WRF development began in the late 1990s through a collaboration between the National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA), U.S. While WRF is primarily used for weather and climate simulation, teams have extended it to support interactions with chemistry, forest fire modeling, and other use cases. Weather Research and Forecasting (WRF) is a popular open-source numerical weather prediction modeling system used by both researchers and operational organizations. With this solution, weather forecasters can get a WRF simulation up and running on Google Cloud in less than an hour! A closer look at WRF To help, weather forecasters can now run the Weather Research and Forecasting (WRF) modeling system easily on Google Cloud using the new WRF VM image from Fluid Numerics, and achieve the performance of an on-premises supercomputer for a fraction of the price.
This is a big shift compared with traditional approaches to weather forecasting, and can appear challenging.
GOOGLE WEATHER SOFTWARE
Ultimately, cloud computing allows forecasters and climate modelers to provide timely and accurate results on a flexible platform using the latest hardware and software systems, in a cost effective manner. Using the cloud instead offers increased flexibility, constantly refreshed hardware, high reliability, geo-distributed compute and networking, and a “pay for what you use” pricing model. They consume a lot of electricity, have largely fixed configurations, and the underlying computer hardware is replaced infrequently. These on-prem HPC systems require significant capital investment and have high long-term operational costs. To provide timely and meaningful predictions, weather forecasters usually rely on high performance computing (HPC) clusters hosted in an on-premises data center. Further, they’re extremely time-sensitive and in high demand - everyone from weekend travelers to large-scale industrial farming operators wants up-to-date weather predictions. Weather forecasting and climate modeling are two of the world's most computationally complex and demanding tasks.
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