Collaborative Research: Climate Diagnostics and Prediction Using Nonlinear Empirical Models

合作研究：使用非线性经验模型进行气候诊断和预测

• 批准号: 9310959
• 负责人: Anastasios Tsonis
• 金额: $ 18.59万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-12-01 至 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9310959&HistoricalAwards=false
• 关键词: Collaborative; Research; Climate; Diagnostics; Prediction

ATM-9310959 Tsonis, A.A. University of Wisconsin-Milwaukee Title: Climate Diagnostics and Prediction Using Nonlinear Empirical Models ABSTRACT This project's main objectives are to develop and test nonlinear statistical models for climate diagnostics and for climate predictions. The methodologies to be applied, based on artificial neural networks and local-approximation models, have been developed recently in the emerging fields of deterministic chaos and parallel distributed processing. The goal is to achieve skillful models of sea surface temperature (SST) anomalies on seasonal to interannual time scales for both the Pacific and Atlantic basins and, through prediction, to enhance understanding of the processes involved. Climate variability on the seasonal to interannual time scale is strongly related to ENSO. Recent research suggests that ENSO, primarily a Pacific signal, can be considered as a low-dimensional chaotic system. In addition, the Atlantic region also has major climate variations shown in atmospheric and oceanic data. Predictions of both ENSO and Atlantic oscillations will be attempted by applying the nonlinear statistical modeling techniques developed for the prediction of chaotic system behavior. The performance of several nonlinear statistical models will be validated and optimized through hindcast experiments with data sets from as early as 1950. The absolute verification experiments will be hindcasts of ENSO SST events in the period of 1985-1990, as established by the Working Group of the TOGA Program on Seasonal to Interannual Prediction (T-POP). The algorithms developed, particularly those for the Connection Machine, are based on new modeling techniques and will be made readily available to the meteorological and oceanographic communities.

ATM-9310959 Tsonis，A.A. 威斯康星大学密尔沃基分校 标题：使用非线性经验模型进行气候诊断和预测 摘要 该项目的主要目标是开发和测试用于气候诊断和气候预测的非线性统计模型。 最近在确定性混沌和并行分布式处理的新兴领域中开发了基于人工神经网络和局部逼近模型的方法。 目标是在太平洋和大西洋盆地的季节到年际时间尺度上建立海面温度（SST）异常的熟练模型，并通过预测增强对所涉及过程的理解。 季节到年际时间尺度上的气候变化与 ENSO 密切相关。 最近的研究表明，ENSO 主要是太平洋信号，可以被视为低维混沌系统。 此外，大西洋地区的大气和海洋数据也显示出重大的气候变化。 将通过应用为预测混沌系统行为而开发的非线性统计建模技术来尝试预测 ENSO 和大西洋涛动。 几个非线性统计模型的性能将通过早在 1950 年的数据集进行后报实验来验证和优化。绝对验证实验将是由 1985 年至 1990 年期间 ENSO 海温事件的后报，由工作组确定。 TOGA 季节到年际预测计划 (T-POP)。 开发的算法，特别是连接机的算法，基于新的建模技术，并将随时可供气象和海洋学界使用。