Discovering New Global Climate Patterns: Curating a 21-Year High Temporal (Hourly) and Spatial (40km) Resolution Reanalysis Dataset

The National Center for Atmospheric Research’s Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40km Reanalysis dataset is a dynamically downscaled dataset with high temporal and spatial resolution. The dataset contains three-dimensional hourly analyses in netCDF format for the global atmospheric state from 1985 to 2005 on a 40km horizontal grid (0.4°grid increment) with 28 vertical levels, providing good representation of local forcing and diurnal variation of processes in the planetary boundary layer. This project aimed to make the dataset publicly available, accessible, and usable in order to provide a unique resource to allow and promote studies of new climate characteristics.

When the curation project started, it had been five years since the data files were generated. Also, although the Principal Investigator (PI) had generated a user document at the end of the project in 2009, the document had not been maintained. Furthermore, the PI had moved to a new institution, and the remaining team members were reassigned to other projects. These factors made data curation in the areas of verifying data quality, harvest metadata descriptions, documenting provenance information especially challenging. As a result, the project’s curation process found that:

• Data curator’s skill and knowledge helped make decisions, such as file format and structure and workflow documentation, that had significant, positive impact on the ease of the dataset’s management and long term preservation. 
• Use of data curation tools, such as the Data Curation Profiles Toolkit’s guidelines, revealed important information for promoting the data’s usability and enhancing preservation planning.
• Involving data curators during each stage of the data curation life cycle instead of at the end could improve the curation process’ efficiency.

Overall, the project showed that proper resources invested in the curation process would give datasets the best chance to fulfill their potential to help with new climate pattern discovery.