 |
| Titel |
Development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia |
| VerfasserIn |
Sojin Lee, Chul-han Song, Rae Seol Park, Mi Eun Park, Kyung Man Han, Jhoon Kim, MyungJe Choi, Young Sung Ghim, Jung-Hun Woo |
| Konferenz |
EGU General Assembly 2016
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250137190
|
| Publikation (Nr.) |
 EGU/EGU2016-18395.pdf |
|
|
|
|
|
| Zusammenfassung |
| To improve short-term particulate matter (PM) forecasts in South Korea, the
initial distribution of PM composition, particularly over the upwind regions,
is primarily important. To prepare the initial PM composition, the aerosol
optical depth (AOD) data retrieved from a geostationary equatorial orbit
(GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers
a part of Northeast Asia (113--146{\degree}\,E; 25--47{\degree}\,N), were used.
Although GOCI can provide a higher number of AOD data in a semicontinuous
manner than low Earth orbit (LEO) satellite sensors, it still has a serious
limitation in that the AOD data are not available at cloud pixels and over
high-reflectance areas, such as desert and snow-covered regions. To overcome
this limitation, a spatiotemporal-kriging (STK) method was used to better
prepare the initial AOD distributions that were converted into the PM
composition over Northeast Asia. One of the largest advantages in using the
STK method in this study is that more observed AOD data can be used to
prepare the best initial AOD fields compared with other methods that use
single frame of observation data around the time of initialization. It is
demonstrated in this study that the short-term PM forecast system developed
with the application of the STK method can greatly improve PM$_{10}$
predictions in the Seoul metropolitan area (SMA) when evaluated with
ground-based observations. For example, errors and biases of PM$_{10}$
predictions decreased by $\sim$\,60 and $\sim$\,70{\%}, respectively, during
the first 6\,h of short-term PM forecasting, compared with those without the
initial PM composition. In addition, the influences of several factors on the
performances of the short-term PM forecast were explored in this study. The
influences of the choices of the control variables on the PM chemical
composition were also investigated with the composition data measured via
PILS-IC (particle-into-liquid sampler coupled with ion chromatography) and low air-volume sample instruments at a site near Seoul. To
improve the overall performances of the short-term PM forecast system,
several future research directions were also discussed and suggested. |
|
|
|
|
|
|