1. Spatial Forecast Methods Inter-Comparison Project -- ICP Spring 2008 Workshop NCAR Foothills Laboratory Boulder, Colorado

2. Welcome !! Main objectives Agenda Subjective Evaluations General (Topic-specific ) Discussion Questions Some other things to think about Where do we go from here?

3. Main Objectives What can a forecaster learn from the results of each approach? –What are the pros and cons of each? –What user-specific needs are met?

4. General (topic) discussions How would one use results from each method? What information is given about scale? Operational concerns? –Summarizing multiple cases –Computational efficiency Uncertainty characterization

5. General Questions cont. Ease of interpretation Appropriate applications –Variables (e.g., precipitation, humidity, winds) –Domains –What information is needed for them to work –Can they characterize different attributes, and how? –Diagnostic information? Comparison of cases –How do they discern good vs. bad?

6. Focus on Fake Cases How do the methods handle the fake cases? Has anything been learned from the cases?

7. Other things to contemplate Categories –Filter Methods Scale-decomposition methods Fuzzy/Neighborhood methods –Motion Methods Features-based methods Field Morphing methods –Not so easy to categorize Methods FQI, CA, Composite, others?

8. Scale Questions All methods can be run for different resolutions. Filter Methods –Scale-decomposition separates scale information –Fuzzy/Neighborhood do not separate scale information, but do directly provide info on scales Motion Methods –Can information about scale be gleaned? –MODE uses quilt plots of threshold against convolution radius. Similar to fuzzy. Other Methods –CA refers to numbers of clusters as “scale.”

9. Filter vs. Motion Filter compares F and O fields at different scales, k, in the manner of Where G is a traditional score (e.g., rmse) B is a filter (smoothing or band pass)

10. Filter vs. Motion Motion moves forecast field (or structures within the fields) in the manner: Where s are coordinates in the domain of the image.