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The ClimTools Software

Dimitrios Gyalistras
Systems Ecology Group, ETH Zurich, Switzerland

Manuscript in preparation
Last edited: 13. October 2004


The computer-based management, manipulation and analysis of geophysical data sets poses major technical challenges that often hamper everyday research work. Some major problems typically encountered in the fields of climate and climate impact research are first outlined.

The paper proposes some design principles for a software that would help to aleviate these problems. A series of existing approaches to deal with the complexities of geophysical data processing were evaluated and some critical limitations of these approaches were identified.

A solution that complements the currently available tools was proposed in the form of a new programs collection named ClimTools. The principles of the ClimTools software, its architecture, and its implementation are presented. The experience from the application of the software to two demanding case studies, as well as possible further developments are discussed.

It is concluded that the ClimTools software is easy to use, powerful, robust, and efficient. In combination with other available tools it facilitates everyday research work and it helps to improve the reproducibility and corectness of the obtained results.

Table of Contents

1. Problems with Geophysical Data Sets

The management, manipulation and analysis of observed or model-simulated geophysical data sets accounts for an important part of everyday work in the fields of climate and climate impact research. Scientists working in these fields are typically confronted with a range of problems:

  1. There is a wide variety of geophysical data types: point data vs. gridded data, observed vs. modelled data, data of varying temporal and spatial resolutions, time series data (e.g., meteorological measurements) vs. statistical data (e.g., climatological parameters) vs. "time-invariant" data (e.g. soil parameters) etc.
  2. The data sets are often very complex: they can be multi-dimensional (e.g., several space-time dependent variables), the data may be irregularly distributed over space and/or time, and the data quality or reliability may vary depending on a range of factors.
  3. There is a large variety of data formats: these range from official formats (such as the World Meteorological Organization's "GRIB" format), or widely accepted quasi-standards (such as the "NetCDF" format of the UNIDATA Program Center of the North American University Corporation for Atmospheric Research), to a countless number of other formats currently in use by individual researchers or institutions.

  4. There is a range of frequently recurring, often conceptually simple, but otherwise in terms of computer skills and ressources quite demanding data processing tasks. These relate, for instance, to the storage, archiving and documentation of individual data sets, to data retrieval, extraction and testing problems, or to the need for transitions between different data formats and software packages.

2. Software Design Principles

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3. Evaluation of Some Existing Solutions

An exhaustive review and intercomparison of the projects that have attempted to solve this kind of problems is far beyond the scope of this paper. Here the evaluation is restricted to five major projects known to the author:
  1. HDF (Hierarchical Data Format)
  2. NetCDF (network Common Data Form)
  3. CDAT (Climate Data Analysis Tools)
  4. GrADS (Gridded Analysis and Display System)
  5. PINGO (Procedural INterface for Grib formatted Objects)

(text in preparation)

4. The ClimTools Approach

According to the above analysis the aims of the ClimTools project were
  1. to introduce a limited number of data types (formats) which according to this author's experience should cover some of the main needs encountered in everyday climate and climate impact research,
  2. to complement existing software packages by a series of programs that take care of specialized tasks (such as specific format conversions) that are currently not well covered by the tools evaluated, and
  3. to enable researchers to perform basic statistical analyses and visualization of complex geophysical data sets with a minimal programming effort.

4.1 Concepts

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4.2 Implementation

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4.3 Testing

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5. Discussion and Concluding Remarks

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6. References

Wirth, N., 1977. What can we do about the unnecessary diversity of notation for syntactic definitions? Commun. ACM, 20(11): 822-823.

This documentation is maintained by Dimitrios Gyalistras. Last updated 10-Oct-2006.