scholarly journals A robust calibration approach for PM<sub>10</sub> prediction from MODIS aerosol optical depth

2012 ◽  
Vol 12 (12) ◽  
pp. 31483-31505 ◽  
Author(s):  
X. Q. Yap ◽  
M. Hashim
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Predictive Power ◽  
Atmospheric Particulate Matter ◽  
Error Characteristic ◽  
Tropical Region ◽  
Spatial Coverage ◽  
Calibration Methods ◽  
Modis Aod ◽  
Calibration Approach

Abstract. Investigating the human health effects of atmospheric particulate matter (PM) using satellite data are gaining more attention due to their wide spatial coverage and temporal advantages. Such epidemiological studies are, however, susceptible to bias errors and resulted in poor predictive output in some locations. Current methods calibrate aerosol optical depth (AOD) retrieved from MODIS to further predict PM. The recent satellite-based AOD calibration uses a mixed effects model to predict location-specific PM on a daily basis. The shortcomings of this daily AOD calibration are for areas of high probability of persistent cloud cover throughout the year such as in the humid tropical region along the equatorial belt. Contaminated pixels due to clouds causes radiometric errors in the MODIS AOD, thus causes poor predictive power on air quality. In contrary, a periodic assessment is more practical and robust especially in minimizing these cloud-related contaminations. In this paper, a simple yet robust calibration approach based on monthly AOD period is presented. We adopted the statistical fitting method with the adjustment technique to improve the predictive power of MODIS AOD. The adjustment was made based on the long-term observation (2001–2006) of PM10-AOD residual error characteristic. Besides, we also incorporated the ground PM measurement into the model as a weighting to reduce the bias of the MODIS-derived AOD value. Results indicated that this robust approach with monthly AOD calibration reported an improved average accuracy of PM10 retrieval from MODIS data by 50% compared to widely used calibration methods based on linear regression models, in addition to enabling further spatial patterns of periodic PM exposure to be undertaken.

scholarly journals A robust calibration approach for PM<sub>10</sub> prediction from MODIS aerosol optical depth

2013 ◽  
Vol 13 (6) ◽  
pp. 3517-3526 ◽  
Author(s):  
X. Q. Yap ◽  
M. Hashim
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Predictive Power ◽  
Atmospheric Particulate Matter ◽  
Error Characteristic ◽  
Tropical Region ◽  
Spatial Coverage ◽  
Calibration Methods ◽  
Modis Aod ◽  
Calibration Approach

Abstract. Investigating the human health effects of atmospheric particulate matter (PM) using satellite data are gaining more attention due to their wide spatial coverage and temporal advantages. Such epidemiological studies are, however, susceptible to bias errors and resulted in poor predictive output in some locations. Current methods calibrate aerosol optical depth (AOD) retrieved from MODIS to further predict PM. The recent satellite-based AOD calibration uses a mixed effects model to predict location-specific PM on a daily basis. The shortcomings of this daily AOD calibration are for areas of high probability of persistent cloud cover throughout the year such as in the humid tropical region along the equatorial belt. Contaminated pixels due to clouds causes radiometric errors in the MODIS AOD, thus causes poor predictive power on air quality. In contrary, a periodic assessment is more practical and robust especially in minimizing these cloud-related contaminations. In this paper, a simple yet robust calibration approach based on monthly AOD period is presented. We adopted the statistical fitting method with the adjustment technique to improve the predictive power of MODIS AOD. The adjustment was made based on the long-term observation (2001–2006) of PM10-AOD residual error characteristic. Furthermore, we also incorporated the ground PM measurement into the model as a weighting to reduce the bias of the MODIS-derived AOD value. Results indicated that this robust approach with monthly AOD calibration reported an improved average accuracy of PM10 retrieval from MODIS data by 50% compared to widely used calibration methods based on linear regression models, in addition to enabling further spatial patterns of periodic PM exposure to be undertaken.

scholarly journals Merging aerosol optical depth data from multiple satellite missions to view agricultural biomass burning in Central and East China

2012 ◽  
Vol 12 (4) ◽  
pp. 10461-10492 ◽  
Author(s):  
Y. Xue ◽  
H. Xu ◽  
L. Mei ◽  
J. Guang ◽  
J. Guo ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Meteorological Data ◽  
Temporal Distribution ◽  
East China ◽  
Agricultural Biomass ◽  
Spatial Coverage ◽  
Modis Aod ◽  
Single Sensor

Abstract. Agricultural biomass burning (ABB) in Central and East China occurs every year from May to October and peaks in June. The biomass burning event in June 2007 was very strong. During the period from 26 May to 16 June 2007, ABB occurred mainly in Anhui, Henan, Jiangsu and Shandong provinces. A comprehensive set of aerosol optical depth (AOD) data, produced by a merger of AOD product data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MIRS), is used to study the spatial and temporal distribution of agricultural biomass aerosols in Central and East China combining with ground observations from both AErosol RObotic NETwork (AERONET) and China Aerosol Remote Sensing NETwork (CARSNET) measurements. We compared merged AOD data with single-sensor single-algorithm AOD data (MODIS Dark Target AOD data, MODIS Deep Blue AOD data, SRAP-MODIS AOD data and MISR AOD data). In this comparison, we found merged AOD products can improve the quality of AOD products from single-sensor single-algorithm data sets by expanding the spatial coverage of the study area and keeping the statistical confidence in AOD parameters. There existed high correlation (0.8479) between the merged AOD data and AERONET measurements. Our merged AOD data make use of synergetic information conveyed in all of the available satellite data. The merged AOD data were used for the analysis of the biomass burning event from 26 May to 16 June 2007 together with meteorological data. The merged AOD products and the ground observations from China suggest that biomass burning in Central and East China has had great impact on AOD over China. Influenced by this ABB, the highest AOD value in Beijing on 12 June 2007 reached 5.71.

scholarly journals Retrieval of Aerosol Optical Depth Using Satellite Data Associated with Ground-based Observations over Urban and Rural Areas

2019 ◽  
Vol 18 (32) ◽  
pp. 4-17
Author(s):  
Le Thi Le ◽  
Lin Tang-Huang ◽  
Canh Van Le ◽  
Lan Thi Pham ◽  
Ha Thi Thu Le ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Rural Areas ◽  
Urban Areas ◽  
Spatial Information ◽  
Satellite Image ◽  
Spatial Coverage ◽  
Modis Aod ◽  
Sun Photometer ◽  
Urban And Rural Areas

Aerosol optical depth (AOD) can be retrieved accurately with sequential ground-based measurements of direct and diffuse solar radiance. However, spatial coverage and location frequency cause certain limitations. Hence, satellite image data are a proper tool for obtaining aerosol optical depth products with more spatial information and patterns of aerosol distribution. Currently, aerosol remote sensing may enhance our understanding of the optimal approach to AOD retrieval over urban and rural areas, and how it differs due to the characteristics of surface reflectivity. The article deals with the concepts of contrast reduction, and dark target approaches are examined using Landsat imaging and the observation of a sun photometer for integrating aerosol optical depth distribution over the city of Taipei in Taiwan. For areas with bright surfaces, such as urban areas, the above concepts were applied using the dispersion coefficient method with a sun photometer, in order to reduce errors considerably in the product. In contrast, a dark target algorithm with a relationship of surface reflectance between the blue (0.49 μm), red (0.66 μm), and infrared (2.1 μm) spectral bands is suitable for moist soils and vegetation areas. The retrieval of AOD spatial distribution is compared with MODIS AOD products and AERONET to verify the accuracy of the results. The RMSE ranged from 0.2 to 0.4, and about 50% of the data were within expected error margins (EE=± (0.05+0.15 AODsunphotometer).

scholarly journals Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis

2014 ◽  
Vol 14 (23) ◽  
pp. 32177-32231 ◽  
Author(s):  
V. Buchard ◽  
A. M. da Silva ◽  
P. R. Colarco ◽  
A. Darmenov ◽  
C. A. Randles ◽  
...  
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Anthropogenic Aerosols ◽  
Aerosol Absorption ◽  
Modis Aod ◽  
Aerosol Index

Abstract. A radiative transfer interface has been developed to simulate the UV Aerosol Index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and Aerosol Absorption Optical Depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the Aerosol Robotic Network (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the South African and South American biomass burning regions indicates that revising the spectrally-dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Sanja Grgurić ◽  
Josip Križan ◽  
Goran Gašparac ◽  
Oleg Antonić ◽  
Zdravko Špirić ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mass Concentration ◽  
Meteorological Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Independent Variables ◽  
Data Variability ◽  
Modis Aod ◽  
The Relationship

AbstractThis study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

scholarly journals Comparison of MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008

2014 ◽  
Vol 14 (4) ◽  
pp. 2015-2038 ◽  
Author(s):  
J. M. Livingston ◽  
J. Redemann ◽  
Y. Shinozuka ◽  
R. Johnson ◽  
P. B. Russell ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Retrieval Algorithm ◽  
Grid Cells ◽  
Data Set ◽  
Clear Sky ◽  
Modis Aod ◽  
Research Aircraft ◽  
Bright Surface ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Airborne sunphotometer measurements acquired by the NASA Ames Airborne Tracking Sunphotometer (AATS-14) aboard the NASA P-3 research aircraft are used to evaluate dark-target over-land retrievals of extinction aerosol optical depth (AOD) from spatially and temporally near-coincident measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the summer 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. The new MODIS Collection 6 aerosol data set includes retrievals of AOD at both 10 km × 10 km and 3 km × 3 km (at nadir) resolution. In this paper we compare MODIS and AATS AOD at 553 nm in 58 10 km and 134 3 km retrieval grid cells. These AOD values were derived from data collected over Canada on four days during short time segments of five (four Aqua and one Terra) satellite overpasses of the P-3 during low-altitude P-3 flight tracks. Three of the five MODIS–AATS coincidence events were dominated by smoke: one included a P-3 transect of a well-defined smoke plume in clear sky, but two were confounded by the presence of scattered clouds above smoke. The clouds limited the number of MODIS retrievals available for comparison, and led to MODIS AOD retrievals that underestimated the corresponding AATS values. This happened because the MODIS aerosol cloud mask selectively removed 0.5 km pixels containing smoke and clouds before the aerosol retrieval. The other two coincidences (one Terra and one Aqua) occurred during one P-3 flight on the same day and in the same general area, in an atmosphere characterized by a relatively low AOD (< 0.3), spatially homogeneous regional haze from smoke outflow with no distinguishable plume. For the ensemble data set for MODIS AOD retrievals with the highest-quality flag, MODIS AOD agrees with AATS AOD within the expected MODIS over-land AOD uncertainty in 60% of the retrieval grid cells at 10 km resolution and 69% at 3 km resolution. These values improve to 65 % and 74%, respectively, when the cloud-affected case with the strongest plume is excluded. We find that the standard MODIS dark-target over-land retrieval algorithm fails to retrieve AOD for thick smoke, not only in cloud-contaminated regions but also in clear sky. We attribute this to deselection, by the cloud and/or bright surface masks, of 0.5 km resolution pixels that contain smoke.

scholarly journals An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

2010 ◽  
Vol 3 (5) ◽  
pp. 4091-4167 ◽  
Author(s):  
E. J. Hyer ◽  
J. S. Reid ◽  
J. Zhang
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Prediction Models ◽  
Hot Spot ◽  
Weather Prediction ◽  
Surface Boundary ◽  
Data Set ◽  
Modis Aod ◽  
Level 3

Abstract. MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2=0.62–0.65 to r2=0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05±20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.

scholarly journals Comparison of PMCAMx aerosol optical depth predictions over Europe with AERONET and MODIS measurements

2016 ◽  
Vol 9 (11) ◽  
pp. 4257-4272
Author(s):  
Antigoni Panagiotopoulou ◽  
Panagiotis Charalampidis ◽  
Christos Fountoukis ◽  
Christodoulos Pilinis ◽  
Spyros N. Pandis
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Transport Model ◽  
Chemical Transport Model ◽  
Secondary Aerosol ◽  
The Balkans ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Dust

Abstract. The ability of chemical transport model (CTM) PMCAMx to reproduce aerosol optical depth (AOD) measurements by the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) over Europe during the photochemically active period of May 2008 (EUCAARI campaign) is evaluated. Periods with high dust or sea-salt levels are excluded, so the analysis focuses on the ability of the model to simulate the mostly secondary aerosol and its interactions with water. PMCAMx reproduces the monthly mean MODIS and AERONET AOD values over the Iberian Peninsula, the British Isles, central Europe, and Russia with a fractional bias of less than 15 % and a fractional error of less than 30 %. However, the model overestimates the AOD over northern Europe, most probably due to an overestimation of organic aerosol and sulfates. At the other end, PMCAMx underestimates the monthly mean MODIS AOD over the Balkans, the Mediterranean, and the South Atlantic. These errors appear to be related to an underestimation of sulfates. Sensitivity tests indicate that the evaluation results of the monthly mean AODs are quite sensitive to the relative humidity (RH) fields used by PMCAMx, but are not sensitive to the simulated size distribution and the black carbon mixing state. The screening of the satellite retrievals for periods with high dust (or coarse particles in general) concentrations as well as the combination of the MODIS and AERONET datasets lead to more robust conclusions about the ability of the model to simulate the secondary aerosol components that dominate the AOD during this period.

scholarly journals Estimation of aerosol optical depth over Dehradun (India) using simple model for atmospheric radiative transfer in multiple scattering approximation

2014 ◽  
Vol XL-8 ◽  
pp. 271-276
Author(s):  
M. Mehta
Keyword(s):  
Simple Model ◽  
Radiative Transfer ◽  
Multiple Scattering ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Land Surface ◽  
Accurate Estimate ◽  
Scattering Approximation ◽  
Modis Aod ◽  
Atmospheric Radiative Transfer

Aerosol optical depth retrieval over land surface using remote sensing employs the use of radiative transfer simulations and/or simultaneous measurements of atmospheric parameters at the time of satellite pass. Also, an accurate estimate of land surface parameters is also required in order to separate the atmospheric component from the land surface reflectance reaching at-sensor. In addition to empirical and semi-empirical approaches, amongst the most widely used methods to retrieve the aerosol properties from satellite measurements are radiative transfer codes used in either forward or inverse modes. As most of them are computationally complex, henceforth, efforts are made to formulate approximate models. In this study, we have tried to estimate aerosol optical depth using one such established physically based model, namely, SMART (Simple Model for Atmospheric Radiative Transfer) code in multiple scattering approximation for aerosols over first band (0.52&ndash;0.59 μm) of RESOURCESAT-AWiFS sensor. The aim of the analysis was to find out an approach to decouple aerosol effects from Top of atmosphere signals recorded by AWiFS sensor using multiple scattering approximations for aerosols. The model is first calibrated for aerosol asymmetry parameter for one dataset each of summer and winter seasons respectively and subsequently validated for 4 different datasets (2 summer and 2 winter) against the MODIS atmosphere product for aerosol optical depth. The results show that the difference between simulated vs. MODIS AOD fall within MODIS expected errors for the aerosol product.

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