SearcharxivSearch

arXiv · 1812.00760

Spatiotemporal Assessment of So2, SO4 and AOD from over MENA Domain from 2006 to 2016 Using multiple satellite Data and Reanalysis MERRA2 Data

Abstract

The sulfur pollutants are the source of a sizeable portion of the air pollution. In this work, the spatial distribution and temporal 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 multiple satellite resources and Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2) reanalysis data. The So2 and So4 data used in these analyses are obtained from (MERRA2) with a resolution of 0.5 x 0.625 deg throughout a period of 10 years (2005 to 2015). On the other hand, the temporal trend and spatial distribution of AOD was 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 to 2015). Data were obtained for the period 2006 to 2015 (2) Multi angle imaging spectroradiometer (MISR) with 0.5 deg spatial resolution for the same 10 years (2006 to 2015). (3) Sea Viewing Wide Field of View Sensor (SeaWIFS) with 0.5 deg for the period (2005 to 2010). (4) Ozone Monitoring Instrument (OMI) AOD at 500 nm wavelength with resolution 1 degree.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muhammed ElTahan, Mohammed Magooda. 2019-01-08. Spatiotemporal Assessment of So2, SO4 and AOD from over MENA Domain from 2006 to 2016 Using multiple satellite Data and Reanalysis MERRA2 Data. https://doi.org/10.4236/gep.2019.74010

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph