An Improved Method for Estimating Aerosol Optical Thickness from Artificial Light Sources Observed by the Visible/Infrared Imaging Radiometer Suite Day/Night Band

Using Visible/Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data, a method, dubbed the “variance method”, is developed for retrieving nighttime aerosol optical thickness (AOT) values based on the dispersion of radiance values above an artificial light source. An improvement of a previous algorithm, this updated method derives a semi-quantitative indicator of nighttime AOT using artificial light sources. Nighttime AOT retrievals from the newly developed method are inter-compared with an interpolated value from late afternoon and early morning ground observations from four AErosol RObotic NETwork (AERONET) sites as well as column-integrated from one High Spectral Resolution LiDAR (HSRL) site at Huntsville, AL during the NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, providing full diel coverage. This method does not account for lunar reflectance from either the surface or the aerosol layer. Sensitivity tests do no indicate large systematic or random errors associated with lunar illumination. VIIRS AOT retrievals yield a coefficient of determination (r^2) of 0.60 and a root-mean-squared-error (RMSE) of 0.18 when compared against straddling daytime-averaged AERONET AOT values. Preliminary results suggest that artificial light sources can be used for estimating regional and global nighttime aerosol distributions in the future

On the Detection and Monitoring of the Transport of an Asian Dust Storm Using Multi-Sensor Satellite Remote Sensing

Dynamical and physical features of a long range transported dust event originating in China affecting Korea early March 2008 are examined using an integrative multi-sensor and multi-algorithm approach. Aerosol loadings and their size mode were analyzed over both ocean and land surfaces using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), employing standard dark target (DT) and deep blue (DB) algorithms, and the Ångström exponent (AE). Anthropogenic absorbing aerosols and smoke were found to be significant over the Indochina Peninsula, the Philippines and southern China, while a mixture of dust and pollution were predominant over central to northern China, as identified by the AE analysis and the Multi-angle Imaging SpectroRadiometer (MISR) spherecitiy and plume height. Remarkable aerosol absorptions in both the near ultraviolet (UV) and the visible were spread over central, central western and northern China, probably due to aerosol mixtures including desert dust and industrial pollution as well as smoke from biomass burning as evidenced from the Ozone Monitoring Instrument (OMI). Long range transport is validated as dust storm reached up to 4–5 km vertically and a mixed cloud layer was identified over the Yellow Sea as disclosed by the vertical structure of dust aerosols as well as observed aerosols subtypes from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The real time detection and monitoring of the dust outbreak and its subsequent evolution are available through the infrared optical depth index (IODI), developed from the MTSAT-1R geostationary satellite imager

Soil Moisture Recognition and the Spatial Distribution of Storm Activity in the Mojave Desert Using High-Resolution ASTER and MODIS Imagery for Thermophysical Mapping

Climate models suggest that the Mojave Desert ecoregion is vulnerable to becoming drier in the future, and as the human population grows and development increases, environmental stresses will likely increase. Determining the spatial distribution and variation of soil moisture on a regional scale is an essential component to climate change, hydrologic, and habitat analyses. Soil permeability and sediment stability are characteristics that have been shown to be measurable from remote sensing observations. The primary objective of this project is to map the mechanical composition of the surface materials in the Mojave Desert ecoregion with implications for soil permeability, sediment stability, and soil moisture. We are using advanced mapping techniques to determine the surface mechanical compositions of the Mojave, with data provided by the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), which provides the spatial resolution necessary to map the composition and thermal properties of arid surfaces and is well suited for mapping the spatial distribution of soil moisture. A full-resolution mosaic of thermal infrared (TIR) and visible to near infrared (VNIR) ASTER images has been constructed for the entire Mojave Desert for mapping surface components. With a 16-day repeat cycle, ASTER provides the high resolution mapping perspective, but lacks the temporal sampling to adequately quantify changes over days to weeks. Moderate Resolution Imaging Spectroradiometer (MODIS) data provides the temporal resolution needed to determine seasonal variations, although at a coarser spatial resolution. Our approach for mapping the Mojave Desert region involves using both ASTER and MODIS to provide the ideal spatial and temporal sampling to map individual storms and their effects on the seasonal conditions of the surface. The viability of the Mojave Desert ecosystem relies solely on infrequent storms and their temporal and spatial distribution over local regions and varied landscapes. Mapping the distribution of individual wetting events with regard to the geomorphology of the region can be a useful component for modeling potential changes as a function of climate change and human development providing a better understand of how random weather events contribute to the hydrologic cycle in the Mojave and potentially other arid regions around the world

Characterising Saharan Dust Sources and Export using Remote Sensing and Regional Modelling

The PhD-thesis aims to characterise the Saharan dust cycle at diffent seasons using satellite remote sensing techniques and regional modelling studies. A dust index based on 15-minute infrared satellite measurements provided by the SEVIRI instrument onboard the Meteosat Second Generation (MSG) satellite is used to infer spatio-temporal charcteristics of dust sources north of 5°N over Africa since March 2006. The spatial distribution of dust sources points towards the importance of endorehic drainage systems in mountain areas. The temporal distribution of the time-of-day when dust mobilisation starts shows maximum activity during local morning hours, pointing towards the role of the breakdown of the nocturnal low-level jet. Details of the role and ability of the low-level jet breakdown for dust entrainment were studied using regional modelling. Furthermore, the seasonal dust export towards the tropical North Atlantic is considered using regional modelling

Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia

This paper describes an operational retrieval algorithm for the sand/dust storm (SDS) from FY-2C/S-VISSR (Stretched-Visible and Infrared Spin-Scan Radiometer) developed at the National Satellite Meteorological Center (NSMC) of China. This algorithm, called Dust Retrieval Algorithm based on Geostationary Imager (DRAGI), is based on the optical and radiative physical properties of SDS in mid-infrared and thermal infrared spectral regions as well as the observation of all bands in the geostationary imager, which include the Brightness Temperature Difference (BTD) in split window channels, Infrared Difference Dust Index (IDDI) and the ratio of middle infrared reflectance to visible reflectance. It also combines the visible and water vapor bands observation of the geostationary imager to identify the dust clouds from the surface targets and meteorological clouds. The output product is validated by and related to other dust aerosol observations such as the synoptic weather reports, surface visibility, aerosol optical depth (AOD) and ground-based PM<sub>10</sub> observations. Using the SDS-IDD product and a data assimilation scheme, the dust forecast model CUACE/Dust achieved a substantial improvement to the SDS predictions in spring 2006

A Synoptic- and Remote Sensing-based Analysis of a Severe Dust Storm Event over Central Asia

Published by [Verlag nicht ermittelbar], Taina