Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Lipponen, Antti; Mielonen, Tero; Pitkänen, Mikko R. A.; Levy, Robert C.; Sawyer, Virginia; Romakkaniemi, Sami; Kolehmainen, Ville; Arola, Antti

doi:10.5194/amt-11-1529-2018

Cited by 27 publications

(28 citation statements)

References 30 publications

(34 reference statements)

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“…In the interests of standardization and in line with other treatments of uncertainty and error in remote sensing (e.g. Rodgers, 2000;Povey and Grainger, 2015;Loew et al, 2017;Merchant et al, 2017;Mittaz et al, 2019;von Clarmann et al, 2019), the GUM terminology is also adopted here. Terms are often used inconsistently in writing or informal conversation (in particular "error" and "uncertainty"), so to assist the reader, definitions of relevant terms are as follows (and see previously cited references).…”

Section: Terminologymentioning

confidence: 99%

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

et al. 2020

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Abstract. Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

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Section: Terminologymentioning

confidence: 99%

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

et al. 2020

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“…From the table we can see that surface concentrations of sulfate and its global burden are significantly larger in SALSA2.0 than in the previous generation model, and they are at the upper end of the estimate of Liu et al (2005). Although our simulation period is not for the same period as for ECHAM5-HAM, by Liu et al (2005) and Textor et al (2006), global sulfate emissions have been suggested to be fairly constant through 2000(Granier et al, 2011. Even larger increases between the two model generations are evident for the BC and OA burdens which are approximately 3 times higher in ECHAM-HAMMOZ-SALSA2.0.…”

Section: Discussionmentioning

confidence: 85%

“…(Bergman et al, 2012). The table also shows the global burdens for these compounds for the same three model versions together with values reported by Liu et al (2005) and Textor et al (2006). Liu et al (2005) have made a synthesis of model data and Textor et al (2006) provide the analysis of global aerosol properties in AeroCom Phase I models for the year 2000.…”

Section: (A) (B)mentioning

confidence: 99%

“…In many cases, modeled BC concentrations exceeded the limits of the variability in ob- Table 3. Comparison of mean NMB in ECHAM5-HAM (with SALSA1), ECHAM-HAMMOZ (with SALSA2.0), and ECHAM HAMMOZ (with M7) for individual compounds at IMPROVE sites, the global burdens (Tg) of all compounds together with those reported by Liu et al (2005) and the mean of AeroCom I models analyzed by Textor et al (2006). servations (gray whiskers in Figs.…”

Section: Evaluation Against Aircraft Observationsmentioning

confidence: 99%

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SALSA2.0: The sectional aerosol module of the aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0

Kokkola¹,

Kühn²,

Laakso³

et al. 2018

Preprint

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Abstract. In this paper, we present the implementation and evaluation of the aerosol microphysics module SALSA2.0 in the framework of the aerosol-chemistry-climate model ECHAM-HAMMOZ. It is an alternative microphysics module to the default modal microphysics scheme M7 in ECHAM-HAMMOZ. The SALSA2.0 implementation is evaluated against the observations of aerosol optical properties, aerosol mass, and size distributions. We also compare the skill of SALSA2.0 in reproducing the observed quantities to the skill of the M7 implementation. The largest differences between SALSA2.0 and M7 are evident over regions where the aerosol size distribution is heavily modified by the microphysical processing of aerosol particles. Such regions are, for example, highly polluted regions and regions strongly affected by biomass burning. In addition, in a simulation of the 1991 Mt Pinatubo eruption in which a stratospheric sulfate plume was formed, the global burden and the effective radii of the stratospheric aerosol are very different in SALSA2.0 and M7. While SALSA2.0 was able to reproduce the observed time evolution of the global burden of sulfate and the effective radii of stratospheric aerosol, M7 strongly overestimates the removal of coarse stratospheric particles and thus underestimates the effective radius of stratospheric aerosol. As the mode widths of M7 have been optimized for the troposphere and were not designed to represent stratospheric aerosol the ability of M7 to simulate the volcano plume was improved by modifying the mode widths decreasing the standard deviations of the accumulation and coarse modes from 1.59 and 2.0, respectively, to 1.2. Overall, SALSA2.0 shows promise in improving the aerosol description of ECHAM-HAMMOZ and can be further improved by implementing methods for aerosol processes that are more suitable for the sectional method, e.g size dependent emissions for aerosol species and size resolved wet deposition.

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“…Ground-based observation by CE-318 instruments is the traditional approach for measuring high-precision aerosol optical depth (AOD) and supports well-accepted atmospheric pollution studies but is limited by its low spatial resolution and high cost in terms of human, material, and financial resources (Che et al, 2009). Thus, satellite remote sensing, which continuously observes the Earth or atmosphere in detail, has the potential to overcome the shortcomings of ground-based observations and has previously been used to quantitatively retrieve AODs at regional and global scales (Kaufman, Tanré, et al, 1997;Kaufman, Wald, et al, 1997;Kaufman et al, 2005;Sun et al, 2017;Kumar et al, 2018;Lipponen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface

Huang

Zhu

et al. 2019

Earth and Space Science

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Modifications such as degrading the retrieval quality of mixed pixels in the coastline area and revising surface characterization scheme have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) product to address inaccuracy over urban areas. In this study, comprehensive evaluations of modifications to the MODIS C6.1 DT AOD product, in comparison with Collection 6 (C6), are conducted over mainland China under different spatial distributions, seasons, and air quality for the period 2010-2017, combined with validation against Aerosol Robotic Network (AERONET) AOD measurements. The preliminary result showed that the C6.1 DT AOD product in China displayed an overall good performance with high R 2 (0.87) and low root-mean-square error (0.23) against ground measurements. Moreover, the C6.1 DT AOD product was an overall improvement over C6, with greater correlation and lower uncertainty against ground measurements, especially for the North China Plain and Central China, although this was not the case for Western China. The improvement was also seasonal, being distinct in spring but less pronounced in winter, and negatively correlated with the level of air pollution. Furthermore, the analysis of DT AOD retrievals in different urbanized areas illustrated that the updated DT algorithm worked well in completely urbanized areas, where 97% of C6.1 DT AOD retrievals were an improvement over C6, while approximately 10% of C6.1 DT AOD retrievals were deteriorated in semiurbanized areas. Additionally, the DT AOD retrievals in areas with relatively low enhanced vegetation index and high surface reflectance were significantly improved, mitigating problems associated with the DT algorithm, further improving the reliability of MODIS DT AOD products. Plain Language Summary Modifications have been made to the Moderate Resolution ImagingSpectroradiometer (MODIS) aerosol optical depth (AOD) product to address inaccuracy over urban areas. This study evaluates the MODIS AOD product in comparison with the old version. Specifically, we evaluated products' performance over mainland China under different spatial distributions, seasons, and air quality for the period 2010-2017. It was demonstrated that the updated AOD retrieval algorithm show a visible improvement in highly urbanized areas, as well as areas with relatively low enhanced vegetation index and high surface reflectance. However, because the surface reflectance scheme was trained over the continental United States, it could not effectively describe the complex surfaces in Eastern and Western China, and therefore, further improvement is required in this regard.

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Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Cited by 27 publications

References 30 publications

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

SALSA2.0: The sectional aerosol module of the aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0

Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface

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