Wind,Temperature,Air Pressure statistics

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The above graphs show statistics from SMHI, the swedish meteorological and hydrographical institute, on air pressure, temperature and wind speed.  For each categrory, (pressure,temp,wind speed) there are two graphs: the first a frequency chart, the second a Q-Q-chart, showing the correlation between the observed data and a specific theoretical distribution, which I’ve chosen to be the Normal distribution for temperature and pressure, and the Rayleigh distribution for wind speed. Note: that’s not to necessarily imply that wind, temp & pressure ARE distributed according to these theoretical models… more on this below.

As can be seen, the best match occurs for wind speed distribution, where the observed data fit the Rayleigh distribution very well – almost all observations fall onto a straight line.

Both air pressure and temperature are mapped against the Normal distribution, and as can be seen, neither of them are a perfect fit, with temperature being clearly most non-conforming to the normal distribution.

To summarize the observations from this data:

Wind speed follows the Rayleigh distribution quite well, Air pressure could perhaps be modelled by the normal distribution, despite it not being a “perfect” match – but then, in reality, no model is a perfect match for reality! – and temperature, at least at my latitudes, where there is large seasonal variation, does not fit neither Rayleigh nor Normal distributions.

About swdevperestroika

High tech industry veteran, avid hacker reluctantly transformed to mgmt consultant.
This entry was posted in development, Non Linear Dynamic Systems, Probability, Python, Statistics and tagged , , , , , , . Bookmark the permalink.

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