 |
| Titel |
Assessment of bias-adjusted PM2.5 air quality forecasts over the continental United States during 2007 |
| VerfasserIn |
D. Kang, R. Mathur, S. Trivikrama Rao |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1991-959X
|
| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 3, no. 1 ; Nr. 3, no. 1 (2010-04-16), S.309-320 |
| Datensatznummer |
250000808
|
| Publikation (Nr.) |
 copernicus.org/gmd-3-309-2010.pdf |
|
|
|
|
|
| Zusammenfassung |
To develop fine particulate matter (PM2.5) air quality forecasts for
the US, a National Air Quality Forecast Capability (NAQFC) system, which
linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's
Community Multiscale Air Quality (CMAQ) model, was deployed in the
developmental mode over the continental United States during 2007. This
study investigates the operational use of a bias-adjustment technique called
the Kalman Filter Predictor approach for improving the accuracy of the
PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor
bias-adjustment technique is a recursive algorithm designed to optimally
estimate bias-adjustment terms using the information extracted from previous
measurements and forecasts.
The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year
at almost all locations. The NAQFC tends to overestimate PM2.5 during
the cool season and underestimate during the warm season in the eastern part
of the continental US domain, but the opposite is true for the Pacific
Coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5
for the whole year. The bias-adjusted forecasts can quickly (after 2–3
days' lag) adjust to reflect the transition from one regime to the other.
The modest computational requirements and systematic improvements in
forecast outputs across all seasons suggest that this technique can be
easily adapted to perform bias adjustment for real-time PM2.5 air
quality forecasts. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|