Why is there a need for parallel architecture when using computers in weather forecasting?

Answer 1

The short answer, one that does not go into the more generalized benefits of multi-processor or parallel computing, is that large amounts of data needing to be incorporated into the weather forecasting process.

Weather forecasting depends on many individual pieces of data that change rapidly. The data may include temperature data gathered from reporting devices or stations across a wide geographic area (such as the United States). The data may also include barometric pressure readings, or the "weight" of the atmosphere at many, many locations. All of this information, represented as data in tables such as those found in databases, may get factored together when determining what is likely to happen with the weather next!

Data is sometimes also used to create the visual representations of what weather looks like now, or what is it likely to look like tomorrow (hence the term "forecasting"). Often times the weather imagery seen on television or the internet is NOT simply a visual imagery feed from a space satellite, but rather a "representation" of how the weather would appear if the viewer were actually looking down at it.

Parallel computing architectures are generally described as faster than computers relying on a single Central Processing Unit (CPU) or "brain".

Parallel computing uses more individual "cores" or brains on a single motherboard computer or networks many computers together to combine their horsepower into a virtual, single computing machine. Though it is unlikely for this level of data processing horsepower to be found at the local news level, it certainly would be found at national weather forecasting organizations such as the center where hurricanes and other tropical weather-related storms are predicted, monitored and forecast. Prior to the invention of modern multi-processored computers, weather forecasters relied on tables and charts describing how previous storms arose, moved, combined with each other, and eventually faded away, in making predictions of how current weather would behave.

Parallel computing architectures may also used to simulate weather that has never happened but which could happen in a given geographic area such as flooding in coastal areas with low lying land elevations (also called topography).

Weather systems where multiple storms are anticipated to collide with each other are considered to be "complex" in nature due to the many sources of data that must be factored into a forecast. Two storms are certainly twice as much data as one, and three storms are three times as much, and this called linear progression (and may be represented as a straight inclined slope from zero at the left to some higher value at the right).

The faster the data processing equipment used in weather forecasting, the more scenarios that can be explored in "What if ..." scenarios (i.e. weather simulations), or the larger the amounts of data that may be incorporated into a forecast model; thereby increasing the overall accuracy of the forecast.

Answer 2

Parallel architecture enables computers to perform multiple operations simultaneously, increasing the processing power and speed. In weather forecasting, parallel architecture is crucial for handling the massive amount of data and complex mathematical modeling required for accurate predictions. By utilizing parallel processing, weather forecasting models can run efficiently and deliver timely forecasts.