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The Science Behind Weather Forecast Models: Improvements in Computing Power and Data Integration

A post by Initwx.com  on May 11, 2023


Accurate weather forecasts allow for better lead time to prepare for environmental disasters. As computing power and data collection efforts evolve, global weather forecast models have the capability to become much more accurate and sophisticated than yesterday’s models. What exactly happens under the hood with these weather models and how do these upgrades help with producing a better weather forecast?


A weather model is a computer program designed to take actual weather observational data as input, and predict a potential outcome based on the laws of physics; the movement of mass, energy, and forces, through space and time. Somewhere in its output, a forecast is made with some degree of accuracy in place, but this will rely on several important factors.



First is the quality of the input data; ideally frequent data with a high degree of accuracy would be best. Frequent can refer to the geographical distribution of measurements or in the frequency at which the measurement occurs (continuous, every 15 seconds, every minute, every hour or even day). According to the U.S. National Weather Service, nearly 1800 weather balloons are launched around the world on a daily basis. This allows for a vertical snapshot of the atmosphere’s weather parameters at multiple levels: temperature, pressure, dew point, wind speed and direction to name a few. This real time data will be used as input for a weather model.


The computing power of the machine that will process this weather data is the second major facet in weather model production. One computing processor is great but a million is exponentially better! A faster machine allows for simultaneous model runs that can each explore various prediction range of potential forecast outcomes. Furthermore, each weather model weighs the microphysical processes of the atmosphere in different ways. Model algorithms will incorporate atmospheric physics processes, the laws of fluid dynamics, mathematical laws, and a statistical analysis of the data crunched through the program.



The forecast skill of any weather model rests on the ability to juggle all of the above, in addition to integrating the influences of other earth’s spheres, especially ocean dynamics and land/water interactions. In recent years, there has been an emphasis on the lack of ocean and atmospheric interactions being integrated into weather models, which is why many of the maritime forecasts lean heavily on the forecaster’s knowledge of geographical influences and nuances of atmospheric pressure patterns.


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