final_program_abstracts[1]

11 IMSC Session Program
Generating verification metrics for optimal model selection
from different NWP models in real time
Wednesday - Parallel Session 11
Laura Huang1, George Isaac1 and Grant Sheng2
1
Science and Technology Branch, Environment Canada, Toronto, Canada
2 Faculty of Environmental Studies, York University, Toronto, Canada
This paper presents a system that generates dynamic verification metrics for selecting
an optimal model from different numerical weather prediction (NWP) models for
nowcasting. Each NWP model has its own strengths and limitations, and the forecast
performance often varies in relation to time, location and other forecasting variables.
The data from traditional verification may not fully reflect the current model
performance. Many researchers found that the nearest past model performance is
strongly correlated with the model performance in the nowcast period (0 to 6 hours).
By combining the performance of dynamic modeling with historical performance,
forecasters are provided with more reliable information with which to select optimal
models for nowcasting. We are developing a novel system that can generate dynamic
verification metrics for pre-selected periods in real time. The system can also
continuously calculate cumulative verification metrics from start time to current time.
The verification metrics include: 1) mean error, 2) bias, 3) mean absolute error, 4)
root mean square error, 5) mean square error, 6) correlation coefficient, 7). confidence
intervals, and 8) mixed contingency tables relevant to events hits and optimal model
hits. The metrics can be generated with respect to models, locations, parameters and
time.
Abstracts 189