Mesoscale Variations of Tropospheric Aerosols*

Anderson, Theodore L.; Charlson, Robert J.; Winker, David M.; Ogren, J. A.; Holmén, Kim

doi:10.1175/1520-0469(2003)060<0119:mvota>2.0.co;2

Cited by 316 publications

(329 citation statements)

References 38 publications

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“…1 indicates that the two datasets show better agreement when the CALIPSO satellite passes within 10 km than when it Figure 1 (black circles) and the autocorrelations of aerosol plumes measured by Anderson et al (2003). Correlations that are significantly less than the Anderson et al (2003) values indicate that other factors are more important than spatial separation. passes at distances of 10-50 km.…”

Section: Spatial Collocationmentioning

confidence: 98%

Comparison of CALIPSO aerosol optical depth retrievals to AERONET measurements, and a climatology for the lidar ratio of dust

et al. 2012

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Abstract. We compared CALIPSO column aerosol optical depths at 0.532 µm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13 % relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97 % confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3 % and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27 %. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29 % and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of R = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 µm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt.

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Section: Spatial Collocationmentioning

confidence: 98%

Comparison of CALIPSO aerosol optical depth retrievals to AERONET measurements, and a climatology for the lidar ratio of dust

et al. 2012

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“…We performed the running mean average of the AERONET measurements in order to approximate a scale transformation from a point to the grid scale, since the direct horizontal average of the AERONET measurements cannot be conducted. Three-day running mean (± one day) was conducted for representing the horizontal average with respect to the similar values of spatial autocorrelation (0.4$0.6) at hundreds-km horizontal scale (100$250 km) to the one-day temporal autocorrelation based on the result of Anderson et al [2003]. The PDF resulted from the corrected AERONET measurements are shifted toward the higher AOD.…”

Section: Probability Distributionmentioning

confidence: 99%

Regional comparison and assimilation of GOCART and MODIS aerosol optical depth across the eastern U.S.

Matsui

Kreidenweis

Pielke

et al. 2004

Geophysical Research Letters

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This study compares aerosol optical depths (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and their integrated products with ground measurements across the eastern U.S. from March 1, 2000 to December 31, 2001. The Terra MODIS Level‐3 (collection 4) AOD at 0.55 μm has better correlation, but consistently overestimates the values of the Aerosol Robotic Network (AERONET) measurements. GOCART has small biases for a 22‐month integration, and slight positive biases appeared for the cold season. These results are also supported by the comparison with the IMPROVE (Interagency Monitoring of Protected Visual Environments) light extinction index. The optimal interpolation improves the daily‐scale RMSE from either MODIS or GOCART alone. However, the regional biases in the aerosol products constitute a major constraint to the optimal estimate of AOD.

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“…The temporal evolution of aerosol has received some interest before: Anderson et al (2003) used hourly nephelometer measurements of dry extinction to study timescales of aerosol evolution over one polluted and one remote site. They found timescales from 2 to 48 h, with aerosol often becoming uncorrelated over a day and a half.…”

Section: Introductionmentioning

confidence: 99%

The importance of temporal collocation for the evaluation of aerosol models with observations

2016

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Abstract. It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20-60 %).Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (aerosol optical thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made.We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation.Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.

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Mesoscale Variations of Tropospheric Aerosols*

Cited by 316 publications

References 38 publications

Comparison of CALIPSO aerosol optical depth retrievals to AERONET measurements, and a climatology for the lidar ratio of dust

Comparison of CALIPSO aerosol optical depth retrievals to AERONET measurements, and a climatology for the lidar ratio of dust

Regional comparison and assimilation of GOCART and MODIS aerosol optical depth across the eastern U.S.

The importance of temporal collocation for the evaluation of aerosol models with observations

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