Dust storm ensemble forecast experiments in East Asia |
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| Authors: | Jiang Zhu Caiyan Lin Zifa Wang |
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| Institution: | State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble
forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting
a dust episode in the East Asia during 23--30 May 2007. The errors in the input wind field, dust emission
intensity, and dry deposition velocity are among important model uncertainties and are considered in the
model error perturbations. These model errors are not assumed to have zero-means. The model error means
representing the model bias are estimated as part of the data assimilation process. Observations from a
LIDAR network are assimilated to generate the initial ensembles and correct the model biases. The ensemble
forecast skills are evaluated against the observations and a benchmark/control forecast, which is a simple
model run without assimilation of any observations. Another ensemble forecast experiment is also performed
without the model bias correction in order to examine the impact of the bias correction. Results show that
the ensemble-mean, as deterministic forecasts have substantial improvement over the control forecasts and
correctly captures the major dust arrival and cessation timing at each observation site. However, the
forecast skill decreases as the forecast lead time increases. Bias correction further improved the forecasts
in down wind areas. The forecasts within 24 hours are most improved and better than those without the bias
correction. The examination of the ensemble forecast skills using the Brier scores and the relative operating
characteristic curves and areas indicates that the ensemble forecasting system has useful forecast skills. |
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| Keywords: | dust storm ensemble forecast data assimilation bias correction |
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