<p>You need a model. You could try to use Bayes theorem to build a model based upon conditional probabilities. See this reference from NOAA about weather forecasting: <a href="http://www.nssl.noaa.gov/users/brooks/public_html/prob/Probability.html" rel="nofollow">Probability Forecasting - Primer</a> . </p>

<p>If I were to try to build a Bayesian model for weather forecasting, with the intention of using a limited data set of observations I had collected myself, I would look into existing software frameworks to see if I could use one of these. For example, the <a href="http://research.microsoft.com/en-us/um/cambridge/projects/infernet/" rel="nofollow">Infer.NET framework</a> is available for free, and has several examples which would be a good starting point for learning about probabilistic modeling. </p>

CC BY-SA 3.0
2014-12-30T13:26:56.577Z
2014-12-31T05:39:50.220Z
2014-12-31T05:39:50.220Z
Mark Rovetta
<p>Assuming I have time evolution of air temperature, air pressure, wind speed, wind direction, relative humidity and cloudiness, how can I predict weather based on this information? </p>

<p>I know that high air pressure increases chance of more concentrated sunlight and low air pressure increases chance of precipitation. I know that a decrease in temperature increases chance of snow or ice and also of low relative humidity and vice versa. I also know that higher wind speed means higher chance of a storm and that certain directions of wind can cause a change in temperature.</p>

<p>However I don't know what meteorological parameters I could predict with a combination of these variables. How would I predict the weather based on the time evolution of these variables?</p>

<meteorology><weather-forecasting><statistics>
Statistical weather prediction
Caters
# Statistical weather prediction

(Asked by: username_0 on 2014-12-30)

Assuming I have time evolution of air temperature, air pressure, wind speed, wind direction, relative humidity and cloudiness, how can I predict weather based on this information?

I know that high air pressure increases chance of more concentrated sunlight and low air pressure increases chance of precipitation. I know that a decrease in temperature increases chance of snow or ice and also of low relative humidity and vice versa. I also know that higher wind speed means higher chance of a storm and that certain directions of wind can cause a change in temperature.

However I don't know what meteorological parameters I could predict with a combination of these variables. How would I predict the weather based on the time evolution of these variables?

**Tags:** meteorology, weather-forecasting, statistics


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## 1 answers

### Answer by username_1 on 2014-12-30. Score: 7

You need a model. You could try to use Bayes theorem to build a model based upon conditional probabilities. See this reference from NOAA about weather forecasting: [Probability Forecasting - Primer](http://www.nssl.noaa.gov/users/brooks/public_html/prob/Probability.html) .

If I were to try to build a Bayesian model for weather forecasting, with the intention of using a limited data set of observations I had collected myself, I would look into existing software frameworks to see if I could use one of these. For example, the [Infer.NET framework](http://research.microsoft.com/en-us/um/cambridge/projects/infernet/) is available for free, and has several examples which would be a good starting point for learning about probabilistic modeling.


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