<p>Unfortunately, unless one is dealing with the big bang theory in cosmology it is difficult to get something from nothing (or near nothing such as low density data points). If you haven't already done so use a different search ellipse size and orientation.</p>
<p>Try orientating the search ellipse so its major axis is orientated in the direction the rainfall was moving. This could be due to wind direction or the direction of weather fronts which influenced the fall of rain, or some other weather phenomenon.</p>
<p>The problem with trying to derive estimates for regions with low data density is an estimate may be obtained but the probability of the estimate being accurate decreases. It's why when geologists estimate a mineral resource for a deposit they categorize different regions of the estimate as either measured, indicated or inferred and data density is one of the main drivers for such a classification system.</p>

CC BY-SA 4.0
2024-12-09T07:18:20.353Z
2024-12-09T07:18:20.353Z
Fred
<p>I'm trying to improve our method of interpolation for rainfall measurements. At the moment we use a specific combination of <code>Rbf</code> interpolation from scipy. Unfortunately it is quite hard to reduce the extrapolation done in areas where there are not many observations.</p>
<p>This picture shows what I mean: the color shadings represent the interpolated field, while the points show the observations I used for the interpolation (colored according to the measured value).
<a href="https://i.sstatic.net/oT8F561A.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oT8F561A.png" alt="Interpolation map" /></a>
As you can see the model works pretty well where the density of observations is good, but fails to extrapolate in areas where the density is not sufficient. There is one particular 'blob' of phantom rain that I indicated with the red arrow, which I would like to correct. What I think is happening is that, since there are no stations in the vicinity, the model just propagates the value of the closest station, which is about 10 mm.</p>
<p>I would like the model to avoid doing that by setting a certain radius above which it just avoids extrapolating. I've experimented with different interpolation method (<code>rbf</code>, kriging, natural neighbour,...) but somehow the interpolated field has always similar features.</p>
<p>Do you know any trick to make the interpolation method more aware of these spatial correlations without having it to train it with radar observations?</p>

CC BY-SA 4.0
2024-12-09T08:48:11.780Z
Not really my field, but I think Voronoi tessellation could be of use in such cases: https://en.wikipedia.org/wiki/Voronoi_diagram#Meteorology/Hydrology
Jean-Marie Prival
CC BY-SA 4.0
2024-12-09T09:51:00.103Z
You might want to consider exploring the correlation with topography in the areas with data, to see whether it can used as a secondary factor to improve interpolation elsewhere
Andy M
<precipitation><interpolation>
Reduce extrapolation in areas with no observations when interpolating precipitation
Droid
# Reduce extrapolation in areas with no observations when interpolating precipitation

(Asked by: username_0 on 2024-12-08)

I'm trying to improve our method of interpolation for rainfall measurements. At the moment we use a specific combination of `Rbf` interpolation from scipy. Unfortunately it is quite hard to reduce the extrapolation done in areas where there are not many observations.

This picture shows what I mean: the color shadings represent the interpolated field, while the points show the observations I used for the interpolation (colored according to the measured value).
[![Interpolation map](https://i.sstatic.net/oT8F561A.png)](https://i.sstatic.net/oT8F561A.png)
As you can see the model works pretty well where the density of observations is good, but fails to extrapolate in areas where the density is not sufficient. There is one particular 'blob' of phantom rain that I indicated with the red arrow, which I would like to correct. What I think is happening is that, since there are no stations in the vicinity, the model just propagates the value of the closest station, which is about 10 mm.

I would like the model to avoid doing that by setting a certain radius above which it just avoids extrapolating. I've experimented with different interpolation method (`rbf`, kriging, natural neighbour,...) but somehow the interpolated field has always similar features.

Do you know any trick to make the interpolation method more aware of these spatial correlations without having it to train it with radar observations?

**Tags:** precipitation, interpolation

> **Comments:**
> **username_1**: Not really my field, but I think Voronoi tessellation could be of use in such cases: https://en.wikipedia.org/wiki/Voronoi_diagram#Meteorology/Hydrology  
> **username_2**: You might want to consider exploring the correlation with topography in the areas with data, to see whether it can used as a secondary factor to improve interpolation elsewhere  

---

## 1 answers

### Answer by username_3 on 2024-12-09. Score: 0

Unfortunately, unless one is dealing with the big bang theory in cosmology it is difficult to get something from nothing (or near nothing such as low density data points). If you haven't already done so use a different search ellipse size and orientation.

Try orientating the search ellipse so its major axis is orientated in the direction the rainfall was moving. This could be due to wind direction or the direction of weather fronts which influenced the fall of rain, or some other weather phenomenon.

The problem with trying to derive estimates for regions with low data density is an estimate may be obtained but the probability of the estimate being accurate decreases. It's why when geologists estimate a mineral resource for a deposit they categorize different regions of the estimate as either measured, indicated or inferred and data density is one of the main drivers for such a classification system.


---

