Computationally Efficient Spatial Forecast Verification Using Baddeley’s Delta Image Metric

Abstract An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images. The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric...

[1]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[2]  B. Brown,et al.  Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas , 2006 .

[3]  D. Stephenson,et al.  A new intensity‐scale approach for the verification of spatial precipitation forecasts , 2004 .

[4]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[5]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[6]  K. Browning,et al.  On the Forecasting of Frontal Rain Using a Weather Radar Network , 1982 .

[7]  Caren Marzban,et al.  Cluster Analysis for Verification of Precipitation Fields , 2006 .

[8]  Douglas W. Nychka,et al.  Design of Air-Quality Monitoring Networks , 1998 .

[9]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[10]  Richard A. Levine,et al.  Wavelets and Field Forecast Verification , 1997 .

[11]  B. Brown,et al.  Object-Based Verification of Precipitation Forecasts. Part II: Application to Convective Rain Systems , 2006 .

[12]  Caren Marzban,et al.  Cluster Analysis for Object-Oriented Verification of Fields: A Variation , 2008 .

[13]  J. Louis,et al.  Distortion Representation of Forecast Errors , 1995 .

[14]  A. Baddeley.  Errors in binary images and an $Lsp p$ version of the Hausdorff metric , 1992 .

[15]  Athanasios C. Micheas,et al.  Cell identification and verification of QPF ensembles using shape analysis techniques , 2007 .

[16]  Jason J. Levit,et al.  Multiscale Statistical Properties of a High-Resolution Precipitation Forecast , 2001 .

[17]  M. Baldwin,et al.  THE WGNE ASSESSMENT OF SHORT-TERM QUANTITATIVE PRECIPITATION FORECASTS , 2003 .

[18]  Jun Du,et al.  Removal of Distortion Error from an Ensemble Forecast , 2000 .

[19]  B. Brown,et al.  APPLICATION OF THE MODE OBJECT-BASED VERIFICATION TOOL FOR THE EVALUATION OF MODEL PRECIPITATION FIELDS , 1992 .

[20]  Louis J. Wicker,et al.  Time-Splitting Methods for Elastic Models Using Forward Time Schemes , 2002 .

[21]  A. ROSENFELD,et al.  Distance functions on digital pictures , 1968, Pattern Recognit..

[22]  Efi Foufoula-Georgiou,et al.  A new metric for comparing precipitation patterns with an application to ensemble forecasts , 2005 .

[23]  J. McBride,et al.  Verification of precipitation in weather systems: determination of systematic errors , 2000 .

[24]  Elizabeth E. Ebert,et al.  Fuzzy verification of high‐resolution gridded forecasts: a review and proposed framework , 2008 .