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climdex2ecad() and
cost2ecad()) have been added. They provide useful tools to
convert data to the ECA&D format (the main format of input data in
INQC). The conversion can be performed for the COST Home and the
RClimDex formats../raw used to store
the data. Station file is always stations.txt and as pass
through readecad(). stations.txt must contain
all stations to be processed. ECA&D seems to to have a comprehensive
station file only subset if country sets are takenhomofolder, so it is declared at first
and does not need to be passed on from routine to routine. Implementing
it progressively once it is completed, will remove this mentionreadecad() changed to adapt the ever changing ECA&D
formatstemperature() starts adapted to cope with the fact that
some ECA&D formats have 4 columns (i.e., not STAID), which is
addedtxtnblend(), which works or blended series. Here,
no big efforts to isolate the “right” station: is is the same staid
changing the variable name … oh, man, I love blended series!!!readecad(), implemented with
readheader()dostats(), function which iterates QCSummary and
provides metastatisticsjummps2() improved to avoid very short series to cause
error. If the series has less than 100 valid values, it is not
checkedflatsun() has been corrected. When ran in
modonube, the values returned in bad where referred to the
sun file instead of the cloud file. Solved by replacing:
z<-merge(x,y,all.x=TRUE,all.y=FALSE) with
if(!modonube){z<-merge(x,y,all.x=TRUE,all.y=FALSE)}; if(modonube){z<-merge(x,y,all.x=FALSE,all.y=TRUE)}listas() reads station files as csv files.
Realized this is not smart for the autodownloading function, but works
for sent filesflat allows to exclude sequences for a value,
i.e., excluding 0, will allow to work flat for
precipitationselepe: results of
badfriki were assigned to flat in the log
files.selepe parametrization,
inadvertently, it was -8000 instead of 8000retornoracha = 500 instead of
retornoracha = 1000 in precip (fine tuning
with benchmark, need to evaluate false positives)rm(liston), which is
executed at the end of the code. It has to be
rm(liston,envir=GlobalEnv). This error was preventing the
generation of new station lists and using previous ones. This caused
txtn to not perform as expectedclosetxtn. Problems that mostly
affected Baboon data, due to its peculiarities. Changes should not
affect real data if this is not the case, revert to a February
versionsunafterdark: it compares sunshine
duration to the maximum possible according to calendar and positionIQRoutliers function to exclude values (e.g.,
0 for precipitation)IQRoutliers to the precip routine,
with very wide limits to avoid flagging excessive number of valuesconsolidator() was only being applied
to temperature!weirddate that was flagging as bad
the 29-th of February of leap yearslistas() made resistant to small format changes in the
input files (e.g. no header or different widths than the “official”
specification)badfriki
was not working well. Replaced by newfriki for pressure
and, most likely, will be deprecated for other variables toojumps2 replaces jumps. Can be ran in two
modes: split by month and with thresholds computed from the distribution
of the differences or with an absolute threshold. In both cases,
jumps2 does not flag BOTH elements in a jump, only the one
which is most likely the culpritQCConsolidated, adds reference to this
software and is “true” ECA&D format, with corrected widths and
comma-separateddrywetlong: the parameter sueco
is the threshold for dividing dry and wet. This is useful to label other
binary sequences, e.g. for 0 radiation. Now it is <= and >,
instead of < and >=txtn improved:
closetxtn is not used anymore (too slow) and the pair
is located by staid, eleid and
parid. If sticking to ECA&D, safe enough.inithome(). Creates all the folders and updates
stations listTG does not exist, the call to
temperature(element='TG') should not break the code
anymore)sundur() had a parameter omitted
(roundmax) which was causing the code to break. Solvedroundprecip().
It used to flag most of the values due to an error. This also made the
code really slow. Solved.drywetlong() resistant to precipitation series
with almost no data (RR_SOUID102169.txt has only ONE valid value, the
rest is NA!!!)roundprecip() made resistant to series with no data
different than 0. Returns NULLpotpareto() made resistant to cases were all values = 0
(e.g, RR_SOUID107781.txt, with < 800 values, and all them 0)drywetlong(): there was a mistake in rle()
usage. Example: rle$values 1 2 2 5, it was interpreted as if the strike
labeled with “5” was starting in position 10, when actually runs from 6
to 10!!!bad<-flat(x$value%%10,roundmax)
for some variables: roundmax was not parameterizedtoomany instead of
toomanymonth and toomanyyear)consolidator, a functions which creates a second
version of the QCed data. Creates a replica of the original series, with
the same name but with the QC column updated to 0 (OK)selepetxtn()<-closetxtn()<-listas()<-distHaversine()
There were a few errors (hopefully, corrected) and now it is much faster
just by limiting the station past as candidates to disthaversine to
those in a radius of 1deg lat and 1deg lon from the candidate. For the
whole ECA&D, the performance is down from 14 seconds to less than 2!
** Further improvements to the closesetation() function:
does not give an error if the tries to open a file listed but
non-existingroundprecip and a bad
parameterization in precipip()potpareto() function to avoid problems with
some matrices encountered in drywetlong:
myfit<-fpot(xx,threshold,std.err=FALSE)drywetlong to avoid problems with desertic
locations with almost no rain eventsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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