Physically-based modelling approaches to weather and climate modelling for energy applications

Transcription

1 Physically-based modelling approaches to weather and climate modelling for energy applications David Brayshaw Willis Lecturer in Weather and Climate Hazards for Insurance Department of Meteorology, University of Reading With particular thanks to: Industrial collaborators, Dirk Cannon, Dan Drew, John Methven, Phil Coker, Pete Inness, Andrew Charlton- Perez, Len Shaffrey (Reading) Kieran Lynch, Marek Kubik, Caroline Ely, Matthew Lang, Rachael Fordham (Reading students) A. Troccoli (CSIRO, Australia), C. Dent (Durham, UK), S. Zachary (Heriot-Watt, UK)

2 Vulnerabilities " Management of Variability and Uncertainty in Energy Systems Focus often on power systems ops/planning (also here) But multiple systems multiple users multiple concerns Interconnected Opera&onal (seconds few days) Trading (days 1 year) Day to day operations (e.g., grid management, plant sched) Anticipating etreme weather (e.g., hurricanes) Longer-term wholesale energy contracts Maintenance planning Medium term resource planning Strategic (long term climate) Characterising demand/supply Impacts of climate change Risk of etreme disruptive weather

3 Overview " European weather and forecasting " Linking to energy impacts " Introduce some ongoing projects UK wind power variability and etremes Month ahead power forecasting Seasonal-scale variability Climate variability and change

4 European weather " Dominated by North Atlantic storm track " What is a storm (cyclone): Forms as instability on temperature gradient Mi the gradient (irreversible wave breaking) Associated with strong, warm, wet westerly flow (windy, warm, wet in winter) L ~ 1000km, T ~ days, eastward propagation ~ 10-20m/s E.g., Brayshaw et al (2008, 2009, 2011); Catto et al 2010; Thorncroft et al ~2001 for discussion Near surface wind Specific humidity (near surface) L L Figure: Lavers et al 2011

5 European weather " Dominated by North Atlantic storm track " Storms transport angular momentum Modifies the background flow (the jet) Blocking (anticyclones) over Europe Associated with weak, cold, dry easterly flow (still, cold, dry in winter) L block > L storm, T ~ several days, often quasi-stationary w.r.t. surface See, e.g., papers by Tim Woollings for dynamical discussion Pressure 10m wind-speed anomaly 2m T anomaly Shown: A composite picture of many blocking events

6 European weather " Dominated by North Atlantic storm track " Storms transport angular momentum Modifies the background flow (the jet) Blocking (anticyclones) over Europe Associated with weak, cold, dry easterly flow (still, cold, dry in winter) L block > L storm, T ~ several days, often quasi-stationary w.r.t. surface See, e.g., papers by Tim Woollings for dynamical discussion Pressure 10m wind-speed anomaly 2m T anomaly Circulation grossly controls day-to-day character of weather, e.g.: Shown: A composite picture of many blocking events Brayshaw, Dent and Zachary (2012, JRR) Weather typing to eplore wind-during-etreme-peak-demand events

7 Weather forecas&ng Initial state analysis

8 Weather forecas&ng Initial state analysis Dynamical core ~ based on Navier-Stokes eqns Physical parameterisation schemes Cloud/moisture processes Surface energy and momentum echange Radiation Fig:

9 Weather forecas&ng Initial state analysis Dynamical core ~ based on Navier-Stokes eqns Physical parameterisation schemes Cloud/moisture processes Surface energy and momentum echange Radiation Forecast

10 Assessing weather forecasts Many approaches: OAen Eulerian (e.g., RMSE of wind speed at a point; spa&al correla&ons; ) Lagrangian (e.g., storm tracking): models represent storm trajectories well See Froude, 2010

11 Initial condition uncertainty IC uncertainty tends to dominate for short-range forecasting (~ few days) Phase space Phase space Best estimate Error Actual conditions Time t = T Time t = T+24h Errors in initial conditions grow to produce bigger errors in forecast

12 Initial condition uncertainty IC uncertainty tends to dominate for short-range forecasting (~ few days) Phase space Phase space Error Best estimate Actual conditions Deterministic forecast Error Actual conditions Time t = T Time t = T+24h Errors in initial conditions grow to produce bigger errors in forecast

13 Ensemble forecasts Region of phase space consistent with current observations Phase space Phase space Best estimate Actual conditions Time t = T Time t = T+24h Multiple plausible estimates of current state used for initial conditions

14 Ensemble forecasts Region of phase space consistent with current observations Region of phase space consistent with forecast Phase space Phase space Best estimate Actual conditions Time t = T Time t = T+24h Multiple plausible estimates of current state used for initial conditions

15 Ensemble forecasts Region of phase space consistent with current observations Region of phase space consistent with forecast Phase space Phase space Best estimate Actual conditions Time t = T Time t = T+24h Ensemble mean outperforms single best model (on average) Multiple plausible estimates of current state used for initial conditions Spread gives measure of uncertainty Propagation of uncertainty

16 Probabilis&c verifica&on Reliability Compare model Pr(B j A i ) vs observed Pr(B j A i ) Resolu-on Can model usefully dis&nguish between different behaviours B j? Many tools/approaches: ROC, Reliability, skill scores Many challenges (especially at lead- &mes >days) Model dria (output calibra&on) Slower components and boundary condi&on forcing

17 Probabilis&c verifica&on Reliability Compare model Pr(B j A i ) vs observed Pr(B j A i ) Resolu-on Can model usefully dis&nguish between different behaviours B j? Many tools/approaches: ROC, Reliability, skill scores Many challenges (especially at lead- &mes >days) Model dria (output calibra&on) Slower components and boundary condi&on forcing Single best deterministic forecast Lagrangian diagnos&c as simple illustra&on Ensemble-mean forecast See Froude, 2010

18 Relationship to energy impacts " Linking to facts on the ground " Interaction with localized topography and specific infrastructure UK orography Installed wind capacity (courtesy Dirk Cannon) National Grid transmission

19 Downscaling " Nested atmospheric models " E.g., UKMO now use ~1km over some regions of UK in operational forecast Figure: for_ag0.jpg

20 Downscaling reanalyses for wind resource assessment " Reanalysis widely used to drive highresolution downscaling (e.g., Hawkins et al 2011) Reanalysis is an optimal merger of NWP model with observations to reconstruct past weather Typically: years of data km grid boes State-of-the-art climate models ~ km Typical modern reanalysis ~ km Typical WRF downscaling ~ 5-25km? Fig: UKMO website

21 Downscaling reanalyses for wind resource assessment " Reanalysis widely used to drive highresolution downscaling (e.g., Hawkins et al 2011) " Positives: Can be used to add important spatiotemporal detail and reduce biases For some situations may help capture new processes (e.g., sting-jets in cyclones associated with damaging gusts) " Negatives: Very costly to perform more than a decade (gradually changing, e.g., CORDEX?) No guarantee that the parent reanalysis samples all possible global situations Often still needs some statistical fies to link to real system (further downscaling) Only deals with one region at a time State-of-the-art climate models ~ km Typical modern reanalysis ~ km Typical WRF downscaling ~ 5-25km? Fig: UKMO website

22 The dangers of short records " Well known and widely studied pressure oscillation in North Atlantic (e.g., Hurrell 2001) " +ve / -ve phase more / less frequent weather systems into N. Europe " Affects wind-speed PDF in UK (estimated to some sites up to ~10% difference in mean winter power between top 1/3 rd and lowest 1/3 rd of NAO years, Brayshaw et al 2011) Relative frequency Blue PDF = Low NAO Red PDF = High NAO Observed hourly mean wind m/s (Great Dun Fell surface station, winter months only) Substantial long-term variability (multi-decadal) NAO patterns and inde from

23 What can reanalysis give us directly? " Avoids computationally epensive downscaling step (=> can use longer timeseries) " Site-by-site comparison MERRA vs MIDAS (thanks to K Lynch, D Cannon, M Kubik) Individual MIDAS 10m wind obs sites (hourly, 10yrs) MIDAS recorded 10m wind speed (m/s) MERRA over or underestimates MIDAS, depending on site MERRA UK average 30 years of data MIDAS Area average better fit (mountain areas problematic) Estimate from bilinear interpolation of MERRA reanalysis data (10m wind, m/s)

24 What can reanalysis give us directly? " Avoids computationally epensive downscaling step " Site-by-site comparison MERRA vs MIDAS (thanks to K Lynch, D Cannon, M Kubik) Individual MIDAS 10m wind obs sites (hourly, 10yrs) MIDAS recorded 10m wind speed (m/s) General rules for using MERRA: MERRA UK average 30 years of data Does not capture small-scale turbulence or local topographic affects BUT Does capture significant variability for Δt > 3-6 h and Δ MIDAS > km MERRA over or underestimates Area average better fit MIDAS, depending on site (mountain areas problematic) (please ask if you would like further details!) Estimate from bilinear interpolation of MERRA reanalysis data (10m wind, m/s)

25 Summary so far " Introduced some: Aspects of meteorology relevant to European weather NWP models Reanalysis " Eamples of ongoing work

26 1 - UK wind variability & etremes " Dirk Cannon with National Grid UK regions (~200 km squares) in MERRA reanalysis Persistent high/low wind Persistence of low wind in regions 2, 5 and both **PRELIMINARY**

27 1 - UK wind variability & etremes " Dirk Cannon with National Grid UK regions (~200 km squares) in MERRA reanalysis Persistent high/low wind; ramping and cut-out " Net steps: Days ahead forecasting and sub-regional etremes **PRELIMINARY** 100 times a year, p 40 % in 6 hours

28 1 - UK wind variability & etremes " Dirk Cannon with National Grid UK regions (~200 km squares) in MERRA reanalysis Persistent high/low wind; ramping and cut-out " Net steps: 1-5 day forecasting and sub-regional etremes Connected project: Wind-power modelling for single wind-farms Dan Drew **PRELIMINARY** 100 times a year, p 40 % in 6 hours

29 2 - Month-ahead forecasting " Kieran Lynch CASE PhD " Use of ECMWF monthly forecast system " Initial results encouraging at 3-4 weeks " Eploring potential uses: Energy trading (risk mgmt) Maintenance scheduling UK 7-day average 10m wind speed anomaly **PRELIMINARY** Date Blue: 51 member ensemble 3-week ahead fcst Red: Observations (Era-Interim)

30 3 - Mied renewable North- Sea grid Much discussion of : 1. enhanced connectivity 2. increasing generation from wind Thought eperiment: Assume Scandinavia perfectly connected to GB with increasing GB wind Use hydro + wind to meet total demand How would climate variability affect such a system compared to today s? Plausible interconnectivity scenarios (from Poyry 2011) Pictures: and blog/massachusetts-tribes-challenge-offshore-wind-farm-1103/

31 3 Mied renewable North Sea grid Precipitation 10m wind Surface temperature Neg Correlation Pos Neg Anomaly Pos Neg Correlation Pos In a negative NAO winter high demand (cold) and low wind => high DNW Also delayed recharge of Norwegian hydro-reserves (cold => late snow melt) Applying 2035 UK wind scenario => 6-fold increase in NAO s affect on DNW => Need for careful management, advance forecasting helpful See Ely et al, in review for Energy Policy for further discussion End figures from NOAA CPC website. Correlation of NAO with January fields. Middle from NCEP data (10m wind NAO high-low)

32 4 - Future climate " New CASE PhD student starting October " Use latest IPCC models to understand impact of future climate on energy system scenarios RCP8.5 Zappa et al (2013)

33 4 - Future climate " Comple problem " Climate uncertainty from: " Initial conditions of slow-evolving components Relative impact of sources of uncertainty in decadal-mean UK surface air temperature " Boundary conditions (eternal forcings) e.g., CO2 pathway " Model uncertainty (parameters, missing processes) Natural variations Emission scenario " Future energy uncertainty: " Scenarios? Model uncertainty " Integrated investment modelling? Figs: Hawkins and Sutton 2009

34 Conclusions/thoughts " Summary: any opportunities for better use of met in energy-sector Process understanding (large-scale and small-scale) Forecasting (hours to decades) Ongoing projects at Reading " Thoughts: Connecting to energy impact challenging Connecting to point-local, high frequency properties challenging Downscaling useful but need to understand/retain large-scale uncertainties Joint statistical-physical approaches useful Better linking of market/energy & weather/climate models Multiple met inputs (e.g., wind => wind-power, temperature => demand) Not just the power system what about other energy systems? Not just policy and operations many other specialist risk mgmt areas (trading, insurance, ) Energy/food/water neus? Unburnable carbon?