A Bayesian Quantitative Precipitation Nowcast Scheme

Abstract Very short-period quantitative precipitation forecast (QPF) or nowcast schemes provide deterministic output that fails to convey explicit measures of the uncertainty in the forecast. Presented here is a forecast methodology based upon a Bayesian hierarchical model that produces a QPF product for a 1-h period along with an associated estimated forecast error field. The precipitation forecast quality is comparable to other nowcast schemes and the uncertainty measures increase the utility of the methodology by allowing forecasters to judge the trustworthiness of the products.

[1]  K. T Smith,et al.  Nowcasting precipitation — a proposal for a way forward , 2000 .

[2]  Neil I. Fox,et al.  The nowcasting of precipitation during Sydney 2000: An appraisal of the QPF algorithms , 2004 .

[3]  Juanzhen Sun,et al.  Nowcasting Thunderstorms: A Status Report , 1998 .

[4]  S. Mecklenburg,et al.  Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm. , 2000 .

[5]  Chris G. Collier,et al.  GANDOLF: a system for generating automated nowcasts of convective precipitation , 2000 .

[6]  M. Dixon,et al.  TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology , 1993 .

[7]  Neill E. Bowler,et al.  Use of a stochastic precipitation nowcast scheme for fluvial flood forecasting and warning , 2005 .

[8]  L. Mark Berliner,et al.  Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions , 2003 .

[9]  P. Driessche,et al.  Dispersal data and the spread of invading organisms. , 1996 .

[10]  A. Seed.  A Dynamic and Spatial Scaling Approach to Advection Forecasting , 2001 .

[11]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[12]  I. Zawadzki,et al.  Scale Dependence of the Predictability of Precipitation from Continental Radar Images. Part II: Probability Forecasts , 2004 .

[13]  Arthur Witt,et al.  The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm , 1998 .

[14]  J. Andrew Royle,et al.  Bayesian Methods in the Atmospheric Sciences , 2007 .

[15]  Christopher K. Wikle.  A Kernel-Based Spectral Approach for Spatiotemporal Dynamic Models , 2002 .

[16]  T. Bird,et al.  The BMRC/NCAR C-Band Polarimetric (C-POL) Radar System , 1998 .

[17]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[18]  L. M. Berliner,et al.  Hierarchical Bayesian space-time models , 1998, Environmental and Ecological Statistics.

[19]  Ke Xu,et al.  A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities , 2005 .

[20]  C. Wikle.  Hierarchical Models in Environmental Science , 2003 .

[21]  L. Mark Berliner,et al.  Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds , 2001 .

[22]  James W. Wilson,et al.  The 3 November Tornadic Event during Sydney 2000: Storm Evolution and the Role of Low-Level Boundaries , 2004 .

[23]  Roman Krzysztofowicz,et al.  The case for probabilistic forecasting in hydrology , 2001 .

[24]  Terri Betancourt,et al.  NCAR Auto-Nowcast System , 2003 .

[25]  C. Wikle.  Spatial Modelling of Count Data: A Case Study in Modelling Breeding Bird Survey Data on Large Spatial Domains , 2002 .

[26]  Chris G. Collier,et al.  On the performance of a conceptual model of convection during preparations for the world weather research project nowcasting intercomparison during the olympic games in Sydney, Australia , 2000 .

[27]  Harold E. Brooks,et al.  Verification of Nowcasts from the WWRP Sydney 2000 Forecast Demonstration Project , 2004 .

[28]  J. Elsner,et al.  A Hierarchical Bayesian Approach to Seasonal Hurricane Modeling , 2004 .

[29]  C. Wikle.  A kernel-based spectral model for non-Gaussian spatio-temporal processes , 2002 .

[30]  I. Zawadzki,et al.  Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology , 2002 .