Dr. Aditi Singh Profile

Dr. Aditi Singh

at NCMRWF

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Proceedings Article | 9 May 2016 Paper

Dust forecast over North Africa: verification with satellite and ground based observations

Aditi Singh, Sumit Kumar, John George

Proceedings Volume 9876, 98762I (2016) https://doi.org/10.1117/12.2223596

KEYWORDS: Atmospheric modeling, MODIS, Aerosols, Satellites, Earth observing sensors, Atmospheric particles, Visibility, Robotics, LIDAR, Polarization, Ocean optics, Clouds, Cesium, Soil science

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Arid regions of North Africa are considered as one of the major dust source. Present study focuses on the forecast of aerosol optical depth (AOD) of dust over different regions of North Africa. NCMRWF Unified Model (NCUM) produces dust AOD forecasts at different wavelengths with lead time upto 240 hr, based on 00UTC initial conditions. Model forecast of dust AOD at 550 nm up to 72 hr forecast, based on different initial conditions are verified against satellite and ground based observations of total AOD during May–June 2014 with the assumption that except dust, presence of all other aerosols type are negligible. Location specific and geographical distribution of dust AOD forecast is verified against Aerosol Robotic Network (AERONET) station observations of total and coarse mode AOD. Moderate Resolution Imaging Spectroradiometer (MODIS) dark target and deep blue merged level 3 total aerosol optical depth (AOD) at 550 nm and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrieved dust AOD at 532 nm are also used for verification. CALIOP dust AOD was obtained by vertical integration of aerosol extinction coefficient at 532 nm from the aerosol profile level 2 products. It is found that at all the selected AERONET stations, the trend in dust AODs is well predicted by NCUM up to three days advance. Good correlation, with consistently low bias (~ ±0.06) and RMSE (~ 0.2) values, is found between model forecasts and point measurements of AERONET, except over one location Cinzana (Mali). Model forecast consistently overestimated the dust AOD compared to CALIOP dust AOD, with a bias of 0.25 and RMSE of 0.40.

Proceedings Article | 5 May 2016 Paper

Dust storm events over Delhi: verification of dust AOD forecasts with satellite and surface observations

Aditi Singh, Gopal Iyengar, John George

Proceedings Volume 9876, 98762S (2016) https://doi.org/10.1117/12.2223706

KEYWORDS: Aerosols, MODIS, Satellites, Earth observing sensors, Visibility, Meteorology, Atmospheric modeling, Remote sensing, Artificial intelligence, Humidity, Ultraviolet radiation, Environmental sensing

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Thar desert located in northwest part of India is considered as one of the major dust source. Dust storms originate in Thar desert during pre-monsoon season, affects large part of Indo-Gangetic plains. High dust loading causes the deterioration of the ambient air quality and degradation in visibility. Present study focuses on the identification of dust events and verification of the forecast of dust events over Delhi and western part of IG Plains, during the pre-monsoon season of 2015. Three dust events have been identified over Delhi during the study period. For all the selected days, Terra-MODIS AOD at 550 nm are found close to 1.0, while AURA-OMI AI shows high values. Dust AOD forecasts from NCMRWF Unified Model (NCUM) for the three selected dust events are verified against satellite (MODIS) and ground based observations (AERONET). Comparison of observed AODs at 550 nm from MODIS with NCUM predicted AODs reveals that NCUM is able to predict the spatial and temporal distribution of dust AOD, in these cases. Good correlation (~0.67) is obtained between the NCUM predicted dust AODs and location specific observations available from AERONET. Model under-predicted the AODs as compared to the AERONET observations. This may be mainly because the model account for only dust and no anthropogenic activities are considered. The results of the present study emphasize the requirement of more realistic representation of local dust emission in the model both of natural and anthropogenic origin, to improve the forecast of dust from NCUM during the dust events.

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