Abstract. Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso-and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.
Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth's surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999-2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and nearinfrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.
An extreme biomass burning event occurred in Indonesia from September through October 2015 due to severe drought conditions, partially caused by a major El Niño event, thereby allowing for significant burning of peatland that had been previously drained. This event had the highest sustained aerosol optical depths (AODs) ever monitored by the global Aerosol Robotic Network (AERONET). The newly developed AERONET Version 3 algorithms retain high AOD at the longer wavelengths when associated with high Ångström exponents (AEs), which thereby allowed for measurements of AOD at 675 nm as high as approximately 7, the upper limit of Sun photometry. Measured AEs at the highest monitored AOD levels were subsequently utilized to estimate instantaneous values of AOD at 550 nm in the range of 11 to 13, well beyond the upper measurement limit. Additionally, retrievals of complex refractive indices, size distributions, and single scattering albedos (SSAs) were obtained at much higher AOD levels than possible from almucantar scans due to the ability to perform retrievals at smaller solar zenith angles with new hybrid sky radiance scans. For retrievals made at the highest AOD levels the fine‐mode volume median radii were ~0.25–0.30 micron, which are very large particles for biomass burning. Very high SSA values (~0.975 from 440 to 1,020 nm) are consistent with the domination by smoldering combustion of peat burning. Estimates of the percentage peat contribution to total biomass burning aerosol based on retrieved SSA and laboratory measured peat SSA were ~80–85%, in excellent agreement with independent estimates.
Direct and indirect aerosol effects are still one of the largest uncertainties related to the Earth energy budget, especially in a wild and remote region like South-East Asia, where ground-based measurements are still difficult and scarce, while endemic cloudy skies make difficult active and passive satellite observations. In this preliminary study, we analyzed and quantitatively assessed the differences between monsoon and inter-monsoon seasons, in terms of radiative effects at surface and columnar heating rate, of clear-sky biomass burning aerosols (no clouds) using ground-based lidar observations obtained with a 355 nm elastic lidar instrument, deployed since 2012 at the Physics Department of Universiti Sains Malaysia (USM). The model-based back-trajectory analysis put in evidence that, during the monsoon seasons (November–March and June–September), the air masses advected towards the observational site transit over active fire hotspot regions, in contrast with the inter-monsoon season. In between the monsoon seasons (April–May, October), the atmosphere over Penang is constituted by local background urban aerosols that originate from road traffic emissions, domestic cooking, and industrial plants emissions. The analysis was carried out using the vertically-resolved profiles of the seasonal averaged aerosol optical properties (monsoon vs. inter-monsoon seasons), e.g., the atmospheric extinction coefficient, to evaluate the seasonal surface aerosol radiative effect and column heating rate differences through the Fu–Liou–Gu (FLG) radiative transfer model. The results put in evidence that the biomass burning advection during the monsoon season (especially during the South West monsoon from June to September) lowers the noon daytime incoming solar shortwave solar radiation reaching the Earth surface with respect to the local background conditions by 91.5 W/m2 (114–69 W/m2). The aerosols also lead to an averaged heating in the first kilometer of the atmosphere of about 4.9 K/day (6.4–3.4 W/m2). The two combined effects, i.e., less absorbed energy by Earth surface and warming of the first kilometer of the boundary layer, increase the low-level stability during monsoon seasons, with a possible reduction in cloud formation and precipitation. The net effect is to exacerbate the haze episodes, as the pollutants rest trapped into the boundary layer. Besides these considerations, the lidar measurements are of great interest in this particular world region and might be used for cal/val of the future space missions, e. g., Earthcare.
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