ALBERT

Description: A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1°×1° and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets. A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that 〉75% of the MSG-retrieved AOD at 0.6 and 0.8 μm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15 τ and a high correlation coefficient (R〉0.86). The AOD datasets derived using the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval and analysis, and the analysis and validation of reflectance will be given in a following paper.

Description: Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of global regions, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by an order of magnitude. Initial measurements indicate that the AOD column to PM2.5 ratio is driven temporally primarily by the vertical profile of aerosol scattering; and spatially by a~ more complex interaction of the aerosol scattering vertical profile and by the mass scattering efficiency.

Description: This paper presents a validation and confutation analysis using the methods of the robust satellite data analysis technique (RST) to detect seismic anomalies within the bi-angular Advanced Along-Track Scanning Radiometer (AATSR) data based on spatial/temporal continuity analysis. The distinguishing feature of our method is that we carried out a comparative analysis of seismic anomalies from bi-directional observation, which could help understanding seismic thermal infrared (TIR) anomalies. The proposed method has been applied to analyse bi-angular AATSR gridded brightness temperature data with longitude from 5 to 25° E and latitude from 35 to 50° N associated with the earthquake that occurred in Abruzzo, Italy, on 6 April 2009, and a full data set of 7 yr data from 2003 to 2009 during the months of March and April has been analysed for validation purposes. Unperturbed periods (March–April 2008) have been considered for confutation analysis. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, along with the analysed results, a number of anomalies could be associated with possible seismic activities, which follow the same time and space. Therefore, we conclude that the anomalies observed from 29 March 2009 to 5 April 2009, about eight days before the Abruzzo earthquake, could be earthquake anomalies.

Description: For the interpretation of the storm event-induced landslide distribution for an area, deterministic methods are frequently used, while a region's landslide susceptibility is commonly predicted via a statistical approach based upon multi-temporal landslide inventories and environmental factors. In this study we try to use an event-based landslide inventory, a set of environmental variables and a triggering factor to build a susceptibility model for a region which is solved using a multivariate statistical method. Data for shallow landslides triggered by the 2002 typhoon, Toraji, in central western Taiwan, are selected for training the susceptibility model. The maximum rainfall intensity of the storm event is found to be an effective triggering factor affecting the landslide distribution and this is used in the model. The model is built for the Kuohsing region and validated using data from the neighboring Tungshih area and a subsequent storm event – the 2004 typhoon, Mindulle, which affected both the Kuohsing and the Tungshih areas. The results show that we can accurately interpret the landslide distribution in the study area and predict the occurrence of landslides in the neighboring region in a subsequent typhoon event. The advantage of this statistical method is that neither hydrological data, strength data, failure depth, nor a long-period landslide inventory is needed as input.

Description: A hydraulic tomography survey (HTS) is a conceptually improved technique that has been recognized to be efficient for estimating high-resolution aquifer parameters. Based on the concept of HTS, this study presents a modified stochastic inverse model for estimating hydraulic conductivity (K) and specific yield (Sy) in shallow and highly permeable unconfined aquifers. A well field with 15 fully screened wells was developed for the purpose of model implementations. In this study a synthetic example was first employed to assess the accuracy of the inverse model. We then implemented the model to field-scale, cross-hole injection tests in a shallow and highly permeable unconfined aquifer near the middle reach of the Wu River in central Taiwan. To assess the effect of constant head boundary conditions on the estimation results, two additional modeling domains were evaluated based on the same field data from the injection tests. Results for the synthetic example show that the modified inverse model can reproduce well the predefined geologic features of the unconfined aquifer. The inverse model can estimate accurately the ln K patterns and magnitudes. However, slightly fewer details of the ln Sy field are obtained due to the insensitivity of transient hydraulic stresses for specified sampling times. Model implementations of field-scale injection tests show that the model can estimate ln K and ln Sy fields with high spatial resolution. The estimated K and Sy values for the test site vary by one order of magnitude, indicating a relatively homogeneous aquifer for the tested well field. Results based on three different modeling domains show similar patterns and magnitudes of ln K and ln Sy near the well locations. This result suggests that the case with domain 40 m × 20 m should be sufficient for the injection tests at the well field.

Description: The transient hydraulic tomography survey (THTS) is a conceptually improved technique that efficiently estimates detailed variations in aquifer parameters. Based on the concept of the THTS, we developed a geostatistical inverse model to characterize saturated hydraulic conductivity (K) and the specific yield (Sy) in transient and unconfined aquifer systems. In this study, a synthetic example was first used to assess the accuracy of the developed inverse model. Multiple random K and Sy realizations with different variances of natural logarithm of K (lnK) were generated and systematically compared to evaluate the effects of joint inversion on K estimations. The model was implemented in field-scale, cross-hole injection tests in a shallow and highly permeable unconfined aquifer near the middle reaches of the Wu River in central Taiwan. To assess the effect of constant head boundary conditions on the estimation results, two additional modeling domains were evaluated on the basis of the same field data from the injection tests. The results of the synthetic example showed that the proposed inverse model can effectively reproduce the predefined K patterns and magnitudes. However, slightly less detail was obtained for the Sy field based on the sampling data from sequential transient hydraulic stresses. The joint inversion by using transient head observations could slightly decrease the accuracy of K estimations. The model implementation for field-scale injection tests showed that the model can estimate K and Sy fields with detailed spatial variations. Estimation results showed a relatively homogeneous aquifer for the tested well field. Results based on the three modeling domains showed similar patterns and magnitudes of K and Sy near the well locations. These results indicated that the THTS is relatively insensitive to artificially drawn boundary conditions even under transient conditions.

Description: A series of wildfires broke out in western Russia starting in late July of 2010. Harmful particulates and gases released into the local Russian atmosphere have been reported, as have possible negative consequences for the global atmosphere. In this study, an extremely hazy area and its transport trajectory on Russian wildfires were analysed using aerosol optical depth (AOD) images retrieved via the synergy method from Moderate Resolution Imaging Spectroradiometer (MODIS) data. In addition, we used trace gases (NO2 and SO2) and CO2 products measured using Ozone Monitoring Instrument (OMI) data, vertical distribution of AOD data retrieved from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the mass trajectory analyses, synoptic maps from a HYSPLIT model simulation and ground-based data, including AERONET (both AOD and Ångström exponent) data and PM2.5. First, an Optimal Smoothing (OS) scheme was used to develop more precise and reliable AOD data based on multiple competing predictions made using several AOD retrieval models; then, integrated AOD and PM2.5 data were related using a chemical transport model (GEOS-Chem), and the integrated AOD and visibility data were related using a 6S model. The results show that the PM2.5 concentration is 3–5 times the normal amount based on both satellite data and in situ values with peak daily mean concentrations of approximately 500 μg m−3. Also, the visibility of many parts of Russia, even Moscow, was less than 100 m; in some areas, the visibility was less than 50 m. Additionally, the possible impact on neighbouring countries due to the long-transport effect was also analysed during 31 July and 15 August 2010. A comparison of the satellite aerosol products and ground observations from the neighbouring countries suggests that wildfires in western Russian have had little impact on most European and Asian countries, the exceptions being Finland, Estonia, Ukraine and Kyrgyzstan. However, a possible impact on the Arctic region was also identified; such an effect would have a serious influence on the polar atmospheric environment and on animals such as polar bears.

Description: A novel approach for the joint retrieval of aerosol optical depth (AOD) and surface reflectance, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a time series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmospheres two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the surface reflectance. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor (g). We assume one aerosol type for a grid of 1° × 1° and the surface reflectance changes little between two consequent scans. A k approximation was used in the inversion to find the best solution of atmospheric properties and surface reflectance. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets. Also, it is assumed that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two consequent scans can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. A detailed analysis of the retrieval results show that it is suitable for AOD retrieval over land. Six Aerosol Robotic Network (AERONET) sites with different surface types were used for detailed analysis and 42 other AERONET sites were used for validation. From 445 collocations representing stable and homogeneous aerosol type, we found that 〉75% of MSG-retrieved AOD values compared to AERONET observed values with an error envelope of ±0.05 ± 0.15τ and a high correlation (R 〉 0.86). The AOD datasets derived using the TS method with SEVIRI data was also compared with collocated AOD products derived from the NASA TERRA and AQUA MODIS data using the dark dense vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. The AOD values derived compare favourably.

Description: Nine years of daily aerosol optical depth (AOD) measurements have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over China for the period from August 2002 to August 2011, comprising AODs at 470, 550, and 660 nm. Then, the variation over China over the nine years was determined from the derived AOD data. Preliminary daily results show the agreement between the Aerosol Robotic Network (AERONET) AOD data and the derived AOD data. From 1219 daily collocations, representing mutually cloud-free conditions, we find that more than 54% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 20%. From 222 monthly averaged collocations, representing mutually cloud-free conditions, we find that more than 63% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 15% and more than 70% within an expected error envelop of 20%. In addition, the long-term SRAP AOD dataset has been implemented in analysing case studies involving dust storms, haze and the characteristics of AOD variation over China over the past nine years. It was found that areas in China with high AOD values generally appear in the Inner Mongolia, the North China Plain, Tarim Basin, the Sichuan Basin, the Tibetan Plateau and the middle and lower reaches of the Yangtze River and area with low AOD values generally appear in the Fujian Province, the Yungui Plateau, and northeast plain. The seasonal averaged AOD results indicate that AOD values generally reach their maximum in spring and their minimum in winter. The yearly mean and monthly mean SRAP AOD were also used to study the spatial and temporal aerosol distributions over China. The results indicate that the AOD over China exhibited no obvious change. Monthly averaged AOD in August in Beijing experienced one decreasing processes from 2006 to 2010, especially after 2007. The monthly mean AOD decreased from 0.46 in 2007 to 0.29 in 2010. SRAP AODs were used to study one haze case and dust case. Combining AOD data from the SRAP AOD dataset and HYSPLIT model can forecast the transport of haze. SRAP AOD data are also sensitive enough to reflect the occurrence and intensity of dust weather. Thus, the SRAP AOD dataset can be used to precisely reflect the spatial distribution, concentration distribution and transmission path of dust.