What makes long-term weather forecasting so challenging?
Is anyone here involved in Atmospheric Sciences? I’m wondering how feasible it is to achieve reasonably accurate weather forecasts 30 days in advance. It seems like we have the data, but weather platforms rarely provide reliable forecasts beyond a week. I’m sure there are complexities I’m not fully aware of.
EDIT: This is why I appreciate Reddit so much. It’s amazing how many people can shed light on questions I’ve always been curious about, no matter how niche the topic.
Yes, long-term forecasts do exist and are known as seasonal forecasts. For instance, El Niño predictions are made using these methods (you can find recent predictions on the International Research Institute for Climate and Society website). Another developing area is the subseasonal to seasonal (S2S) forecasts, which bridge the gap between short-term weather forecasts and longer-term seasonal predictions.
These forecasts operate similarly to weather forecasts but extend further in time, using both dynamic and statistical models. Unlike weather forecasts, which primarily use atmospheric models, seasonal forecasts often couple atmospheric and oceanic models.
Ocean-atmosphere interactions can span a wide range of timescales and spatial scales, so long-range predictability is possible. Larger phenomena, like El Niño, have more inertia, making them easier to predict over longer periods. The ocean’s slower adjustment compared to the atmosphere also plays a significant role. While long-range forecasts might lack the detailed resolution of short-term forecasts, deterministic chaos like the butterfly effect sets limits on predictability but doesn’t entirely preclude long-term forecasting.
Machine learning is beginning to be incorporated into weather forecasting (see recent developments like ECMWF AIFS), though it hasn’t yet been widely adopted for seasonal forecasts. However, it’s likely just a matter of time before it becomes more common in this area.
This is fantastic information! It’s really enlightening to see how seasonal forecasting works and how it extends our understanding beyond short-term weather predictions. The way it bridges the gap between immediate weather patterns and long-term climate phenomena is impressive. It’s fascinating to learn about the role of oceanic models and the intricate balance between atmospheric and oceanic interactions. Plus, the potential of machine learning in this field adds an exciting layer of innovation. Thanks for sharing such valuable insights.