Different kinds of observations feature different strengths, e.g. visible-infrared imagery for clouds and radar for precipitation, and, when integrated, better constrain scientific models and hypotheses. Even critical, fundamental operations such as cross-calibrations of related sensors operating on different platforms or orbits, e.g. spacecraft and aircraft, are integrative analyses. The great variety of Earth Science data types and the spatiotemporal irregularity of important low-level (ungridded) data has so far made their integration a customized, tedious process which scales in neither variety nor volume. Generic, higher-level (gridded) data products are easier to use, at the cost of being farther from the original observations and having to settle with grids, interpolation assumptions, and uncertainties that limit their applicability. The root cause of the difficulty in scalably bringing together diverse data is the current rectilinear geo-partitioning of Earth Science data into conventional arrays indexed using consecutive integer indices and then packaged into files. Such indices suffice for archival, search, and retrieval, but lack a common geospatial semantics, which is mitigated by adding on floating-point encoded longitude-latitude information for registration. An alternative to floating-point, the SpatioTemporal Adaptive Resolution Encoding (STARE) provides an integer encoding for geo-spatiotemporal location and neighborhood that transcends the use of files and native array indexing, allowing diverse data to be organized on scalable, distributed computing and storage platforms.

不同类型的观测具有不同的优势，例如云的可见红外图像和降水的雷达，并且在整合时可以更好地约束科学模型和假设。即使是关键的基本操作，例如在不同平台或轨道（例如航天器和飞机）上运行的相关传感器的交叉校准，也是综合分析。迄今为止，地球科学数据类型的多样性和重要的低级（非网格）数据的时空不规则性使它们的集成成为一个定制的、乏味的过程，无论是在种类还是数量上都没有规模。通用的、更高级别（网格化）的数据产品更易于使用，代价是距离原始观测值更远，并且不得不解决限制其适用性的网格、插值假设和不确定性。难以将不同数据可扩展地汇集在一起​​的根本原因是当前地球科学数据的直线地理分区到使用连续整数索引索引的传统数组中，然后打包到文件中。这些索引足以用于存档、搜索和检索，但缺乏通用的地理空间语义，这可以通过添加浮点编码的经纬度信息进行注册来缓解。作为浮点数的替代方案，时空自适应分辨率编码 (STARE) 为地理时空位置和邻域提供整数编码，超越了文件和本机数组索引的使用，允许在可扩展的分布式计算和存储上组织不同的数据平台。