meteoForecast 0.43: GFS, NAM, and RAP included

Etiquetas

forecast, meteorological data, NWP, R, WRF

Some months ago I published the meteoForecast package, with functions to download data from the Meteogalicia and OpenMeteo NWP-WRF services. Now there is a new version available at CRAN. This update includes a bunch of bug fixes, some additional features, and three new services: GFS, NAM, and RAP.

You will find detailed information and examples here.

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Meteorological Data Sources

Etiquetas

data, forecast, meteorological data, wiki

I have started a wiki with a collection of meteorological data sources I have collated in the framework of the research project PVCROPS. It includes services that publish observations, forecasts and reanalysis. Most of them offer products free of charge.

If you find typos or errors, or if you want some source to be referenced, please contribute.

Matrices de diagramas de dispersión con R y lattice

Etiquetas

lattice, meteorological data, R, scatterplot matrices, solar, splom

Dentro de lattice, existe una función llamada splom, útil para visualizar matrices de diagramas de dispersión (scatter plot matrices). Para conjuntos de datos de gran tamaño la función panel.hexbinplot del paquete hexbin es una mejor opción para el panel que usa splom por defecto. Veamos un ejemplo utilizando datos meteorológicos de MAPA-SIAR:

How to display scatter plot matrices with R and lattice

Etiquetas

lattice, meteorological data, R, scatterplot, solar

In lattice, there is a function called splom for the display of scatter plot matrices. For large datasets, the panel.hexbinplot from the hexbin package is a better option than the default panel.

As an example, let’s use some meteorological data from MAPA-SIAR:

library(solaR)
library(hexbin)

aranjuez <- readSIAR(prov=28, est=3, start='01/01/2004', end='31/12/2010')

aranjuezDF <- subset(as.data.frame(getData(aranjuez)),
 select=c('TempMedia', 'TempMax', 'TempMin',
 'HumedadMedia', 'DirViento', 'EtPMon',
 'Precipitacion', 'G0'))

Now we can use splom with panel.hexbinplot and panel.loess. Besides, I have included some changes to diag.panel in order to show the univariate density of each variable (adapted from here). UPDATED: I include the use of colramp and BTC to change the color of the hexagon bins.

splom(aranjuezDF,
 panel=panel.hexbinplot,
 colramp=BTC,
 diag.panel = function(x, ...){
 yrng <- current.panel.limits()$ylim
 d <- density(x, na.rm=TRUE)
 d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )
 panel.lines(d)
 diag.panel.splom(x, ...)
 },
 lower.panel = function(x, y, ...){
 panel.hexbinplot(x, y, ...)
 panel.loess(x, y, ..., col = 'red')
 },
 pscale=0, varname.cex=0.7
 )

Finally, it is interesting to identify some points. This task is easy with panel.link.splom. The points are selected via mouse clicks. Clicks other than left-clicks terminate the procedure.

trellis.focus('panel', 1, 1)
idx <- panel.link.splom(pch=13, cex=0.6, col='green')
aranjuezDF[idx,]