The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation

• Published in: SOLA Vol. 7A; no. Special_Edition; pp. 40 - 43
• Main Authors: Sekiyama, Tsuyoshi Thomas, Tanaka, Taichu Y., Maki, Takashi, Mikami, Masao
• Format: Journal Article
• Language: English; Japanese
• Published: Tokyo Meteorological Society of Japan 2011; Japan Science and Technology Agency
• Subjects: Data assimilation; Dust emission; Kalman filter; Lidar applications; Numerical simulations; Snow cover; Soil moisture; Soil moisture estimation
• ISSN: 1349-6476; 1349-6476
• DOI: 10.2151/sola.7A-011

This paper is the second of a series that describes the effects of snow cover and soil moisture on Asian dust during spring. Whereas the first paper in this series discussed the importance of snow cover and soil moisture estimation, here, we focus on the correctness of the dust emission intensity results based on data assimilation under the assumption that simulation models yield errors in snow cover and soil moisture. We utilized global satellite lidar measurements and a four-dimensional ensemble Kalman filter to optimize the dust emission simulation. The data assimilation results were evaluated by a comparison with independent ground-based lidar measurements. The data assimilation procedure resulted in an increase in the dust emission in the Gobi region during the dust event from March 25 to April 3, 2007, and it improved the analysis of dust concentrations in the leeward region. Without data assimilation, the dust concentrations were underestimated owing to the wet surface conditions of the dust source region. This paper confirms that the improvement of snow cover and soil moisture estimation is important in the analysis of Asian dust levels, and it demonstrates that data assimilation is a powerful tool that can contribute to such improvement.