Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

Berrocal, Veronica J.; Raftery, Adrian E.; Gneiting, Tilmann
April 2007
Monthly Weather Review;Apr2007, Vol. 135 Issue 4, p1386
Academic Journal
Forecast ensembles typically show a spread–skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at individual locations. At any site individually, spatial BMA reduces to the original BMA technique. The spatial BMA method provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the flow-dependent information contained in the dynamical ensemble. The members of the spatial BMA ensemble are obtained by dressing the weather field forecasts from the dynamical ensemble with simulated spatially correlated error fields, in proportions that correspond to the BMA weights for the member models in the dynamical ensemble. Statistical ensembles of any size can be generated at minimal computational cost. The spatial BMA technique was applied to 48-h forecasts of surface temperature over the Pacific Northwest in 2004, using the University of Washington mesoscale ensemble. The spatial BMA ensemble generally outperformed the BMA and GOP ensembles and showed much better verification results than the raw ensemble, both at individual sites, for weather field forecasts, and for forecasts of composite quantities, such as average temperature in National Weather Service forecast zones and minimum temperature along the Interstate 90 Mountains to Sound Greenway.


Related Articles

  • A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size. Lawrence, Andrew R.; Hansen, James A. // Monthly Weather Review;Apr2007, Vol. 135 Issue 4, p1424 

    An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast...

  • Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging. Wilson, Laurence J.; Beauregard, Stephane; Raftery, Adrian E.; Verret, Richard // Monthly Weather Review;Apr2007, Vol. 135 Issue 4, p1364 

    Bayesian model averaging (BMA) has recently been proposed as a way of correcting underdispersion in ensemble forecasts. BMA is a standard statistical procedure for combining predictive distributions from different sources. The output of BMA is a probability density function (pdf), which is a...

  • Fog Forecasting for Melbourne Airport Using a Bayesian Decision Network. Boneh, Tal; Weymouth, Gary T.; Newham, Peter; Potts, Rodney; Bally, John; Nicholson, Ann E.; Korb, Kevin B. // Weather & Forecasting;Oct2015, Vol. 30 Issue 5, p1218 

    Fog events occur at Melbourne Airport, Melbourne, Victoria, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption, and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult...

  • Weather Watch.  // Stonewall County Courier (Aspermont, TX);9/6/2007, Vol. 21 Issue 45, p1 

    The article forecasts the weather in Aspermont, Texas from August 27 to October 2, 2007 based on the statistics shared by the National Weather Service.

  • Probabilistic Evaluation of the Dynamics and Predictability of the Mesoscale Convective Vortex of 10–13 June 2003. Hawblitzel, Daniel P.; Zhang, Fuqing; Meng, Zhiyong; Davis, Christopher A. // Monthly Weather Review;Apr2007, Vol. 135 Issue 4, p1544 

    This study examines the dynamics and predictability of the mesoscale convective vortex (MCV) of 10–13 June 2003 through ensemble forecasting. The MCV of interest developed from a preexisting upper-level disturbance over the southwest United States on 10 June and matured as it traveled...

  • Comments on “Statistical Single-Station Short-Term Forecasting of Temperature and Probability of Precipitation: Area Interpolation and NWP Combination”. Leslie, Lance M.; Speer, Milton S. // Weather & Forecasting;Dec2001, Vol. 16 Issue 6, p765 

    Comments on the statistical single-station short-term forecasting of temperature and probability of precipitation. Benefits and disadvantages of RB99 forecasts system; Role of statistical techniques in short-term forecasting; Features of RB99 system.

  • Performance Evaluation Statistics Applied to Derived Fields of NWP Model Forecasts. Satyamurty, Prakki; Bittencourt, Daniel Pires // Weather & Forecasting;Oct99, Vol. 14 Issue 5, p726 

    NWP model skill as obtained from the standard statistics applied to derived atmospheric fields such as thermal advection and moisture convergence is different from that obtained by the same statistics applied to basic model output fields such as temperature or wind components. An analysis with a...

  • Tubing: An Alternative to Clustering for the Classification of Ensemble Forecasts. Atger, Frederic // Weather & Forecasting;Oct99, Vol. 14 Issue 5, p741 

    Tubing is a method of classification of meteorological forecasts. The method has been designed to facilitate a human interpretation of the distribution of forecasts produced by an ensemble prediction system (EPS). This interpretation aims to complement probabilistic forecasts generated from EPS...

  • A Data Assimilation Case Study Using a Limited-Area Ensemble Kalman Filter. Dirren, Sébastien; Torn, Ryan D.; Hakim, Gregory J. // Monthly Weather Review;Apr2007, Vol. 135 Issue 4, p1455 

    Ensemble Kalman filter (EnKF) data assimilation experiments are conducted on a limited-area domain over the Pacific Northwest region of the United States, using the Weather Research and Forecasting model. Idealized surface pressure, radiosoundings, and aircraft observations are assimilated every...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics