# How to transform the inventory data from degrees to kilometers

First all, my question was different from the question Converting grid resolution from degrees to kilometers.

In chemical transport modeling, the pollutant source inventory was an essential input which contain residential, industrial, agriculturial, vehicle emission in the area of interest.

The source inventory can be built independently or downloaded from the websites of several institutions which support some global/regional source information dataset.

For example:

1. It contain different emission species like SO2, NOx, CO, NMVOC in the resolution of 0.5 degree x 0.5 degree

2. Tracer-P

3. REAS -> Regional Emission inventory in ASia
Spatial resolution: 0.25 degree by 0.25 degree

4. EDGAR The Emissions Database for Global Atmospheric Research
spatial resolution: 0.1deg x0.1deg and 0.5deg x0.5deg

These source inventories are all gridded in geographic coordinate. But for regional/small scale research, the simulation grid was set in the unit of kilometers(For example, 36 km for regional simulation, 4 km for urban simulation).

Then, I want to re-arrange the source inventory into km x km cell. Due to different distance corresponding to the same degrees gap along the latitude, the degree-cell can't be transform into kilometer-cell directly.

If directly, the width of each cell

$$\Delta y=2\pi R_e\frac{\Delta lat}{360^\circ}$$

The length of each cell:

$$\Delta x=2\pi R_e\cos(lat)\frac{\Delta lon}{360^\circ}$$

So, every cell has its own size in kilometers. The uneven grid networks enhance the difficulty for simulation. I'm wondering to transform the inventory into uniform grid in kilometers(for example: 0.25 degree x 0.25 degree -> 36 km x 36 km)

How to achieve it? I'm familiar with Python language.
• A colleague of mine does the emission preparation for our whole working group. He uses his own interpolation routines. I use cdo (cdo remap) when I do interpolations. Maybe we should switch to the chat, if you have more questions? Jul 7, 2016 at 9:25