eliocamp / metR

Tools for Easier Analysis of Meteorological Fields
https://eliocamp.github.io/metR/
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Uniform streamlines #166

Closed d-siden closed 1 year ago

d-siden commented 1 year ago

Hi,

I'm using the geom_streamline() (v. 0.12.0) on my final paper on Meteorology and I came to a problem when plotting streamlines based on U and V wind components. The higher speed sector had extremely long lines, while the slower had only the arrow head. I solved this using some functions to remove the magnitude of vectors and remake the field with same length speed. I think this should be an easier feature to have as a parameter in this function, so I'm showing how I'm doing.

First I create a dataframe which correlates the way R outputs angles ("r") and the conventional meteorological direction ("buss"): tabela.de.graus <- data.frame("r"=c(seq(-179,180)), "buss"=c(seq(269,0),seq(359,270))) This gives 1 degree of resolution, which is enough for my observation scale. Then, when converting the U and V components into a dataframe, I do the following: Find the direction where this vector is pointing at, convert to degrees: atan2(V, U)*180/pi) Round it and check if it became -180 if, yes, convert to 180, if not, procceed with value. ifelse(round(atan2(V, U)*180/pi)==-180, 180, round(atan2(V, U)*180/pi)) Now we have our R angle, whose meteorological equivalent we shall find in the tabela.de.graus: tabela.de.graus[match(ifelse(round(atan2(V, U)*180/pi)==-180, 180, round(atan2(V, U)*180/pi)),tabela.de.graus$r),"buss"] The table was written in degrees, so we convert it back to radians to use in our sin() and cos(), to decompose it in U and V relative to the north-south axis: Uu=sin(tabela.de.graus[match(ifelse(round(atan2(V, U)*180/pi)==-180, 180, round(atan2(V, U)*180/pi)),tabela.de.graus$r),"buss"]*pi/180) Vv=cos(tabela.de.graus[match(ifelse(round(atan2(V, U)*180/pi)==-180, 180, round(atan2(V, U)*180/pi)),tabela.de.graus$r),"buss"]*pi/180) Comparison between "crude" values and "uniformized", the equatorial streamlines are much more clearer, and the jet stream is less polluted: compara The parameters for the right image are: L = 7, res = 5, jitter = 0. The ones for the left image I forgot.

eliocamp commented 1 year ago

Hi! This is intended behaviour. Streamlines are closer together in regions of larger amplitude and the length of the streamline is also a measure of (Lagrangian) speed of the particle.

A quick way of getting uniform streamlines might be by normalising the X and y speeds by the total velocity: aes(dx = u/(u^2 + v^2), dy = v/(u^2 + v^2)).

d-siden commented 1 year ago

Thank you!