The Impact of Ground-Based Observations on the Inverse Technique of Aeolian Dust Aerosol

Abstract

We studied the amount of emission of Aeolian dust aerosol from the Gobi desert area using the inverse technique, an Aeolian dust model (MASIGNAR), and surface observation data shared in the Triplet Environmental Ministers Meeting (TEMM) joint research project during the dust and sand storm (DSS) event in the spring of 2007. We constructed the first high-temporalresolution (three hours) dust-emission estimating system using the Bayesian synthesis inversion and PM 10 observation data. Our research shows that we could modify MASINGAR's Aeolian dust concentration to match the observation data with an increase in MASINGAR's Aeolian dust flux. The estimated total dust emission from March 26 to April 3 is from 23 to 43Tg from different prior flux uncertainties. This study suggests that there was a greater Aeolian dust flux than that estimated by MASINGAR in the middle part of the Gobi desert on March 30. The results are sensitive to the observational network, the prior flux uncertainty and the observational error from sensitivity tests. In addition, we found that the observation data gathered in the neighborhood of the dust-emission area could modify the model result more effectively. Furthermore, the time resolution and data uncertainty of the observation data are also important for precise analysis. To obtain a robust estimation of the Aeolian dust-emission flux, it is critically important to share quality-controlled observation data among neighboring countries. We consider that inverse technique will become a powerful tool for estimating dust aerosol flux more precisely.

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

“…1c therein) using 4D-Var data assimilation. This spatial pattern in the Gobi region also agrees qualitatively with the result of the classic inverse analysis by Maki et al (2011).…”

Chemical Data Assimilation—An Overview

“…A growing number of applications employ the 3D-Var technique [3,[65][66][67][68][69][70][71][72]. The 4D-Var approach has been used to adjust gas phase chemical tracer initial conditions [10,11,61,69,[73][74][75][76][77], to improve estimates of pollutant emissions, i.e., emission inversion, [15,78,79], and to improve aerosol fields [80][81][82]. Suboptimal Kalman filters have been employed successfully in chemical data assimilation for over a decade [63,[83][84][85][86][87][88][89].…”

Inverse Modeling of Asian Dust Emissions with POPC Observations: A TEMM Dust Sand Storm 2014 Case Study

“…For example, Maki et al (2011) used Bayesian synthesis inversion and surface PM 10 concentration data to optimize dust emission fluxes in the Gobi desert, and Yumimoto and Takemura (2015) performed an 8-year inverse modeling study of Asian dust with a global aerosol transport model that used a four-dimensional variational assimilation method and satellite-measured aerosol optical thickness (AOT).…”