Collaborative Research: Climate Diagnostics and Predictions 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: 9310715
• 负责人: James Elsner
• 金额: $ 9.95万
• 依托单位: Florida State University
• 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 1996-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9310715&HistoricalAwards=false
• 关键词: Collaborative; Research; Climate; Diagnostics; Predictions

ATM-9310715 Elnser, James B. Florida State University Title: Climate Diagnostics and Predictions 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-9310715 Elnser，James B. 佛罗里达州立大学 使用非线性经验模式的气候诊断和预测 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。 应用的方法，人工神经网络和局部近似模型的基础上，最近已经开发的新兴领域的确定性混沌和并行分布式处理。 目标是建立太平洋和大西洋海盆季节至年际时间尺度上的海表温度异常的熟练模型，并通过预测加强对所涉过程的了解。 季节到年际时间尺度上的气候变率与ENSO密切相关。 最近的研究表明，ENSO，主要是一个太平洋信号，可以被认为是一个低维混沌系统。 此外，大西洋区域也有大气和海洋数据显示的重大气候变化。 将尝试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预测ENSO和大西洋振荡。 几个非线性统计模型的性能将验证和优化，通过后测实验数据集早在1950年。 绝对验证试验将是TOGA季节至年际预报方案工作组确定的1985-1990年期间ENSO SST事件的后报。 所开发的算法，特别是用于连接机的算法，是基于新的建模技术，并将随时提供给气象和海洋学界。