Climate Risk Management engine (CRMe)
The Climate Risk Management engine (CRMe) is an extensible, efficient NCL-based code set (with capability to integrate R packages) used to process and analyze large climate data sets (daily or monthly) from a variety of differernt sources (observations, model output) and on any horizontal resolution (global, regional, local). CRMe can handle various processing tasks such as:
- preprocessing, subsetting, and regridding data,
- standardizing the data into CF-compliant netCDF,
- restructuring daily data to enable very efficient computations,
- calculation of a variety of sector-oriented indices for various application groups,
- aggregation of data into climatological period statistics,
- visualization of the various output datasets,
- tailored data delivery through a variety of modalities, such as a search-enabled Earth System Grid Federation portal, to Dashboards, or even into dynamic Excel spreadsheets.
CRMe provides consistent data provenance by preserving metadata from source datasets and implementing a structured metadata schema with controlled vocabulary. These provisions enable downstream applications (such as Open Climate GIS) and enable for controlled extensible search.
CRMe implements a simple form of workflow control and is flexible to be run on a desktop, a server, or a supercomputer. The result is that CRMe can take a ~1 TB dataset of high resolution global observational data and compute 1600+ calculation datasets and visualize these in a matter of hours.
CRMe employs workflow parallelization by dataset, so scores of climate model datasets can be run simultaneously over multiple climate periods. CRMe can process 10+ TB of CMIP5 climate data into several hundred sector-oriented indices with a turn-around time of a few days.
CRMe supports "Big Data"-sized problems (e.g., 100+ years of global data at half degree grid spacing, or 60+ years of global data at quarter degree grid spacing).
These capabilities, coupled with the CRMe Viewer, provide powerful new ways to browse and compare large sets of climate data in past and future climates.