عنوان مقاله [English]
Mineral dust can directly affect the solar and terrestrial radiation in both visible and infrared (IR) spectral regions through scattering and absorption processes. Due to specific optical properties of dust particles, satellite observed radiances carry the spectral signatures of dust particles that are different from molecular, cloud, and underlying surface. Based on these differences, various detection schemes have been developed to distinguish dust. In practice, the detection is based on the analysis of reflectance (or radiance) in visible bands or brightness temperature (BT) in IR bands. The magnitude of the difference in BT in selected bands (or channels) can be used to infer the signature of dust. This is the essence of aerosol imagery detection algorithms (Zhao et al, 2010).
Iran is located in a region that is strongly affected by dust storms. The frequency and intensity of these storms have increased in recent years. Recent studies have shown that numerical weather models alone are not able to track and detect dust storms and in many cases have significant errors (Taghavi, 2010). Remote sensing provides a valuable tool for detecting this phenomenon.
In west of Iran some areas are much more prone to dust storms than others due to differing soils and climates. Even in bare deserts, the sandy areas, such as those found on the Arabian Peninsula, generally do not generate dust storms. Generally, areas with silt- and clay-rich soils are responsible for most dust storms. These storms occur when the sub-tropical jet stream migrates northward from south of the Arabian Peninsula and the polar front jet stream moves southward from the European continent (Taghavi, 2008). In this study, we enhance and survey two dust events that occurred in the west of Iran on March the 4th and April the 13th, 2011, using two different algorithms. The first algorithm uses MNDVI index and threshold temperature of 290 K in MODIS band-32 to differentiate dust from semi-arid areas with low vegetation cover and clouds, respectively. Surveys show that MNDVI index cannot clearly detect dust over water surfaces. Therefore, we use the algorithm of combining brightness temperature difference of dust between the wavelengths of 8.5μm (MODIS band-29) and 11μm (MODIS band-31) with negative values of bands-31 and 32 brightness temperature differences. For defining dust areas we use (BT8.5-BT11) – (BT11-BT12) values larger than the obtained threshold and (BT11-BT12) smaller than zero. To study the dust loading, we also use the Dust Regional Atmospheric Model (DREAM-8b). The Earth Sciences Department of Barcelona Supercomputing Center (BSC) uses the DREAM-8b model (Nickovic et al. 2001; Perez et al. 2006a, Perez et al. 2006b) to conduct modelling research and development for short-term prediction of dust. The model predicts the atmospheric life cycle of the eroded desert dust and was developed as a pluggable component of the Eta/NCEP (National Centers for Environmental Prediction) model.
The recent method (using thermal infrared spectrum) detects dust well, especially over water. Comparison with quantitative aerosol optical thickness (AOT) retrieval is performed to validate the enhancement algorithms. At the end with comparing the enhanced images using IR technique with synoptic maps, MODIS AOT values, DREAM 8b model outputs and synoptic stations data, it is found that the applied enhancement algorithms provide a more reliable approach for monitoring dust storms compared to MODIS AOT retrievals or model outputs. For both dust storm cases, a low pressure was the main cause of the dust storms. Using trajectory maps, we can track the transport of dust from the main sources. Results show that for the dust storm occurred on the 4th March dust originated from the border of Iran and Iraq, then moved southward towards the Persian Gulf coasts, while that occurred on the case 13th April moved northward and approached Caspian Sea.