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WaterScienceProf OP t1_j22oqd4 wrote

Temperature! See our figure from our paper and on wikipedia: As seen, the energy needs is overlaid on a psychometric chart. The map itself is a yearly average, so it looks worse for regions that have dry seasons, even though AWH may be very easy in their wet seasons.. e.g. Look at Africa North and south of the Congo rainforest, or just South of the Amazon. Also, the map shows a strong influence of Hadley cells, with bands of low and high energy needs:


somedave t1_j2349qx wrote

Your graph doesn't go to zero for the humidity ratio. Presumably because the energy required becomes infinite. Does it blow up as the log of this ratio or the inverse?


wasdlmb t1_j22v10x wrote

Thank you. Weather is wild.

The chart makes it look like the energy need mirrors relative humidity, but multiplying relative humidity by a constant wouldn't require high performance computing. Nor even just a defined two input map applied to every point. What was it that took the cluster so much time to compute? Did you have to model the local relative humidity yourself, or is there some more complex relation that I'm not seeing?


WaterScienceProf OP t1_j25u4s8 wrote

The model calculates the Gibbs free energy from removing water vapor, which takes property lookups at each condition. The data intensity comes into play because it's a full year averaged using hourly data, all across the globe.

To the other question: as the humidity goes to zero, the energy needs become extremely high. It also becomes very challenging for practical systems.