Retrieving aerosol in a cloudy environment: aerosol product availability as a function of spatial resolution

Remer, L. A.; Mattoo, S.; Levy, Robert C.; Heidinger, Andrew K.; Pierce, R. Bradley; Chin, Mian

doi:10.5194/amt-5-1823-2012

Cited by 62 publications

(84 citation statements)

References 34 publications

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“…Even partly cloudy scenes are problematic because the highly reflective cloudy fraction contributes disproportionately to the total backscattered radiation from the pixel. A major advantage of finer pixel resolution is thus to increase the probability of clear-sky scenes (Remer et al, 2012). The GOSAT retrievals exclude cloudy scenes by using a simultaneous retrieval of the oxygen column in the 0.76 µm A band.…”

Section: Tir Swirmentioning

confidence: 99%

Satellite observations of atmospheric methane and their value for quantifying methane emissions

et al. 2016

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Abstract. Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100-1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.

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Section: Tir Swirmentioning

confidence: 99%

Satellite observations of atmospheric methane and their value for quantifying methane emissions

et al. 2016

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“…While not meeting requirements of 4 times daily observation, the EOS system observes aerosol over nearly the entire globe, with spatial and temporal resolution sufficient to study the regional and local scales. One of the primary instruments of EOS is the Moderate Resolution Imaging Spectroradiometer (MODIS; Salomonson et al, 1989), which has been flying on Terra since December 1999 and on Aqua since May 2002. From MODIS-observed reflectance in the visible (VIS), nearinfrared (NIR) and shortwave-IR (SWIR) wavelength regions, the so-called "dark-target" (DT) aerosol algorithm (e.g., Levy et al, 2007aLevy et al, , b, 2010Levy et al, , 2013Remer et al, 2005Remer et al, , 2008 has been retrieving total aerosol optical depth (AOD) at 0.55 µm over land and ocean at nominal 10×10 km spatial resolution.…”

Section: R C Levy Et Al: Long-term Global Aerosol Optical Depth Rementioning

confidence: 99%

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

et al. 2015

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Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 µm AOD and 0.55/0.86 µm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODISlike (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products.Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1 • × 1 • AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ∼ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

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“…The MODIS Dark Target aerosol algorithms are well documented in the literature Tanré et al, 1997;Remer et al, 2005Remer et al, , 2012Levy et al, 2007aLevy et al, , b, 2013). …”

Section: Modis Aerosol Retrieval At 10 Km and 3 Km Resolutionmentioning

confidence: 99%

“…The algorithm avoids clouds, ocean sediments, glint, snow, ice, inland water and bright surfaces. Clouds are identified by means of spatial variability, ratio and threshold tests with additional assistance from specific tests from the MOD/MYD35 product (Martins et al, 2002;Gao et al, 2002;Frey et al, 2008;Ackerman et al, 2010;Remer et al, 2012). Sediments are identified in the ocean using spectral tests and sun glint is eliminated through the use of a 40 • glint mask.…”

Section: A Remer Et Al: Modis 3 Km Aerosol Product: Algorithm Anmentioning

confidence: 99%

MODIS 3 km aerosol product: algorithm and global perspective

et al. 2013

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After more than a decade of producing a nominal 10 km aerosol product based on the dark target method, the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol team will be releasing a nominal 3 km product as part of their Collection 6 release. The new product differs from the original 10 km product only in the manner in which reflectance pixels are ingested, organized and selected by the aerosol algorithm. Overall, the 3 km product closely mirrors the 10 km product. However, the finer resolution product is able to retrieve over the ocean closer to islands and coastlines, and is better able to resolve fine aerosol features such as smoke plumes over both ocean and land. In some situations, it provides retrievals over entire regions that the 10 km product barely samples. In situations traditionally difficult for the dark target algorithm such as over bright or urban surfaces, the 3 km product introduces isolated spikes of artificially high aerosol optical depth (AOD) that the 10 km algorithm avoids. Over land, globally, the 3 km product appears to be 0.01 to 0.02 higher than the 10 km product, while over ocean, the 3 km algorithm is retrieving a proportionally greater number of very low aerosol loading situations. Based on collocations with ground-based observations for only six months, expected errors associated with the 3 km land product are determined to be greater than that of the 10 km product: ± 0.05 ± 0.20 AOD. Over ocean, the suggestion is for expected errors to be the same as the 10 km product: ± 0.03 ± 0.05 AOD, but slightly less accurate in the coastal zone. The advantage of the product is on the local scale, which will require continued evaluation not addressed here. Nevertheless, the new 3 km product is expected to provide important information complementary to existing satellite-derived products and become an important tool for the aerosol community

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Retrieving aerosol in a cloudy environment: aerosol product availability as a function of spatial resolution

Cited by 62 publications

References 34 publications

Satellite observations of atmospheric methane and their value for quantifying methane emissions

Satellite observations of atmospheric methane and their value for quantifying methane emissions

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

MODIS 3 km aerosol product: algorithm and global perspective

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