File:Mean precipitation 240ma 2.png

From Wikimedia Commons, the free media repository
Jump to navigation Jump to search

Original file (1,800 × 800 pixels, file size: 625 KB, MIME type: image/png)

Captions

Captions

Mean precipitation, Trias 240 Ma

Summary

[edit]
Description
English: Mean precipitation in mm, Trias 240 Ma
Date
Source Own work
Author Merikanto

This image is based data from exoplasim simulation and Scotese paleodem maps.

https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/

PaleoDEM Resource – Scotese and Wright (2018) 11 August, 2018 by Sabin Zahirovic

https://www.earthbyte.org/webdav/ftp/Data_Collections/Scotese_Wright_2018_PaleoDEM/Scotese_Wright_2018_Maps_1-88_1degX1deg_PaleoDEMS_nc.zip

PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic

Scotese, Christopher R, & Wright, Nicky M. (2018). PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5460860

Scotese, Christopher R; Wright, Nicky M https://zenodo.org/record/5460860

https://zenodo.org/record/5460860/files/Scotese_Wright_2018_Maps_1-88_6minX6min_PaleoDEMS_nc.zip?download=1

Params from exoplasim

    1. triassic middle

a_eccentricity1=0.0167022 a_obliquity1=23.441 a_lonvernaleq1=102.7

a_pCO21=1400.0e-6

Code to obtain data

Basic codes here

https://commons.wikimedia.org/wiki/File:World_200ma_6.webp

Post processing codes:

    1. process input rasters
    2. 15.6.2022 v 0000.00025
    1. input: georeferenced netcdf
    2. must create with qgis georeferencer from
    3. you need
    4. koppenpasta output, scaling factor 1
    5. default scaling

input1=$1 inputdem1=$2

echo "Inputs"

echo $input1 echo $inputdem1

  1. exit(-1)
    1. georef koppenpasta file

gdal_translate -a_srs EPSG:4326 -a_ullr -180 90 180 -90 $input1 ./origo/koppen.tif

    1. translate to netcdf

gdal_translate -of NetCDF ./origo/koppen.tif ./origo/koppen.nc

rm ../origo/k10.nc rm ../origo/k1.nc rm ../origo/k20.nc rm ../origo/k2.nc rm ../origo/k30.nc rm ../origo/k3.nc

ncks -v Band1,lon,lat ./origo/koppen.nc ./origo/k10.nc -O ncks -v Band2,lon,lat ./origo/koppen.nc ./origo/k20.nc -O ncks -v Band3,lon,lat ./origo/koppen.nc ./origo/k30.nc -O

ncrename -vBand1,k1 ./origo/k10.nc ./origo/k1.nc -O ncrename -vBand2,k2 ./origo/k20.nc ./origo/k2.nc -O ncrename -vBand3,k3 ./origo/k30.nc ./origo/k3.nc -O

    1. process dem to aspect, distance mask ...

gdal_translate -of GTiff $inputdem1 ./origo/dem0.tif gdal_calc.py -A ./origo/dem0.tif --outfile=./origo/dem6.tif --calc="A*(A>0)" gdal_calc.py -A ./origo/dem0.tif --outfile=./origo/sea6.tif --calc="A*(A<0)" gdal_translate -of NetCDF ./origo/dem6.tif ./origo/dem6.nc gdal_translate -of NetCDF ./origo/sea6.tif ./origo/sea6.nc gdaldem aspect ./origo/dem6.tif ./origo/aspect6.tif gdaldem slope ./origo/dem6.tif ./origo/slope6.tif gdaldem TPI ./origo/dem6.tif ./origo/tpi60.tif gdaldem TRI ./origo/dem6.tif ./origo/tri60.tif

gdal_calc.py -A ./origo/tpi60.tif --outfile=./origo/tpi6.tif --calc="nan_to_num(A)" gdal_calc.py -A ./origo/tri60.tif --outfile=./origo/tri6.tif --calc="nan_to_num(A)"

gdal_translate -of NetCDF ./origo/aspect6.tif ./origo/aspect6.nc gdal_translate -of NetCDF ./origo/slope6.tif ./origo/slope6.nc gdal_translate -of NetCDF ./origo/tpi6.tif ./origo/tpi6.nc gdal_translate -of NetCDF ./origo/tri6.tif ./origo/tri6.nc

gdal_proximity.py ./origo/sea6.tif ./origo/distance6.tif gdal_translate -of NetCDF ./origo/distance6.tif ./origo/distance6.nc gdal_calc.py -A ./origo/dem6.tif --outfile=./origo/mask6.tif --calc="256*(A>0)" gdal_translate -of NetCDF ./origo/mask6.tif ./origo/mask6.nc

gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/mask6.tif ./origo/mask1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/dem6.tif ./origo/dem1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/sea6.tif ./origo/sea1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/distance6.tif ./origo/distance1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/aspect6.tif ./origo/aspect1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/slope6.tif ./origo/slope1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/lons6.tif ./origo/lons1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/lats6.tif ./origo/lats1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/tri6.tif ./origo/tri1600.tif gdalwarp -of GTiff -overwrite -ts 1600 800 ./origo/tpi6.tif ./origo/tpi1600.tif

  1. gdal_translate -of png -ot Uint16 ./origo/mask1600.tif ./origo/mask1600.png

gdal_translate -of png -ot Uint16 ./origo/dem1600.tif ./origo/dem1600.png gdal_translate -ot Int16 -of GTiff ./origo/dem1600.tif ./origo/dem16.tif gdal_translate -of png -ot Uint16 ./origo/mask1600.tif ./origo/mask1600.png gdal_translate -ot Int16 -of GTiff ./origo/mask1600.tif ./origo/mask16.tif

gdaldem hillshade ./origo/dem16.tif -of GTiff -b 1 -z 1.0 -s 1.0 -az 315.0 -alt 45.0 ./origo/emboss0.tif

gdal_translate -of png ./origo/emboss0.tif ./origo/emboss0.png

convert ./origo/emboss0.png -fill white -colorize 80% ./origo/emboss.png

cp ./origo/emboss.png ./output/emboss.png

convert ./origo/dem16.tif -compose Copy_Opacity -auto-level ./origo/koppendem.tif

convert ./origo/koppendem.tif ./origo/koppendem.png

convert ./origo/mask16.tif -compose Copy_Opacity -auto-level ./origo/koppenmask.tif convert ./origo/koppenmask.tif ./origo/koppenmask.png

cp ./origo/koppendem.png ./output/koppendem.png cp ./origo/koppenmask.png ./output/koppenmask.png cp ./origo/landsea.png ./output/landsea.png

echo "."

Draw precip map

    1. draw precip map
      1. 19.6.2021 0000.0001


import netCDF4 as nc import numpy as np from scipy import interpolate from scipy.interpolate import griddata

import matplotlib.pyplot as plt from matplotlib.pylab import * import matplotlib.mlab as mlab


def ncread(fn1, var1): ds2 = nc.Dataset(fn1) varr1=ds2[var1] return(varr1)


def savenetcdf_single_frommem(outfilename1, outvarname1, xoutvalue1,xoutlats1,xoutlons1): nlat1=len(xoutlats1) nlon1=len(xoutlons1) #indata_set1=indata1 print(outfilename1) ncout1 = nc.Dataset(outfilename1, 'w', format='NETCDF4') outlat1 = ncout1.createDimension('lat', nlat1) outlon1 = ncout1.createDimension('lon', nlon1) outlats1 = ncout1.createVariable('lat', 'f4', ('lat',)) outlons1 = ncout1.createVariable('lon', 'f4', ('lon',)) outvalue1 = ncout1.createVariable(outvarname1, 'f4', ('lat', 'lon',)) outvalue1.units = 'Unknown' outlats1[:] = xoutlats1 outlons1[:] = xoutlons1 outvalue1[:, :] =xoutvalue1[:] ncout1.close() return 0

def ncsave_T21(oname1, ovar1, ovals1): newx=64 newy=32 X = np.arange(-180, 180, 360/newx) Y = np.arange(-90, 90, 180/newy) savenetcdf_single_frommem(oname1, ovar1,ovals1,Y, X)


from matplotlib.colors import LinearSegmentedColormap

cm_data = [[0.2081, 0.1663, 0.5292], [0.2116238095, 0.1897809524, 0.5776761905], 

[0.212252381, 0.2137714286, 0.6269714286], [0.2081, 0.2386, 0.6770857143], 
[0.1959047619, 0.2644571429, 0.7279], [0.1707285714, 0.2919380952, 
 0.779247619], [0.1252714286, 0.3242428571, 0.8302714286], 
[0.0591333333, 0.3598333333, 0.8683333333], [0.0116952381, 0.3875095238, 
 0.8819571429], [0.0059571429, 0.4086142857, 0.8828428571], 
[0.0165142857, 0.4266, 0.8786333333], [0.032852381, 0.4430428571, 
 0.8719571429], [0.0498142857, 0.4585714286, 0.8640571429], 
[0.0629333333, 0.4736904762, 0.8554380952], [0.0722666667, 0.4886666667, 
 0.8467], [0.0779428571, 0.5039857143, 0.8383714286], 
[0.079347619, 0.5200238095, 0.8311809524], [0.0749428571, 0.5375428571, 
 0.8262714286], [0.0640571429, 0.5569857143, 0.8239571429], 
[0.0487714286, 0.5772238095, 0.8228285714], [0.0343428571, 0.5965809524, 
 0.819852381], [0.0265, 0.6137, 0.8135], [0.0238904762, 0.6286619048, 
 0.8037619048], [0.0230904762, 0.6417857143, 0.7912666667], 
[0.0227714286, 0.6534857143, 0.7767571429], [0.0266619048, 0.6641952381, 
 0.7607190476], [0.0383714286, 0.6742714286, 0.743552381], 
[0.0589714286, 0.6837571429, 0.7253857143], 
[0.0843, 0.6928333333, 0.7061666667], [0.1132952381, 0.7015, 0.6858571429], 
[0.1452714286, 0.7097571429, 0.6646285714], [0.1801333333, 0.7176571429, 
 0.6424333333], [0.2178285714, 0.7250428571, 0.6192619048], 
[0.2586428571, 0.7317142857, 0.5954285714], [0.3021714286, 0.7376047619, 
 0.5711857143], [0.3481666667, 0.7424333333, 0.5472666667], 
[0.3952571429, 0.7459, 0.5244428571], [0.4420095238, 0.7480809524, 
 0.5033142857], [0.4871238095, 0.7490619048, 0.4839761905], 
[0.5300285714, 0.7491142857, 0.4661142857], [0.5708571429, 0.7485190476, 
 0.4493904762], [0.609852381, 0.7473142857, 0.4336857143], 
[0.6473, 0.7456, 0.4188], [0.6834190476, 0.7434761905, 0.4044333333], 
[0.7184095238, 0.7411333333, 0.3904761905], 
[0.7524857143, 0.7384, 0.3768142857], [0.7858428571, 0.7355666667, 
 0.3632714286], [0.8185047619, 0.7327333333, 0.3497904762], 
[0.8506571429, 0.7299, 0.3360285714], [0.8824333333, 0.7274333333, 0.3217], 
[0.9139333333, 0.7257857143, 0.3062761905], [0.9449571429, 0.7261142857, 
 0.2886428571], [0.9738952381, 0.7313952381, 0.266647619], 
[0.9937714286, 0.7454571429, 0.240347619], [0.9990428571, 0.7653142857, 
 0.2164142857], [0.9955333333, 0.7860571429, 0.196652381], 
[0.988, 0.8066, 0.1793666667], [0.9788571429, 0.8271428571, 0.1633142857], 
[0.9697, 0.8481380952, 0.147452381], [0.9625857143, 0.8705142857, 0.1309], 
[0.9588714286, 0.8949, 0.1132428571], [0.9598238095, 0.9218333333, 
 0.0948380952], [0.9661, 0.9514428571, 0.0755333333], 
[0.9763, 0.9831, 0.0538]]

parulamap = LinearSegmentedColormap.from_list('parula', cm_data)



infilename1="./origo/pravg.nc"

varname1="Band1" kaption1="Mean precip. mm, 240 Ma" infilename2="./origo/dem.nc" savename1="mean_precipitation_240ma_2.png"


varr0=ncread(infilename1, varname1) varr1=np.flipud(np.asarray(varr0))

mask1=np.flipud(np.asarray(ncread(infilename2, "z")))

  1. plt.imshow(mask1)
  1. plt.show()


  1. quit(-1)


  1. ncsave_T21("tas_min.nc", "tas", minitable1)


  1. kmap2 = plt.cm.get_cmap('cividis')
  2. kmap2 = plt.cm.get_cmap('viridis')

kmap2=parulamap


contourrange1=[0,200,400,800,1000,1500,2000,5000]

contourrange2=[0,1000,2000]

contourrange3=np.arange(0,2000,50)

  1. range3=np.arange(-50,50,0.1)

range3=np.arange(0,2000,10)


    1. dravar1=minitable1

dravar1=varr1

meanmean1=np.mean(dravar1)

print("Mean avg.", meanmean1)

plt.rcParams["figure.figsize"] = (18,8)

  1. plt.rcParams["figure.figsize"] = (8,4)


plt.title(kaption1, fontsize=24)

plt.xlabel("Longitude", fontsize=18) plt.xticks(fontsize=18) plt.ylabel("Latitude", fontsize=18) plt.yticks(fontsize=18)


  1. plt.imshow( mask1, extent=(-180,180,-90,90) )

cs0 = plt.contourf( mask1,extent=(-180,180,-90,90), levels=[-10000,0], color="Black", extend='both')

cs2 = plt.contourf( dravar1,extent=(-180,180,-90,90), levels=range3, cmap=kmap2.reversed(), extend='both')


cs = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, color="Blue", levels=contourrange1, alpha=0.65 )


  1. cs0a = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, cmap=kmap2.reversed(), linestyles=':', alpha=0.65, linewidths=0.4, levels=contourrange3)


plt.clabel(cs, fontsize=20, inline=1,fmt = '%3.0f')


  1. cs3 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[1],cmap=plt.cm.binary,alpha=0.8, extend='both')

cs3 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[0],color="Black", alpha=0.8, extend='both')

  1. cs4 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[0,2000],color="Black", alpha=0.4, extend='both')


plt.savefig(savename1)


plt.show()










Licensing

[edit]
I, the copyright holder of this work, hereby publish it under the following license:
w:en:Creative Commons
attribution share alike
This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeThumbnailDimensionsUserComment
current12:20, 19 June 2022Thumbnail for version as of 12:20, 19 June 20221,800 × 800 (625 KB)Merikanto (talk | contribs)Update
11:17, 19 June 2022Thumbnail for version as of 11:17, 19 June 20221,800 × 800 (574 KB)Merikanto (talk | contribs)Update
11:11, 19 June 2022Thumbnail for version as of 11:11, 19 June 20221,800 × 800 (581 KB)Merikanto (talk | contribs)Uploaded own work with UploadWizard

You cannot overwrite this file.

There are no pages that use this file.

Metadata

This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.

Retrieved from "https://commons.wikimedia.org/w/index.php?title=File:Mean_precipitation_240ma_2.png&oldid=839998825"