"We are entering an era in which high-resolution models realistically simulate regional mesoscale circulations, when mesoscale ensembles produce calibrated regional-scale probabilistic guidance, and forecasters can no longer consistently 'beat' objectively produced forecasts. The frequent use of human beings to laboriously alter deterministic forecasts for a week into the future would be a serious mistake that would lessen forecasters' time for more productive work. IFPS [the Interactive Forecast Preparation System] must be altered to facilitate short-term forecasting and the communication of such forecasts. It should also evolve into a more probability-capable platform that can make use of ensemble information and can disseminate probabilistic information. Last, human forecasters must be left with sufficient time to fill an irreplaceable role--to explain and interpret the probabilistic and deterministic forecasts created by automated numerical weather prediction systems and to provide critical advisories, watches, and warnings." Source: Mass, Clifford F., 2003: IFPS and the Future of the National Weather Service. Weather and Forecasting: Vol. 18, No. 1, pp. 75-79.

Using current tools, expert forecasters cannot make full use of available knowledge and data for nowcasting. 1 There is an increasing amount of remotely sensed data, a "data deluge", more than humans can intelligently and consistently and quickly process during rapidly changing weather situations. 2 In many non-meteorological professions, experts systems have been developed to help experts multiply their effectiveness by doing much of the most repetitive, systematic work that was traditionally done by experts. A commonly used definition of artificial intelligence (AI) is: "The application of computers to areas normally regarded as requiring human intelligence." Because the development of meteorological expert systems has not kept up with the increases in data and the increases in demand for forecast products, forecasters are increasingly challenged to attend to the details that experts are expected to attend to, and in so doing, are increasingly losing sight of the "big picture". As Henry David Thoreau might have put it: "Men have become the tools of their tools." The "What" to "How" spectrum of computing In the "man-machine mix", AI can mediate between perception and decision making and computation. According to Edward Feigenbaum, AI enables people to spend more time controlling "what" computers do and less time controlling "how" computers do.

"The 'What' to 'How' spectrum of computing. Off to the left, the user wishes to express goals and needs in his or her own terms, not in terms of the computer's step-by-step instructions. The history of the computer software is a series of steps that allow the user more of that freedom. AI software aspires to be at the point closest to the user's desires and problems." Edward A. Feigenbaum, How the "What" Becomes the "How", Communications of the ACM, Volume 39, No. 12 (Dec. 1996).

Currently, nowcasting tools use very little AI. To produce nowcasts, forecasters have to: 1) use their heads, to perceive, think, and decide; and 2) use their hands, to specify details, step-by-step, in computers' terms. When nowcasting tools use more AI, forecasters will spend less time working with their hands, to specify how forecasts are constructed, and will spend more time working with their heads, applying their meteorological expertise, to determine what forecasts say. Nowcasting tools, for both regular and severe weather, should be designed to enable synergistic use of the complementary abilities of expert forecasters and AI. For this, the different needs and abilities of forecasters and AI should be initial considerations in design of nowcasting tools. National Meteorological Services need to develop and use intelligent nowcasting tools to enable intelligent integration of predictive, heterogeneous fields -- such as observations, model based guidance, and projected radar and satellite images -- to enable such data to be intelligently and consistently and quickly processed into better nowcasts. Better nowcasts would be highly reliable and precise, site-specific, up-to-the-minute, 0-to-6-hour graphical forecasts of weather, with intelligent, situation-specific descriptions of forecast confidence; in other words, equivalent to what forecasters can currently only provide to individuals through personal and direct interaction. By using intelligent nowcasting tools, weather service providers could increase the quality and the number and the availability of such nowcasts. By disseminating such nowcasts via the Internet and wireless technology, practically anyone could have customized and current nowcasts. Anyone involved in a weather-sensitive activity or industry could choose to be automatically alerted to the development of significant weather, with "significant" conditions being one of the customizable service options. Three types of intelligent nowcasting tools Three types of intelligent nowcasting tools -- heuristic, non-fuzzy, and fuzzy rule base -- are described here, along with their relative advantages (+), disadvantages (-), and a few examples: 1. Heuristic: experience-based, useful nuggets of wisdom gleaned from experience, perception, observation, and analysis. Basis of rules of thumb, expertise, insight, and intuition. (+) Sound results, accord with knowledge and experience; (-) Inconsistent and "expensive", in development and in application; (-) Context dependent; (+/-) Refer to abstract (fuzzy) features, such as "high to the north", which people process well, computers not so well. Examples: "Calm, clear, long, humid nights are prone to radiation fog"; "Forecast alternate conditions or above at Stephenville in easterlies"; "Don't clear Gander until its pressure exceeds St. Anthony's"; Checklist for forecasting severe weather; and many other forecasting rules of thumb. 2. Non-fuzzy: basically, a decision tree with answers at terminal nodes. Often implemented as "if-then-else ladders" or as conditional "equations". Discrete thresholds between predictors/conditions. (+) Time-tested "work horse", effective in many situations; (+) All meteorologists and developers of meteorological systems are familiar with decision trees; (-) Lack explanation capability. Usually a "black box". This is a debatable point, as all three intelligent nowcasting approaches can be made to offer explanations for their results. However, good expert systems can offer intelligible and scrutable explanations for their results, and thus enable expert users to attach appropriate confidence in the results. Heuristic and fuzzy systems tend to provide more intelligible and more scrutable explanations than non-fuzzy systems, and thus results that expert users are more likely to appreciate and less likely to regard as "data overload". (-) Lack facility for intelligent data fusion. For example, given fields of current observations, model output, and radar and satellite projections, how would a system "intelligently" compose a projection of a field of interest (e.g., severe thunderstorm at CYYZ at t+1 hr); (-) ... hence, non-fuzzy systems are poor for nowcasting and "brittle" if "misapplied" (e.g., recent radar should "outweigh" older MOS). Examples: Most nomograms; Conditional persistence; Non-fuzzy conditional climatology; Precipitation typing based on forecast layer thicknesses; Statistics based systems, such as MOS and CART; Neural networks; Interactive Forecast Preparation System (IFPS) and Scribe knowledge base systems (KBS) which translate collections of data and forecaster-adjusted "weather concepts" into forecast products. 3. Fuzzy rule base: answers based on a weighted sum of all rules "firing" simultaneously, each rule activated to the degree to which its conditions are valid. Predictors/conditions specified by fuzzy sets. Rules expressed in fuzzy language (hence fuzzy logic has been described as "Computing with Words"). For example, the method used in a classic example of a fuzzy rule base for controlling an industrial furnace has been adapted directly for forecasting radiation fog. (+) Numerous: www.rap.ucar.edu/technology/iws/advantages.htm; (+) Rules are clear to developers and forecasters, easy to develop, tune, and maintain (www.nrlmry.navy.mil/~medex/tutorial/toc/toc.html); (+) Can incorporate heuristics and non-fuzzy decision trees and other forecast models and heterogeneous data with intelligent integration. According to Richard Wagoner, Deputy Director/Program Development Manager at the Research Applications Program, National Center for Atmospheric Research (RAP/NCAR): Over 90% of systems developed at RAP use fuzzy logic as the intelligence integrator. (+) Operates intelligently on heterogeneous data (e.g., observations, model output, and radar and satellite projections) to help nowcasting, as explained by the Research Applications Program (RAP) in an article about Intelligent Weather Systems design. (+) ... hence fuzzy rule based systems are good for nowcasting, more capable than heuristics and non-fuzzy decision trees by themselves, and less brittle; (-) Most meteorologists and developers of meteorological systems are [still] not familiar with fuzzy logic. Examples: Intelligent Weather Systems, www.rap.ucar.edu/technology/iws; Applications of fuzzy logic for nowcasting, www.chebucto.ca/Science/AIMET/applications; Fuzzy Logic in Environmental Sciences: A Bibliography, www.chebucto.ca/Science/AIMET/fuzzy_environment; Fuzzy logic based conditional climatology, www.bjarne.ca/ai3 (data mining); RAP Ceiling and Visibility Forecast Algorithm, www.meted.ucar.edu/dlac/website/rap.htm (data fusion). I've only roughly outlined three basic and distinct intelligent nowcasting approaches. Of course, in practice, forecasting experts, researchers, and developers combine the approaches. For example: Forecasting experts routinely combine heuristics and non-fuzzy techniques to make forecast and warning decisions; Maner and Joyce combined heuristics and fuzzy logic in WXSYS; Burrows and Monpetit combined non-fuzzy and fuzzy techniques in CANFIS (CART + neuro-fuzzy inference system). I would appreciate any suggestions, comments, or criticisms. Just send me an e-mail at bjarne.hansen@ec.gc.ca. Bjarne Hansen 1. This webpage describes how to improve forecasting tools through intelligent integration of frequently updated predictive fields, for example, regular observations, and current and projected weather radar images. Such data is very valuable for forecasting a few hours forwards, but its value drops off after a few hours. I refer to such forecasting as "nowcasting" to emphasize the value of current data. However, all three of the following types of forecasts could benefit from the development of tools for intelligent integration of data. nowcast - A short-term weather forecast, generally for the next few hours. The U.S. National Weather Service specifies zero to three hours, though up to six hours may be used by some. very short-range forecast - A weather forecast made for a time period of generally less than six hours. short-range forecast - A weather forecast made for a time period up to 48 hours. Glossary of Meteorology, Second Edition, Todd S. Glickman (ed.), American Meteorological Society, Boston, Massachusetts, U.S.A., 2000. 2. If weather forecasting is like flying an airplane, then nowcasting is like landing an airplane. To help to explain why human forecaster expertise is needed in the forecasting process, an autopilot analogy is often used. For example, as Richard Verret recently put it: "Radars, satellites and better computer models have all helped to improve the forecasting of both precipitation and temperature over the past decade, ... But the knowledge and experience of human forecasters is also crucial, especially in hazardous weather. 'You can fly on an airplane on autopilot from Paris to London, but you need a pilot when things go bad.'" Source: Kate Jaimet, 2003: Today's forecast will be 90% correct, The Ottawa Citizen. Flying an airplane consists of three stages: takeoff, flight, and landing. Pilots generally fly on autopilot during flight, when events requiring rapid and intelligent intervention are relatively rare. However, they still fly manually during takeoffs and landings, when events requiring rapid and intelligent intervention are relatively common. A forecast "takes off" when, for example, a forecaster decides whether to choose model A or model B. A forecast "flies" when, for example, a forecaster performing weather watch perceives no reasons to revise a forecast. A forecast "lands", for example, when a forecaster decides whether or not an existing thunderstorm is likely to track near a target, such as a major city or an airport. Last updated 28 June 2003. Opinions expressed here are solely those of the author except where indicated as otherwise.