Prediction (NCEP) of the National Weather Service and the Geophysical Fluid Dynamics Laboratory (GFDL), both of which are part of the National Oceanic and Atmospheric Administration, and with the director of the European Centre for Medium-Range Weather Forecasts (ECMWF). Particular assistance was received from Louis Uccellini, Ben Kyger, and Steve Lord from NCEP, Brian Gross from GFDL, Dominique Marbouty of the ECMWF, James Hack from the National Center for Atmospheric Research (NCAR), and other colleagues. Special thanks go to Jeremy D. Ross, Storm Exchange, Inc., for assistance with the discussion of challenges and techniques of high-resolution mesoscale modeling.


This section identifies the major challenges facing the atmospheric sciences. Each challenge is given a ranking of [1], [2], or [3], representing the committee’s consensus on the degree to which advances in HECC will impact progress. A ranking of [1] indicates that progress would immediately accelerate if advances in HECC were available. Other resources might be partially limiting, too, such as field or laboratory instruments, personnel, or funds for field programs. A ranking of [2] indicates that HECC is currently playing or will soon play a key role, but that other factors, such as immaturity of the models or data sets, are more limiting because they prevent advances in HECC from having an immediate impact. A ranking of [3] indicates that HECC will probably not be a limiting resource within 5 years because the models, data sets, and theories are not fully mature. Even for the challenges ranked [3], however, some computer-intensive modeling or data analysis activities are already under way.

In the brief discussion that accompanies each challenge, references are made to the physical or mathematical aspects of the problem that necessitate attacking it with HECC. These aspects are described in more detail in the section on computational challenges in the atmospheric sciences.

Major Challenge 1:
Extend the Range, Accuracy, and Utility of Weather Prediction [1]

Many sectors of the economy and the public at large have come to depend on accurate forecasting of day-to-day weather. Examples include agriculture, transportation, energy, construction, and recreation. Over the last 50 years, the accuracy and range of weather forecasting have steadily improved, owing in equal measure to underlying improvements in

  • Physical models of the atmosphere;

  • Algorithms for solving partial differential initial- and boundary-value problems;

  • Coverage and quality of data for model initialization;

  • Algorithms for model initialization; and

  • Computers that are faster and have larger memories, allowing higher spatial resolution and running of ensembles of models.

These advances are the result of massive, sustained research efforts in atmospheric physics and dynamics, instrumentation, the mathematics of chaos, and several areas of data filtering and numerical analysis, all of them capitalizing on concurrent progress in supercomputers themselves. While today’s weather forecasts are good by previous standards, several serious limitations can nonetheless be noted:

  • Even a short-term forecast for one or two days out may occasionally miss a significant weather event or misjudge its strength or trajectory.

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