For all such training examples and each time lag from 1…kmax days, link all values of k within 100 km of grid cell to map type V G M. For each time lag from 1…kmax days, calculate the frequency of extreme within 100 k V km of G. Since the distribution of weather stations does not match the 64-km grid used for map types, a buffer distance was required to link map types and dependent variables spatially. As shown above, we chose a buffer distance of 100 km. In experiments (not shown), SOM performance was similar for buffer distances of 50 and 200 km. We chose 100 km subjectively, as this produced the easiest maps for us to interpret. The buffer distance could easily be changed, depending on users’ needs. 1.4. Forecast evaluation Evaluation consists of two steps: (a) predict dependent variables for test cases and (b) compare predictions with true values. The following procedure was used to predict test cases for each dependent variable on V, date at grid cell Again, let be the maximum time lag for D G. kmax V. For each time lag from 1…kmax days, find the most similar map type (Mk) on date k D – k. For each time lag from 1…kmax days, find the frequency of extreme within 100 km k V, of grid cell under map type Let this frequency (calculated in Section 1.3) be G, Mk. fk. Combine all frequencies into a single value. This is done with an exponential fk weighting scheme:
For all such training examples and each time lag k from 1…kmax days, link all values of V within 100 km of grid cell G to map type M. For each time lag k from 1…kmax days, calculate the frequency of extreme V within 100 km of G. Since the distribution of weather stations does not match the 64-km grid used for map types, a buffer distance was required to link map types and dependent variables spatially. As shown above, we chose a buffer distance of 100 km. In experiments (not shown), SOM performance was similar for buffer distances of 50 and 200 km. We chose 100 km subjectively, as this produced the easiest maps for us to interpret. The buffer distance could easily be changed, depending on users’ needs. 1.4. Forecast evaluation Evaluation consists of two steps: (a) predict dependent variables for test cases and (b) compare predictions with true values. The following procedure was used to predict test cases for each dependent variable V, on date D at grid cell G. Again, let kmax be the maximum time lag for V. For each time lag k from 1…kmax days, find the most similar map type (Mk) on date D – k. For each time lag k from 1…kmax days, find the frequency of extreme V, within 100 km of grid cell G, under map type Mk. Let this frequency (calculated in Section 1.3) be fk. Combine all frequencies fk into a single value. This is done with an exponential weighting scheme:
cjfr-2017-0063suppla.docx