[R] Classification of wind events
Stefano Sofia
@te|@no@@o||@ @end|ng |rom reg|one@m@rche@|t
Mon May 18 12:14:06 CEST 2020
Sorry for my fault.
I am very grateful for such code, which is extremely efficient. I would have never been able to reach these results.
In order to preserve the quality of this code, I dare to ask you a final question: once identified each single period in the column foehn1c, this period can be taken into consideration only if within it the mean of max_speed is higher than 8.0 (which is speed in m/s).
Could you please help me in this final step?
Thank you again for all 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.sofia using regione.marche.it
---Oo---------oO----------------
________________________________________
Da: Jeff Newmiller [jdnewmil using dcn.davis.ca.us]
Inviato: sabato 16 maggio 2020 21.04
A: Stefano Sofia; Jim Lemon; r-help mailing list
Oggetto: RE: [R] Classification of wind events
Please run your code before posting it... you forgot the quotes in your main_dir column.
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)
# mark candidate rows
mydf$foehn1a <- mydf$main_dir %in% c( "WSW", "SW" )
# mark unstable conditions
mydf$foehn1b <- with( mydf
, cumsum( !foehn1a )
)
# find minimum length of foehn conditions
mydf$foehn1c <- ave( rep( 1, nrow( mydf ) )
, mydf$foehn1b
, FUN=function(v) 10 < length( v )
)
# find starts of foehns
mydf$foehn1d <- with( mydf
, 0 < diff( c( 0, foehn1c ) )
)
# identify foehns distinctly (multiple days)
mydf$foehn1e <- with( mydf
, ifelse( foehn1c
, cumsum( foehn1d )
, 0
)
)
mydf[ , c( 1, 2, 8 ) ]
On May 16, 2020 3:21:24 AM PDT, Stefano Sofia <stefano.sofia using regione.marche.it> wrote:
>Dear Jim and Jeff,
>thank you for your comments. You are right, it is quite difficult to
>detect this process through a single observation point, I am awre of
>it.
>I need to set up an automatic algorithm to filter 20 years of data, and
>I have to find an easy way to do it.
>I know quite well my automatic stations, the wind direction is very
>stable during these situations, and therefore I would like to start
>from it. (I should use also wind speed, relative humidity and
>temperature, but I will introduce them only once I will be able to
>manage the direction).
>In the case of the example below reported, I know that the directions
>of this particular automatic station must be only SW or WSW.
>
>My biggest problem, obviously, is to find the beginning and the end of
>each event, when there is a change in the main direction.
>Thinking about categorical data in general, is there a way to detect
>periods when one particular category is more frequent?
>
>Here I reproduce a real example 24 hours long, where these Foehn
>condition start between 09 and 10 and finish after 19:
>
>first_day_POSIX <- as.POSIXct("2020-02-19-00-00",
>format="%Y-%m-%d-%H-%M")
>last_day_POSIX <- as.POSIXct("2020-02-20-00-00",
>format="%Y-%m-%d-%H-%M")
>mydf <- data.frame(data_POSIX=seq(first_day_POSIX, last_day_POSIX,
>by="10 min"))
>
>mydf$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.sofia using regione.marche.it
>---Oo---------oO----------------
>
>________________________________________
>Da: Jim Lemon [drjimlemon using 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.sofia using 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.sofia using regione.marche.it
>> ---Oo---------oO----------------
>>
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