Raspberry Pi lightning strikes detection station that tweets storm information

A few days ago I was browsing the world wide web, when suddenly I stumbled upon an article speaking about an IC sensor made by AMS, called the AS3935 Franklin Lightnin Sensor, which could detect the electrical signature of lightning strikes up to 40km and give the “strength” of the lightning as well as an estimation of the distance to the head of the storm. And all this in a tiny 4x4mm SMD circuit designed to be embedded in personal weather stations, watches, cars, etc.

My dark nerd side was tickled and I suddenly needed wanted to buy one and build a lightning detection station that could tweet about incoming storms, using my Raspberry Pi which is lying on a table doing nothing most of the time. And so I did it ! What I ended up with is a Twitter account on which lightning strikes info is tweeted in real time, with energy and distance estimation : https://twitter.com/toulouse_orages

After some research, I found that Embedded Adventure was selling a module with a pre-soldered sensor with the right antenna and components added to the board, ready to be used via an I2C interface. Some more research and I found that somebody already implemented a Python library to use such sensors on a Raspberry Pi. Perfect ! We have everything we need.


Wiring the sensor to the Raspberry Pi

Here is the wiring diagram to connect the sensor to the Raspberry Pi (you can use a different GPIO pin for the IRQ, but remember to change code accordingly):

AS3935 PinMOD-1016 PinRaspberry Pi Pin
4 (GND)GND25 (Ground)
5 (VDD)VCC1 (3v3 Power)
10 (IRQ)IRQ11 (GPIO 17)
11 (I2CL)SCL5 (SCL)
13 (I2CD)SDA / MOSI3 (SDA)

Here is a picture of my setup. For now, it’s just a quick prototype set up on a breakboard :

Raspberry Pi + AS9535 Lightning sensor

Writing the code to make it work

The Python library is pretty well self-documented, so just browse through the code and it should be enough. If you want to understand exactly how each of the sensor parameter works (Watchdog Threshold, Signal Rejection, etc, even some of which are not implemented in the library), refer to the AS9535 datasheet.
It could be useful to change those parameters that are not directly exposed, and it’s pretty easy to do so by directly calling RPi_AS3935.set_byte(register, value) (just be sure to call RPi_AS3935.read_data() first, and get the current value of the register so you only change the bits you need in it). For the Twitter part of the script, I chose to use the Tweepy module.

I just wanted to write a script that would listen for lightning, then tweet when a storm is happening. So first, to avoid getting false positives, I set the “minimum strikes” parameter to 5 (the sensor waits for 5 strikes in a predefined period before it starts sending IRQs for each subsequent strike). Then I have a handler which is run each time an IRQ is received. If it’s a strike, we tweet about it. But to avoid spamming tons of tweets, I set up a 5 minutes delay : if more than one strike happen during those 5 minutes, I’ll just tweet the number of strikes that have been detected. After 30min with no activity from the sensor, we can declare that the storm is over and tweet people that they should be okay now.

Here is an example tweet (yes it tweets in French, because I live in France. Baguette.) :

The translation would be “/!\ 117 strikes detected in the last 5 minutes. Power of the last strike : 429714 – distance to the head of the storm : 1km“.
As you can see, it was a pretty violent storm, with tons of intra-cloud strikes happening and being detected almost every other second (which is close to the maximum the sensor can detect, since in the datasheet we can read it waits for 1,5s after a strike has been detected and analyzed, before it starts listening again).
The Twitter account is : https://twitter.com/toulouse_orages

Final words

I had the chance to set it up right before a period of storms, here in Toulouse. At first I thought it might be crazy because it detected hundreds of strikes every few minutes. But no… it’s been 4 days now and I saw no false positives. The sensor didn’t detect any lightning for 48 hours, and started going crazy anytime a storm was approaching ! So I’m really happy with the results, that are much better than I would have thought from a little sensor like it. Especially since I didn’t take any time to calibrate it properly and mainly used factory settings. I just used the antenna capacitor tuning value given by Embedded Adventure, but it can be a good idea calibrating it again since temperature and other factors can influence it. A way to calibrate the antenna by reading the oscillation speed from the IRQ pin using an Arduino is explained here : as you can see, the process could be easily automatized.

If you’re interested in building your own station, you can find the final Python code for this bot on Github : Github – Hexalyse / LightningTweeter

Feel free to fork it, modify it and hack it to your needs ! If you have any question, remark or observation, do not hesitate to post a comment.

Hope you enjoyed 🙂 Happy storm-hunting !

37 Replies to “Raspberry Pi lightning strikes detection station that tweets storm information”

    1. Hey Michael !
      Thanks for your comment and the article about my project you posted on your website 🙂

  1. Bonjour
    c’est la bonne époque pour les détecteurs d’orage ! 🙂 bel article, je viens de commander une carte chez Embedded Adventures 😀
    me donnes tu l’autorisation d’utiliser ton article (en traduction française) sur framboise314 (avec des liens vers ta page et vers facebook) ?

    1. Bonjour François,

      Désolé ton commentaire avait été marqué en spam par Akismet (tsss…), je ne viens que de le voir. Ravi d’avoir fait naître quelques projets chez d’autres personnes aussi 🙂 Oui bien sûr, tu peux traduire, partager etc. mon travail. Tant que tu linkes/cites pour référence, ça me va 🙂
      J’ai d’ailleurs hésité à tenir ce blog en français dans un premier temps, mais je me suis dit que ce serait dommage de limiter autant mon public potentiel, et qu’en anglais ça permettrait à bien plus de gens de comprendre.

        1. Merci François.
          Article super détaillé et bien imagé (je devrais prendre modèle pour mes prochains articles afin de les rendre un peu plus agréables à lire).

  2. Thank you Hexalyse!!!!

    I am in to automation here…too bad we cannot automate lightning yet 😉

    I enjoy playing with lightning lately….



  3. Nice!

    Could you comment on or provide a picture of your enclosure for this project when deployed it? Did you deploy it to a roof top?


    1. For now it’s still on this little breakboard, because its small enough that id doesn’t bother me. I don’t play to export it outside for now, because it’s working fine from inside (it’s right behind a window, and I doubt I would benefit a lot from taking it outside).

      Thanks for your interest 🙂

    1. Unfortunately, I don’t think I can. If I remember correctly, they work with people who maintain better stations, with much more expensive hardware, which GPS receiving capability for very precise time measurement, etc.

    1. But for that, I would need a way to get a very precise time clock… for example with a GPS receiving circuit, which can be pretty expensive.
      Because we’re not talking about sound delays, but radiowaves, which, over a few km, would be delayed by a few microseconds maximum.

      1. Won’t something like a Ublox Neo 6 give you the timestamp you need ? Cost less than €10 ?


        1. Indeed it would (thanks for the info I didn’t know this module). The problem is that the AMS sensor takes some time to process a lightning before it triggers the IRQ pin : a few ms to determine if it’s a real lightning strike or a “disturber” (electromagnetic interferences), then quite some time processing data to compute strenght of the strike and statistical distance of the storm.
          All in all, when the IRQ pin is triggered, sometimes up to 1 second has passed since the lightning stroke. So… even if you get the timestamp from the Ublox Neo 6 at this moment, it would be completely inaccurate.

          In conclusion, this sensor won’t allow somebody to get a precise timing of when the lightning occurred because of how it works.

  4. Hello,

    why not add the Pi camera so it would take pictures when there is a storm then tweet the pics too…? 🙂

    1. It’s a pretty good idea. But I’m afraid at the time the sensor triggers the interrupt, the lightning has already passed (the electric arc lasts for a very short amount of time). So I don’t think it’s a viable way of triggering a camera to take lightning pictures.

  5. Bonjour j’ai commandé le lightning sensor v8 et reçu hier j’ai installé LightningTweeter-master et RaspberryPi-AS3935-master j’ai une petite question comment faire en sorte pour que ça twitte sur mon compte twitter
    merci d’avance

    1. Bonjour lolo,

      Alors il te faut obtenir une clé d’API pour Twitter. Cherche sur Google comment faire, c’est pas très compliqué. Une fois que tu l’as, tu as juste à les renseigner dans le code là où il faut (le nom des variables est assez explicite, et correspond au nom des différentes clés à renseigner).

      Have fun tweeting storms 🙂

      1. Cool ! Je suis content que ça marche et que ça te plaise. N’hésite pas à partager ce que tu as créé avec 😉

  6. I have a AS3935,with interface named “MOSI” “MISO”.
    I want to kown,if “MOSI”is the same of “MOSI SDA”?
    Why I connect “MOSI” with “sda” of Raspberry.I could not get anything by use “i2cdectect -y 1” in raspberry. I am suse the i2c is right.
    chould you give me an e-mail? my:346236337@qq.com
    I got a sensor like this:https://www.aliexpress.com/item/AS3935-Sensor-Breakout-Board-SPI-I2C-Interface-Strikes-Thunder-Rainstorm-Storm-Distance-Detection-2-4V-5/32838837144.html

  7. Found your blog through Adafuit.com. Thanks for the tutorial about your project. It was helpful to follow and assemble a lightning detector.

Leave a Reply to micheal wang Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.