scholarly journals Forecast verification: relating deterministic and probabilistic metrics

2021 ◽  
Author(s):  
Tsz Yan Leung ◽  
Martin Leutbecher ◽  
Sebastian Reich ◽  
Theodore G. Shepherd
Keyword(s):  
Forecast Verification ◽  
Probabilistic Metrics

scholarly journals Intercomparison of Spatial Forecast Verification Methods: Identifying Skillful Spatial Scales Using the Fractions Skill Score

2010 ◽  
Vol 25 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Marion Mittermaier ◽  
Nigel Roberts
Keyword(s):  
Spatial Scales ◽  
Wrf Model ◽  
Skill Score ◽  
Area Ratio ◽  
Forecast Verification ◽  
Care Needs ◽  
Forecast Performance ◽  
Verification Methods ◽  
Upper Level ◽  
Formed Part

Abstract The fractions skill score (FSS) was one of the measures that formed part of the Intercomparison of Spatial Forecast Verification Methods project. The FSS was used to assess a common dataset that consisted of real and perturbed Weather Research and Forecasting (WRF) model precipitation forecasts, as well as geometric cases. These datasets are all based on the NCEP 240 grid, which translates to approximately 4-km resolution over the contiguous United States. The geometric cases showed that the FSS can provide a truthful assessment of displacement errors and forecast skill. In addition, the FSS can be used to determine the scale at which an acceptable level of skill is reached and this usage is perhaps more helpful than interpreting the actual FSS value. This spatial-scale approach is becoming more popular for monitoring operational forecast performance. The study also shows how the FSS responds to forecast bias. A more biased forecast always gives lower FSS values at large scales and usually at smaller scales. It is possible, however, for a more biased forecast to give a higher score at smaller scales, when additional rain overlaps the observed rain. However, given a sufficiently large sample of forecasts, a more biased forecast system will score lower. The use of percentile thresholds can remove the impacts of the bias. When the proportion of the domain that is “wet” (the wet-area ratio) is small, subtle differences introduced through near-threshold misses can lead to large changes in FSS magnitude in individual cases (primarily because the bias is changed). Reliable statistics for small wet-area ratios require a larger sample of forecasts. Care needs to be taken in the choice of verification domain. For high-resolution models, the domain should be large enough to encompass the length scale of the typical mesoscale forcing (e.g., upper-level troughs or squall lines). If the domain is too large, the wet-area ratios will always be small. If the domain is too small, fluctuations in the wet-area ratio can be large and larger spatial errors may be missed. The FSS is a good measure of the spatial accuracy of precipitation forecasts. Different methods are needed to determine other patterns of behavior.

Forecast Verification for S2S Timescales

2019 ◽  
pp. 337-361 ◽  
Author(s):  
Caio A.S. Coelho ◽  
Barbara Brown ◽  
Laurie Wilson ◽  
Marion Mittermaier ◽  
Barbara Casati
Keyword(s):  
Forecast Verification
Sign in / Sign up

Export Citation Format

Share Document