Temperature Record Frequency Dashboard
urth-core-watch patchand a few hacks.
- ToDo: revisit code after v0.1.1 is release of ```urth`` components.
This analytical notebook is a component of a package of notebooks. The package is intended to serve as an exercise in the applicability of Juypter Notebooks to public weather data for DIY Analytics.
This notebook makes use of the following Project Jupyter features:
There has been a great deal of discussion around climate change and global warming. Since NOAA has made their data public, let us explore the data ourselves and see what insights we can discover.
- How many weather stations in US?
- For US weather stations, what is the average years of record keeping?
- For each US weather station, on each day of the year, identify the frequency at which daily High and Low temperature records are broken.
- Does the historical frequency of daily temperature records (High or Low) in the US provide statistical evidence of dramatic climate change?
- What is the average life-span of a daily temperature record (High or Low) in the US?
If there is scientific evidence of extreme fluctuations in our weather patterns due to human impact to the environment then we should be able to identify factual examples of increase in the frequency in extreme temperatures.
This notebook was developed using a March 16, 2015 snapshot of USA-Only daily temperature readings from the Global Historical Climatology Network. The Data Munging project was used to generate datasets in CSV format.
Data Preparation Options
- Use the NOAA data Munging project to generate CSV files for the latest NOAA data.
- Use the sample March 16, 2015 snapshot provided in this repo. Open a terminal session and run these commands:
$ cd /home/main/notebooks/noaa/hdtadash/data/ $ tar -xvf station_summaries.tar
- Open the
- Run all cells
- Change view to dashboard mode
- GHCN-Daily journal article: Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910.
- Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), [Version 3.20-upd-2015031605], NOAA National Climatic Data Center [March 16, 2015].