Climactic modeling in order to make predictions about upcoming weather patterns is computationally intensive. At a high level, it involves taking some input data about the current weather conditions, segmenting the atmosphere in this data up into a large, three-dimensional grid of cubes some certain length on a side, and then running simulations of how weather conditions evolve over a certain time period. The data required here often has to be very up to date, necessitating some data transfer setup to bring the latest weather data in regularly. These simulations are extremely complex, and typically take into account factors like air pressure, humidity, sunlight intensity, time of year, and even more granular details such as expected heat rise from cities. Weather modeling is done both for scientific research as well as for practical purposes; for example, learning more about the Earth’s climate on a global scale relative to industrial growth, or a public power utility running weather models to determine what level of power will be available from renewable energy sources within the coming few days.