Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary co...Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary conditions are needed for such of these numerical methods. Predication and simulations for different case studies are major sources for the great importance of these models. Satellite data from different wide ranges of sensors provide observations that indicate system state. So both numerical models and satellite data provide estimation of system states, and between the different estimations it is required the best estimate for system state. Assimilation of observations in numerical weather models with data assimilation techniques provide an improved estimate of system states. In this work, highlights on the mathematical perspective for data assimilation methods are introduced. Least square estimation techniques are introduced because it is considered the basic mathematical building block for data assimilation methods. Stochastic version of least square is included to handle the error in both model and observation. Then the three and four dimensional variational assimilation 3dvar and 4dvar respectively will be handled. Kalman filters and its derivatives Extended, (KF, EKF, ENKF) and hybrid filters are introduced.展开更多
The sulfur pollutants are the source of a sizeable portion of the air pollution. In this work, the recent spatiotemporal distribution and trend of the mass concentration of two of the critical sulfur pollutants, SO2 a...The sulfur pollutants are the source of a sizeable portion of the air pollution. In this work, the recent spatiotemporal distribution and trend of the mass concentration of two of the critical sulfur pollutants, SO2 and SO4, in addition to the aerosol optical properties (AOD) were analyzed over the region of the Middle East and North Africa (MENA) from satellite and Modern Era-Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis data. The SO2 and SO4 data used in these analyses are obtained from (MERRA-2) with a resolution of 0.5° × 0.625° throughout a period of 10 years (2005-2015). On the other hand, the temporal trend and spatial distribution of AOD were identified from four different satellite data. 1) moderate-resolution imaging spectroradiometer (MODIS) Level 3 AOD data at 550 nm wavelengths from Collection 6 algorithm (combined dark target and deep blue algorithms) are used for 10 years temporal analysis (2006-2015). 2) Multi-angle imaging spectroradiometer (MISR) with 0.5 deg spatial resolution for the same 10 years (2006-2015). 3) Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) with 0.5 deg for the period (2005-2010). 4) Ozone Monitoring Instrument (OMI) AOD at 500 nm wavelength with resolution 1 degree. This study presents more resent 10 years of Spatiotemporal of SO2, SO4 and AOD over MENA domain.展开更多
文摘Environmental systems including our atmosphere oceans, biological… etc. can be modeled by mathematical equations to estimate their states. These equations can be solved with numerical methods. Initial and boundary conditions are needed for such of these numerical methods. Predication and simulations for different case studies are major sources for the great importance of these models. Satellite data from different wide ranges of sensors provide observations that indicate system state. So both numerical models and satellite data provide estimation of system states, and between the different estimations it is required the best estimate for system state. Assimilation of observations in numerical weather models with data assimilation techniques provide an improved estimate of system states. In this work, highlights on the mathematical perspective for data assimilation methods are introduced. Least square estimation techniques are introduced because it is considered the basic mathematical building block for data assimilation methods. Stochastic version of least square is included to handle the error in both model and observation. Then the three and four dimensional variational assimilation 3dvar and 4dvar respectively will be handled. Kalman filters and its derivatives Extended, (KF, EKF, ENKF) and hybrid filters are introduced.
文摘The sulfur pollutants are the source of a sizeable portion of the air pollution. In this work, the recent spatiotemporal distribution and trend of the mass concentration of two of the critical sulfur pollutants, SO2 and SO4, in addition to the aerosol optical properties (AOD) were analyzed over the region of the Middle East and North Africa (MENA) from satellite and Modern Era-Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis data. The SO2 and SO4 data used in these analyses are obtained from (MERRA-2) with a resolution of 0.5° × 0.625° throughout a period of 10 years (2005-2015). On the other hand, the temporal trend and spatial distribution of AOD were identified from four different satellite data. 1) moderate-resolution imaging spectroradiometer (MODIS) Level 3 AOD data at 550 nm wavelengths from Collection 6 algorithm (combined dark target and deep blue algorithms) are used for 10 years temporal analysis (2006-2015). 2) Multi-angle imaging spectroradiometer (MISR) with 0.5 deg spatial resolution for the same 10 years (2006-2015). 3) Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) with 0.5 deg for the period (2005-2010). 4) Ozone Monitoring Instrument (OMI) AOD at 500 nm wavelength with resolution 1 degree. This study presents more resent 10 years of Spatiotemporal of SO2, SO4 and AOD over MENA domain.
