File:Mis3 france warm interpolated tjuly 1.svg

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Original file ‎(SVG file, nominally 1,650 × 1,275 pixels, file size: 1.24 MB)

Captions

Captions

July Temperature during MIS3 warm stage, interpolated

Summary

[edit]
Description
English: Temperature of MIS3 warm interstadial in France.
Date
Source Own work
Author Merikanto
This SVG image contains embedded raster graphics.[1] Such images are liable to produce inferior results when scaled to different sizes (as well as possibly being very inefficient in file size). If appropriate to do so, they should be replaced with images created using vector graphics.
Note: This template is only supposed to be used if the SVG file mixes vector and raster graphics. If the SVG file only contains raster graphics {{FakeSVG}} is supposed to be used. See also {{TopoSVG}}.

This image is based on OIS3 stage three project data.

Arbitrary selected points from plottings. Maybe some inaccuracy.

Raster to modify w/lapse rate 6.5/1000m interpolated is from processed etopo w/ utility under this image

File:CCSM3_17000BP_JulyT_1.svg Tjuly data is from Davies& Gollop 2003

https://www.researchgate.net/publication/236109423_The_Human_Presence_in_Europe_during_the_Last_Glacial_Period_II_Climate_Tolerance_and_Climate_Preferences_of_Mid-and_Late_Glacial_Hominids

Davies, William & Gollop, Piers. (2003). The Human Presence in Europe during the Last Glacial Period II: Climate Tolerance and Climate Preferences of Mid-and Late Glacial Hominids.

Original Tjuly data is from Pollard et al OIS3 project.

https://www.researchgate.net/figure/Simulated-patterns-of-late-Quaternary-mean-temperatures-in-Europe-for-summer-JJA_fig2_236109423

Barron, E., & Pollard, D. (2002). High-resolution climate simulations of oxygen isotope stage 3 in Europe. Quaternary Research, 58(3), 296-309. https://doi.org/10.1006/qres.2002.2374

Some points data is intuitively manually selected from plottings.

Interpolation and dalta raster add with puthon. vaery fast process compared to kriging. Visualization with NASA Panoply. Conversion to SVG on net.

Python code to interpolate

import csv import xarray as xr

import matplotlib.pyplot as plt import numpy as np import scipy

from scipy.interpolate import griddata from scipy.interpolate import Rbf

from pykrige.uk import UniversalKriging from pykrige.ok import OrdinaryKriging

csvnimi1="./daatta/mis3w1.txt"

  1. csvnimi1="./daatta/mis3g2.txt"
  1. csvnimi1="./daatta/mis3x0.txt"

outname1="./out.nc"

dataname0="./daatta/oro330.nc" dataname1="./daatta/result330.nc"

dataname0="./daatta/area_neworog.nc" dataname1="./daatta/result.nc"

inx=[] iny=[] ints=[] inh=[]

  1. points1=points1=[]

with open(csvnimi1) as csv_file1: csvreader1 = csv.reader(csv_file1, delimiter=',') nn = 0 lc=0 for row1 in csvreader1: if (lc>0): xx=float(row1[1]) yy=float(row1[2]) tt=float(row1[3]) hh=float(row1[4]) inx.append(xx) iny.append(yy) ints.append(tt) inh.append(hh) points1.append([]) points1[nn].append(xx) points1[nn].append(yy) nn=nn+1

lc=lc+1

  1. print(points1)

datakoko1=len(inx) print (datakoko1)

  1. exit(-1)

hila0 = xr.open_dataset(dataname0) hila1 = xr.open_dataset(dataname1)

elevhila0=hila0.Elev elevhila1=np.flipud(elevhila0)

tshila0=hila1.TS tshila1=np.flipud(tshila0)

x1=-15 x2=40 xsiz1=3300 xu1=x2-x1 xd1=xu1/xsiz1

y1=30 y2=70 ysiz1=2400 yu1=y2-y1 yd1=yu1/ysiz1

lons1 = np.arange(x1, x2, xd1) lats1 = np.arange(y1, y2, yd1)

gx1, gy1 = np.mgrid[ 0:xsiz1, 0:ysiz1 ]

xin2=[] yin2=[] elev2=[] points2=[]

for b in range(0, datakoko1): xxx1=points1[b][0]-x1 yyy1=points1[b][1]-y1 xxx2=int(xxx1/xd1) yyy2=int(yyy1/yd1) zzz2=elevhila1[yyy2,xxx2] elev2.append(zzz2) xin2.append(xxx2) yin2.append(yyy2) points2.append([]) points2[b].append(xxx2) points2[b].append(yyy2)

naytekoko=20

print( points2)

  1. exit(0)

xobs1 = np.random.random((naytekoko, 2))

xobs1[0:,0]=(xobs1[0:,0]*xsiz1) xobs1[0:,1]=(xobs1[0:,1]*ysiz1)

xx1=xobs1[0:,0] yy1=xobs1[0:,1]

  1. xobs1[][0]=xobs1[][0]*xu1

xobs2=xobs1.astype(int)

naytee1=[] naytet1=[]

for b in range(0, naytekoko): xxx1=xobs2[b,0] yyy1=xobs2[b,1] zzz1=tshila1[yyy1,xxx1] zzz2=elevhila1[yyy1,xxx1] naytet1.append(zzz1) naytee1.append(zzz2)

  1. print (xobs2)
  2. print (naytez1)
  1. print (tshila1)
  1. points1=
  1. img = plt.imshow(tshila1)
  1. plt.scatter(xx1,yy1,marker='o',c='b',s=5)
  1. plt.show()
  1. exit(-1)
  1. print("Interpolating, slow, please wait patiently ... ")
    1. orig test
  2. z1 = griddata(xobs2, naytet1, (gx1,gy1), method='cubic')
  3. z0 = griddata(xobs2, naytee1, (gx1,gy1), method='cubic')

z1 = griddata(points2, ints, (gx1,gy1), method='cubic') z0 = griddata(points2, elev2, (gx1,gy1), method='cubic')

  1. rbf = Rbf(yin2,xin2,ints, epsilon=2)
  2. z0 = rbf(gx1,gy1)
  3. OK = OrdinaryKriging(xx1,yy1, naytez1, variogram_model='linear', variogram_parameters=[1,20,1])
  4. OK = OrdinaryKriging(xx1,yy1, naytez1, variogram_model='exponential', variogram_parameters=[1,40,3])
  5. z0, ss0 = OK.execute('grid', gx1,gy1)

eh0= np.transpose(elevhila1) print( len(eh0)) print( len(z0))

  1. exit(0)

deltae=eh0-z0 deltat=deltae*-0.0065

rout=z1+deltat

plt.scatter(xx1,yy1,marker='o',c='b',s=5)

  1. img = plt.imshow(z0, extent=(0,330,0,240), origin="upper")

img = plt.imshow(rout, extent=(0,330,0,240), origin="upper")

plt.show()

  1. rout=z0



outdata1 = xr.Dataset( 

       data_vars={'TS': (('lon', 'lat'), rout) },
       coords={'lon': lons1, 'lat':lats1})

outdata1.to_netcdf(outname1)

Points

"ID","lon","lat","TS","Elev" "Calais",1.85,50.95,11.0,0 "Marse",5.0,41.0,21.0,0.0 "Marse2",4.0,43.0,19.0,0.0 "P5",0.0,50.0,12.0,0.0 "Up1",15.0,47.5,12,1000.0 "Up2",15.0,46.5,8,2000.0 "Mare1",-10.0,46.5,10,00.0 "Aqvit1",0.5,44.5,16.5,200 "LesEyzies",1.018,44.936,16.0,75 "Loire",0,47,16.5,250 "T1",-10,40,18,0 "T2",-10,50,10,0 "T3",0,40,12.5,0 "T4",0,50,10,0 "T5",10,40,18,0 "T6",10,50,13,300 "T7",20,40,22,100 "T8",20,50,12,0 "T9",-3,45.5,12,0 "T9",-3,49,12,0

Licensing

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Date/TimeThumbnailDimensionsUserComment
current05:41, 4 October 2019Thumbnail for version as of 05:41, 4 October 20191,650 × 1,275 (1.24 MB)Merikanto (talk | contribs)User created page with UploadWizard

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