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In Aesop's fable, 'The Gnat and the Bull,' a tiny gnat overestimates his importance by presuming that he disturbed a large bull by landing on his horn. However, when the [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.solener.2008.07.010 Byline: Isaac Moradi (a), Richard Mueller (b), Bohloul Alijani (c), Gholam Ali Kamali (d) Keywords: Heliosat; Meteosat; Solar radiation atlas; Geostationary satellite; Iran Abstract: Efficient use of solar radiation needs detailed knowledge of its spatial and temporal variations. Such information can be achieved using interpolating measured irradiance by ground stations. But more reliable results can be obtained by processing geostationary satellite images. Heliosat is an algorithm which has been developed to estimate global horizontal irradiance at ground level from images taken in the visible band by the Meteosat satellites. The aim of this study was to evaluate the Heliosat-II model by using daily global solar irradiation data measured at the four radiometric stations in Iran as well as Meteosat-5 images which are recorded by a spacecraft over 63[degrees]E. Mean RMSD% and MBD% for all stations were 11.7% and 1.9%, respectively. The mean values of intercept, slope and correlation coefficient were 0.82 (kWhm.sup.-2), 1.05 and 0.93, respectively. Seasonally, the maximum RMSD occurs in autumn (22.1%) and the minimum is experienced in spring (8.4%). This accuracy is a great achievement for producing a high quality solar radiation atlas in a country such as Iran with very sparse radiometric network and frequently unreliable measured irradiation data. Author Affiliation: (a) Satellite Atmospheric Science (SAT) Group, Institutionen for Rymdvetenskap (Department of Space Science) (IRV), LuleA[yen] University of Technology (LTU), Kiruna, Sweden (b) German National Meteorological Service (DWD), Climate Monitoring Satellite Application Facility (CM-SAF), Offenbach, Germany (c) Department of Geography, Tarbiat Moalem University, Tehran, Iran (d) Atmospheric Sciences and Meteorological Research Center, Tehran, Iran Article History: Received 24 November 2006; Revised 23 October 2007; Accepted 10 July 2008 Article Note: (miscellaneous) Communicated by: Associate Editor David Renne

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2008.04.004 Byline: Ammarin Makkeasorn (a), Ni-Bin Chang (a), Jiahong Li (b) Abstract: Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method - the RIparian Classification Algorithm (RICAL) - to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future. Author Affiliation: (a) Civil and Environmental Engineering Department, University of Central Florida, Orlando, FL 32816, USA (b) Research Scientist, Smithsonian Environmental Research Center, Kennedy Space Center, FL 32829, USA Article History: Received 8 March 2007; Revised 18 January 2008; Accepted 6 April 2008

Several meteorological services in Africa now issue seasonal climate forecasts on an operational basis. However, the failure to develop a comprehensive profile of users has resulted in a considerable gap between the information that is likely to be useful to farmers and that provided and disseminated by these services. The present study develops a methodology to characterize smallholder production systems in order to identify farmer groups who may adopt and benefit from the climate forecast information in sub-Saharan Africa. Through an extensive literature review, data and information was derived from a national household survey of 1540 smallholders in 1995-1997 by the Kenya Agricultural Research Institute and spatial georeferenced data from leading world data centers. The data were analysed and synthesized using the GIS. Considerable opportunities exist for farming communities to improve their profitability using climate forecasts. Although the needs and demand for climate forecasts vary according to the production systems and market forces that determine credit, demand and input availability and, thus, the usability of forecasts depend on the characteristics of the farmers and their place in space. Based on production strategies and options available to farmers, three zones were identified grouping farmers with highly probable, probable and less probable potential of adopting climate forecasts to alter their production practices. Although a climate forecast may be useful to all farmers in the region considered, due to different options available to individual groups of farmers, however, the benefits derived from its use may not be equitable. Some of the options available to farmers in Kenya were considered in this study with a view to highlighting why some may benefit more than others. The methodology demonstrated here could be adopted for other parts of the world for: (1) selecting survey sites to determine the benefits of climate forecasts using farmers participatory rapid rural appraisals and simulation approach, and (2) target climate information where it would be most useful.

The 2002 Winter Olympic and Paralympic Games will be hosted by Salt Lake City, Utah, during February-March 2002. Adverse weather during this period may delay sporting events, while snow and ice-covered streets and highways may impede access by the athletes and spectators to the venues. While winter snowstorms and other large-scale weather systems typically have widespread impacts throughout northern Utah, hazardous winter weather is often related to local terrain features (the Wasatch Mountains and Great Salt Lake are the most prominent ones). Examples of such hazardous weather include lake-effect snowstorms, ice fog, gap winds, downslope windstorms, and low visibility over mountain passes. A weather support system has been developed to provide weather information to the athletes, games officials, spectators, and the interested public around the world. This system is managed by the Salt Lake Olympic Committee and relies upon meteorologists from the public, private, and academic sectors of the atmospheric science community. Weather forecasting duties will be led by National Weather Service forecasters and a team of private weather forecasters organized by KSL, the Salt Lake City NBC television affiliate. Other government agencies, commercial firms, and the University of Utah are providing specialized forecasts and support services for the Olympics. The weather support system developed for the 2002 Winter Olympics is expected to provide long-term benefits to the public through improved understanding, monitoring, and prediction of winter weather in the Intermountain West.