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

项目摘要/叙述 极端高温和寒冷事件(欧洲高等教育和欧洲经委会)与一系列不良健康后果有关 加重既往疾病至精神疾病和呼吸系统及心血管疾病 研究往往通过简单化的方法确定受欧洲高等教育/欧洲经委会影响的地区和人口 将最近气象站的温度数据分配给正在研究的人口(例如，人口普查 地区或邮政编码)。我们的初步分析表明，动态时空 方法论大大减少了在传统方法中往往会发生的错误分类。 实施更复杂的具有更高空间和时间分辨率的模型可以设置计算量 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 利用云计算资源通过以下方式简化和扩展EHE/ECA识别工作流 改进了规范，以帮助配置内部部署计算。目标1：提高可伸缩性和 云中检测极端气候事件的计算效率与现场计算相比，我们将 开发和实施计算方法，以(1)扩大空间内插方法 大陆规模使用并行和分布式计算算法，(2)监控和评估性能 在计算时间、内存分配和存储资源方面与 专用服务器利用率和传统高性能计算(HPC)方法。我们假设 云计算将提高已在内部实施的当前方法的效率 使用All-in内存解决方案和序列化数据流水线的计算基础设施。我们将利用 空间数据库以及DevOps工具和服务(如集装箱化)的效率，以及 自动化部署，以简化我们的研究工作流程。目标2：提高准确性和稳健性 云中极端气候事件识别与本地计算的对比我们将评估 应用于大陆非均匀气候资料的动态EHE/ECE值划分方法 地理和更远的地方。具体地说，我们将评估云计算改善 时空方法在确定受EHE/ECA影响的人口和地区方面的准确性 不同的模型参数化方案。我们假设云计算将提高精度 通过(1)促进开发更复杂的模型，提高EHE/欧洲经委会识别方法的稳定性 考虑到其他环境变量，(2)通过简化可再生性实践 使更广泛的科学界能够在多个范围内测试和验证模型，从而产生更多 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