Environmental Engineering Reference
In-Depth Information
It is important for an air quality forecast product to be able to accurately predict
exceedance and non-exceedance events (categorical predictions). Categorical
evaluations for the raw model and KF bias-adjusted forecasts for daily maximum
O
3
and daily mean PM
2.5
concentrations have shown that the KF bias-adjusted
forecasts were able to significantly reduce False Alarm Ratio (FAR) values and
increase Hit rate (H) values for both daily maximum 8-h O
3
forecasts and daily
mean PM
2.5
forecasts.
4. Summary
The near real-time KF bias-adjustment technique was applied to NAM-CMAQ O
3
and PM
2.5
air quality forecasts over the continental United States. These bias-adjust-
ment forecasts were implemented to run daily for improving next-day forecasts.
The bias-adjustment post-processing adds minimal computational burden; on a
daily-basis, it required less than 10 min of CPU on a single processor Linux machine.
Hourly O
3
and PM
2.5
bias-adjusted forecasts were provided at all the locations
where the observations were available from the AIRNOW network. The performance
evaluation of the bias-adjusted forecasts for both O
3
and PM
2.5
has shown significant
improvement over the raw model forecasts for a variety of performance evaluation
statistical measures. Specifically, errors and biases were systematically reduced,
the correlation coefficients were increased, false alarm ratios went down, and hit
rates went up. The robustness of this technique was also manifested through time
and space and over all the concentration bins; the forecast skills were improved at
all the locations within the domain during all the seasons.
References
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