Inverse modeling of Asian dust emissions with POPC observations: A TEMM dust sand storm 2014 case study

Keiya Yumimoto, Itsushi Uno, Xiaole Pan, Tomoaki Nishizawa, Sang Woo Kim, Nobuo Sugimoto

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

An inverse modeling system for estimating Asian dust emissions was developed by combining the GEOS-Chem chemical transport model with the Green's function method. We applied the system to two heavy dust storms that occurred in 2014 (10-25 March and 24 May to 5 June), using surface-based polarization optical particle counter (POPC) observations at Fukuoka. Validation by independent observation datasets, including POPC measurements and PM10 observations at Seoul, showed that the use of a posteriori dust emissions improved overestimations in the a priori simulation and achieved much better agreement with observations. Satellite observations, surface synoptic observations, and modeled wind fields indicated that the major dust source region differed between the two dust storms; the major dust outbreak of one storm occurred in the northeastern Gobi Desert, whereas that of the other occurred in the southern Gobi Desert. The a posteriori dust emissions successfully reproduced this difference. Thus, the inverse modeling system developed in this study was able to improve the estimation of not only the intensity but also the geographical distribution of dust emissions.

Original languageEnglish
Pages (from-to)31-35
Number of pages5
JournalScientific Online Letters on the Atmosphere
Volume13
DOIs
Publication statusPublished - Jan 1 2017

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polarization
dust
sand
modeling
dust storm
desert
Green function
EOS
wind field
geographical distribution
particle
simulation

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

Inverse modeling of Asian dust emissions with POPC observations : A TEMM dust sand storm 2014 case study. / Yumimoto, Keiya; Uno, Itsushi; Pan, Xiaole; Nishizawa, Tomoaki; Kim, Sang Woo; Sugimoto, Nobuo.

In: Scientific Online Letters on the Atmosphere, Vol. 13, 01.01.2017, p. 31-35.

Research output: Contribution to journalArticle

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