An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

Abstract

Abstract. To better understand current uncertainties in the important observational constraint to climate models of AOD (Aerosol Optical Depth), we evaluate and intercompare fourteen satellite products, representing 9 different retrieval algorithm families using observations from 5 different sensors on 6 different platforms. The satellite products, super-observations consisting of 1° × 1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010, are evaluated with AERONET (AErosol RObotic NETwork) and MAN (Maritime Aerosol Network) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15–25 % (depending on product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1° × 1° grid cells. Up to 50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations, and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Changes in satellite retrievals of atmospheric composition over eastern China during the 2020 COVID-19 lockdowns

“…Around 78% of the Deep Blue retrievals are within the expected error range of ±0.05±0.20*AOD (Sayer et al, 2013). MODIS AOD data have been extensively used by the modeling and remote sensing scientific communities and inter-compared with a wide range of satellite AOD products (see Schutgens et al (2020) and references therein).…”

Validation of MODIS C6.1 and MERRA-2 AOD Using AERONET Observations: A Comparative Study over Turkey

“…The result obtained with MODIS Terra is consistent with that of Levy et al [43], who found significantly larger bias for this satellite sensor compared to MODIS Aqua. Therefore, the MODIS Aqua AOD was generalized to represent the MODIS dataset by Schutgens et al [44]. However, Georgoulas et al [45] found that MODIS Terra AOD exhibited the best agreement with AERONET over the eastern Mediterranean.…”

AEROCOM/AEROSAT AAOT & SSA study, part I: evaluation and intercomparison of satellite measurements

“…We need to allow some flexibility in the time separation between data (here 3 hours) to ensure sufficient numbers of collocated data pairs for further analysis. Schutgens et al (2020) showed that shorter time separations greatly limited the number of pairs but did not substantially alter the correlation of satellite AOD with AERONET. On the other hand, longer time separations appear to negatively affect the correlation of satellite AAOD with AERONET, see Fig.…”

“…Since an individual AERONET site cannot be expected to be representative for a 1 o × 1 o grid-box, satellite evaluation may be negatively affected. To select only sites with high representativity we use a list published in Kinne et al (2013) as described in Schutgens et al (2020), where we also describe some tests for its suitability (based on 14 satellite AOD products). The Kinne list was developed with the AERONET Direct Sun product (i.e.…”