摘要
为了提高预报员对重大天气过程的预报分析能力,减少重复劳动浪费的时间,使重大天气过程复盘分析变得更加高效。研究将面向国内天气预报业务和科研应用的通用型天气学诊断分析的Python工具包MetDig开发为可共享的Web应用程序,实现多模式预报数据及再分析实况数据可定制的、可视化的诊断分析功能(包括热力诊断、动力诊断、水汽诊断、天气学诊断、降水诊断、要素诊断等共六大类28种综合图),为重大天气过程预报、复盘、机理研究等应用场景提供诊断分析技术支持。
In order to improve the ability of forecasters to forecast and analyze major weather processes, reduce the time wasted by repetitive labor, and make the multi-disk analysis of major weather processes more efficient, in this paper, MetDig, a Python toolkit for general synoptic diagnostic analysis for domestic weather forecasting business and scientific research applications, is developed into a shareable Web application. It can realize customized and visual diagnostic analysis functions of multi-model forecast data and reanalysis of live data (including thermal diagnosis, dynamic diagnosis, water vapor diagnosis, weather diagnosis, precipitation diagnosis, factor diagnosis, etc., a total of six categories and 28 kinds of comprehensive maps), and provide diagnostic analysis technical support for application scenarios such as major weather process prediction, review and mechanism research.
出处
《计算机科学与应用》
2024年第5期14-22,共9页
Computer Science and Application
