Assimilation and forecasting experiment for heavy siberian wildfire smoke in may 2016 with himawari-8 aerosol optical thickness

Abstract

The Japan Meteorological Agency (JMA) launched a next-generation geostationary meteorological satellite (GMS), Himawari-8, on October 7, 2014, which began its operation on July 7, 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 observational bands that enable the retrieval of full-disk maps of aerosol optical properties (AOPs), including aerosol optical thickness (AOT) and the Ångström exponent (AE), with unprecedented spatial and temporal resolutions. In this study, we combined an aerosol transport model with the Himawari-8 AOT using the data assimilation method and performed aerosol assimilation and forecasting experiments on smoke from an intensive wildfire that occurred over Siberia between May 15 and 18, 2016. To effectively utilize the high observational frequency of Himawari-8, we assimilated 1-h merged AOTs generated through the combination of six AOT snapshots taken over 10-min intervals, three times per day. The heavy smoke originating from the wildfire was transported eastward behind a low-pressure trough and covered northern Japan from May 19 to 20. The southern part of the smoke plume then traveled westward, in a clockwise flow associated with high pressure. The forecast without assimilation reproduced the transport of the smoke to northern Japan; however, it underestimated AOT and the extinction coefficient compared with observed values mainly because of errors in the emission inventory. Data assimilation with the Himawari-8 AOT compensated for the un-derestimation and successfully forecasted the unique C-shaped distribution of the smoke. In particular, the assimilation of the Himawari-8 AOT in May 18 greatly improved the forecast of the southern part of the smoke flow. Our results indicate that the inheritance of assimilation cycles and the assimilation of more recent observations led to better forecasting in this case of a continental smoke outflow.