Augmented mapping of the Extreme Heat and Cold Events (EHE/ECE) at continental scale with cloud-based computing

项目摘要/叙述 极端高温和低温事件（EHE/ECE）与一系列不良健康结果有关， 精神疾病、呼吸道疾病和心血管疾病的恶化 研究往往通过简单的方法确定受EHE/ECE影响的地区和人口 将来自最近气象站的温度数据分配给正在研究的人群（例如，普查 区域或邮政编码）。我们的初步分析表明，动态时空 这些方法显著地减轻了在传统方法中倾向于发生的错误分类。 实现具有更高空间和时间分辨率的更复杂的模型可能会造成计算困难。 复杂性阻碍了动态模型的应用和可扩展性。在这里，我们提出了一种混合方法 利用云计算资源简化和扩展EHE/ECE识别工作流程， 改进的规范，以帮助配置本地计算。目标1：提高可伸缩性， 云计算与本地计算检测极端气候事件的计算效率我们将 开发和实施计算方法，以（1）扩大空间插值方法， 大陆规模使用并行和分布式计算算法，（2）监测和评估性能 这些算法的计算时间，内存分配和存储资源相比， 专用服务器利用率和传统的高性能计算（HPC）方法。我们假设 云计算将提高目前在本地实施的方法的效率 计算基础设施使用全内存储器解决方案和串行化数据管道。我们将利用 空间数据库的效率沿着提高，以及DevOps工具和服务（如容器化），以及 自动化部署，以简化我们的研究工作流程。目标2：提高 云计算与本地计算的极端气候事件识别我们将评估 动态EHE/ECE划分方法在应用于大陆非均匀气候资料时 地理和超越。具体来说，我们将评估云计算在多大程度上改善了 使用EHE/ECE确定受影响人口和地区的时空方法的准确性 不同的模式参数化方案。我们假设云计算将提高 和鲁棒性EHE/ECE识别方法（1）促进更复杂的模型的开发 考虑到额外的环境变量，（2）通过简化再现性实践， 使更广泛的科学界能够在多个尺度上测试和验证模型， 可靠的模型

Project Summary/Narrative Extreme heat and cold events (EHE/ECE) have been linked to a range of adverse health outcomes from exacerbated pre-existing conditions to mental illness and respiratory and cardiovascular disease Previous research has often determined the areas and population impacted by EHE/ECE through simplistic methods that assign temperature data from the closest weather station to the population being studied (e.g., a census tract or postal zipcode). Our preliminary analysis has demonstrated that dynamic spatial-temporal methodologies significantly alleviate misclassifications that tend to occur in conventional approaches. Implementing more sophisticated models with higher spatial and temporal resolution can pose computational complexity which hinders application and scalability of the dynamic models. Here we propose a hybrid method to leverage cloud computing resources to streamline and scale up EHE/ECE identification workflows with improved specification to help configuration of on-premises computing. Aim 1: Improving scalability and computational efficiency of detecting extreme climate events in-cloud versus on-premises computing We will develop and implement computational methodologies to (1) scale up the spatial interpolation methods at continental scale using parallel and distributed computing algorithms, (2) monitor and assess the performance of these algorithms in terms of computational time, memory allocation and storage resources compared to the dedicated server utilization and conventional High-Performance Computing (HPC) approach.We hypothesize that cloud computing will improve efficiency of current methods which have been implemented on on-premises computing infrastructure using all-in memory solutions and serialized data pipeline. We will leverage efficiencies of spatially enabled databases along with DevOps tools and services such as containerization, and automated deployment to streamline our research workflows. Aim 2: Improving accuracy and robustness of extreme climate events identification in-cloud versus on-premises computing We will assess the robustness of dynamic EHE/ECE delineation methods when applied to heterogeneous climatological data at continental geographies and beyond. Specifically, we will evaluate the extent to which cloud computing improves the accuracy of the spatial-temporal methods in identifying populations and areas impacted by EHE/ECE using different model parameterization scenarios. We hypothesize that cloud computing will improve the accuracy and robustness of EHE/ECE identification methods by (1) facilitating development of more complex models that take into account additional environmental variables, (2) by streamlining reproducibility practices that enables the wider scientific community to test and validate the models at multiple scales that results in more reliable models