I designed and developed my Fire Weather Index (FWI) for Personal Weather stations (PWSs) starting with basic logic reasoning (wet wood does not burn), with scientific background and made it usable in the context of PWSs. I found a very interesting and confirming article from October 2016: Impact of anthropogenic climate change on wildfire across western US forests .
In my previous blog I described why and how the predictions on pwsFWI were implemented. Also I promised a short analysis:
We’re now in version 1.8.3 and it seems all to be working OK. The testing is back on the meteorological level, trying to find out how much a two day ahead prediction will differ from the actual calculation when the day has past.
In this blog I will show the results and try to interpret them.
Please note that this is not a scientific or even a full analysis as I am lacking the resources and the data for that.
The past months I blogged several times about my Fire Weather Index for personal weather stations on the basis of Cumulus as data acquisition software. So if you’re new here and don’t know what pwsFWI is about, check out the previous blogs on the subject. Currently, pwsFWI is part of a small software package named CumulusUtils. This meteo website/blog is the home of the package which is distributed through the Cumulus support forum. If you have a weather station which can cooperate with Cumulus, don’t hesitate and get it, it’s free.
Last night Daylight Saving Time switched back to its winter position. That itself is nothing special. Well, maybe it is because the EU is really thinking of abandoning the twice per year hour shift, it is biologically deregulating.
But that is not why I write this [short] message. The switch itself showed up nicely on the temperature graph of my weather station and, as if it was saying Winter is Coming, exactly on the time of the hour switch, the temperature fell from 15 to 10 degrees within an hour and a half.
It shows all nicely on the chart (after the break). Read More »Winter is coming
One of the goals of the current implementation of pwsFWI is to see its behaviour under all conditions. It is therefore very interesting to see that behaviour in the two stations in semi-arid zones (in Australia and in Spain) where long dry periods may suddenly alternate with rain after which the drought returns. Read More »Behaviour of pwsFWI with first rain
You know it is coming, the end of summer, but where I live it usually comes slowly and somewhere end of October you know it is gone. Not this year. This year it was in an instant, well, we may have some nice weather in October of course, but the end was there. And I will show you with some graphs.
Now that version 0.9.0 of my Fire Weather Index for personal weather stations (pwsFWI) has been released in the context of CumulusUtils (I may separate it later into an independent program), it is time to look at formula corrections.
This blog message contains a list of sites, which carry the pwsFWI Fire Weather Index. Please look here for more information, or look here for it’s distribution location on the Cumulus support forum. Look here for its scientific background. Check out the map as well for stations carrying CumulusUtils.
Having developed theoretically a Fire Weather Index (see my pwsFWI) and creating a first implementation of it in C for analytical purposes, I needed to take the software a bit further to make it more robust and useful. I had some feedback which made me realize it was urgent to have a good Fire Weather Index (FWI) for Personal Weather Stations (PWSs).
This blog is about the context and the actual programming requirements.
In An effort for a Simpler Fire Weather Index I described my new FWI and the theory behind it. In short, this pwsFWI (as I have baptised it) is meant to be a generic FWI, valid everywhere and independent of geology and vegetation.
The pwsFWI is a (not too) complex measure of local meteorology, an indicator composed of humidity, wind speed and temperature. It fluctuates under ‘normal’ conditions and if it becomes dryer (a longer period without rain, the number becomes higher. As soon as it starts raining, the value starts dropping.