explain why weather can be predicted only as probable.

Weather can only be predicted as probable because it is a complex and dynamic system that is influenced by numerous factors. These factors include temperature, humidity, pressure systems, air currents, and even the interactions between oceans and atmosphere. Due to the sheer number of variables involved and their constantly changing nature, it is impossible to accurately predict the exact weather conditions for a specific location and time.

Instead, meteorologists use scientific models and data analysis techniques to make predictions about the most likely weather conditions. These models take into account historical weather patterns, current atmospheric conditions, and mathematical equations that describe the behavior of the atmosphere. However, even with these sophisticated tools, there is still inherent uncertainty in weather prediction.

Small variations in initial conditions or inaccuracies in the models can lead to significant differences in the predicted weather. This is known as the butterfly effect, where even a minor change in one part of the system can have a large impact on the overall weather outcome. Additionally, the atmosphere is a chaotic system, meaning that tiny errors in measurements or calculations can amplify over time, making accurate long-term predictions difficult.

Meteorologists express weather forecasts as probabilities to reflect this uncertainty. For example, a forecast of a 60% chance of rain predicts that, based on current information, there is a higher likelihood of rain occurring than not. However, this does not guarantee that it will rain for sure. It is more like an educated estimation based on the available information.

Overall, weather prediction is a challenging task due to the complexity and chaotic nature of the atmosphere. While advances in technology and improved understanding of atmospheric processes have enhanced forecast accuracy, there will always be an inherent uncertainty in predicting the exact weather conditions.