Improving National Air Quality Forecasts with Satellite Aerosol Observations

Al-Saadi, J. A.; Szykman, J.; Pierce, R. Bradley; Kittaka, C.; Neil, D. O.; Chu, D. A.; Remer, L. A.; Gumley, Liam E.; Prins, E. M.; Weinstock, Lewis; MacDonald, Clinton P.; Wayland, Richard; Dimmick, Fred; Fishman, Jack

doi:10.1175/bams-86-9-1249

Cited by 315 publications

(223 citation statements)

References 18 publications

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“…Many successful applications of these data to global-and regional-scale questions are already presented in the literature. They range from assessing zonal mean or global aerosol short-wave forcing [37][38][39][40][41][42] and regional long-wave forcing [43], to improving aerosol forecasting through data assimilation [44,45], monitoring dust and pollution plume evolution [46,47] and air quality [48,49], mapping aerosol air mass type evolution [50], and validating aerosol transport model AOD simulations [51,52]. In each case, ways of exploiting the strengths of the MISR and MODIS data have been found, and in many cases, independent validation was performed specific to the application.…”

Section: Application Of Misr and Modis Aerosol Productsmentioning

confidence: 99%

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Kahn

Garay

Nelson

et al. 2011

Journal of Quantitative Spectroscopy and Radiative Transfer

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A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD) products, and reports much poorer agreement than that obtained by the instrument teams and others. We trace the reasons for the discrepancies primarily to differences in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account.

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Section: Application Of Misr and Modis Aerosol Productsmentioning

confidence: 99%

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Kahn

Garay

Nelson

et al. 2011

Journal of Quantitative Spectroscopy and Radiative Transfer

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“…Second, we do not know whether the tuning of the algorithm for the Indonesian region will hold for other situations of heavy smoke from wildfires. The Indonesian smoke proved to be relatively non-absorbing, which might be similar to smoke from peat burnings in other places 20 such as those from Alaskan fires in summer of 2004and 2005(Eck et al, 2009), but may be inappropriate for more absorbing smoke in other places and situations. Third, there is also a philosophical question of how fragmented should a global aerosol retrieval become?…”

Section: Discussionmentioning

confidence: 96%

“…The AErosol RObotic NETwork (AERONET) is a global aerosol-monitoring network of sun/sky observing radiometers that is commonly used as a benchmark for validating satellite-retrieved AOD (Holben et al, 1998;Levy et al, 2010Levy et al, , 2013Remer et al, 2005;Sayer et al, 2013;Reid, 2006, Shi et al, 2011). The instruments measure attenuated solar energy through two modes: direct sun and scanning sky (Holben et al, 1998).…”

Section: Aeronet Sun/sky Aerosol Productsmentioning

confidence: 99%

“…Satellite aerosol products are one important data source used by a wide range of disciplines in general studies of fire and smoke. Examples of applications of satellite products include: fire intensity estimation (Petrenko et al, 2012), aerosol transport modeling and visibility forecasts (Collins et al, 2001;Zhang et al, 2008), air quality prediction (Al-Saadi et al, 2005;Wang et al, 2003), and human health assessments (Van Donkelaar et al, 2010;Lighty et al, 2000). Satellite aerosol products, especially those derived from the 10 passive sensors, have difficulties retrieving aerosol signals when the smoke plumes are very optically thick (van Donkelaar et al, 2011;Zhang et al, 2016;Witte et al, 2011).…”

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confidence: 99%

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Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Shi¹,

Levy²,

Eck³

et al. 2018

Preprint

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Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-globalaveraged uncertainties in DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. 15Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval we created a research algorithm, and applied it to 80 appropriate MODIS granules during the event.Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS 20 retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5° grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm. 25

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“…The study of aerosols or any other atmospheric pollutant dispersion patterns relies on spatial and temporal data series obtained from air quality monitoring networks or measurement campaigns (Al-Saadi et al 2005). Such monitoring programs entail a high implementation and maintenance costs and are limited in terms of spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

Hadjimitsis

2009

Air Qual Atmos Health

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The aim of this study is to present and apply the proposed algorithm on archived time series Landsat TM over an urban area in the vicinity of Heathrow Airport (UK) acquired in 1984-1986 and to two up-to-dated Landsat TM images in the vicinity of Paphos Airport in Cyprus acquired in July-August 2008. The monitoring of aerosol concentrations becomes a high environmental priority particularly in urban areas. The proposed algorithm has been developed to allow the quantification of the aerosol optical thickness (AOT) over land. The algorithm compares multitemporal satellite data sets and evaluates radiometric alterations due to the optical atmospheric effects of aerosols. Novel features of this algorithm which is based on the application of radiative transfer calculations are the inclusion of applying iteration procedures for selecting the suitable object for determining the aerosol optical thickness and the automatic division into working grid cells. AOT retrieval from Landsat TM band 1 images of Cyprus has been crossvalidated satisfactorily by comparing AOT values found from handheld MICROTOPS II sun photometer.

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Improving National Air Quality Forecasts with Satellite Aerosol Observations

Abstract: Satellite aerosol observations-which are particularly helpful in tracking long-range transport aloft--can overcome some of the limitations of surface monitoring networks and enhance daily air quality forecasts associated with particle pollution.

Cited by 315 publications

References 18 publications

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Response to “Toward unified satellite climatology of aerosol properties. 3. MODIS versus MISR versus AERONET”

Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

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