This is the first time I'm using R to model climate.
I have three variables of 1-min data as per below:
x<-data.frame(matrix(c("2012-02-04", "2012-02-04", "2012-02-04", "2012-02-04", "00:00",
"00:01","00:02","00:03", "960.0244", "960.0258", "960.0272", "960.0286",
"12", "12.2", "12", "12.1", "0", "0.1","4", "2"), ncol=5))
names(x)<-c("date","time","pressure","temperature","precipitation")
date time pressure temperature precipitation
1 2012-02-04 00:00 960.0244 12 0
2 2012-02-04 00:01 960.0258 12.2 0.1
3 2012-02-04 00:02 960.0272 12 4
4 2012-02-04 00:03 960.0286 12.1 2
Of course, the original data is much much larger (approximately 8 variables) and much much longer (3 million rows, total 8 years of data).
I'm afraid conventional regression technique i.e. linear regression lm(precipitation~temperature+pressure) or even polynomial or multiple linear may not be sufficient for modeling this kinda research.
So I would like to know what kind of modeling technique can I use to model the relationship between precipitation to other variables?