Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System
Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System
复制标题
多模型多物理系综：扩展范围预测系统的未来方法
作者：
A. Sahai;M. Kaur;S. Joseph;A. Dey;R. Phani;R. Mandal;R. Chattopadhyay
为了设计更好的实时预测工具，目前的工作突出了多模型多物理系综相对于其可操作的前身版本的优势。现有的业务扩展范围预测系统（ERPv1）将耦合的、偏差校正的海面温度强迫大气模型与扰动的初始条件系综以两种分辨率运行相结合。该系统实现了次季节尺度精细预报的重要目标；然而，系统的技能最多只能限制2周。该 ERP 系统的下一版本在分辨率上是无缝的，并且基于多物理多模型集成 (MPMME)。与早期版本类似，该系统包括耦合气候预报系统版本 2 (CFSv2) 和强制使用来自 CFSv2 的实时偏差校正海面温度的大气全球预报系统。在新版本中，每月进行六次模型集成以进行实时预测，选择对流和微物理参数化方案的组合。此外，还针对这些初始条件生成超过 15 年的后报。该系统的初步结果表明，在预测大规模低变率信号和长达 3 周的周平均降雨量方面，较其前身有了显着的改进。细分技能分析表明，MPMME 表现更好，尤其是在印度西北部和中部地区。
In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.