 4

5 Data Renewables.ninja (Staffel and Pfenninger, 2016) 5

6 Data Renewables.ninja (Staffel and Pfenninger, 2016) Country-aggregated simulated wind power dataset since 1985 Hourly resolution 6

7 Data Renewables.ninja (Staffel and Pfenninger, 2016) Country-aggregated simulated wind power dataset since 1985 Hourly resolution Wind park as of today & reanalysis data Bias corrected & quality checked Most European countries CF (Capacity Factors) 7

8 Data Renewables.ninja (Staffel and Pfenninger, 2016) Country-aggregated simulated wind power dataset since 1985 Hourly resolution Wind park as of today & reanalysis data Bias corrected & quality checked Most European countries ERA-Interim Global Reanalysis dataset since hourly temporal resolution 1 spatial resolution CF (Capacity Factors) Geopotential height 100m wind speeds 8

9 Germany Stronger polar vortex Weaker polar vortex 9

10 Germany Stronger polar vortex Weaker polar vortex Lower than normal month-ahead wind power during weak polar vortex Highest wind power for moderately strong polar vortex Saturation towards normal for very strong polar vortex conditions 10

11 Spain Stronger polar vortex Weaker polar vortex 11

12 Spain Stronger polar vortex Weaker polar vortex Lower than normal month-ahead wind power during strong polar vortex For near normal and weak polar vortex conditions no signal 12

13 Sweden Stronger polar vortex Weaker polar vortex 13

14 Sweden Stronger polar vortex Weaker polar vortex Strongest modulation by polar vortex Above normal wind power for strong polar vortex Below normal wind power for below normal polar vortex 14

15 Sweden Germany Spain Stronger polar vortex Weaker polar vortex 15

16 500 hpa geopot Introduction Data Results Conclusions Synoptic pattern: Strong vortex P0 P2 P0 P10 Very strong vortex cases (2% and 10% strongest): NAO+ dipole very far north 16

17 500 hpa geopot 100m wind Introduction Data Results Conclusions Synoptic pattern: Strong vortex P0 P2 P0 P10 Very strong vortex cases (2% and 10% strongest): NAO+ dipole very far north High winds North Sea Low winds in Iberia Germany at southern edge high wind corridor Therefore German wind power saturates for very strong polar vortex conditions 17

18 Synoptic pattern: Strong vortex Moderately strong vortex cases (10% to 20% strongest): P10 P20 NAO+ dipole further south High winds further south Local maximum of German wind power 18

19 500 hpa geopot 100m wind Introduction Data Results Conclusions Synoptic pattern: Weak vortex P98 P100 P90 P100 Very weak vortex cases (2% and 10% weakest): NAO- dipole Low winds in North Sea No clear signals for Iberia 19

20 Empirical wind power forecasts Probabilistic 3-categorical forecasts of of the lower stratospheric circulation ( ) based on the state 20

21 Empirical wind power forecasts Probabilistic 3-categorical forecasts of of the lower stratospheric circulation ( ) based on the state Three predictability groups: Nordics UK, Germany, Poland France and southern Europe 21

22 Empirical wind power forecasts Stronger polar vortex Weaker polar vortex 22

23 Empirical wind power forecasts Skill is highly conditional: Sweden: High skill for both strong and weak polar vortex conditions Stronger polar vortex Weaker polar vortex 23

24 Empirical wind power forecasts Skill is highly conditional: Sweden: High skill for both strong and weak polar vortex conditions Germany: Highest skill for moderately strong/weak polar vortex, but low skill for extremely strong/weak polar vortex Stronger polar vortex Weaker polar vortex 24

25 Empirical wind power forecasts Skill is highly conditional: Sweden: High skill for both strong and weak polar vortex conditions Germany: Highest skill for moderately strong/weak polar vortex, but low skill for extremely strong/weak polar vortex Spain: Skill for strong polar vortex France almost no skill Stronger polar vortex Weaker polar vortex 25

26 Conclusions Robust link between the stratospheric circulation and month-ahead wind power Shape and magnitude of it differs among suregions of Europe 26

27 Conclusions Robust link between the stratospheric circulation and month-ahead wind power Shape and magnitude of it differs among suregions of Europe The reason for this relation are prolonged phases of strat-trop coupling that lead to quasipersistent periods of NAO+ or NAO- 27

28 Conclusions Robust link between the stratospheric circulation and month-ahead wind power Shape and magnitude of it differs among suregions of Europe The reason for this relation are prolonged phases of strat-trop coupling that lead to quasipersistent periods of NAO+ or NAO- Statistical forecasts showed that anomalous lower stratopsheric circulation states are windows of enhanced predictability of month-ahead wind power 28

29 Conclusions Robust link between the stratospheric circulation and month-ahead wind power Shape and magnitude of it differs among suregions of Europe The reason for this relation are prolonged phases of strat-trop coupling that lead to quasipersistent periods of NAO+ or NAO- Statistical forecasts showed that anomalous lower stratopsheric circulation states are windows of enhanced predictability of month-ahead wind power Beerli R, Wernli H., Grams CM: Does the lower stratosphere provide predictability for month-ahead wind electricity generation in Europe? Submitted to the Quarterly Journal of the Royal Meteorological Society. 29

30 References Baldwin, M. P. and Dunkerton, T. J. (2001). Stratospheric Harbingers of Anomalous Weather Regimes. Science, 294(5542): Staffell, I. and Pfenninger, S. (2016). Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114: Zubiate, L., McDermott, F., Sweeney, C., and O'Malley, M. (2017). Spatial variability in winter NAO-wind speed relationships in western Europe linked to concomitant states of the East Atlantic and Scandinavian patterns. Q. J. Roy. Meteorol. Soc., 143(702):

31 Empirical wind power forecasts Probabilistic country-aggregate 3-categorical month-ahead wind power forecasts based on the lower polar stratosphere. An example for Sweden: (19JAN1986)= -80.8m (19JAN %) = -53.7m (19JAN %)=-119.4m From m to -53.7m features the following population of its climatological terciles: Lower tercile: 10.6% Middle tercile: 23.1% Upper tercile: 66.3% 31

32 Data: Renewables.ninja From Staffel and Pfenninger (2017) 32

33 Introduction Research questions Data Results Conclusions The role of strat-trop coupling [hpa] Strong polar vortex P0 P2 Weak polar vortex [hpa] P98 P P0 P P90 P P10 P P80 - P P20 P P70 P lead/lag [days] Bins with a 30-day mean NAO+/NAOdipole also feature long-lived strat-trop coupling 34

34 Introduction Research questions Data Results Conclusions Synoptic pattern: Weak vortex P80 P90 Moderately weak vortex cases (10% to 20% weakest): No significant NAO dipole No significant surface wind anomalies in Europe but the northern half of Europe tends to be calm 36

35 Appendix 37

36 38

37 Outlook A conditional hindcast study is planned to explore the following questions: How does the skill of such a simple empirical approach compare to the skill of numerical subseasonal models? Are anomalous states of the stratospheric circulation also windows of enhanced predictability of wind power in numerical models? 39