New Ideas in Statistical Seasonal Forecasting: Application to North American Temperature and Precipitation

该项目将单独或合并发展和比较最近提出的进一步改进统计季节预报的几种设想，具体应用于北方的表面温度和降水。这些想法是：1）使用来自19世纪而不是仅来自20世纪世纪中期的海表温度（SST）训练数据（这是常规的），以提高拟合模型的统计稳定性; 2）除了SST外，还使用了额外的低频表面预报因子，特别是来自北美积雪的预报因子;（3）典型相关分析、最大协方差分析和冗余度分析作为统计预测框架的探索和比较; 4）由于持续的气候变化和潜在的其他低-频率变化，通过一个时间依赖性的“铰链”的平均函数;和5）探索一种新的方法来过滤明显不可预测的季节内变化从predictandseasonal手段，通过计算潜在的更可预测的“慢”的模式，网格predictand值可以投射。利用这五个要素的各种组合构建的完全样本外的回顾性预报将在模拟季节预报业务生产中的现实限制的实验环境中进行评估和比较。该项目的更广泛影响包括：1）产生实际结果，从而基于动力和统计预报工具的共识改进季节预报，为了更好地支持对季节性气候变化敏感的各种企业的长期决策; 2）对天气和气候风险管理产生重大影响，可能使企业，消费者和公共政策制定者受益。该项目将有助于培养一名博士。统计气候诊断和预测领域的学生。

The project will develop and compare, alone and in combination, several recently suggested ideas for further improvement in statistical seasonal forecasts, with specific application to northern hemisphere surface temperature and precipitation. These ideas are: 1) use of sea surface temperature (SST) training data from the 19th century rather than from the mid-20th century only (which is conventional ), in order to improve statistical stability of the fitted models; 2) use of additional low-frequency surface predictors in addition to SSTs, specifically predictors derived from North American snow cover; 3) exploration and comparison of Canonical Correlation Analysis, Maximum Covariance Analysis, and Redundancy Analysis as statistical prediction frameworks; 4) modeling and accounting for nonstationarity in predictand means due to ongoing climate change and potentially other low-frequency variations through a time-dependent "hinge" mean function; and 5) exploration of a novel approach to filtering apparently unpredictable intraseasonal variations from predictand seasonal means, through computation of potentially more predictable "slow" patterns onto which gridded predictand values can be projected. Fully out-of-sample retrospective forecasts constructed using various combinations of these five elements will be evaluated and compared in an experimental setting that simulates real-world constraints in the operational production of seasonal forecasts.Broader impacts of this project include the potential to 1) produce practical results leading to improved seasonal forecasts based on a consensus of dynamical and statistical forecast tools, in order to better support long-range decision making in a variety of enterprises sensitive to seasonal climate variations; 2) have a significant impact on weather and climate risk management, potentially benefiting businesses, consumers and public policy makers. The project will contribute to the training of a Ph.D. student in the area of statistical climate diagnostics and prediction.