Say, what are you doing this afternoon?

Bus Stop - Under The Rain by Leonid Afremov

Here's a happy little afternoon project for new users trying to play with a new
language or script, and getting their feet in the open-source ecosystem.

Your phone's handy weather app depends upon the goodwill of a for-profit data provider, and their often-opaque API (14 degrees? Where was it observed? When?) That's a shame because most data collection is paid for by you, the taxpayer.

Let's take the profit-from-data out of that system. Several projects have tried to do this before (including libgweather), but each tried to do too much and replicate the one-data-provider-to-rule-them-all model. And most ran aground on that complexity.

Here's where you come in

One afternoon, look up your weather service's online data sources. And knock together a script to publish them in a uniform format.

Here's the one I did for the United States:
Worldwide METAR observation sites
US DOD and NOAA weather radars
US Forecast/Alert zones

• Looking for data on non-METAR (non-airport) observation stations, weather radar sites, and whatever forecast and alert areas your country uses.


• Use the same format I did: Lat (deg.decimal), Lon (deg.decimal), Location Code, Long Name. Use the original source's data, even if it's wrong. Area and zones should use the lat/lon of the centroid.


• The format is simple CSV, easy to parse and publish.


• Publish on GitHub, for easy version control, permalinking, free storage, and uptime.


• Here's the key: Your data must be automatically-updated. Regularly, your program must check the original source and update your published version. How I did it with a cron job. Publish both the data and your method on GitHub. 


• When you have published, drop me an e-mail so I can link to your data and source.

If you do it right, one afternoon to setup your country's self-updating database. Not a bad little project, you learn a little, and you help make the world a better place.

My country doesn't have online weather data

Sorry to hear that. You're missing some great stuff.

If you live in a country with a reasonably free press and reasonably fair elections, make a stink about it. You are probably already paying for it through taxes, why can't you have it?

If you live somewhere else, then next time you have a revolution or coup, add 'open data' to the long list of needed reforms.

What will this accomplish?

This will create a free, sustainably updated, uniform, crowdsourced set of accurate worldwide data that will be easy to compile into a single global database. If you drop out, your online code will ensure another volunteer can step in.

This is one fundamental tool that other free-weather projects have lacked. And any weather project can use this.

The global database of locations is really small by most database standards. Small enough to easily fit on a phone. Small enough to be bundled with apps that can request data directly from original sources...once they can look up the correct source to use.

How will this change the world?

It's about simple tools that make
it easy to create free, cool software.
And it's about ensuring free access to data you already paid for.

Not bad for one afternoon's contribution.

How to set up automatic Github uploads

Back to basics

For years, I have been playing with US Weather Data.

A couple years ago, I came up with a lookup server for US weather locations, and -of course- had all kinds of plans for how to expand it to more services and more countries.

So, of course, it went nowhere.

This week, I took a look at the issue again, and came up with a simple set of lookup tables (yes, a tiny database) to replace the server. It reduced the amount of code by about 70%, and it's finally in a maintainable, expandable form. It's simple, and ready for a few drive-by patches.

Which leads to the next question...how to share it with the world?

Well, in this decade, sharing software and data means putting it on GitHub.

Creating the GitHub repositories

We have a Python 3 script that creates the database, another Python 3 example script that consumes the database, and the three CSV files that make up the database tables. Most consumers will probably just want the data for their own purposes - they really don't need the scripts (and it would be quite rude for millions of users to hit NOAA servers to produce their own databases each week)

So I created two repositories on GitHub, one for the data and another for the optional scripts.

If you haven't used GitHub, you first go online and create your account and create the repositories (with initial README file) in a web browser. Then:

cd /tmp
git clone https://github.com/ian-weisser/data   # Create a clone of the online repository
cd data

cp /home/somewhere/else/data/* ./               # Add existing data files into the local clone
git add ./*                                     # Tell git to track the new data files
git commit -m "Add initial data"
git push origin master                          # Push the changes back to GitHub
  Username for 'https://github.com':
  Password for 'https://me@github.com': 

cd /tmp 

rm -rf /tmp/data                                # Clean up

Overall, it's pretty simple. For the next iteration:

• We must automate the upload credentials so it doesn't prompt for a username and password each time.
• We must add a cron job that creates the data for upload.
• Let's move this project from /tmp on my laptop to the server where it will live permanently. This also means that the server should pull the latest Python script...which is also hosted on GitHub.

Let's setup the server:

sudo apt-get install git    # On this server, about 3 MB of download
sudo apt-get install python3-httplib2  # Script dependency
mkdir uploads
cd uploads
git clone https://github.com/ian-weisser/data          # Setup the local 'data' repository
git clone https://github.com/ian-weisser/weather       # Setup the local 'software' repository
weather/nws_database_creator.py                        # Use the script to update CSVs in ~/uploads/data
cd data
git config --global user.email "my_email@example.com"  # First time setup
git config --global user.name "My Name"                # First time setup
git config credential.helper store                     # First time using this repo setup
git add -A
git commit -m "Test update"
git push origin master
  Username for 'https://github.com': my-github-login-name   # First time using this repo setup
  Password for 'https://my-github-login-name@github.com':   # First time using this repo setup

Automating the process

So now subsequently, without all the first time setup:

#!/bin/sh

dir=/home/ian/uploads
hub="https://github.com/ian-weisser"
now=$(/bin/date +%F)

cd $dir/weather
/usr/bin/git pull "$hub"/weather      # Update the database_creator script

cd $dir/data
/usr/bin/git pull "$hub"/data         # Need to pull to be able to push later

$dir/weather/nws_database_creator.py  # Do the update
/usr/bin/git add -A                   # Record any changes
/usr/bin/git commit -m "Scheduled update $now"
/usr/bin/git push origin master
exit 0

And now we can run the shell script weekly from a user cron job:

5 6 * * 7 /home/ian/uploads/cron/weekly_weather > /home/ian/uploads/weather.log 2>&1

A couple of tests, and it works.

It's completely automatic.

Once a week, my server downloads the latest make-a-database script, builds the database, and uploads the database to GitHub for the world to use.

The data

If you want to see the final product (the lat/lon lookup tables for US weather), you can get them at:
https://raw.githubusercontent.com/ian-weisser/data/master/metar.csv
https://raw.githubusercontent.com/ian-weisser/data/master/radar.csv
https://raw.githubusercontent.com/ian-weisser/data/master/zone.csv

They will, of course, be automatically updated weekly.