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A Growing Intelligence Around Earth. Science@NASA (October 26, 2006). "EO-1 [Earth Observing 1] is a new breed of satellite that can think for itself. 'We programmed it to notice things that change (like the plume of a volcano) and take appropriate action,' [Steve] Chien explains. EO-1 can re-organize its own priorities to study volcanic eruptions, flash-floods, forest fires, disintegrating sea-ice -- in short, anything unexpected. Is this real intelligence? 'Absolutely,' he says. EO-1 passes the basic test: 'If you put the system in a box and look at it from the outside, without knowing how the decisions are made, would you say the system is intelligent?' Chien thinks so. And now the intelligence is growing. 'We're teaching EO-1 to use sensors on other satellites. ...'" Observing Volcanoes, Satellite Thinks for Itself. By Michon Scott. Earth Observatory (December 6, 2007). "At the onset of the November 2006 eruption, volcanologists watching the distant glow in the nighttime sky knew that Nyamuragira produced fast-flowing lava. If it flowed very far, it might reach the nearby town of Sake, perhaps mimicking the 2002 nightmare. ... On December 1, 2006, the international science community received an appeal for assistance from the scientists at the Goma Observatory asking for any satellite imagery that could help them safely monitor the situation. One of the scientists who fielded this appeal was Ashley Davies, a research scientist at NASA’s Jet Propulsion Laboratory (JPL). Davies is part of NASA’s Volcano Sensor Web, an experimental artificial intelligence (AI) project that links orbiting and ground-based volcanic sensors with NASA’s Earth Observing-1 satellite. When the satellite receives an alert of a volcanic trouble spot, it can change its data collection and transmission plans accordingly, without waiting for a human command. The Sensor Web is an offshoot of the lab’s Autonomous Sciencecraft Experiment, a broader effort to test advanced software and AI technology that could network space exploration assets -- satellites, orbiters, and rovers -- and make them more self-directed. ... Besides planning future refinements to Earth-based autonomous systems, Davies looks beyond our home planet to envision how they might work elsewhere. 'Mars is a good example,' he says. 'We’re looking at a time in the future when there are multiple assets [orbiters, rovers], and they can communicate with each other. A smart system can help maximize the resource usage and data return. There’s also the safety aspect. Something in orbit could detect a storm moving in, and it could warn assets on the ground to hunker down for the duration.'" Air Quality Information Service, Europe. Technology News Daily (August 9, 2007). "Weather forecasters can tell you if the sun is going to shine or if it is going to rain, but they rarely make predictions about something that may be even more important: the quality of the air we breathe. A new air quality information service, unlike any developed to date, promises to make obtaining data about pollution levels as easy as finding out tomorrow’s temperature. ... The Marquis system takes raw data from air quality monitoring stations – so far covering five European regions -- and uses advanced assessment and interpretation models incorporating artificial intelligence and machine learning to generate information and predictions on air conditions. That information is then adapted to meet the requirements of individual end users by changing the way it is displayed -- from text to graphs and pictograms -- and translating it into their language. 'We are the first project in this field to use natural language processing. Our multilingual text processor generates information for all communication media from scratch, using generation grammars, instead of using a prefabricated response based on templates,' [Leo] Wanner explains." New Method Predicts Monster Waves. By Robin Lloyd. LiveScience (May 3, 2005). "The seven-story freak wave that slammed into the cruise ship Norwegian Dawn last month was not so freakish after all. Rogue waves are more common than most people realize, and scientists are starting to predict when and where they will strike. ... In the future, [Vijay] Panchang and his Texas A&M colleague Shreenivas Londhe hope to show that 'learning' computers called neural networks, a form of artificial intelligence, can also do the job with help from buoys in the ocean that have been collecting wave height information at particular locations for decades. These arrays of processors imitate the way the human brain works. The researchers’ networks crank through all the data for previous years, hour by hour, to find the most likely wave pattern to follow conditions just like the current conditions. In a test of wave height predictions for the past month in coastal Massachusetts, Galveston, and Dauphin Island off Alabama, the network 'brain' has accurately forecast what buoys in those locations later recorded." Software learns to recognize spring thaw. By Jane Platt,Jet Propulsion Lab. NASA - Goddard News (July 19, 2005). "Spring thaw in the Northern Hemisphere was monitored by a new set of eyes this year -- an Earth-orbiting NASA spacecraft carrying a new version of software trained to recognize and distinguish snow, ice, and water from space. Using this software, the Space Technology 6 Autonomous Sciencecraft Experiment autonomously tracked changes in the cryosphere, the section of Earth that is frozen, and relayed the information and images back to scientists. ... While other spacecraft only capture images when they receive explicit commands to do so, for the last year Earth Observing-1 has been making its own decisions. Based on general guidelines from scientists, the spacecraft automatically tracks events such as volcano eruptions, floods and ice formation. The most recent software upgrade allows the spacecraft to accurately recognize cryosphere changes such as ice melting. ... 'This new software is capable of a rudimentary form of learning, much the way a child learns the names of new objects,' said Dominic Mazzoni, the JPL computer scientist who developed the software. ... Similar software has been used to distinguish between different types of clouds in images captured by JPL's Multi-angle Imaging SpectroRadiometer...."
NOAA Using Artificial Intelligence to Improve Navigational Safety Data. NOAA News (June 23, 2003). "The NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is now using artificial intelligence to extend and improve its existing real-time quality control monitoring system. This system, called CORMS (Continuous Operational Real-time Monitoring System) operates 24 hours a day, seven days a week ensuring the availability and accuracy of the real-time water levels, currents and meteorological data provided by CO-OPS for navigational safety. CO-OPS is part of the NOAA Ocean Service. ... The benefits of using artificial intelligence are four-fold: 1) the ability to monitor more sites; 2) provide more information to CORMS managers to assist them in decision-making; 3) ensure consistency in monitoring performance; and 4) significantly reduce reaction time to any instrument failures." The MEDEX Project - A Fuzzy Expert System for Assisting Gale-Force Wind Forecasting in the Mediterranean Region from the Naval Research Laboratory Marine Meteorology Division. "MEDEX is a fuzzy rule-based system for predicting the onset and cessation of seven gale-force winds in the Mediterranean region. The rules were compiled by several meteorological experts and implemented using a commercial software package."
Use of unmanned aircraft was big milestone for '05. By Kim Lanier. The Mobile Register & al.com (January 2, 2006). "The 2005 hurricane season saw more than unprecedented tropical activity. It also marked the first flight of an unmanned aircraft that could soon provide researchers and forecasters with regular observations from an area where meteorologists have had difficulty obtaining data. ... In September, the Aerosonde, a small, unmanned autonomous vehicle, became the first aircraft to provide near-surface observations when it flew into Tropical Storm Ophelia on Sept. 16, the morning after it had been downgraded from a Category 1 hurricane. ... Use of the Aerosonde is a joint venture between NOAA and the National Aeronautics and Space Administration with the Aerosonde Co. ... As computer forecast models improve, the high-resolution data collected by Aerosondes could eventually lead to improved model initialization, resulting in better routine weather forecasts as well as hurricane forecasts, [Joe] Cione said." Machine Learning for Meteorology at the Navy Center for Applied Research in Artificial Intelligence. Follow the link to the WWW projects page where you'll find projects such as: American Meteorological Society's Committee on Artificial Intelligence Applications to Environmental Science. "The term artificial intelligence denotes a large body of advanced computer techniques that are useful to meteorologists and those working in related disciplines. Society members concerned with artificial intelligence include forecasters who wish to improve forecast skill; psychologists who wish to improve the human forecasting environment by using intelligent and human computer assistance; statisticians who are interested in evaluating the performance of both humans and machines; researchers who wish to create an organized, modified base of current meteorological knowledge; and computer scientists who wish to develop sophisticated and useful computer programs."
AI systems may blow weathermen away. New Scientist (September 24, 2005; Issue 2518: page 27). "Weather forecasters could find themselves pushed out of a job by an artificial intelligence system designed to write clearer, less ambiguous reports. Computer scientists at the University of Aberdeen, UK, were asked to generate an 'artificial weatherperson' by operators of offshore oil rigs, who wanted more clarity in their forecasts. ... To remove such uncertainties, the team programmed a natural language generation (NLG) software package to transform data on the forecast weather into an unambiguous written bulletin (Artificial Intelligence, vol 167, p 137)."
AIMet Group: Research Group in Artificial Intelligence & Applied Meteorology University of Cantabria - INM (Spanish Weather Service) - CSIC CMOS 2005, Canadian Meteorological and Oceanographic Society's 39th Annual Congress: Sea to Sky.
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