scholarly journals Improving PM<sub>2.5</sub> retrievals in the San Joaquin Valley using A-Train Multi-Satellite Observations

2011 ◽  
Vol 11 (11) ◽  
pp. 30563-30598 ◽  
Author(s):  
A. W. Strawa ◽  
R. B. Chatfield ◽  
M. Legg ◽  
B. Scarnato ◽  
R. Esswein
Keyword(s):  
Linear Regression ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Ground Level ◽  
San Joaquin Valley ◽  
Satellite Observations ◽  
Target Area ◽  
Simple Linear Regression ◽  
Data Set ◽  
Modis Aod

Abstract. This paper demonstrates the use of a combination of multi-platform satellite observations and statistical data analysis to dramatically improve the correlation between satellite observed aerosol optical depth (AOD) and ground-level retrieved PM2.5. The target area is California's San Joaquin Valley which has a history of poor particulate air quality and where such correlations have not yielded good results. We have used MODIS AOD, OMI AOD, AAOD (absorption aerosol optical depth) and NO2 concentration, and a seasonal parameter in a generalized additive model (GAM) to improve retrieved/observed PM2.5 correlations (r2 at six individual sites and for a data set combining all sites. For the combined data set using the GAM, r2 improved to 0.69 compared with an r2 of 0.27 for a simple linear regression of MODIS AOD to surface PM. Parameter sensitivities and the effect of multi-platform data on the sample size are discussed. Particularly noteworthy is the fact that the PM retrieved using the GAM captures many of the PM exceedences that were not seen in the simple linear regression model.

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 Evaluation of MODIS-Retrieved Aerosol Optical Depth in Alaska: Implications for Surface Air Quality Applications

2018 ◽  
Author(s):  
Alyson McPhetres ◽  
Srijan Aggarwal
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
National Parks ◽  
Urban Areas ◽  
Ground Level ◽  
Modis Data ◽  
Particulate Pollution ◽  
Modis Aod ◽  
Pollution Concentrations

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks leaving a large proportion of the state unmonitored. The use of MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world, and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from AERONET ground-based sunphotometers in Barrow and Bonanza Creek to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it could only be validated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10 km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. On the other hand, the individual Terra and Aqua MODIS collection 6 AOD products at 10 km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3 km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

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

2011 ◽  
Vol 4 (3) ◽  
pp. 379-408 ◽  
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 MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008

2013 ◽  
Vol 13 (6) ◽  
pp. 15007-15059 ◽  
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) from 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), and 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-contaminated 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 Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska

2018 ◽  
Vol 10 (9) ◽  
pp. 1384 ◽  
Author(s):  
Alyson McPhetres ◽  
Srijan Aggarwal
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
National Parks ◽  
Urban Areas ◽  
Ground Level ◽  
Modis Data ◽  
Particulate Pollution ◽  
Modis Aod ◽  
Pollution Concentrations

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks, leaving a large proportion of the state unmonitored. The use of Moderate Resolution Imaging Spectroradiometer MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from two ground-based Aerosol Robotic Network (AERONET) sunphotometers in Alaska, located at Utqiagvik (previously known as Barrow) and Bonanza Creek, to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from years 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it was only evaluated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10-km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. Note that the individual Terra and Aqua MODIS collection 6 AOD products at 10-km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3-km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

scholarly journals The relevance of aerosol optical depth to cumulus fraction changes: a five-year climatology at the ACRF SGP site

2007 ◽  
Vol 7 (4) ◽  
pp. 11797-11837 ◽  
Author(s):  
E. I. Kassianov ◽  
L. K. Berg ◽  
C. Flynn ◽  
S. McFarlane
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Great Plains ◽  
Value Added ◽  
Cloud Fraction ◽  
Satellite Observations ◽  
Time Dependent ◽  
Size Dependent ◽  
Cloud Properties ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The objective of this study is to investigate, by observational means, the magnitude and sign of the actively discussed relationship between cloud fraction N and aerosol optical depth τa. Collocated and coincident ground-based measurements and Terra/Aqua satellite observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site form the basis of this study. The N–τa relationship occurred in a specific 5-year dataset of fair-weather cumulus (FWC) clouds and mostly non-absorbing aerosols. To reduce possible contamination of the aerosols on the cloud properties estimation (and vice versa), we use independent datasets of τa and N obtained from the Multi-filter Rotating Shadowband Radiometer (MFRSR) measurements and from the ARM Active Remotely Sensed Clouds Locations (ARSCL) value-added product, respectively. Optical depth of the FWC clouds τcld and effective radius of cloud droplets re are obtained from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We found that relationships between cloud properties (N,τcld, re) and aerosol optical depth are time-dependent (morning versus afternoon). Observed time-dependent changes of cloud properties, associated with aerosol loading, control the variability of surface radiative fluxes. In comparison with pristine clouds, the polluted clouds are more transparent in the afternoon due to smaller cloud fraction, smaller optical depth and larger droplets. As a result, the corresponding correlation between the surface radiative flux and τa is positive (warming effect of aerosol). Also we found that relationship between cloud fraction and aerosol optical depth is cloud size dependent. The cloud fraction of large clouds (larger than 1 km) is relatively insensitive to the aerosol amount. In contrast, cloud fraction of small clouds (smaller than 1 km) is strongly positively correlated with τa. This suggests that an ensemble of polluted clouds tends to be composed of smaller clouds than a similar one in a pristine environment. One should be aware of these time- and size-dependent features when qualitatively comparing N–τa relationships obtained from the satellite observations, surface measurements, and model simulations.

scholarly journals Variations in optical properties of aerosols on monsoon seasonal change and estimation of aerosol optical depth using ground-based meteorological and air quality data

2014 ◽  
Vol 14 (13) ◽  
pp. 19747-19789
Author(s):  
F. Tan ◽  
H. S. Lim ◽  
K. Abdullah ◽  
T. L. Yoon ◽  
B. Holben
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Statistical Tests ◽  
Air Pollutant ◽  
Quality Data ◽  
Supplementary Information ◽  
Northeast Monsoon ◽  
Data Set ◽  
Proposed Model

Abstract. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the aerosol optical depth (AOD) against the Angstrom exponent. A modified algorithm based on the prototype model of Tan et al. (2014a) was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET because of frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The predicted AOD of the model was generated based on the coefficients and compared against the measured data through standard statistical tests. The predicted AOD in the proposed model yielded a coefficient of determination R2 of 0.68. The corresponding percent mean relative error was less than 0.33% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Validation tests were performed on the model against selected LIDAR data and yielded good correspondence. The predicted AOD can beneficially monitor short- and long-term AOD and provide supplementary information in atmospheric corrections.

scholarly journals Estimating ground-level PM<sub>2.5</sub> in Eastern China using aerosol optical depth determined from the GOCI Satellite Instrument

2015 ◽  
Vol 15 (12) ◽  
pp. 17251-17281 ◽  
Author(s):  
J. Xu ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
J. Kim ◽  
M. Choi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Transport Model ◽  
Eastern China ◽  
Ground Level ◽  
East China ◽  
Chemical Transport Model ◽  
Near Surface ◽  
Significant Agreement

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in East China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean Geostationary Ocean Color Imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5/AOD ratio exhibits high consistency with ground-based measurements (MFB = −0.52–8.0 %). We evaluate the satellite-derived PM2.5 vs. the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in-situ observations in both annual averages (r = 0.81, N = 494) and monthly averages (MFB = 13.1 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical speciation of GOCI-derived PM2.5 reveals that secondary inorganics (SO42−, NO3−, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating are responsible for an increase in the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over East China for 2013 is 53.8 μg m−3, threatening the health and life expectancy of its 600 million residents.

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