Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud services.

中文翻译：

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基于网络的生物气象研究支持系统

数据是开展科学研究的基本组成部分，旨在了解空间和时间中的自然现象。数据处理的概念无处不在，几乎适用于任何需要从数据中获得洞察力的项目。向用户提供的信息源、数据格式和下载服务的可用性不断增加，使得在多个科学领域中重用或开发这些新资源的潜力变得困难。在本文中，我们提出了一种用于下载大气数据集的空间提取-转换-加载（spatial-ETL）方法，以生成新的生物气象指标并将其公开以供研究重用。我们工作中涉及的技术和流程在 GDAL 库和 Python 编程语言是地理处理工具开发和实施的关键要素的上下文中得到了明确定义。由于国家海洋和大气管理局 (NOAA) 是信息来源，因此每次该服务发布更新的大气预测模型时都会执行 ETL 过程，从而获得不同的时空分析预测。因此，我们提出了一个 Web 应用程序，用于在处理后下载这些新创建的数据集，并将交互式 Web 地图与许多地理处理任务的结果可视化。我们还详细说明了用于设计这些流程的所有功能和技术，