Log‐normal distribution based Ensemble Model Output Statistics models for probabilistic wind‐speed forecasting

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles: they are usually underdispersive and uncalibrated and require statistical post‐processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind‐speed forecasts based on the log‐normal (LN) distribution and we also show a regime‐switching extension of the model, which combines the previously studied truncated normal (TN) distribution with the LN.

[1]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

[2]  W. R. Hargraves,et al.  Methods for Estimating Wind Speed Frequency Distributions. , 1978 .

[3]  Roberto Buizza,et al.  Computation of optimal unstable structures for a numerical weather prediction model , 1993 .

[4]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[5]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[6]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[7]  J. Torres,et al.  Fitting wind speed distributions: a case study , 1998 .

[8]  A. Frigessi,et al.  A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection , 2002 .

[9]  Frederick Anthony Eckel,et al.  Effective mesoscale, short-range ensemble forecasting , 2003 .

[10]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[11]  Anton H. Westveld,et al.  Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .

[12]  R. Buizza,et al.  A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems , 2005 .

[13]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[14]  Clifford F. Mass,et al.  Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting , 2005 .

[15]  R. Stull,et al.  Probabilistic aspects of meteorological and ozone regional ensemble forecasts , 2006 .

[16]  Andras Horanyi,et al.  The ARPEGE/ALADIN mesoscale numerical modeling system and its application at the Hungarian Meteorological Service , 2006 .

[17]  Namir,et al.  Authors , 1947, Praxis der Kinderpsychologie und Kinderpsychiatrie.

[18]  Tilmann Gneiting,et al.  ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging , 2007 .

[19]  A. Raftery,et al.  Probabilistic forecasts, calibration and sharpness , 2007 .

[20]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[21]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[22]  Tim N. Palmer,et al.  Ensemble forecasting , 2008, J. Comput. Phys..

[23]  T. Gneiting.  Making and Evaluating Point Forecasts , 2009, 0912.0902.

[24]  Tilmann Gneiting,et al.  Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression , 2010 .

[25]  A. Raftery,et al.  Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging , 2010 .

[26]  T. Gneiting,et al.  Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules , 2011 .

[27]  Adrian E. Raftery,et al.  Probabilistic Weather Forecasting in R , 2011 .

[28]  D. S. Wilks,et al.  Chapter 8 - Forecast Verification , 2011 .

[29]  Z. B. Bouallègue,et al.  Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries , 2011 .

[30]  AndrÁS HorÁNyi,et al.  Latest developments around the ALADIN operational short-range ensemble prediction system in Hungary , 2011 .

[31]  P. Friederichs,et al.  Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE , 2012 .

[32]  Pierre Pinson,et al.  Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations , 2012 .

[33]  Thordis L. Thorarinsdottir,et al.  Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas , 2012, 1202.3956.

[34]  J. M. Sloughter,et al.  Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging , 2010 .

[35]  S. Baran,et al.  Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction , 2013, 1312.3763.

[36]  S. Baran,et al.  Statistical post-processing of probabilistic wind speed forecasting in Hungary , 2012, 1202.4442.

[37]  T. Thorarinsdottir,et al.  Comparison of non-homogeneous regression models for probabilistic wind speed forecasting , 2013, 1305.2026.

[38]  Z. B. Bouallègue,et al.  Enhancing COSMO-DE ensemble forecasts by inexpensive techniques , 2013 .

[39]  Sándor Baran,et al.  Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components , 2013, Comput. Stat. Data Anal..

[40]  T. Gneiting.  719 Calibration of Medium-Range Weather Forecasts , 2014 .

[41]  Christopher A. T. Ferro,et al.  A comparison of ensemble post‐processing methods for extreme events , 2014 .

[42]  Willem A. Landman,et al.  Statistical Methods in the Atmospheric Sciences (3rd Edition), Daniel S. Wilks : book review , 2015 .