Aiming at these problems of the current data service of Automatic Weather Stations (AWS), including data processing delay, slow interactive response, and low statistical efficiency, a new method based on Spark Streaming and HBase technologies was proposed and introduced to process massive streaming AWS data by integrating stream computing framework and distributed database system. Flume was used for data collection, and data processing was conducted by Spark Streaming and data were stored into HBase. In framework of Spark, two algorithms, one for writing streaming AWS data into HBase database, the other for realizing real-time statistical calculation of different observed AWS meteorological elements were designed. Finally, a stable and high-efficient system for real-time acquisition, processing, and statistics of AWS data was developed on Cloudera platform. Based on comparative analysis and running monitoring, performances of the system were confirmed, including low latency, high I/O efficiency, stable running status and excellent load balance. The experimental results show that the response time of Spark Streaming-based real-time operational processing for AWS data can reach to millisecond level, which includes paralleled data writing into HBase, HBase-based data query and statistics on different meteorological elements. The system can fully meet needs of operational applications to AWS data, and provides effective support to weather forecast.