A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

Castellanos, Patricia; Silva, Arlindo da; Darmenov, Anton; Buchard, Virginie; Govindaraju, Ravi; Ciren, Pubu; Kondragunta, Shobha

doi:10.3390/atmos10010002

Cited by 15 publications

(9 citation statements)

References 68 publications

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“…The GEOS-5 NR is driven by prescribed sea-surface temperature and sea-ice, daily volcanic and biomass burning emissions, as well as highresolution inventories of anthropogenic sources. A description of the GEOS-5 model configuration used for the Nature Run can be found in Putman et al (2014), while results from a validation exercise appear in Gelaro et al (2015) and Castellanos et al (2019).…”

Section: Modis Channels As Permentioning

confidence: 99%

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Wind

Silva

Meyer

et al. 2021

Preprint

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Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE = 0.107. When only near-nadir pixels were considered (view zenith angle within +/−20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset −0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

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Section: Modis Channels As Permentioning

confidence: 99%

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Wind

Silva

Meyer

et al. 2021

Preprint

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“…Global sea-salt AOT is similar to MERRA within 10%. (Note that Castellanos et al (2019) derived global rescaling factors for aerosol speciated AOD in G5NR. How such scaling factors will affect AAOD is unknown.…”

Section: Geos-5 Nature Runmentioning

confidence: 99%

Site representativity of AERONET and GAW remotely sensed AOT and AAOT observations

Schutgens

2019

Preprint

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Abstract. Remote sensing observations from the AERONET and GAW networks are intermittent in time and have a limited field-of-view. A global high-resolution simulation (GEOS5 Nature Run) is used to conduct an Observing System Simulation Experiment (OSSE) for AERONET and GAW observations of AOT and AAOT and estimate the spatio-temporal representativity of individual sites for larger areas (from 0.5° to 4° in size). G5NR and the OSSE are evaluated and shown to have sufficient skill, although daily AAOT variability is significantly underestimated while the frequency of AAOT observations is over-estimated (both resulting in an under-estimation of temporal representativity errors in AAOT). Yearly representation errors are provided for a host of scenarios: varying grid-box size, temporal collocation protocols, and site altitudes are explored. Monthly representation errors are shown to correlate strongly throughout the year, with a pronounced annual cycle. The collocation protocol for AEROCOM model evaluation (using daily data) is shown to be sub-optimal and the use of hourly data advocated instead. A previous subjective ranking of site spatial representativity (Kinne et al., 2013) is analysed and a new objective ranking proposed. Several sites are shown to have yearly representation errors in excess of 40 %. Lastly, a recent suggestion by Wang et al. (2018) that AERONET observations of AAOT suffer a positive representation bias of 30 % globally is analysed and evidence is provided that this bias is likely an overestimate (the current paper finds 4 %) due methodological choices.

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“…The G5NR is a global 7 km non-hydrostatic mesoscale simulation based on the Ganymed version of GEOS-5 (Putman et al, 2014). The aerosol component is described and evaluated by Castellanos et al (2018). This is a two-year (May 2005(May -2007 simulation, and while some factors (e.g.…”

Section: Geos-5 Nature Runmentioning

confidence: 99%

“…Buchard et al (2015). Data from the simulated year 2006 only are used; Castellanos et al (2018) noted that G5NR aerosol fields were initialised to zero, and so did not use the initial six months of the simulation to ensure that equilibrium had been reached. The final six months of the simulation are also discarded here to ensure that each calendar month has equal representation in the analysis.…”

Section: Geos-5 Nature Runmentioning

confidence: 99%

How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

Sayer¹,

Knobelspiesse²

2019

Preprint

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<p><strong>Abstract.</strong> Many applications of geophysical data &#8211; whether from surface observations, satellite retrievals, or model simulations &#8211; rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily, monthly) resolution than the highest available from the technique. Almost all these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for Normally-distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to Lognormal, for which geometric mean and standard deviation would be more appropriate. This study presents a method to assess whether a given sample of data are more consistent with an underlying Normal or Lognormal distribution, using the Shapiro-Wilk test, and tests AOD frequency distributions on spatial scales of 1&#176; and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse; MISR and MODIS represent different satellite retrieval techniques and sampling; as a model simulation, G5NR is spatiotemporally complete. As time scales increase from days to months to seasons, data become increasingly more consistent with Lognormal than Normal distributions, and the differences between arithmetic and geometric mean AOD become larger, with geometric mean becoming systematically smaller. Assuming Normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is sufficiently small (<&#8201;0.01) as to be unimportant for many applications, especially on daily timescales. However, in continental outflow regions and near source regions over land, and on monthly or seasonal time scales, the difference is frequently larger than the Global Climate Observation System (GCOS) goal uncertainty on a climate data record (the larger of 0.03 or 10&#8201;%). This is important because it shows the sensitivity to averaging method can and often does introduce systematic effects larger than the total goal GCOS uncertainty. Using three well-studied AERONET sites, the magnitude of estimated AOD trends is shown to be sensitive to the choice of arithmetic vs. geometric means, although the signs are consistent. The main recommendations from the study are that (1) the distribution of a geophysical quantity should be analysed in order to asses how best to aggregate it; (2) ideally AOD aggregates such as satellite level 3 products (but also ground-based data and model simulations) should report geometric mean or median rather than (or in addition to) arithmetic mean AOD; and (3) as this is unlikely in the short term due to the computational burden involved, users can calculate geometric mean monthly aggregates from widely-available daily mean data as a stopgap, as daily aggregates are less sensitive to the choice of aggregation scheme than those for monthly or seasonal aggregates. Further, distribution shapes can have implications for the validity of statistical metrics often used for comparison and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.</p>

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A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

Cited by 15 publications

References 68 publications

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Site representativity of AERONET and GAW remotely sensed AOT and AAOT observations

How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

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