Development of a Low-Cost Portable Cluster for Numerical Weather Prediction

Abstract

Numerical weather prediction (NWP) systems are crucial tools in atmospheric science education and weather forecasting, and high-performance computing (HPC) is essential for achieving such science. The goals of NWP systems are to simulate different scales of weather systems for educational purposes or to provide future weather information for operational purposes. Supercomputers have traditionally been used for NWP systems; however, supercomputers are expensive, have high power consumption, and are difficult to maintain and operate. In this study, the Raspberry Pi platform was used to develop an easily maintained high-performance NWP system with low cost and power consumption—the Improved Raspberry Pi WRF (IRPW). With 316 cores, the IRPW had a power consumption of 466 W and a performance of 200 Gflops at full load. IRPW successfully simulated a 48-h forecast with a resolution of 1 km and a domain of 32,000 km2 in 1.6 h. Thus, IRPW could be used in atmospheric science education or for local weather forecasting applications. Moreover, due to its small volume and low power consumption, it could be mounted to a portable weather observation system.

Keywords

• numerical weather prediction system
• high-performance computing
• Raspberry Pi
• weather research and forecasting