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Spatial datasets operations: an overview of global climate dataset and interpolation

Climate dataset is literally the most important data in climate change research. Great efforts have been made to prepare climate dataset over the decades.

In my recent project, I need to prepare some climate dataset at global scale. Then I have to take some time to finish a review of current state of climate dataset at global scale.

First, I want to emphasize "global scale" including both arctic and antarctic. Because I will use this data in a three-dimensional ecosystem simulation, a special configuration of the data structure will be used, which is completely different from traditional approaches.

Second, where or how can we get the climate data?

The easiest source we can turn to is existing global climate dataset, such as CRU dataset, NCEP dataset. I try not to get into details of these data but rather list the source and state some critical aspects that we have to take into consideration.

CRU:
http://www.cru.uea.ac.uk/data
http://www.ipcc-data.org/observ/clim/cru_climatologies.html
NCEP:
https://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml
ECMWF
https://www.ecmwf.int/en/research/climate-reanalysis
WorldClim
http://www.worldclim.org/

Pros:
  • Easy to use
  • Global coverage

Cons:
  • Missing polar regions
  • Coarse resolution


Based on meteorology site observations, many spatial interpolation methods can also be used to produce spatial dataset.
IDW
https://en.wikipedia.org/wiki/Inverse_distance_weighting
Kriging
https://en.wikipedia.org/wiki/Kriging

Pros:
  • Easy to implement

Cons:
  • Data not well distributed
  • Algorithm uncertainty


There are also advanced tools can be used to prepare climate data.
ANUSPLIN
http://fennerschool.anu.edu.au/research/products/anusplin-vrsn-44
PRISM
http://www.prism.oregonstate.edu/

Pros:

  • Could potential on global scale


Cons:
  • Computational demand
  • Hard to implement 

Recently, Artificial Neural Network has also been used for spatial interpolation. But current tests show that ANN may not be as good as traditional approaches.

Pros:
  • Easy to use

Cons:
  • Uncertainty

I want to highlight that our use of latitude/longitude in spatial resolution will cause issues in polar regions. This is because of the underlying mathematics definition of latitude/longitude. There are a few solutions to this issue but that is currently out of the scope in this post. I will provide some hint here that may be used to address this issue.
http://www.discreteglobalgrids.org/

So what could potentially be the best choice?
As for now, I see the potential in ANN since it is nonlinear and could potentially performance well compared with traditional approaches. 




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