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Showing posts from June, 2017

Spatial datasets operations: a hexagon-based discrete grid systems for global simulation

After finished my three-dimensional coupled water and carbon cycle model, I have been thinking whether I can apply this approach at large spatial domain or even global scale.
During this process, I realized that most (or all) global scale land surface modeling work are based on the square grid system, which is widely used in Earth science. This grid is also common recognized as pixel, grid cell.

Can we still use grid in global scale land surface model simulation?
Yes and no. If you do not consider lateral flow, then interactions between grid cells are omitted. In this scenario, grid cell might be the easiest approach to do so.
However, if horizontal interactions are considered. Then the grid-based structure will fail. This is because latitude/longitude based structure will create singularity in polar regions like this.

And due to the distortion, it is impossible to calculate interactions within this area across polar regions.

Most maps of various variables at global scale express the …

Scientific writing: from LaTex to BibTex

I am not very familiar with Tex system, but I use it for several purposes.

To produce a high quality manuscript using Latex itself can be a little bit of challenge so I am trying to do it once for all here.

Here are steps I generally follow if everything works fine.

You will need an Overleaf account because I prefer to write in a browser.Find the template of the document. If Overleaf has one, use it directly, if not, download it and upload it as a project;Start writing your manuscript just like normal;For figures, I suggest use an separated folder and indexed names;Use Mendeley to manage all your references and enable Mendeley Bibtex feature;Connect your Overleaf with Mendeley,Add the Bibliography file from Mendeley;Add all citations into your manuscript;Download the whole project including all output;Install Bibtex on a Linux machine;Upload all the results to Linux machine;Install the bibexport package;Remove unwanted citations from the Mendeley local file using bibexport;Upload the r…

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.