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:
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.
CRMe has been in nearly continuous development since 2013.
We thank James McCreight for assistance in parallelizing some of the R packages that CRMe uses.
We also acknowledge the NCL Development Team for their great support. CRMe development has sometimes uncovered bugs in NCL that have been quickly fixed by the NCL Development Team. The NCL Development team includes: Mary Haley, David Brown, Wei Wang, Richard Brownrigg, Dennis Shea, and others.
This is the project that launched CRMe. The goal was to evaluate a variety of downscaling methods over the continental United States. CRMe was used to generate 1600 metrics and indices for evaluation and comparison of the various downscaling techniques. Three types of output were generated: publication-quality plots, CF-conforming netCDF files, and XML files containing the metadata that corresponded to the data. These output datasets were loaded into an Earth System Grid Federation (ESGF) portal with machine-enabled search capability. These outputs supported the QED Workshop that was held at NCAR in August 2013.
http://earthsystemcog.org/search/ncpp/ - Search the NCPP Data Collection on the UC-Boulder ESGF Portal (requires an OpenID to access)
CRMe was used to generate some of the datasets for the next generation Climate Change Knowledge Portal. CRMe generated monthly means, anomalies, and ensemble aggregations (10th, 50th, and 90th percentiles) for 16 CMIP5 model datasets for four scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) for the five climate periods (baseline climate period: 1986-2005, future climate periods: 2020-2039, 2040-2059, 2060-2079, and 2080-2099).
CRMe generated climate data used to populate an interactive "climate inspector" tool for the United Arab Emirates AGEDI project. This work involved taking dynamically downscaled WRF model data, aggregating into climate data, and comparing against gridded observations (the Princeton global quarter degree dataset).
CRMe was used to process a set of dynamically-downscaled WRF model data over a region of South America. These datasets were used in support of a training workshop that was held in Peru.
CRMe is being used to compute indices and datasets for the purpose of verifying climate models. This project has resulted in the addition of several new indices, such as the heat index and heating/cooling degree days. This project has also supported the creation of a set of datasets for various indices using the quarter degree global Princeton dataset. Due to the computational requirements of running such a large global dataset, this work has improved CRMe's support of "Big Data"-sized problems.
Recently CRMe has been used to calculate and distill a massive amount of climate data to be used as input for climate risk screening tools for various industry sectors (forestry, ports and waterways, and insurance). Through collaboration with several consulting teams and industry experts, 100+ new sector-oriented indices were developed to use in assessing sector-specific climate risks. CRMe was then used to process 33 CMIP5 model datasets for RCP4.5 and RCP8.5 scenarios, and then aggregate the resulting ensembles into the 50th and 66th percentiles. The unique data delivery requirements of this project resulted in the development of a new data delivery modality: import of climate data directly into dynamic Excel spreadsheets.
CRMe was used to process the underlying climate data used in a paper examining the climate impacts on Salmonella:
This material is based on work supported by the National Science Foundation under Grant No. NSF - AGS 1243030.
This work was also supported by the National Oceanographic and Atmospheric Administration through Grant No. NOAA - M0936098.
Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies.
The NCAR Command Language (Version 6.3.0) [Software]. (2016). Boulder, Colorado: UCAR/NCAR/CISL/TDD. http://dx.doi.org/10.5065/D6WD3XH5