NCAR STATMOS/SAMSI Project: Representing Extremes in High-Resolution Gridded Climate Products

This project is part of the NCAR STATMOS/SAMSI workshop held at NCAR from July 17-21, 2017. The objective of the project is to investigate spatial interpolation methods for climate data that eliminate or minimize smoothing, so that extreme values are better represented. Original project proposal can be found here.

Project participants

• Liz Drenkard, Scripps Institution of Oceanography, liz.drenkard@gmail.com
• Hossein Moradi Rekabdarkolaee, Virginia Commonwealth University, moradirekabh@vcu.edu
• Jared Oyler, Penn State University, jared.oyler@psu.edu

Data

The main analysis data for the project are 1948-2015 daily precipitation observations from the GHCN-D archive. Spatial domains of analysis include the U.S. Mid-Atlantic and Colorado. Data can be found on the Google Drive folder for the project.

Data are provided in netcdf format. Observations are stored as 2D space-wide matrices where each row is a day in time and each column is a station. Missing values are represented as nan. Examples for reading the netcdf files are provided:

• R example
• Python example

Background reading

• High resolution gridded climate products
• Global vs. local accuracy of interpolation methods