In 2021 the Dutch Meteorlogical Institute (which has the predicate royal, koninklijk in Dutch, hence KNMI) started using new normal values. Something they do every 10 years shifting the normal to the most recent three decades.
So from the first of january the normal values are calculated from the years 1990 until 2020 (included). However, the KNMI only publishes the normal values for their official numbers i.e. De Bilt where their office and main weather station is.
So how do I follow? How do I get my normal values for Wagenborgen which is some 175 km north east of De Bilt as the crow flies. The nearest official KNMI stations are Nieuw Beerta and the Airport Eelde. Usually I use Nieuw Beerta for my temperature and humidity calibrations but unfortunately, Nieuw Beerta does not measure rain.
As Airport Eelde is more land inwards, the temperature will likely deviate from Nieuw Beerta so I made the choice to create the normal temperatures from Nieuw Beerta and the normal rain values from Eelde.
I took the values from the daily KNMI measurements page: rain for Eelde and Temperature for Nieuw Beerta. Then I wrote a short program to read the output and calculate the resulting normal values for monthly temperatures and monthly rain. The resulting values I filled in in the CumulusMX settings for the NOAA reports from where they are fetched to be used in the CumulusUtils reporting style you see on the website.
The new normal values used for my Wagenborgen weather station for the period of 1990 – 2020 are shown in the following table:
pwsFWI is now a bit more than a year old and has proven its value : it relates very well to the existing indices and the EFFIS Current Situation viewer. This means that the note : Behavioural testing still under way! will be removed from the pwsFWI page at the end of the European fire season (21 September).
Having said that, I have noticed that some users have reverted to the FWI calculations and graphs as shown by FWIcalc as if that one is better than pwsFWI.
This is a short blog on some fire related research and organisations. Unrelated directly to pwsFWI but definitely useful to follow if you are interested in fire – wildfire and/or society related issues regarding large wildfires. Especially the running webinars are interesting (and free) !
Recently (in 2019) an ITN project PyroLife started (what does ITN stand for btw?), led by Wageningen scientist dr. Cathelijne Stoof.
Apart from being a great initiative, it became quickly very interesting because of the free webinar which started 3 June 2020 (and running till the end of july 2020). The presentations of this Symposium are published on the YT channel of PyroLife (don’t start binge watching these presentations, prevent overkill).
I would like to point to the the presentation of Adrián Cordil of 10 June 2020 (at moment of writing not yet published) on thePyroLife YT channel linked above, scientist at the University of LLeida, Spain and associated with Tecnosylva S.L. (twitter account). He presented the work of Technosylva regarding simulation software, put to work in different parts of the world in the context of combatting wildfire situations.
Estimating Fire Weather is one aspect of this. An interesting piece of software to look at is Wildfire Analyst also available for Android. Check it out.
pwsFWI is unfortunately not related to this [large] project, but the results of your weather station, and your understanding of meteorology, definitely might give you an entry to understanding what they are doing there in Spain.
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.
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). Continue reading
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. Continue reading
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.
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.