GIST-PM-Asia v1: development of a numerical system to improve particulate  matter forecasts in South Korea using geostationary satellite-retrieved  aerosol optical data over Northeast Asia

Lee, S.; Song, C. H.; Park, R. S.; Park, M. E.; Han, K. M.; Kim, J.; Choi, M.; Ghim, Y. S.; Woo, J.-H.

doi:https://doi.org/10.5194/gmd-9-17-2016

Articles | Volume 9, issue 1

https://doi.org/10.5194/gmd-9-17-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-17-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 1

Development and technical paper

|

15 Jan 2016

Development and technical paper |

| 15 Jan 2016

GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

S. Lee, C. H. Song, R. S. Park, M. E. Park, K. M. Han, J. Kim, M. Choi, Y. S. Ghim, and J.-H. Woo

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May 2016	20	14	5	39
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Cumulative views and downloads (calculated since 08 Jul 2015)

Month	HTML	PDF	XML	Total
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Aug 2015	34	13	0	47
Sep 2015	34	10	0	44
Oct 2015	12	5	0	17
Nov 2015	6	12	0	18
Dec 2015	4	14	0	18
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Latest update: 07 Nov 2025

Short summary

We developed an integrated air quality modeling system using AOD data retrieved from a geostationary satellite sensor, GOCI (Geostationary Ocean Color Imager), over Northeast Asia with an application of the spatiotemporal-kriging (STK) method and conducted short-term hindcast runs using the developed system. It appears that the STK approach can greatly reduce not only the errors and biases of AOD and PM₁₀ predictions but also the computational burden of a chemical weather forecast (CWF).