How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies. Issue 9 (24th September 2020)

• Record Type: Journal Article
• Authors: Sayer, Andrew M.
• Is Part Of: Earth and space science. Volume 7:Issue 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-24
• Subjects: aerosol -- variogram -- validation -- time series -- AERONET; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2020EA001290
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Geophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers. Plain Language Summary: It is common to compare different data sets that are measuring the same thing against each other, to check how consistent they are. Satellite data sets measuring aerosols—small particles in the atmosphere such as dust, smoke, smog, or volcanic ash—are often compared with ground‐based data sets, which are very accurate but sparse. When comparing data sets like this, often the ground measurements are not made at exactly the same time as the satellite flies over. This time mismatch leads to a source of uncertainty in such comparisons, because a difference in the data could be from both measurement error and real variability. This study presents a method to quantify the typical level of variability of aerosols for different time mismatches, to allow for a better understanding of differences in such comparisons between different data sets. Key Points: Variograms can characterize size and time scales of aerosol variation These can inform matchup thresholds in data validation and intercomparisons Biomass burning regions tend to show fastest aerosol variation