<p>I have managed to plot various points on a map and interpolate with ordinary kriging. However, my data does not look anything like it should. Do I need to use an algorithm to fill in the missing data? Does anyone know how the RCWIP2 model does this?</p>
<p>My data looks like this:
<a href="https://i.sstatic.net/matEB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/matEB.png" alt="enter image description here" /></a></p>
<p>and it should look like this:</p>
<p><a href="https://www.researchgate.net/figure/Global-mean-annual-average-leaf-water-d-18-O-and-d-2-H-isoscapes-for-the-sites-of_fig1_226462314" rel="nofollow noreferrer">https://www.researchgate.net/figure/Global-mean-annual-average-leaf-water-d-18-O-and-d-2-H-isoscapes-for-the-sites-of_fig1_226462314</a></p>
<p>Does anyone have an idea where to get an algorithm (open source) for this? Or is it possible to create a better interpolation using the GNIP data (without an algorithm)?</p>
<p>I have adjusted the colour scale (it doesn't make sense to use a larger colour range) and displayed the measuring points. But you can see that the interpolation is much too coarse. What can I do?
<a href="https://i.sstatic.net/os5OI.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/os5OI.png" alt="enter image description here" /></a></p>
<p>The values central europe, for example, look like this:</p>
<pre><code>0,0,100400,78.91666667,11.93333333,-11.948727272727274
1,1,284500,66.4968611111111,25.7551805555556,-15.65725
2,2,291701,62.8917944444444,27.6254222222222,-14.696333333333333
3,3,297401,60.1800888888889,24.8328611111111,-12.793636363636363
4,4,304401,58.15,-4.975,-5.424
5,5,365302,51.6,-1.1,-8.102181818181817
6,6,395300,51.93,-10.25,-6.069957643495706
7,7,403000,64.13,-21.93,-9.823909326232151
8,8,432000,76.7666666666667,-18.6666666666667,-22.848
9,9,628001,53.23,6.55,-8.19874809968471
10,10,660100,47.5426086672,7.58371692988,-11.620857142857144
11,11,661700,46.9814140076,6.60799965476,-9.690999999999999
12,12,663100,46.9522256303,7.4393205714,-12.386181818181818
13,13,663300,47.3723359688,8.08313824523,-11.575
14,14,663700,46.7273863351,8.18678216294,-11.338727272727272
15,15,668100,47.4269289828,9.4007768793,-10.921999999999999
16,16,670500,46.3986411863,6.23380378665,-9.955272727272726
17,17,672000,46.2203086749,7.33848445042,-12.540444444444445
18,18,673401,46.657725176,8.29270683241,-12.813636363636364
19,19,674400,46.5724847069,8.33271962058,-12.234545454545453
20,20,676000,46.1737858341,8.78855605127,-9.962909090909092
21,21,679201,46.4914336729,9.89818088768,-13.835999999999999
</code></pre>
<p>You can see that the data is very variable and that should have an effect on the interpolation, right?</p>

CC BY-SA 4.0
2022-06-04T13:33:26.290Z
You appear to be using a different scale. You're using -24 to +3 while the other site is using -19 to +30. Try changing that first?
Barry Carter
CC BY-SA 4.0
2022-06-05T23:31:43.070Z
You say your data looks like this map, but isn't that the model? Either way, it looks very smooth. What's the spatial sampling of your data like? How did you fit the kernel (covariance matrix) and tune the other hyperparameters? Are you applying regularization? Last thing: [please don't use this colourmap](https://www.nature.com/articles/s41467-020-19160-7).
Matt Hall
CC BY-SA 4.0
2022-06-06T11:46:03.817Z
@kwinkunks I modified my question
Weiss
CC BY-SA 4.0
2022-06-06T12:28:38.937Z
@kwinkunks Hi I don't know much about machine learning etc.:) I use the package Pykrige (Python) for the interpolation and assume that this package contains everything. Or do I have to do everything you mentioned separately? But Thanks for your reply
Weiss
CC BY-SA 4.0
2022-06-06T14:57:14.073Z
As a start, try changing the `variogram_model` and `variogram_parameters` because the default is a regularized linear variogram, hence your very smooth map.
Matt Hall
CC BY-SA 4.0
2022-06-06T14:58:12.147Z
I use in this case gaussian. I allready tried outher modells!
Weiss
CC BY-SA 4.0
2022-06-06T15:03:51.037Z
@kwinkunks however thanks allready :)
Weiss
<climate-models><mapping><python><isotopic><interpolation>
Algorithm for the interpolation?
Weiss
# Algorithm for the interpolation?

(Asked by: username_0 on 2022-06-04)

I have managed to plot various points on a map and interpolate with ordinary kriging. However, my data does not look anything like it should. Do I need to use an algorithm to fill in the missing data? Does anyone know how the RCWIP2 model does this?

My data looks like this:
[![enter image description here](https://i.sstatic.net/matEB.png)](https://i.sstatic.net/matEB.png)

and it should look like this:

<https://www.researchgate.net/figure/Global-mean-annual-average-leaf-water-d-18-O-and-d-2-H-isoscapes-for-the-sites-of_fig1_226462314>

Does anyone have an idea where to get an algorithm (open source) for this? Or is it possible to create a better interpolation using the GNIP data (without an algorithm)?

I have adjusted the colour scale (it doesn't make sense to use a larger colour range) and displayed the measuring points. But you can see that the interpolation is much too coarse. What can I do?
[![enter image description here](https://i.sstatic.net/os5OI.png)](https://i.sstatic.net/os5OI.png)

The values central europe, for example, look like this:

```
0,0,100400,78.91666667,11.93333333,-11.948727272727274
1,1,284500,66.4968611111111,25.7551805555556,-15.65725
2,2,291701,62.8917944444444,27.6254222222222,-14.696333333333333
3,3,297401,60.1800888888889,24.8328611111111,-12.793636363636363
4,4,304401,58.15,-4.975,-5.424
5,5,365302,51.6,-1.1,-8.102181818181817
6,6,395300,51.93,-10.25,-6.069957643495706
7,7,403000,64.13,-21.93,-9.823909326232151
8,8,432000,76.7666666666667,-18.6666666666667,-22.848
9,9,628001,53.23,6.55,-8.19874809968471
10,10,660100,47.5426086672,7.58371692988,-11.620857142857144
11,11,661700,46.9814140076,6.60799965476,-9.690999999999999
12,12,663100,46.9522256303,7.4393205714,-12.386181818181818
13,13,663300,47.3723359688,8.08313824523,-11.575
14,14,663700,46.7273863351,8.18678216294,-11.338727272727272
15,15,668100,47.4269289828,9.4007768793,-10.921999999999999
16,16,670500,46.3986411863,6.23380378665,-9.955272727272726
17,17,672000,46.2203086749,7.33848445042,-12.540444444444445
18,18,673401,46.657725176,8.29270683241,-12.813636363636364
19,19,674400,46.5724847069,8.33271962058,-12.234545454545453
20,20,676000,46.1737858341,8.78855605127,-9.962909090909092
21,21,679201,46.4914336729,9.89818088768,-13.835999999999999

```

You can see that the data is very variable and that should have an effect on the interpolation, right?

**Tags:** climate-models, mapping, python, isotopic, interpolation

> **Comments:**
> **username_1**: You appear to be using a different scale. You're using -24 to +3 while the other site is using -19 to +30. Try changing that first?  
> **username_2**: You say your data looks like this map, but isn't that the model? Either way, it looks very smooth. What's the spatial sampling of your data like? How did you fit the kernel (covariance matrix) and tune the other hyperparameters? Are you applying regularization? Last thing: [please don't use this colourmap](https://www.nature.com/articles/s41467-020-19160-7).  
> **username_0**: @kwinkunks I modified my question  
> **username_0**: @kwinkunks Hi I don't know much about machine learning etc.:) I use the package Pykrige (Python) for the interpolation and assume that this package contains everything. Or do I have to do everything you mentioned separately? But Thanks for your reply  
> **username_2**: As a start, try changing the `variogram_model` and `variogram_parameters` because the default is a regularized linear variogram, hence your very smooth map.  
> **username_0**: I use in this case gaussian. I allready tried outher modells!  
> **username_0**: @kwinkunks however thanks allready :)  

---

## 0 answers

