The Atmospheric moNitoring to Assess the availability of Optical LInks through the Atmosphere (ANAtOLIA) is a station developed in the framework of a project funded by the European Space Agency which aims to ground-sites selection and assess their availabilities for optical links through the atmosphere. In addition to cloud cover, space-to-ground optical communications are limited by aerosols and atmospheric turbulence. Therefore, we are developing in the framework of the ANAtOLIA project, an innovative and efficiency instrumentation and studies to specify, accurately measure, analyze, characterize, and ultimately predict critical atmospheric parameters for the purposes of the selection of the Optical Ground Station (OGS) sites and the evaluation of their availability. The main objectives of ANAtOLIA project are to design, manufacture, procure and assembly a self-standing and autonomous ground support equipment, comprising cloud, aerosol and turbulence monitoring to deliver precise measurements of the atmosphere transmission. Then, to install and commission of these atmosphere monitors at selected ground locations in ESA member states or in their vicinity and to record continuously local cloud, aerosol information and atmospheric turbulence conditions for 24 months. The last objective is to correlate these local ground measurements with data available from other sources of atmospheric conditions. The main goal of these correlations is to improve knowledge of the optical link availability for selected OGS locations and to carry out a long-term validation of the optical link availability prediction methods. ANAtOLIA is a compact 24h mobile station consisting of the Generalized Monitor of Turbulence (GMT), the Reuniwatt Sky Insight camera and the Cimel photometer CE318-T.

ANAtOLIA (Atmospheric monitoring to Assess the availability of Optical Links through the Atmosphere) is a European Space Agency project aimed at selecting sites for optical communication in the atmosphere. The main monitored parameters are cloud cover, aerosol in relation to atmospheric turbulence aimed at monitoring and forecasting the influence of aerosol and cloud cover in reducing optical communication through the atmosphere in selected sites by ESA. In this work, a novel algorithm that uses both the Pearson correlation coefficient and Fourier analysis is used to assess such influences. Aerosol and cloud cover data are obtained from ground stations and satellite over Calern (France), Catania (Italy), Cebreros (Spain), and Lisbon (Portugal). The novel algorithm provides a preliminary long-, medium-, and short-term aerosol-cloud interaction for these four candidate sites, obtaining respectively the variability, the seasonal, and hourly trend of the aerosol concentration; the main medium-term periodicities of aerosols as clouds precursors; the short-term correlation between morning-afternoon aerosol concentration. The use of aerosols as a precursor parameter of cloud cover through a Fourier analysis, makes the algorithm versatile and usable for all sites of optical communication and astronomical importance in which optical transparency is a fundamental requirement, and therefore it is a potential tool to be developed to implement forecasting models.

ANAtOLIA (Atmospheric moNitoring to Assess the availability of Optical LInks through the Atmosphere) is a project funded by the European Space Agency and aims to ground-sites selection and assess their availabilities for optical links through the atmosphere. In addition to cloud cover, space-to-ground optical communications are limited by aerosols and atmospheric turbulence. Therefore, we are developing in the framework of the ANAtOLIA project, an innovative and efficiency instrumentation and studies to specify, accurately measure, analyze, characterize, and ultimately predict critical atmospheric parameters for the purposes of the selection of the OGS (Optical Ground Station) sites and the evaluation of their availability. The main mission objectives of ANAtOLIA are to design, manufacture, procure and assembly a self-standing and autonomous ground support equipment, comprising cloud, aerosol and turbulence monitoring to deliver precise measurements of the atmosphere transmission. Secondary study goals are to install and commission of these atmosphere monitors at selected ground locations in ESA member states or in their vicinity and to record continuously local cloud, aerosol information and atmospheric turbulence conditions for 24 months. The last objective is to correlate these local ground measurements with data available from other sources of atmospheric conditions. The main goal of these correlations is to improve knowledge of the optical link availability for selected OGS locations and to carry out a long-term validation of the optical link availability prediction methods. This compact 24h mobile station consists of the Generalized Monitor of Turbulence (GMT), Reuniwatt Sky Insight camera and Cimel CE318-T.

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust).

Fire is a terrifying weapon, with nearly unlimited destructive power. Fire accidents are a major cause of human suffering and material loss and the one that perhaps are predicted the least accurately. Most existing work in fire occurrence prediction focuses on prediction of wildfires in forests and those caused by volcanic eruptions. Surprisingly prediction of fire occurrence in residential and official buildings has not been fully explored because the factors that influence fires are too many. The idea behind this research is to provide an alert to fire stations in the event of fire in hospitals, official and commercial buildings by the use of Image processing and Artificial Intelligence techniques that might significantly reduce the death toll and loss of property

Fire is a terrifying weapon, with nearly unlimited destructive power. Fire accidents are a major cause of human suffering and material loss and the one that perhaps are predicted the least accurately. Most existing work in fire occurrence prediction focuses on prediction of wildfires in forests and those caused by volcanic eruptions. Surprisingly prediction of fire occurrence in residential and official buildings has not been fully explored because the factors that influence fires are too many. The idea behind this research is to provide an alert to fire stations in the event of fire in hospitals, official and commercial buildings by the use of Image processing and Artificial Intelligence techniques that might significantly reduce the death toll and loss of property caused by fire accidents. The Digital snapshots of the building can be taken (1,600 x 1,200 pixels at 1MB image per second) continuously using Closed circuit digital photography (CCDP) and these snapshots are then automatically sent to the server for storage as timed and dated JPEG files. The digital images are converted from RGB to XYZ color space and then segmented by utilizing anisotropic diffusion to identify the presence of fires. Subsequently, Radial Basis Function Neural Network is trained with the color space values of the segmented fire regions and is employed in the design of this novel system. The proposed intelligent system will thus aid in alerting the fire stations with the help of a Global System for Mobile Communications in event of any fire to take immediate actions before fire spreads quickly and causes traumatizing loss.

The MODIS Rapid Response (RR) System was developed to meet the near real time needs of the applications community. Generally, its products are available online within hours of the satellite overpass. We recently adapted the standard MODIS land surface temperature (LST) split-window algorithm for use in the RR System. To minimize latency, we eliminated the algorithm's dependency on upstream MODIS products. For example, although the standard MODIS LST requires prior retrieval of air temperature and water vapor from the MODIS scene, the RR LST employs a climatological database of atmospheric values based on a 25-year record of NOAA TOVS observations. The standard and RR algorithms also differ in upstream processing, surface emissivity determination, and use of a cloud mask (RR product does not contain one). Comparison of the MODIS RR and standard LST products suggests that biases are generally less than 0.1 K, and root-mean-square differences are less than I K despite the presence of some larger outliers. Initial validation with field data suggests the absolute uncertainty of the RR product is below I K. The MODIS RR land surface temperature algorithm is a stand-alone computer code. It has no dependencies on external products or toolkits, and is suitable for Direct Broadcast and other processing systems. (c) 2006 Elsevier Inc. All rights reserved.

Rapidly providing image products and remote sensing derived information to wildfire managers is necessary to maximize their utility for current wildfire situation assessment and strategic planning. In an effort to address this need, the USDA Forest Service Remote Sensing Applications Center (RSAC) coordinates with NASA-Goddard Space Flight Center (GSFC) and the University of Maryland to provide the USDA Forest Service MODIS Active Fire Mapping Program. The program utilizes high temporal resolution image data acquired by the MODIS sensor onboard the Earth Observing System (EOS) satellites TERRA and AQUA to facilitate active fire monitoring. The primary objective of the program is to provide real time MODIS imagery and MODIS-derived fire products for active fire assessment in the western United States and near-real time fire products for the rest of the continental United States and Alaska. In 2004, the program plans to coordinate with other MODIS Direct Readout facilities to provide real time coverage to other areas of the United States and Canada

Experience with the first 2 years of high quality data from the Moderate Resolution Imaging Spectroradiometer (MODIS) through quality control and validation has suggested several improvements to the original MODIS active fire detection algorithm described by Kaufman, Justice et al. [Journal of Geophysical Research 103 (1998) 32215]. In this paper, we present an improved replacement detection algorithm that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate. Performance of both the original and improved algorithm is established using a theoretical simulation and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes. In general, the new algorithm can detect fires roughly half the minimum size that could be detected with the original algorithm while having an overall false alarm rate 10–100 times smaller.

The Moderate-resolution Imaging Spectroradiometer (MODIS) instrument on board the Terra satellite offers an unprecedented combination of daily spatial coverage, spatial resolution, and spectral characteristics. These capabilities make MODIS ideal to observe a variety of rapid events: active fires, floods, smoke transport, dust storms, severe storms, iceberg calving, and volcanic eruptions. A new processing system has been developed at NASA's Goddard Space Flight Center to provide a rapid response to those events, with initial emphasis on active fire detection and 250-m resolution imagery. MODIS data of most of the Earth's land surface is processed within a few hours of data acquisition. Collaboration between NASA, the University of Maryland and the USDA Forest Service has been developed to provide fire information derived from MODIS to the fire managers. Active fire locations in the conterminous United States are produced by the MODIS Rapid Response System and communicated to the Forest Service within a few minutes of production. These active fire locations are used to generate regional fire maps, updated daily and provided to the fire managers to help them allocate adequate resources to firefighters. Active fire locations are also distributed to the Global Observation of Forest Cover (GOFC) user community through a Web interface integrating MODIS active fire locations and geographic information system datasets.

Fire products are now available from the Moderate Resolution Imaging Spectroradiometer (MODIS) including the only current global daily active fire product. This paper describes the algorithm, the products and the associated validation activities. High-resolution ASTER data, which are acquired simultaneously with MODIS, provide a unique opportunity for MODIS validation. Results are presented from a preliminary active fire validation study in Africa. The prototype MODIS burned area product is described, and an example is given for southern Africa of how this product can be used in modeling pyrogenic emissions. The MODIS Fire Rapid Response System and a web-based mapping system for enhanced distribution are described and the next steps for the MODIS fire products are outlined.

A suite of global land surface products is made on an operational basis from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument data. Quality assessment (QA) is an integral part of this production chain and is focused on evaluating and flagging product quality with respect to expected performance. This task is challenging because of the different error sources that may affect product quality, the large volume of products produced, and the dependencies that exist between them. This paper describes the QA approach adopted by the MODLAND Science Team and coordinated by the MODIS Land Data Operational Product Evaluation (LDOPE) facility

A global monthly reflectance dataset from September 1997 to December 1999 at 8 km spatial resolution was derived from Sea Wide Field of view Sensor (SeaWiFS). This dataset, used for prototyping MODIS Land/ Atmosphere algorithms, is now available for release to the broader community. This Letter describes the data processing, data format, and how these value-added data and some tools for their analysis may be obtained.

Early results are described for some of the land products from MODIS generated in test and evaluation mode prior to operational product release. These products give a first glimpse of the potential of the MODIS instrument for land surface studies. Outline descriptions are provided for the following products: surface reflectance, land surface temperature, vegetation indices, LAI/FPAR, snow and BRDF/albedo. Quality assurance of products conducted by members of the MODIS land team are described. These initial MODIS products show an enhanced capability for moderate-resolution imaging over previously available operational systems

The POLDER instrument is devoted to global observations of the solar radiation reflected by the Earth–atmosphere system. The airborne version of the instrument was operated during the ACE-2 experiment, more particularly as a component of the CLOUDYCOLUMN project of ACE-2 that was conducted in summer 1997 over the subtropical northeastern Atlantic ocean. CLOUDYCOLUMN is a coordinated project specifically dedicated to the study of the indirect effect of aerosols. In this context, the airborne POLDER was assigned to remote measurements of the cloud optical and radiative properties, namely the cloud optical thickness and the cloud albedo. This paper presents the retrievals of those 2 cloud parameters for 2 golden days of the campaign 26 June and 9 July 1997. Coincident spaceborne ADEOS-POLDER data from 2 orbits over the ACE-2 area on 26 June are also analyzed. 26 June corresponds to a pure air marine case and 9 July is a polluted air case. The multidirectional viewing capability of airborne POLDER is here demonstrated to be very useful to estimate the effective radius of cloud droplet that characterizes the observed stratocumulus clouds. A 12 μm cloud droplet size distribution appears to be a suitable cloud droplet model in the pure marine cloud case study. For the polluted case the mean retrieved effective droplet radius is of the order of 6–10 μm. This only preliminary result can be interpreted as a confirmation of the indirect effect of aerosols. It is consistent with the significant increase in droplet concentration measured in polluted marine clouds compared to clean marine ones. Further investigations and comparisons to in-situ microphysical measurements are now needed.

In this conclusion paper, remote sensing retrievals of cloud optical thickness performed during the EUCREX mission 206 are analyzed. The comparison with estimates derived from in situ measurements demonstrates that the adiabatic model of cloud microphysics is more realistic than the vertically uniform plane parallel model (VUPPM) for parameterization of optical thickness. The analysis of the frequency distributions of optical thickness in the cloud layer then shows that the adiabatic model provides a good prediction when the cloud layer is thick and homogeneous, while it overestimates significantly the optical thickness when the layer is thin and broken. Finally, it is shown that the effective optical thickness over the whole sampled cloud is smaller than the adiabatic prediction based on the mean geometrical thickness of the cloud layer. The high sensitivity of the optical thickness on cloud geometrical thickness suggests that the effect of aerosol and droplet concentration on precipitation efficiency, and therefore on cloud extent and lifetime, is likely to be more significant than the Twomey effect.

Solar eclipses evident in short wavelength data sensed by polar orbiting Sun-synchronous wide field of view instruments such as SeaWiFS and POLDER are illustrated, and some implications for global dataset production are considered.

This paper describes the theoretical model for simulating Moderate-Resolution Imaging Spectroradiometer (MODIS) observations under varying environmental conditions. The model includes mathematical descriptions of the orbit of the Earth Observing System (EOS) AM platform, imaging process of the optical sensor, spectral response of the detectors, and realistic illumination and observation geometry for the scene being viewed. It includes a data base that supplies the model with variable surface and atmospheric conditions. Land surface elements are characterized with biome type, and reflectance is described using a time-dependent model bidirectional reflectance distribution function (BRDF). Ocean whitecap and Dun glint are dependent on National Meteorological Center (NMC) analysis wind speed and direction, and water leaving radiance is determined from chlorophyll concentration obtained from the Coastal Zone Color Scanner (CZCS). Cloud cover comes from the International Satellite Cloud Climatology Project (ISCCP) climatology data set. The mathematical descriptions and databases are implemented in a software package that produces data sets of calibrated MODIS data in the exact format that operational processing of real data will provide. The results are used in the test and development of MODIS data processing software

This study investigates the validity of the plane‐parallel cloud model and in addition the suitability of water droplet and ice polycrystal phase functions for stratocumulus and cirrus clouds, respectively. To do that, we take advantage of the multidirectional viewing capability of the Polarization and Directionality of the Earth's Reflectances (POLDER) instrument which allows us to characterize the anisotropy of the reflected radiation field. We focus on the analysis of airborne‐POLDER data acquired over stratocumulus and cirrus clouds during two selected flights (on April 17 and April 18, 1994) of the European Cloud and Radiation Experiment (EUCREX'94) campaign. The bidirectional reflectances measured in the 0.86 μm channel are compared to plane‐parallel cloud simulations computed with the microphysical models used by the International Satellite Cloud Climatology Project (ISCCP). Although clouds are not homogeneous plane‐parallel layers, the extended cloud layers under study appear to act, on average, as a homogeneous plane‐parallel layer. The standard water droplet model (with an effective radius of 10 μm) used in the ISCCP analysis seems to be suitable for stratocumulus clouds. The relative root‐mean‐square difference between the observed bidirectional reflectances and the model is only 2%. For cirrus clouds, the water droplet cloud model is definitely inadequate since the rms difference rises to 9%; when the ice polycrystal model chosen for the reanalysis of ISCCP data is used instead, the rms difference is reduced to 3%.