Characterization of satellite-based proxies for estimating nucleation mode particles over South Africa

Abstract

Abstract. Proxies for estimating nucleation mode number concentrations and further simplification for their use with satellite data have been presented in Kulmala et al. (2011). In this paper we discuss the underlying assumptions for these simplifications and evaluate the resulting proxies over an area in South Africa based on a comparison with a suite of ground-based measurements available from four different stations. The proxies are formulated in terms of sources (concentrations of precursor gases (NO2 and SO2) and UVB radiation intensity near the surface) and a sink term related to removal of the precursor gases due to condensation on pre-existing aerosols. A-Train satellite data are used as input to compute proxies. Both the input data and the resulting proxies are compared with those obtained from ground-based measurements. In particular, a detailed study is presented on the substitution of the local condensation sink (CS) with satellite aerosol optical depth (AOD), which is a column-integrated parameter. One of the main factors affecting the disagreement between CS and AOD is the presence of elevated aerosol layers. Overall, the correlation between proxies calculated from the in situ data and observed nucleation mode particle number concentrations (Nnuc) remained low. At the time of the satellite overpass (13:00–14:00 LT) the highest correlation is observed for SO2/CS (R2 = 0.2). However, when the proxies are calculated using satellite data, only NO2/AOD showed some correlation with Nnuc (R2 = 0.2). This can be explained by the relatively high uncertainties related especially to the satellite SO2 columns and by the positive correlation that is observed between the ground-based SO2 and NO2 concentrations. In fact, results show that the satellite NO2 columns compare better with in situ SO2 concentration than the satellite SO2 column. Despite the high uncertainties related to the proxies calculated using satellite data, the proxies calculated from the in situ data did not better predict Nnuc. Hence, overall improvements in the formulation of the proxies are needed.

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

“…Very large particles, larger than several tens of micrometers, occur in very low concentrations and therefore contribute little to the radiance measured at TOA. Hence, the particles observed from satellites, during clear-sky conditions, are in the size range of about 100 nm to several tens of micrometers and thus do not include newly formed particles , unless proxies are used (Kulmala et al, 2011;Sundström et al, 2015). On the other hand, cloud droplets with sizes on the order of 10 µm do affect the thermal infrared (TIR) radiances, and this is used for cloud detection.…”

The Silk Road agenda of the Pan-Eurasian Experiment (PEEX) program

“…Optical instruments are not sensitive to particles smaller than about 100 nm (Sundström et al, 2015) and therefore the production of new aerosol particles through GTP cannot be directly observed. However, considering the processes a scheme was presented by Kulmala et al (2011) for the use of satellite data to detect nucleation on a global scale.…”

Forecasting ultrafine particle concentrations from satellite and in situ observations

“…3. Sundstrom et al [2015] sought to overcome the issues with highly uncertain satellite observations of SO 2 by instead using remote sensing estimates of columnar nitrogen dioxide (NO 2 ) as a proxy for sulfuric acid. Their proxy was developed and applied over Southern Africa and for nucleation mode concentrations (Dp < 30 nm).…”

“…We estimate UFP concentrations by using lagged estimates of UFP and concurrent satellite-based observations of aerosol optical properties, ultraviolet solar radiation flux, and trace gas concentrations, wherein an expectation maximization algorithm is used to impute missing values in the satellite observations. The resulting model of UFP (derived by using an autoregressive moving average model with exogenous inputs) explains 51 and 28% of the day-to-day variability in concentrations at two sites in eastern North America.Recent innovations in remote sensing technologies and data retrievals offer the potential for predicting UFP concentrations from satellite-borne radiometer measurements Kulmala et al, 2011a;Sundstrom et al, 2015]. The major methodological assumptions and primary findings from previous research in this field can be summarized as follows:1.…”

“…Recent innovations in remote sensing technologies and data retrievals offer the potential for predicting UFP concentrations from satellite-borne radiometer measurements Kulmala et al, 2011a;Sundstrom et al, 2015]. The major methodological assumptions and primary findings from previous research in this field can be summarized as follows:…”