GPU-Accelerated High-Resolution Geostationary-Satellite-Based Atmospheric Motion Vector Retrieval

Satellite-derived atmospheric motion vectors (AMVs) provide essential wind field data crucial for numerical weather prediction (NWP) and nowcasting applications. However, current operational AMV products typically offer relatively low spatial resolution, limiting their effectiveness in meeting precise meteorological forecasting requirements. Retrieving AMVs at higher spatial resolutions significantly increases computational demand, hindering their application in real-time operational scenarios. To address this challenge, this article introduces a novel GPU-accelerated (graphics processing unit) algorithm, based on OpenACC (ACCelerators), designed specifically to improve computational efficiency in the target-tracking part of the AMV retrieval algorithm. The proposed method decomposes complex calculations into parallel tasks suitable for efficient processing by GPUs. Additionally, to address the limited memory capacity of individual GPUs, a block-based computational strategy is developed, allowing for efficient use of multiple GPUs to process larger datasets. A comparative analysis at 48, 12, and 6 km resolutions showed that, at 6 km, OpenMP on a 48-core CPU achieved a 40 × speedup, while single- and dual-GPU configurations reached approximately 50 × and 110 ×, respectively. The hybrid OpenACC+OpenMP strategy, combining one GPU with 48 CPU cores, delivered the highest acceleration at around 140 ×, with all GPU-based methods maintaining near-lossless accuracy relative to the CPU baseline. This article provides a practical and effective solution for real-time high-resolution AMV retrieval, significantly improving the timeliness of critical meteorological services such as typhoon monitoring and data assimilation in NWP systems.