基于Python的生活气象指数预报制作的优化
Optimization of Life Weather Index Forecasting Based on Python

菏泽市气象局开展生活气象指数预报已经有十几年时间，数据获取是由预报员主观制作，不能自动制作上传，耗时费力。因此，需要对气象指数预报的制作进行优化。随着数值预报模式越来越准确，除短历时强降水和突发灾害性天气有待提高外，其他要素预报基本不需要主观订正，这就为指数预报自动化提供了数据支持。运用Python开发工具，通过全国综合气象信息共享平台(CIMISS)的气象数据统一访问接口(MUSIC接口)的REST服务读取气象预报内容，依据预报模型和预报方程计算生活气象指数，自动制作指数预报并上传到气象监测预警平台。
It has been more than ten years since the Heze Meteorological Bureau carried out the forecast of daily Life Meteorological Index. Data acquisition is subjectively produced by forecasters, and cannot be automatically produced and uploaded, which is time-consuming and labor-intensive. Therefore, it is necessary to optimize the production of meteorological index forecast. As the numerical prediction model becomes more and more accurate, except for short-duration heavy rainfall and sudden disastrous weather, the subjective correction is not needed for other elements, which provides data support for the automation of index prediction. Using the Python development tool, the weather forecast content is read through the REST service of the national integrated weather information sharing platform (CIMISS) weather data unified access interface (MUSIC interface), and the living weather index is calculated according to the forecast model and forecast equation, and automatically make the index forecast and upload it to the weather monitoring and early warning platform.