Application of spectral analysis techniques in the intercomparison of aerosol data: 1. An EOF approach to analyze the spatial‐temporal variability of aerosol optical depth using multiple remote sensing data sets

Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

doi:10.1002/jgrd.50686

Cited by 52 publications

(54 citation statements)

References 23 publications

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“…These three modes, as shown in Fig. 9, are also consistent with Li et al (2013Li et al ( , 2014a and reveal aerosol-source regions and their interannual variability. It is encouraging that all four satellite data sets agree well with AERONET qualitatively.…”

Section: Global Analysis -Anomaly Data Setsupporting

confidence: 75%

“…Previously, we have demonstrated that spectral decomposition techniques such as principal component analysis (PCA) can be effectively used to examine the spatial and temporal variability in multidimensional aerosol observations (Li et al, , 2011(Li et al, , 2013. Many global and regional aerosol-source regions and their seasonal and interannual variability are successfully captured by the dominant orthogonal modes.…”

Section: Introductionmentioning

confidence: 99%

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Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

Carlson

Lacis

2014

Atmos. Meas. Tech.

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Abstract. In this paper, we introduce the usage of a newly developed spectral decomposition technique -combined maximum covariance analysis (CMCA) -in the spatiotemporal comparison of four satellite data sets and groundbased observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for both the Gangetic Basin and southern India, while on interannual timescales it has the poorest agreement.

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Section: Global Analysis -Anomaly Data Setsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

Carlson

Lacis

2014

Atmos. Meas. Tech.

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“…Black carbon, primary and secondary organics, and sulfate are maximal in this region during the summer, whereas mineral dust and sea salt peak in spring. A formal approach could include combined principal component analysis of the daily model-simulated or satelliteretrieved burdens of multiple aerosol components in candidate deployment regions (e.g., Li et al 2013).…”

Section: Deployment Site Selection and Completion Strategiesmentioning

confidence: 99%

SAM-CAAM: A Concept for Acquiring Systematic Aircraft Measurements to Characterize Aerosol Air Masses

Kahn

Berkoff

Brock

et al. 2017

Bulletin of the American Meteorological Society

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Since 1995, the Intergovernmental Panel on Climate Change (IPCC) assessment reports have highlighted, as leading uncertainties in understanding Earth's climate, the direct impact of airborne particles on the planetary energy balance and the indirect effects they have on clouds, atmospheric stability, regional circulation, and the hydrologic cycle. For example, the confidence with which future climate can be predicted depends to first order on the relationship between the near-surface warming response and the radiative forcing, primarily by greenhouse gases and aerosol effects. This relationship is characterized, in its simplest form, as a linear factor-the climate sensitivity. The quantity is determined using presentday and retrospective values of forcing and response; AFFILIATIONS: Kahn and hansiCo-Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland; BeRKoff, Chen, and feRRaRe-NASA Langley Research Center, Hampton, Virginia; BRoCK and muRphy-Chemical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, Colorado; Ghan-Department of Energy, Pacific Northwest National Laboratory, Richland, Washington; heGG-Department of Atmospheric Sciences, University of Washington, Seattle, Washington; maRTins-Department of Physics, and Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland; mCnauGh- 2215OCTOBER 2017 AMERICAN METEOROLOGICAL SOCIETY | currently, the largest uncertainty in climate sensitivity is due to uncertainty in the aerosol forcing (IPCC 2013;Schwartz et al. 2014;Forster 2016).Further, the presence of aerosols often necessitates large corrections to other space-based measurements of independent parameters, such as ocean color and productivity (e.g., Gordon 1997), and they cause greater premature mortality than ozone, NO x , or other pollutants (Lelieveld et al. 2015). Frequent, global aerosol airmass-type mapping, of value itself for air quality, material transport, and other applications, also represents critical test, validation, and constraint data for climate modeling. Here, we expand the definition of "aerosol type" normally used in satellite remote sensing, which covers those categorical distinctions among particle components and mixtures that can be made from optical constraints, of varying sensitivity, to particle size, shape, and spectral absorption. To these we add particle hygroscopicity, mass, and composition, which are critical for treating aerosol direct and indirect forcing in climate models and for air quality applications. These additional characteristics cannot be derived from remote sensing alone and thus require in situ measurement. Further, measurements of these quantities make it possible to better represent aerosol light-absorption properties needed to address many radiative and dynamical questions, yet cannot be retrieved with sufficient accuracy from satellite observations alone.Single-view satellite instruments, such as the NASA EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and the ...

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“…The AOD products from these spaceborne sensors are mainly available in two levels, i.e., swath (Level-2) and gridded (Level-3) formats. Several studies involving aerosol characterization, aerosolclimate effects and aerosol trend analysis utilize the monthlymean gridded AOD products from MISR and MODIS (Level-3 data) (Shrestha and Barros, 2010;Zhang and Reid, 2010;Li et al, 2013). The Level-3 MODIS AOD dataset includes aerosol retrievals over ocean as well as DarkTarget retrievals over land (Levy et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…If the time series consists of a number of missing values above a certain threshold, the resultant trend obtained from the time series may not be a reliable or accurate representation of the change over time. Empirical Orthogonal Functions (EOFs) are another technique used widely in the seasonal decomposition of aerosols (or other environmental/climatic variables) based on satellite datasets (Shrestha and Barros, 2010;Li et al, 2013), where central to the EOF analysis is the variance-covariance matrix. The elements of this matrix are computed using the covariance between two time series.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer

Singh

Venkatachalam

Gautam

2017

Aerosol Air Qual. Res.

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The Aerosol Optical Depth (AOD) retrieved from satellite remote sensing measurements such as from MISR and MODIS, both onboard the Terra platform, are widely used for studying regional and global patterns of aerosol loading. Aerosol products from these sensors are also used for analyzing feedbacks and relationship between aerosols and climatic variables including clouds, precipitation, and radiation fluxes. Several statistical techniques leading to the understanding of such relationships, including empirical orthogonal function and temporal trend extraction methods, require spatially complete AOD data records. Inherent to remote sensing of aerosols, cloud cover significantly affects aerosol retrievals and results in missing data across the AOD products. This paper demonstrates widely-used geostatistical techniques, such as Co-Kriging (CK) and Regression Kriging (RK), for spatially-filling missing data in the MISR AOD product for the period 2001-2013. Among the unique characteristics of this data-filling algorithm is that it utilizes additional AOD information obtained from MODIS. The mean accuracy of the predicted MISR AOD using CK method is estimated to be 0.05, globally. The gap-filled MISR AOD data are also compared with 131 ground-based Aerosol Robotic Network (AERONET) stations, located around the world. It is found that Root Mean Squared Error of the gap-filled AOD dataset and the original MISR AOD product with respect to AERONET data are 0.143. The gap-filled AOD dataset can be used in applications where the presence of missing values is undesirable such as for global/regional aerosol variability and trend analysis.

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Application of spectral analysis techniques in the intercomparison of aerosol data: 1. An EOF approach to analyze the spatial‐temporal variability of aerosol optical depth using multiple remote sensing data sets

Cited by 52 publications

References 23 publications

Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

SAM-CAAM: A Concept for Acquiring Systematic Aircraft Measurements to Characterize Aerosol Air Masses

Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer

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