OPeNDAP Access in Python to Derive Climate Normals and Anomalies of Daymet netCDF4 Yearly Data

Author: ORNL DAAC

Date: February 28, 2018

Contact for ORNL DAAC: [email protected]

Keywords: ORNL DAAC, Daymet, weather estimates, Anomalies, Normals, THREDDS, OPeNDAP, Earthdata, pydap

Overview

In this tutorial, we will explore using the pydap package to plot Daymet data. Specifically, we will calculate anomalies and normals for a weather estimate such as tmin, tmax, and prcp. To learn more about how to use Jupyter Notebooks, check out this YouTube video.

Source Data

A spatial subset of the North American Daymet dataset daily data; https://daymet.ornl.gov . In this example, we will access the data from this ORNL DAAC's THREDDS server.

Prerequisites:

Python 2.7 or greater. Python modules: pydap, basemap, matplotlib. To run this notebook locally, you will also need: iPython, ipywidgets, jupyter. Requirements are also in requirements.txt

For scientific computing, anaconda is recommended as it come pre-installed with packages such as numpy, iPython, and matplotlib.

Most operating systems, however, come with Python. To install the necessary python libraries, you can copy the requirements.txt from this repository and run:

pip install -r requirements.txt

Procedure

In this tutorial the Daymet data is separated by year rather than aggregated into one tile. You can access the Jupyter Notebook here.