Computationally Efficient Dynamical Downscaling with an Analog Ensemble

Transcription

1 ENERGY Computationally Efficient Dynamical Downscaling with an Analog Ensemble Application to Wind Resource Assessment Daran L. Rife 02 June 2015 Luca Delle Monache (NCAR); Jessica Ma and Rich Whiting (DNV GL) 1 SAFER, SMARTER, GREENER

2 Motivation New innovative methods for generating virtual time series and mesoscale wind maps Advance state-of-the-art Significantly reduce computational time & expense Enable next era of advanced proven mesoscale downscaling methodologies Monte-Carlo-based ensemble simulation methods to reduce uncertainty 2

3 Analog ensemble: Concept Two states of the atmosphere which resemble one other are termed analogues. Edward Lorenz (1969) One analog prediction method: Identify historical dates where meteorological patterns are similar to those forecast to occur today Make prediction based on distribution of observed weather states on those dates Used extensively in other industries/disciplines Stock Market Seasonal climate prediction Economics Voting trends Hydrology Trajectory of disease outbreaks 3

4 Analog ensemble: Practical implementation EXAMPLE: Downscale coarse-res WRF simulation at Sample Time 1 (solid black circle) 1) Find all dates in 2014 training period where coarse-res WRF simulations closely match Sample Time 1 (open circles); termed analogs 2) Rank analogs according to their similarity to Sample Time 1 3) Use fine-res WRF simulations for ranked dates (open squares) to compute ensemble mean 4) Repeat Steps 1-3 for remaining hourly outputs in ; complete WRF fine-res tseries Analogs Observations Adapted from Delle Monache et al. (2012) 4

5 Theory and practical application Nagarajan, B., L. Delle Monache, J. P. Hacker, D. L. Rife, K. Searight, J. C. Knievel, and T. N. Nipen, 2015: An evaluation of analog-based post-processing methods across several variables and forecast models. Provisionally accepted for publication in Wea. Forecasting. Alessandrini, S., Delle Monache, L., Sperati, S., and Nissen, J, A novel application of an analog ensemble for short-term wind power forecasting. Renewable Energy, 76, Rife, D., L. Delle Monache, J. Ma, and R. Whiting, 2014: Toward computationally efficient virtual time series with an analog ensemble. WindPower 2014 Conference, American Wind Energy Association, Las Vegas, NV. Vanvyve, E., L. Delle Monache, A. J. Monaghan, and J. O. Pinto, 2014: Wind resource estimates with an analog ensemble approach. Renewable Energy, 74, Delle Monache, L., F. A. Eckel, D. L. Rife, B Nagarajan, K. Searight, 2013: Probabilistic weather prediction with an analog ensemble. Mon. Wea. Rev., 141, Delle Monache, L., T. Nipen, Y. Liu, G. Roux, and R. Stull, 2011: Kalman filter and analog schemes to postprocess numerical weather predictions. Mon. Wea. Rev., 139,

6 Analog ensemble: Practical benefits Overall 60-80% decrease in computational expense/time compared to brute force mesoscale calculations High resolution mesoscale grid often accounts for 90% of calculation expense Analog ensemble only requires high resolution training data for limited period (1-3 years), rather than entire year period Algorithm requires ~90 sec of compute time to downscale 10-year time series to 1 km resolution (87,600 hourly records) Computational savings greatly reduces timescales, and enables use of advanced modeling techniques, such as Monte Carlo ensembles. 6

7 Performance of analog ensemble GOAL: Quantify how closely analog ensemble replicates brute force WRF calculations at 2 km resolution Evaluation at 22 European mast sites in 11 countries Brute force WRF simulations performed at all 22 sites Nested 10 km 2 km grid configuration Complete 9-year hourly time series Analog ensemble used to downscale 10 km WRF Result: Complete 6-year hourly time series at 2 km resolution ( ) Training data: 3-year hourly WRF 2 km brute force ( ) Predictors: Wind speed and wind direction at 4 heights 7

8 Site specific examples 8

9 Southern Europe site: Time series for 2 representative months Hourly wind speed December 10 km brute force 2 km brute force 2 km analog ensemble June 9

10 Site 1: Southern Europe Hourly wind speed WRF 2km brute force R² = 0,90 2km Analog Ensemble [m/s] 10 DNV GL June 2015 RMSD [m/s] 1.36 BIAS [m/s] 0.11

11 Frequency Frequency Site 1: Southern Europe GFE = 10 % GFE = 1 nbins GFE = 34 % % simulated % observed % observed 2km brute force 2km analog ensemble 2km brute force 10km brute force Wind Speed [ hourly ] Wind Speed [ hourly ] Hourly wind speeds

12 Site 2: Northern Europe Hourly wind speed WRF 2km brute force R² = 0,92 2km Analog Ensemble [m/s] 12 DNV GL June 2015 RMSD [m/s] 1.28 BIAS [m/s] 0.05

13 Frequency Frequency Site 2: Northern Europe GFE = 1 nbins % simulated % observed % observed GFE = 14 % GFE = 22 % 2km brute force 2km analog ensemble 2km brute force 10km brute force Wind Speed [ hourly ] Wind Speed [ hourly ] Hourly wind speeds

14 Overall results for analog ensemble Hourly wind speed Correspondence between analog ensemble and WRF brute force

15 Analog ensemble: Wind mapping R 2 = Bias = m/s CRMSD = 0.20 m/s 15

16 Summary and trajectory Analog ensemble yields 60-80% decrease in computational expense compared to standard mesoscale calculations Greatly reduces timescales for virtual time series and wind mapping products Analog closely matches brute force WRF simulations Enables next era of advanced proven mesoscale downscaling methodologies Monte-Carlo-based methods to decrease uncertainty in virtual datasets o Multiphysics ensemble (run multiple versions of WRF) o Multi-reanalysis ensemble (drive WRF with different reanalyses) Can be done for same cost / timeline as current standard mesoscale calculations 16

17 Thank you! Daran Rife SAFER, SMARTER, GREENER 17