Sergei Rodionov

Introduction

ECliPS is a knowledge management system designed to organize and process data and information about the climate system. Due to existence of so-called "teleconnections," or linkages between climate anomalies over great distances, an accurate seasonal forecast in one area is practically impossible without taking into account climatic processes in other areas of the world, as well as external factors, such as solar activity. This leads to a huge information overload, which is one of the main problems in climate forecasting. In this situation, the organization of information, along with reconciliation and connection of evidence and ideas, plays a very important role. 

There are two different ways to deal with the information overload. One large group of methods tries to resolve this problem by reducing the dimensionality of the system. This group includes principal component analysis, singular value decomposition, multidimensional scaling and other methods. These methods are very popular in climatology. Indeed, they can be useful in a number of situations, although there is often a problem of interpreting the results. Another important drawback of those methods is that they do not preserve information about the relationships between climate variables.

An alternative approach to the problem of information overload is to use those methods or tools that can help manage information in such a way that only the information relevant to the problem or question at hand is provided to the user at any given point of the analysis. This approach can be realized in the form of an expert system, decision support system or information management system. Climate Logic advocates such a system approach to climate forecasting, when the entire climate system is taken into consideration, even at the expense of some simplification.

The first climatic expert system called CESNA was developed by Rodionov and Martin (1996). For several years it was used in forecasting of winter atmospheric circulation and temperature in the North Atlantic and Europe (Rodionov and Martin, 1999). These experimental forecasts demonstrated the advantages of symbolic processing, which allowed taking into account both quantitative and qualitative information, and thereby catching many aspects of climate variability that were escaped from other conventional methods. Unfortunately, it was written in a rare language (KnowledgeMan/GURU) for DOS operating system and was somewhat cumbersome to use. 

Unlike CESNA, ECliPS is written in VB.NET with the use of several off-the-shelf Microsoft products: Word, Excel, Access, and Visio. It is more user-friendly and much easier to use than CESNA. It streamlines the inference process and makes it more transparent to the user.