Advancing Sub-Seasonal Weather Predictability Through Machine Learning Techniques

目前的天气预报系统可以对日常天气做出有价值的预测，但这种预测只能提前一周左右。 在较长的次季节时间尺度上，例如在两到八周之间，也可能对平均情况和极端事件的可能性进行有用的预测。但基础科学问题，包括亚季节变化的来源和机制，其潜在的可预测性，以及可靠预测所需的基本要素，尚未得到解决。 这些问题是实际的，以及科学的兴趣，从亚季节预报的指导可能有各种各样的用途，包括农业规划和应急管理，该项目旨在确定当地的气候变化的利益和大规模模式的相关预测，如海表温度（SST），土壤湿度和大气环流的经验预测关系。首席研究员（PI）及其同事先前的工作证明了德克萨斯州热浪（包括与2011年干旱相关的热浪）与覆盖北太平洋大部分地区的SST模式之间的关系。 这种预测方法的一个关键限制是观察到的记录太短，无法识别统计上显著的预测关系。 因此，在过去的情况下测试时似乎成功的方法在用于实时预测时可能会失败。PI规避这一限制的策略是使用天气和气候模型的输出代替观测来确定预测关系，因为从各种建模项目中可以获得数千年的模拟天气和气候变率。这里使用的模式输出来自北美多模式Ensemble（NMME），亚季节到季节（S2S）预测项目和耦合模式相互比较项目（CMIP）。 开发经验预测方法的另一个问题是需要正则化，这意味着一种消除预测模式中虚假小尺度特征的方法，这些特征是由于用于表示预测字段的大量数据点而产生的。这些特征通常在不同模型之间或模型输出与观测之间并不一致。 PI使用正则化方案，其中拉普拉斯算子的特征向量用于分解小尺度，从而产生更鲁棒的预测关系。 为了进一步确保稳健的预测，PI应用了一种创新的交叉验证技术，该技术绕过了交叉验证中确定的最佳统计模型，而采用了最简单的模型，该模型在最佳模型的标准差范围内。一旦确定了在不同模型和观察中保持的稳健预测关系，将探索这些关系的来源和机制。 如果经验方法可以重现后报从NMME存档模型，然后模型输出可以用来了解随时间变化的动态过程中连接的预测模式预测variability.Although的社会效益的次季节预测，该项目提供了支持和培训博士后，从而促进劳动力发展在这一研究领域。该项目还通过由PI举办的关于次季节预测的公众研讨会来提高公众的科学素养，这是公众感兴趣的话题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Current operational weather forecasting systems can produce valuable predictions of day-to-day weather, but such predictions are only skillful up to a week or so in advance. On the longer subseasonal timescale, say between two and eight weeks, useful forecasts of mean conditions and the likelihood of extreme events may also be possible. But basic science questions, including the sources and mechanisms of subseasonal variability, their potential predictability, and the essential elements necessary for robust prediction, have not yet been resolved. These questions are of practical as well as scientific interest, as guidance from subseasonal forecasts could have a variety of uses including agricultural planning and emergency management.This project seeks to identify empirical predictive relationships between local climate variability of interest and large-scale patterns in relevant predictors such as sea surface temperature (SST), soil moisture, and atmospheric circulation. Prior work by the Principal Investigator (PI) and colleagues demonstrated such a relationship between heat waves in Texas, including the heat wave associated with the 2011 drought, and an SST pattern covering much of the North Pacific. A key limitation to such prediction methods is that the observed record is too short to identify statistically significant predictive relationships. Thus methods which seem successful when tested on past cases may fail when used for realtime prediction. The PI's strategy for circumventing this limitation is to identify predictive relationships using output from weather and climate models in place of observations, as many thousands of years of simulated weather and climate variability are available from a variety of modeling projects. Model output used here comes from the North American Multimodel Ensemble (NMME), the Subseasonal to Seasonal (S2S) Prediction Project, and the Coupled Model Intercomparison Project (CMIP). A further concern in developing empirical prediction methods is the need for regularization, meaning a way to eliminate spurious small-scale features in predictor patterns which arise due to the large number of data points used to represent the predictor fields. Such features are not usually consistent from model to model or between model output and observations. The PI uses a regularization scheme in which eigenvectors of the Laplacian operator serve to factor out small scales, leading to more robust predictive relationships. To further ensure robust predictions, the PI applies an innovative cross validation technique which bypasses the best statistical model identified in cross validation in favor of the simplest model which is within a standard deviation of the best model.Once robust predictive relationships are identified that hold across different models and in observations, the sources and mechanisms responsible for the relationships will be explored. If the empirical methods can reproduce hindcasts from the models in the NMME archive then the model output can be used to understand the time-evolving dynamical processes linking the predictor pattern to the predicted variability.In addition to the societal benefits of subseasonal forecasts, the project provides support and training to a postdoc, thereby promoting workforce development in this research area. The project also addresses public scientific literacy through public seminars by the PI on sub-seasonal prediction, a topic of interest to the general public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.