Adaptive Kalman Filtering for Postprocessing Ensemble Numerical Weather Predictions

AbstractForecasts from numerical weather prediction models suffer from systematic and nonsystematic errors, which originate from various sources such as subgrid-scale variability affecting large scales. Statistical postprocessing techniques can partly remove such errors. Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially postprocess the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training datasets do not exist. Here, a new adaptive postprocessing technique for ensemble predictions (called AEMOS) is introduced. The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the postprocessing equation. A verification study for the region of Campania in southern Italy is performed. Two years (2014–15) of daily meteorological observ...

[1]  S. Vannitsem,et al.  Dynamical Properties of MOS Forecasts: Analysis of the ECMWF Operational Forecasting System , 2008 .

[2]  George Galanis,et al.  A new Kalman filter based on Information Geometry techniques for optimizing numerical environmental simulations , 2017, Stochastic Environmental Research and Risk Assessment.

[3]  Roman Schefzik,et al.  Ensemble calibration with preserved correlations: unifying and comparing ensemble copula coupling and member‐by‐member postprocessing , 2017 .

[4]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[5]  Shu-li Sun,et al.  Multi-sensor optimal information fusion Kalman filters with applications , 2004 .

[6]  M. Homleid,et al.  Diurnal Corrections of Short-Term Surface Temperature Forecasts Using the Kalman Filter , 1995 .

[7]  Pierre Pinson,et al.  Adaptive calibration of (u,v)‐wind ensemble forecasts , 2012 .

[8]  George Galanis,et al.  A new methodology for the extension of the impact of data assimilation on ocean wave prediction , 2009 .

[9]  Roland B. Stull,et al.  Hydrometeorological Accuracy Enhancement via Postprocessing of Numerical Weather Forecasts in Complex Terrain , 2008 .

[10]  Tiziana Paccagnella,et al.  Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges , 2011 .

[11]  George Galanis,et al.  Applications of Kalman filters based on non-linear functions to numerical weather predictions , 2006 .

[12]  Dara Entekhabi,et al.  Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations , 1994, IEEE Trans. Geosci. Remote. Sens..

[13]  Philippe Crochet,et al.  Adaptive Kalman filtering of 2‐metre temperature and 10‐metre wind‐speed forecasts in Iceland , 2004 .

[14]  Eric A. Wan,et al.  Dual Extended Kalman Filter Methods , 2002 .

[15]  Georges Kariniotakis,et al.  Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. , 2005 .

[16]  H. Madsen,et al.  Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark , 2014 .

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

[18]  Bert Van Schaeybroeck,et al.  Ensemble post‐processing using member‐by‐member approaches: theoretical aspects , 2015 .

[19]  Rui A. P. Perdigão,et al.  Dynamics of Prediction Errors under the Combined Effect of Initial Condition and Model Errors , 2009 .

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

[21]  Andrew W. Western,et al.  Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs , 2014 .

[22]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[23]  F. Cassola,et al.  Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output , 2012 .

[24]  C. Nicolis,et al.  Dynamical Properties of Model Output Statistics Forecasts , 2008 .

[25]  George Galanis,et al.  Wind power prediction based on numerical and statistical models , 2013 .

[26]  Jeffrey L. Anderson.  A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations , 1996 .

[27]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction , 2006 .

[28]  R. Stull,et al.  Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions , 2011 .

[29]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[30]  Luca Delle Monache,et al.  Probabilistic Weather Prediction with an Analog Ensemble , 2013 .

[31]  Isabel F. Trigo,et al.  Correction of 2 m-temperature forecasts using Kalman Filtering technique , 2008 .

[32]  Nunzio Romano,et al.  Kalman filters for assimilating near-surface observations into the Richards equation – Part 2: A dual filter approach for simultaneous retrieval of states and parameters , 2012 .

[33]  H. Hersbach.  Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .

[34]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[35]  W. J. Steenburgh,et al.  Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United States , 2007 .

[36]  H. Medina,et al.  Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes , 2012 .

[37]  Henryk Modzelewski,et al.  Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction , 2003 .

[38]  Andrea Buzzi,et al.  Validation of a limited area model in cases of mediterranean cyclogenesis: Surface fields and precipitation scores , 1994 .

[39]  Guido D'Urso,et al.  Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system , 2016 .

[40]  Zoltan Toth,et al.  An Ensemble Forecasting Primer , 1997 .

[41]  Wade T. Crow,et al.  An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting , 2014 .

[42]  George Galanis,et al.  A one‐dimensional Kalman filter for the correction of near surface temperature forecasts , 2002 .

[43]  Andrew W. Western,et al.  Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags , 2013 .

[44]  A. Pelosi,et al.  An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy) , 2015 .