Meteorological Training Course, 20 March /25 Using Combined Prediction Systems (CPS) for wind energy applications European Centre for Medium-Range Weather Forecasts (ECMWF) Michael Denhard, Renate Hagedorn

Meteorological Training Course, 20 March /25 Safe Wind “Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration.” EU-FP7 project

Meteorological Training Course, 20 March /25 September 2008 August 2012 Safe Wind

Meteorological Training Course, 20 March /25 TIGGE (10 global EPS) Thorpex Interactive Grand Global Ensemble TIGGE-LAM: archive at ECMWF (-24 hours) COSMO LEPS Limited area medium range ensemble (2-5 days) GLAMEPS LAMEPS (Hungary) ALADIN-LAEF (Austria) NORLAMEPS (Norway) AEMET SREPS Spain MOGREPS - NAE UK COSMO SREPS Italy SRNWP-PEPS “Poor mans” ensemble Eumetnet, Germany PEACE France Limited area short range ensembles (1-3 days) Numerical forecast systems in Europe global high resolution models

Meteorological Training Course, 20 March /25 SafeWind WP5: Summary 6 Tasks, 11 Deliverables, 5 (direct) Partners ForWind(OL), ARMINES, ECMWF, ENERGINET ECMWF, ForWind(OL) ForWind(OL), ECMWF, Meteo France ForWind(OL), ECMWF ECMWF, ForWind(OL), MeteoFrance ECMWF, ForWind(OL) Partners CPS applied to wind power forecasts5.6 Combined meteorological Prediction Systems5.5 Weather regime dep. Extreme Forecast Index5.4 Use of Local Area Model EPS5.3 Evaluation of novel ensemble techniques5.2 Probabilistic verification tool (wind gust verif.)5.1 DescriptionTask

Meteorological Training Course, 20 March /25 Combination of: ECMWF Ensemble Perturbations (50 ) ECMWF EPS control (1) ECMWF high resolution model forecast (1) Combined Prediction System

Meteorological Training Course, 20 March /25 ECMWF high resolution deterministic system Sea Level Pressure und 10 m Winds 00 UTC, 12 December 2005 Anlysis and forecasts (a) T799L91 and (b) T511L60 ECMWF global high resolution deterministic model

Meteorological Training Course, 20 March /25 RMSE Wind Power % installed capacity (~60MWatt) RMSE Wind speed model level 88 (116m) 5 best members OpFC: deterministic high resolution model of ECMWF Single point forecasts at the FINO1-site (100m) mean RMSE (Dec July 2008) EPS mean EPS control

Meteorological Training Course, 20 March / June 2008 Combined Prediction System

Meteorological Training Course, 20 March /25 Brier-Score based combination Mark Rodwell et. al.

Meteorological Training Course, 20 March /25 The forecast of a combined prediction system is: In principal there are two ways of determining the weights:  calculate the forecast skill of each component of the CPS separately and determine the weights according to the differences of the score values of the subsystems  optimize an overall score value of the CPS forecasts by changing the weights of its components Combined Prediction System with is the forecasted probability of system k : a single deterministic forecast, a group of forecasts with predefined equal skill or any other probabilistic forecast

Meteorological Training Course, 20 March /25 If the observable is binned in categories is 2, no matter what the distance between the outcomes is ! and Combined Prediction System Combining deterministic and probabilistic forecast systems

Meteorological Training Course, 20 March /25 : forecast observation pairs of the sample. to exceed threshold of observable forecasted probability observed probability Brier Score

Meteorological Training Course, 20 March /25 Summing over all Brier Scores of possible event thresholds leads to: The RPS measures the difference between the cumulative density function of the forecast and the observation. This enables the RPS to measure the overall difference between all kinds of probability distributions, including deterministic delta functions. Ranked Probability Score (RPS)

Meteorological Training Course, 20 March /25 Ranked Probability Score category f(y) category F(y) 1 PDF CDF

Meteorological Training Course, 20 March /25 Weighting of ensemble components One may distinguish between three different ways of estimating the weights:  single skill, by measuring the forecast skill of each component separately and setting the weights according to the differences between the individual scores.  multiple skill, by using analytical or regression methods to jointly determine the weights.  iterative, by starting from a first guess for the weights and minimizing a penalty function or a score until convergence is reached.

Meteorological Training Course, 20 March /25 Total Error Variance of the individual model forecasts k can be used to estimate the weights This only holds, if the errors of the models are linearily independent, which indeed is not the case for numerical weather forecasts.

Meteorological Training Course, 20 March /25 EOF-filter

Meteorological Training Course, 20 March /25 Number of predictors (ensemble size of the CPS) in a reduced MLR-model with positive coefficients Linear Regression of single members  COMO-DE-LAF ensemble (COMSO-DE) with 4 delayed members  SRNWP-PEPS (PEPS) with 17 members  COSMO-LEPS (LEPS) with 16 members. PEPS/COSMO-DE CPS: (+) add lagged LEPS systems

Meteorological Training Course, 20 March /25 Linear Regression of single members

Meteorological Training Course, 20 March /25 Linear Regression of single members 12h accumulated precipitation (6UTC to 18UTC) summer 2007 training (70%,T) validation (30%,V) The full regression model (MLR) is compared with a reduced model (MLR w>0) with positive coefficients

Meteorological Training Course, 20 March /25 Methods for estimating Weights of CPS members Error Variance problem: covariances of forecast errors EOF-Filter reduce covariances of forecasts errors Best Member statistic Multiple Linear Regression (MLR) iterate until all coefficients are positive Brier Score/ Ranked Probability Score (under investigation!) analytical solution Combined System [ ]

Meteorological Training Course, 20 March /25 Combined System 10 m Windspeed, Training period: 30 days Forecast validation: 10 days Mean for Europe ECMWF hres: deterministic run ECMWF-EPS ctrl: control run pert: 50 perturbations pert ~ 2% per member

Meteorological Training Course, 20 March /25 Combined System Ranked Probability Skill Score (RPSS) relative to raw EPS 10 m Windspeed,

Meteorological Training Course, 20 March /25 Summary  Started Safe Wind Project  First Results for Combined Systems  Does sorting out members really generate better probabilistic forecasts ?