 |
| Titel |
Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns |
| VerfasserIn |
K. Miyazaki, H. J. Eskes, K. Sudo |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 5 ; Nr. 12, no. 5 (2012-03-01), S.2263-2288 |
| Datensatznummer |
250010844
|
| Publikation (Nr.) |
 copernicus.org/acp-12-2263-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| A data assimilation system has been developed to estimate global nitrogen
oxides (NOx) emissions using OMI tropospheric NO2 columns
(DOMINO product) and a global chemical transport model (CTM), the Chemical
Atmospheric GCM for Study of Atmospheric Environment and Radiative Forcing
(CHASER). The data assimilation system, based on an ensemble Kalman filter
approach, was applied to optimize daily NOx emissions with a
horizontal resolution of 2.8° during the years 2005 and 2006. The
background error covariance estimated from the ensemble CTM forecasts
explicitly represents non-direct relationships between the emissions and
tropospheric columns caused by atmospheric transport and chemical processes.
In comparison to the a priori emissions based on bottom-up inventories, the
optimized emissions were higher over eastern China, the eastern United
States, southern Africa, and central-western Europe, suggesting that the
anthropogenic emissions are mostly underestimated in the inventories. In
addition, the seasonality of the estimated emissions differed from that of
the a priori emission over several biomass burning regions, with a large
increase over Southeast Asia in April and over South America in October. The
data assimilation results were validated against independent data: SCIAMACHY
tropospheric NO2 columns and vertical NO2 profiles obtained
from aircraft and lidar measurements. The emission correction greatly
improved the agreement between the simulated and observed NO2 fields;
this implies that the data assimilation system efficiently derives
NOx emissions from concentration observations. We also demonstrated
that biases in the satellite retrieval and model settings used in the data
assimilation largely affect the magnitude of estimated emissions. These
dependences should be carefully considered for better understanding
NOx sources from top-down approaches. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|