Hi Stefano,
I don't know whether this will help you, but here is a way to
visualize wind speed and direction using clock24.plot:
first_day_POSIX <- as.POSIXct("2020-02-19-00-00", format="%Y-%m-%d-%H-%M")
last_day_POSIX <- as.POSIXct("2020-02-20-00-00", format="%Y-%m-%d-%H-%M")
mydf <- data.frame(data_POSIX=seq(first_day_POSIX, last_day_POSIX, by="10 min"))
mydf$main_dir<-c("WSW","WSW","SW","SW","W","WSW","WSW","WSW","W","W","SW",
"WSW","SSW","S","SW","SW","WSW","WNW","W","WSW","WSW","SE","SE","SE",
"NW","NNE","ENE","SE","NNW","NW","NW","NW","NW","NW","NW","NE","NW","NW",
"NW","NW","NW","N","WNW","NW","NNW","NNW","NW","NW","NW","WNW","ESE","W",
"WSW","SW","SW","SW","WSW","SW","S","S","SSW","SW","WSW","WSW","WSW","WSW",
"WSW","WSW","WSW","SW","WSW","WSW","WSW","WSW","SW","SW","WSW","WSW","WSW",
"WSW","WSW","SW","SW","SW","SW","SW","SW","SW","SW","SW","WSW","WSW","WSW",
"WSW","SW","SW","SW","SW","WSW","SW","SW","SW","SW","SW","WSW","SW","SW",
"W","WSW","WSW","SSW","S","WNW","SW","W","WSW","WSW","SE","SE","SE","NW",
"NNE","ENE","SE","NNW","NW","NW","NW","NW","NW","NW","NE","NW","NW","NW",
"NW","NW","N","WNW","NW","NNW","NNW","NW","NW","NW")
mydf$max_speed <- c(4.60, 4.60, 3.40, 3.10, 4.80, 4.20, 4.10, 4.50,
4.70, 4.30, 2.40, 2.30, 2.20, 2.10, 2.90, 2.80, 1.80, 2.70, 4.30,
3.30, 2.30, 2.30, 3.20, 3.20, 2.90, 2.30, 1.50, 1.80, 2.90, 2.40,
1.80, 2.40, 2.30, 2.60, 1.80, 2.30, 1.90, 2.20, 2.80, 2.40, 1.00,
1.10, 1.60, 2.30, 2.50, 3.30, 3.40, 3.20, 4.50, 3.90, 3.10, 2.40,
6.00, 7.80, 6.30, 7.80, 8.10, 6.10, 7.40, 9.50, 8.90, 9.10, 10.10,
10.50, 11.10, 10.10, 10.90, 11.30, 13.40, 13.50, 12.80, 11.50, 13.10,
13.50, 11.10, 10.50, 8.50, 10.10, 10.70, 13.60, 11.90, 14.90, 10.90,
10.90, 12.80, 12.10, 9.10, 8.30, 8.80, 7.40, 8.40, 10.30, 10.00, 7.00,
8.50, 8.40, 8.60, 6.70, 7.30, 6.20, 5.90, 5.90, 5.10, 5.80, 5.60,
6.50, 6.60, 11.70, 11.30, 8.70, 7.10, 6.90, 4.30, 3.80, 4.30, 3.30,
2.30, 2.30, 3.20, 3.20, 2.90, 2.30, 1.50, 1.80, 2.90, 2.40, 1.80,
2.40, 2.30, 2.60, 1.80, 2.30, 1.90, 2.20, 2.80, 2.40, 1.00, 1.10,
1.60, 2.30, 2.50, 3.30, 3.40, 3.20, 4.50)
mydf$main_dir<-factor(mydf$main_dir,
levels=c("N","NNE","NE","ENE","E","ESE","SE","SSE",
"S","SSW","SW","WSW","W","WNW","NW","NNW"))
dir2degrees<-seq(0,337.5,by=22.5)
mydf$wind_dir<-dir2degrees[as.numeric(mydf$main_dir)]
library(plotrix)
png("SS_spd_dir.png")
clock24.plot(mydf$max_speed,minutes=TRUE,mar=c(4,2,3,2),
line.col=rainbow(16)[as.numeric(mydf$main_dir)],
main="Wind speed and direction (color)")
legend(0,-17.5,levels(mydf$main_dir),fill=rainbow(16),
xjust=0.5,ncol=8,cex=0.8)
dev.off()
Jim
On Sun, May 17, 2020 at 8:07 PM Abby Spurdle <spurdl...@gmail.com> wrote:
>
> Sorry, please put my last post aside.
> I mis-read your question.
>
> I agree with Jim's comments.
>
>
>
>
>
>
>
>
>
> On Sun, May 17, 2020 at 9:52 PM Abby Spurdle <spurdl...@gmail.com> wrote:
> >
> > Hi Stefano,
> >
> > I don't have any specific suggestions, but...
> >
> > If you could convert your (character) direction vector into a
> > (numeric) time-ordered direction vector giving radians or degrees, and
> > provide the corresponding speed vector...
> > (i.e. North -> 90 degrees).
> >
> > I'd be happy to try and plot the data in a variety of ways...
> > ...and see if there's any obvious relationships, or anything else useful.
> >
> >
> > On Sat, May 16, 2020 at 10:23 PM Stefano Sofia
> > <stefano.so...@regione.marche.it> wrote:
> > >
> > > Dear Jim and Jeff,
> > > thank you for your comments. You are right, it is quite difficult to
> > > detect this process through a single observation point, I am awre of it.
> > > I need to set up an automatic algorithm to filter 20 years of data, and I
> > > have to find an easy way to do it.
> > > I know quite well my automatic stations, the wind direction is very
> > > stable during these situations, and therefore I would like to start from
> > > it. (I should use also wind speed, relative humidity and temperature, but
> > > I will introduce them only once I will be able to manage the direction).
> > > In the case of the example below reported, I know that the directions of
> > > this particular automatic station must be only SW or WSW.
> > >
> > > My biggest problem, obviously, is to find the beginning and the end of
> > > each event, when there is a change in the main direction.
> > > Thinking about categorical data in general, is there a way to detect
> > > periods when one particular category is more frequent?
> > >
> > > Here I reproduce a real example 24 hours long, where these Foehn
> > > condition start between 09 and 10 and finish after 19:
> > >
> > > first_day_POSIX <- as.POSIXct("2020-02-19-00-00", format="%Y-%m-%d-%H-%M")
> > > last_day_POSIX <- as.POSIXct("2020-02-20-00-00", format="%Y-%m-%d-%H-%M")
> > > mydf <- data.frame(data_POSIX=seq(first_day_POSIX, last_day_POSIX, by="10
> > > min"))
> > >
> > > mydf$main_dir <- c(WSW, WSW, SW, SW, W, WSW, WSW, WSW, W, W, SW, WSW,
> > > SSW, S, SW, SW, WSW, WNW, W, WSW, WSW, SE, SE, SE, NW, NNE, ENE, SE, NNW,
> > > NW, NW, NW, NW, NW, NW, NE, NW, NW, NW, NW, NW, N, WNW, NW, NNW, NNW, NW,
> > > NW, NW, WNW, ESE, W, WSW, SW, SW, SW, WSW, SW, S, S, SSW, SW, WSW, WSW,
> > > WSW, WSW, WSW, WSW, WSW, SW, WSW, WSW, WSW, WSW, SW, SW, WSW, WSW, WSW,
> > > WSW, WSW, SW, SW, SW, SW, SW, SW, SW, SW, SW, WSW, WSW, WSW, WSW, SW, SW,
> > > SW, SW, WSW, SW, SW, SW, SW, SW, WSW, SW, SW, W, WSW, WSW, SSW, S, WNW,
> > > SW, W, WSW, WSW, SE, SE, SE, NW, NNE, ENE, SE, NNW, NW, NW, NW, NW, NW,
> > > NW, NE, NW, NW, NW, NW, NW, N, WNW, NW, NNW, NNW, NW, NW, NW)
> > >
> > > mydf$max_speed <- c(4.60, 4.60, 3.40, 3.10, 4.80, 4.20, 4.10, 4.50, 4.70,
> > > 4.30, 2.40, 2.30, 2.20, 2.10, 2.90, 2.80, 1.80, 2.70, 4.30, 3.30, 2.30,
> > > 2.30, 3.20, 3.20, 2.90, 2.30, 1.50, 1.80, 2.90, 2.40, 1.80, 2.40, 2.30,
> > > 2.60, 1.80, 2.30, 1.90, 2.20, 2.80, 2.40, 1.00, 1.10, 1.60, 2.30, 2.50,
> > > 3.30, 3.40, 3.20, 4.50, 3.90, 3.10, 2.40, 6.00, 7.80, 6.30, 7.80, 8.10,
> > > 6.10, 7.40, 9.50, 8.90, 9.10, 10.10, 10.50, 11.10, 10.10, 10.90, 11.30,
> > > 13.40, 13.50, 12.80, 11.50, 13.10, 13.50, 11.10, 10.50, 8.50, 10.10,
> > > 10.70, 13.60, 11.90, 14.90, 10.90, 10.90, 12.80, 12.10, 9.10, 8.30, 8.80,
> > > 7.40, 8.40, 10.30, 10.00, 7.00, 8.50, 8.40, 8.60, 6.70, 7.30, 6.20, 5.90,
> > > 5.90, 5.10, 5.80, 5.60, 6.50, 6.60, 11.70, 11.30, 8.70, 7.10, 6.90, 4.30,
> > > 3.80, 4.30, 3.30, 2.30, 2.30, 3.20, 3.20, 2.90, 2.30, 1.50, 1.80, 2.90,
> > > 2.40, 1.80, 2.40, 2.30, 2.60, 1.80, 2.30, 1.90, 2.20, 2.80, 2.40, 1.00,
> > > 1.10, 1.60, 2.30, 2.50, 3.30, 3.40, 3.20, 4.50)
> > >
> > >
> > > Thank you for your attention
> > > Stefano
> > >
> > >
> > > (oo)
> > > --oOO--( )--OOo----------------
> > > Stefano Sofia PhD
> > > Civil Protection - Marche Region
> > > Meteo Section
> > > Snow Section
> > > Via del Colle Ameno 5
> > > 60126 Torrette di Ancona, Ancona
> > > Uff: 071 806 7743
> > > E-mail: stefano.so...@regione.marche.it
> > > ---Oo---------oO----------------
> > >
> > > ________________________________________
> > > Da: Jim Lemon [drjimle...@gmail.com]
> > > Inviato: mercoledì 13 maggio 2020 11.01
> > > A: Stefano Sofia; r-help mailing list
> > > Oggetto: Re: [R] Classification of wind events
> > >
> > > Hi Stefano,
> > > Given only one observation point you will find it difficult. If your
> > > automatic weather station is in the low area where the foehn wind is
> > > felt, it can only be distinguished from a dry katabatic wind if the
> > > upwind conditions are known. There is a similar but milder version of
> > > this in eastern Australia, but it is usually of the latter sort. There
> > > may be a way to measure turbulence above the peak of the high ground
> > > with radar or something, but I'm not familiar with that.
> > >
> > > Jim
> > >
> > > On Tue, May 12, 2020 at 6:13 PM Stefano Sofia
> > > <stefano.so...@regione.marche.it> wrote:
> > > >
> > > > Dear R list users,
> > > > I am aware that this question is not strictly related, at the present
> > > > moment, to R code and it is more general. Please forgive me, but I need
> > > > to share my thoughts with you.
> > > >
> > > > Foehn conditions on the southern slope of Alps happen with strong
> > > > northerly flows that impact perpendicularly over the Apls. This
> > > > situation triggers strong northerly leeward winds.
> > > > Given a single automatic weather station, I would like to identify
> > > > these periods starting from wind direction and wind intensity data.
> > > > Frequency of data is quarter of hour.
> > > > I would really find difficult to detect the moving windows of these
> > > > events:
> > > > - I can't analyse data day by day;
> > > > - at the beginning and at the end of each event, when the process is
> > > > not at full speed yet, the rotation is not always perfectly
> > > > identifiable;
> > > > - I cannot claim in principle that the direction of each consecutive
> > > > observation is costantly and strictly from the chosen direction.
> > > >
> > > > Does anybody have a clue on how to start to build this process in the
> > > > right way?
> > > >
> > > > Thank you for your attention and your help
> > > > Stefano
> > > >
> > > > (oo)
> > > > --oOO--( )--OOo----------------
> > > > Stefano Sofia PhD
> > > > Civil Protection - Marche Region
> > > > Meteo Section
> > > > Snow Section
> > > > Via del Colle Ameno 5
> > > > 60126 Torrette di Ancona, Ancona
> > > > Uff: 071 806 7743
> > > > E-mail: stefano.so...@regione.marche.it
> > > > ---Oo---------oO----------------
> > > >
> > > > ________________________________
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