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The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction system. It is designed to serve the needs of both operational forecasting and atmospheric research for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Microphysics plays an important role in weather and climate prediction. Microphysics includes explicitly resolved water vapor, cloud, and precipitation processes. Several bulk water microphysics schemes are available within the WRF, with different numbers of simulated hydrometeor classes and methods for estimating their size, fall speeds, distributions and densities. Stony Brook University scheme is a 5-class scheme with riming intensity predicted to account for the mixed-phase processes. In this paper, we develop an efficient Graphics Processing Unit (GPU) based Stony Brook University scheme. The GPU-based Stony Brook University scheme was compared to a CPU-based single-threaded counterpart on a computational domain of 422 × 297 horizontal grid points with 34 vertical levels. The original Fortran code was first rewritten into a standard C code. After that, C code was verified against Fortran code and CUDA C extensions were added for data parallel execution on GPUs. On a single GPU, we achieved a speed-up of 213× with data I/O and 896 × without I/O on NVIDIA GTX 590. Using multiple GPUs, a speed-up of 352 × is achieved with I/O for 4 GPUs. We will also discuss how data I/O will be less cumbersome if we ran the complete WRF model on GPUs.