# Forecasting rain from weather conditions

Please bear with me - I have a very limited knowledge in statistics. Here is my case (simplified):
A weather pattern is derived from 1 year of data. We are monitoring:

• sky conditions (sunny/overcast)
• wind direction (E/W/N/S)

Goal: We are trying to predict probability of "rain within 1hr".

Observations:

• if sky="overcast" AND wind="East": 18 out of 20 times it rains within 1hr. - p=0.9
• if sky="overcast" AND wind="South": 15 out of 20 times it rains within 1hr. - p=0.75

My problem is that there is only 20 occurrences on each pattern that gives me very low confidence (approx. +-22% at 95% confidence lvl) - apparently the predominant wind in the area is N/W :(

Question: Can I re-formulate my statement/pattern like this:
if sky="overcast" AND (wind="East" OR wind="South") 33 out of 40 times it rains within 1hr. e.g. p=0.83?
If this is the case now that I have 40 occurrences would that give me better confidence interval, say approx. 15% at 95% cl?

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