In the summer of 2015, from June 1st to July 15th, a fleet of scientists set out each evening in the Midwest. Their goal? To gain insight on the nocturnal mesoscale convective storms (MCS's) that plague America's heartland. While MCS's are necessary for irrigation and the replenishing of aquifers, often these storms can become vicious; especially at night when they are least detected.
With the implementation of the field experiment PECAN (Plains Elevated Convection at Night) scientists from eight research laboratories and fourteen Universities including Millersville University of Pennsylvania hope to gain insight to better predict these nocturnal storms. After the data were collected, the Unidata Program Center (UPC) worked closely with Millersville students and academic staff to help standardize their PECAN study data.
Earlier this year, the Unidata Users Committee asked members of the Unidata community to participate in a survey regarding their use of scientific software packages, software training, and community services, and to favor us with their insights into possible future directions for the program. You can read an overview of the survey results in 2016 Community Survey Results.
While Unidata's governing committees and the Unidata Program Center staff will continue to analyze the survey comments in the process of crafting Unidata's next Strategic Plan, individual Program Center development groups are also using the survey as input into their own development plans. This series of articles provides responses from different development groups to comments or concerns raised by the survey. Under review in this article: Python activities at Unidata.
The Python AWIPS Data Access Framework can be used to query available grid parameters and levels if given a known Grid name (as of AWIPS 15.1.3 we can not query derived parameters, only parameters which have been directly decoded).
Unidata is pleased to announce the availability of the Python Data Access Framework (DAF). The DAF provides access to an AWIPS II Environmental Data EXchange (EDEX) server directly from Python code. Created by Unidata Program Center software engineer Michael James, the DAF strengthens Unidata's AWIPS II offerings by making it easier to retrieve data from the AWIPS II data storage system from outside the Common AWIPS Visualization Environment (CAVE).
Users have always been able to request real time NCEP/NWS weather data using AWIPS II, but now, with the addition of the Python DAF, users can request this data using only simple python commands.
The 2016 UCAR Software Engineering Assembly (SEA) Software Engineering Conference and Tutorials (https://sea.ucar.edu/conference/2016) just wrapped up at UCAR's Center Green facility in Boulder, Colorado. The week long conference took place April 4–8, 2016, and focused on “Data Science for scientific disciplines.” Unidata staff were on hand, and on the final day, held a python tutorial titled “Visualizing meteorological data with Python: Use cases with Siphon and MetPy.” Over 40 people participated in the tutorial.
This is Part 2 of a series of notebooks showing how to plot GINI-formatted satellite data from a THREDDS server using MetPy and Siphon. In Part 1 we covered how to access and parse the data file. In this part, we cover:
This is the first of what we hope will be a series of posts showing how to use Python for weather analysis and create graphics for a variety of purposes. In this two-part post, we demonstrate plotting a water vapor satellite image, specifically using GINI formatted data. GINI is the format currently used for satellite data transmitted across NOAAPORT, and is available on Unidata's demonstration THREDDS server. This first part focuses on accessing the data using Siphon and MetPy; the second part will introduce plotting using CartoPy.
