A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes
A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes
复制标题
DOI：
10.1002/2015rg000513
复制
发表时间：
2016-06
影响因子：
25.2
通讯作者：
T. N. Krishnamurti;Vinay Kumar;A. Simon;Amit Bhardwaj;T. Ghosh;R. Ross
中科院分区：
文献类型：
--
作者：
T. N. Krishnamurti;Vinay Kumar;A. Simon;Amit Bhardwaj;T. Ghosh;R. Ross
这篇综述总结了天气、气候、海洋和飓风集合预报领域的工作。这包括多个预测模型结果的组合，这些结果不局限于整体平均值，而是使用独特的集体偏差减少程序。利用由著名的洛伦兹低阶非线性系统构建的一套模型，提供了该过程的理论框架。提供了一个教程，其中包括一个演练表并说明了多模型超集成原理的内部工作原理。单个确定性模型中的系统误差是由一系列特征引起的，这些特征包括模型的初始状态（数据同化）、分辨率、物理、动力学和海洋过程的表示、地形的局部方面、水体和陆地表面的细节。模型以其对这些特征的多样性表示，最终会留下系统错误的独特特征。多模型超级集成使用多达 1000 万个权重来考虑多模型的这些不同特征所产生的偏差误差。这里提供了单个确定性预测模型的设计，该模型利用大量权重的多个特征。这使得我们能够更好地理解不同模型中几个物理参数化的误差增长和集体偏差减少，例如积云对流、行星边界层物理和辐射传输。提供了成员模型、集合平均值和超集合的天气、季节性气候、飓风和地下海洋预报技能的许多示例。
This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well‐known Lorenz low‐order nonlinear system. A tutorial that includes a walk‐through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.