Hello Ferreters,
I am analyzing output from CMIP5 models at 26 N and some of the models provide their data in x,y coordinates rather than lat,lon. I can plot the data using latitude,longitude however to integrate the velocity to find the volume transport I have to use the x,y values.
For example how I would calculate the gulf stream transport with a model with output given in lat,lon:
let voltans = vo[x=81W:74W@DIN,y=26N@ITP,k=0:37@DIN]
For a model in which the data is not already in latitude longitude but x and y .... say x goes from 1:182 and y from 1:149, I am not sure how to determine which y value corresponds to 26N besides plotting identical images one using the lat,lon and the other without and using the where command to identify the closest point to what looks like 26N on the lat,lon plot.
To get around this I thought I could just go ahead and regrid all of the data to a rectilinear coordinate system using the following script:
let lonin = lon[d=1]
let latin = lat[d=1]
define axis/x=0.5:359.5:1/modulo/unit=degrees xax
define axis/y=-79.5:89.5:1/unit=degrees yax
let lonlatout = y[gy=yax] + x[gx=xax]
let map = curv_to_rect_map(lonin,latin,lonlatout,10)
can var/all
use noresm_map
show data
let vo_regrid = curv_to_rect(vo[d=1],map[d=2])
However I am getting errors when I do this however they vary by model. For example when I compare the regridded data with the data on the native grid for ESM2M I get an increase of ~1.4 Sv when looking at the Gulf Stream Transport. When comparing for ESM2G I get an increase of ~10 Sv which seems unrealistic.
Taking a look at the two files I noticed that when I regrid I am losing some of the topography / bathemetry and am getting values for velocity in regions where there used to be none.
I have tried varying the radius I use when regridding however this does not seem to be solving the problem.
Has anyone had similar issues when regridding ocean velocity data or know a way to determine which y value on a models native grid corresponds to a certain latitude?
Any help would be great!
Thank you,
Rebecca Beadling