A low-cost post-processing technique improves weather forecasts around the world

Computer-generated forecasts divide the earth's surface into gridboxes, each now ~25% of the size of London, and predict one value per gridbox. If weather varies markedly within a gridbox forecasts for specific sites inevitably fail. A completely new statistical post-processing method, using ensemble forecasts as input, anticipates two gridbox-weather-dependant factors: degree of variation in each gridbox, and bias on the gridbox scale. Globally, skill improves substantially; for extreme rainfall, for example, useful forecasts extend 5 days ahead. Without post-processing this limit is < 1 day. Relative to historical forecasting advances this constitutes ground-breaking progress. The key drivers, incorporated during calibration, are meteorological understanding and abandoning classical notions that only local data be used. Instead we simply recognise that "showers are showers, wherever they occur worldwide" which delivers a huge increase in calibration dataset size. Numerous multi-faceted applications include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

[1]  Albrecht Weerts,et al.  Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales ☆ , 2013 .

[2]  T. W. Anderson,et al.  Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes , 1952 .

[3]  J. Done,et al.  Mesoscale simulations of organized convection: Importance of convective equilibrium , 2006 .

[4]  Emmanuel Roulin,et al.  Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts , 2012 .

[5]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[6]  Thomas M. Hamill,et al.  Comparison of Ensemble-MOS Methods Using GFS Reforecasts , 2007 .

[7]  5 . 1 OPERATIONAL CALIBRATED PROBABILITY FORECASTS FROM THE ECMWF ENSEMBLE PREDICTION SYSTEM : IMPLEMENTATION AND VERIFICATION , 2001 .

[8]  Gregory R. Herman,et al.  “Dendrology” in Numerical Weather Prediction: What Random Forests and Logistic Regression Tell Us about Forecasting Extreme Precipitation , 2018, Monthly Weather Review.

[9]  Roberto Buizza,et al.  Ensemble Forecasting and the Need for Calibration , 2018 .

[10]  T. D. Hewson,et al.  NWP Grid Editing at the Met Office , 2005 .

[11]  Antonino Cancelliere,et al.  Quality control of daily rainfall data with neural networks , 2009 .

[12]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[13]  J. Flowerdew.  Calibration and combination of medium- range ensemble precipitation forecasts , 2012 .

[14]  Thomas M. Hamill,et al.  Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application , 2006 .

[15]  Matthew J. Bunkers,et al.  An Observational Examination of Long-Lived Supercells. Part II: Environmental Conditions and Forecasting , 2006 .

[16]  J. Fritsch,et al.  Predicting the Movement of Mesoscale Convective Complexes , 1996 .

[17]  Sue Ellen Haupt,et al.  Artificial Intelligence Methods in the Environmental Sciences , 2008 .

[18]  Caren Marzban,et al.  MOS, Perfect Prog, and Reanalysis , 2006 .

[19]  T. Gneiting,et al.  Receiver Operating Characteristic (ROC) Curves , 2018, 1809.04808.

[20]  J. Whitaker,et al.  REFORECASTS An Important Dataset for Improving Weather Predictions , 2006 .

[21]  Marion Mittermaier,et al.  A long‐term assessment of precipitation forecast skill using the Fractions Skill Score , 2013 .

[22]  Niels Bormann,et al.  Representing Equilibrium and Nonequilibrium Convection in Large-Scale Models , 2014 .

[23]  T. Haiden,et al.  A new equitable score suitable for verifying precipitation in numerical weather prediction , 2010 .

[24]  Susan Joslyn,et al.  Progress and challenges in forecast verification , 2013 .

[25]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[26]  Jeffrey M. Bielicki,et al.  The stationarity of two statistical downscaling methods for precipitation under different choices of cross‐validation periods , 2018 .

[27]  F. Pappenberger,et al.  Trends in the predictive performance of raw ensemble weather forecasts , 2014 .

[28]  A. Thorpe,et al.  Attribution concepts applied to the omega equation , 1996 .

[29]  Giles M. Foody,et al.  Crowdsourcing for climate and atmospheric sciences: current status and future potential , 2015 .

[30]  R. Houze.  Orographic effects on precipitating clouds , 2012 .

[31]  P. Friederichs,et al.  Postprocessing for Extreme Events , 2018 .

[32]  R. Plant,et al.  Characterisation of convective regimes over the British Isles , 2016 .

[33]  Jong‐Jin Baik,et al.  Urban impacts on precipitation , 2014, Asia-Pacific Journal of Atmospheric Sciences.

[34]  N. Roberts,et al.  Realism of Rainfall in a Very High-Resolution Regional Climate Model , 2012 .

[35]  Mithat Gönen,et al.  Receiver Operating Characteristic (ROC) Curves , 2006 .

[36]  Renate Hagedorn,et al.  Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation , 2008 .

[37]  L. Isaksen,et al.  Use of in situ surface observations at ECMWF , 2018 .

[38]  Olivier Mestre,et al.  Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting , 2019, Weather and Forecasting.

[39]  N. Roberts,et al.  Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events , 2008 .

[40]  Nicolai Meinshausen,et al.  Quantile Regression Forests , 2006, J. Mach. Learn. Res..

[41]  C. Keil,et al.  Regime-dependent forecast uncertainty of convective precipitation , 2011 .

[42]  Shengping Yang,et al.  The receiver operating characteristic (ROC) curve , 2017 .

[43]  Thomas M. Hamill,et al.  Measuring forecast skill: is it real skill or is it the varying climatology? , 2006 .

[44]  Matthew R. Peroutka,et al.  The U.S. National Blend of Models for Statistical Postprocessing of Probability of Precipitation and Deterministic Precipitation Amount , 2017 .

[45]  James Bruce Lee,et al.  Theory and Application , 2019, Wearable Sensors in Sport.

[46]  T. Hewson,et al.  A classification of FASTEX cyclones using a height‐attributable quasi‐geostrophic vertical‐motion diagnostic , 2002 .

[47]  S. Herath,et al.  Improving global rainfall forecasting with a weather type approach in Japan , 2017 .

[48]  Fuqing Zhang,et al.  Is precipitation a good metric for model performance? , 2019, Bulletin of the American Meteorological Society.

[49]  Thomas M. Hamill,et al.  Probabilistic Precipitation Forecast Postprocessing Using Quantile Mapping and Rank-Weighted Best-Member Dressing , 2018, Monthly Weather Review.

[50]  George Wright,et al.  The limits of forecasting methods in anticipating rare events , 2010 .

[51]  Susanne Crewell,et al.  A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation;A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation , 2017 .

[52]  Maurice Schmeits,et al.  Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods , 2018, Monthly Weather Review.

[53]  Khadija Iqbal,et al.  An introduction , 1996, Neurobiology of Aging.

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

[55]  Robin P. Fawcett,et al.  Theory and application , 1988 .

[56]  Thomas M. Hamill,et al.  Analog Probabilistic Precipitation Forecasts Using GEFS Reforecasts and Climatology-Calibrated Precipitation Analyses* , 2015 .

[57]  Thomas M. Hamill,et al.  Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* , 2015 .

[58]  Christian Keil,et al.  Aspects of short‐term probabilistic blending in different weather regimes , 2014 .

[59]  Maurice Schmeits,et al.  Statistical Postprocessing and Multivariate Structuring of High-Resolution Ensemble Precipitation Forecasts , 2018, Journal of Hydrometeorology.

[60]  Pablo A. Mendoza,et al.  Statistical Postprocessing of High-Resolution Regional Climate Model Output , 2015 .

[61]  Gregory R. Herman,et al.  Money Doesn't Grow on Trees, but Forecasts Do: Forecasting Extreme Precipitation with Random Forests , 2018 .

[62]  David S. Richardson,et al.  Economic Value and Skill , 2012 .

[63]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[64]  Thomas M. Hamill.  Practical Aspects of Statistical Postprocessing , 2018 .

[65]  J. Gutiérrez,et al.  Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions , 2013 .