A re-sampling based weather generator

Transcription

1 A re-sampling based weather generator Sara Martino 1 Joint work with T. Nipen 2 and C. Lussana 2 1 Sintef Energy Resources 2 Norwegian Metereologic Institute Berlin 19th Sept Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

2 Overview 1 Motivation 2 Resampling 3 The Dataset 4 Joining Segments 5 Results 6 Concluding remarks Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

3 Motivation Energy market in Europe becomes more and more integrated Largest energy companies need to account also for what happens in other countries Renewable energy production increases in many countries in Europe Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

4 Need for a larger simulator Increased spatial domain Increased number of variables Precipitation Temperature Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

5 Need for a larger simulator Increased spatial domain Increased number of variables Precipitation Temperature Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

6 Need for a larger simulator Increased spatial domain Increased number of variables Precipitation Temperature Humidity, Radiation, Wind Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

7 Challenges The larger domain and the increased number of variables of interest creates many challenges The size of the spatial model Modeling the covariances spatial temporal inter-variables Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

8 Challenges The larger domain and the increased number of variables of interest creates many challenges The size of the spatial model Modeling the covariances spatial temporal inter-variables Many such characteristics are embedded in deterministic models used in numerical weather prediction There are dataset with stored NWP ensembles or hindcast ensembles. Such datasets are steadily increasing. Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

9 Challenges The larger domain and the increased number of variables of interest creates many challenges The size of the spatial model Modeling the covariances spatial temporal inter-variables Many such characteristics are embedded in deterministic models used in numerical weather prediction There are dataset with stored NWP ensembles or hindcast ensembles. Such datasets are steadily increasing. Idea: Try to re-sample from such data set! Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

10 Re-sampling idea Create a new weather sequence by joining randomly chosen 10-days segments We do not try to model the transition but look for an analog day in the database We do not model climate change and assume stationary weather for the next 5/10 years Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

11 Pros and Cons Advantages At daily scale: physical correlation between variables At daily scale: physical spatial correlation No need to model time transition The model is easy to understand and to implement It can be applied in any part of the world Disadvantages Need an algorithm to join sequences You get the physics that is in the forecast model! ara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

12 The Dataset Based on ECMWF ensemble re-forecasts Twice a week, the ECMWF reruns their current operational model for a number of past days (only 5 ensemble) Time span (growing) Spatial resolution ( in Norway ca Km) 10 days long forecast Total of ca days segments In contrast, a reanalysis dataset would have ca days Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

13 The Dataset South of Norway as temporary spatial domain 8 variables (2m temperature (K), precipitation (mm), sea level pressure (Pa), cloud area fraction (0-1), zonal wind speed (m/s), meridional wind speed (m/s), incoming solar radiation (W/m2), relative humidity ) We are not directly interested in sea level pressure but use it for its large scale behavior Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

14 Seasonal Dataset The dataset is monthly based to ensure seasonal variation Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

15 Drift Correction Precipitation Temperature We found a drift in the forecast model Use QQ map to correct it (independently for each cell) Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

16 How to join segments I For the current segment y, for each candidate segment x in the relevant database Compute the score: Where S x = k ν=1 wν RMSD ( f (y 10 ν ), f (x 1 ν ) ) k is the number of variables that are included in the score w ν is a weight (depends also on the units of the variable) y 10 and x 1 are the 10th day in the current segment and the rst day in the candidate segment RMSD( ) is the root mean squared dierence f ( ) is a functional of the relevant map Randomly sample one of the top N segments Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

17 How to join segments II Need to reduce dimension Aggregate over whole domain or use wavelets Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

18 How to join segments III Important choices to make: Which variables to include in the score Which weight to give to each variable in the score Which spatial resolution to choose Still working to nd the optimal set up Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

19 How to join segments III Important choices to make: Which variables to include in the score (Pressure and temperature) Which weight to give to each variable in the score Which spatial resolution to choose Still working to nd the optimal set up Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

20 How to join segments III Important choices to make: Which variables to include in the score Which weight to give to each variable in the score (Proportional to the sd of the variable) Which spatial resolution to choose Still working to nd the optimal set up Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

21 How to join segments III Important choices to make: Which variables to include in the score Which weight to give to each variable in the score Which spatial resolution to choose Still working to nd the optimal set up Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

22 Verication of the scenarios characteristics In out verication the truth is the day 0 forecast Smoothness of the time series Time structure Spatial structure Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

23 Smoothness of the scenarios Mean inter day jump near Oslo Even matching on the spatial mean gives reasonable results Wavelets improves the smoothness locally Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

24 Smoothness of the scenarios Cold and dry in winter season Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

25 Seasonal and Annual variation Realistic seasonal pattern Manage to create warm/cald, dry/wet years Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

26 Autocorrelation Autocorrelation for temperature and precipitation Cell close to Oslo Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

27 Length of dry spell Distribution of dry spell length in Oslo and Bergen Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

28 Spatial correlation - Temperature Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

29 Spatial correlation - Precipitation Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

30 Variance at dierent aggregation scales Too low variance at higher aggregation times Ideas to improve it: Include external forcing (NAO??) for longer time trends Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

31 Open source software Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

32 Open source software wxgen Downloadable from: What it does: Generate scenarios wxgen sim -db db.nc -n 21 -t 365 -o sim.nc Generate truth scenario wxgen truth -db database.nc -o truth.nc Verify scenario characteristics wxgen verif -v 0 truth.nc -m variance Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26

33 Conclusions and further work Flexible framework Applicable to areas with bad data coverage Does not need to explicitly model covariances (time, space and inter-variables) Preliminary results: The simulated series are smooth and respect the correlation between covariates There is both seasonal and annual variation The variance of long time aggregated variables is a bit too low Next step: Increase the database and work on a European scale Sara Martino Joint work with T. Nipen and C. Lussana Berlin 19th Sept / 26