Why accurate weather forecasting is still so difficult — and why real-time data matters

Accurate weather forecasting remains an ongoing challenge due to the dynamic and complex nature of the atmosphere. A model must first accurately determine the current state of the atmosphere before predicting its future behavior. This current state is derived by combining observations from various sources such as weather radars, satellites, ground stations, weather balloons, and aircraft.

However, each source has its own limitations, including varying resolutions, update frequencies, coverage gaps, and measurement errors. Combining these diverse data sources into a coherent picture represents a complex process called data assimilation, which establishes an estimate of the atmosphere's current state, known as an analysis, upon which the forecast is based.

The quality of the forecast heavily relies on the precision of this analysis. Even a highly accurate model will fail if the starting point is incomplete, inaccurate, or outdated.

Optical flow techniques are commonly used for short-term forecasting. These methods examine consecutive radar images, estimate the movement of precipitation, and extrapolate this motion into the future. This approach works efficiently when rain bands move steadily. However, storms are not static; they form, intensify, weaken, split, and merge.

A new convective cell may emerge where no rain existed just 20 minutes prior. For instance, a storm approaching a mountain might be predicted to continue moving at the same speed. Yet, the terrain can disrupt airflow, slow the system, and lead to heavier rain or hail. The challenge lies not only in predicting the movement of existing precipitation but also in determining how the weather system will evolve.

Large-scale models are proficient at depicting weather fronts, pressure systems, and atmospheric circulation across countries and continents. However, many weather events that impact people and businesses occur on a smaller scale. A thunderstorm might affect a specific city area while leaving another part almost unaffected. A cloud front can significantly reduce energy output at one solar farm but leave another plant 20 kilometers away unscathed.

Wind conditions can vary significantly between the ground, rooftop, and the altitude at which a drone operates. These variations can't be accurately predicted by models.

The butterfly effect underscores the sensitivity of weather to its initial state. Small uncertainties in temperature, humidity, or wind can escalate over time, making long-term predictions increasingly uncertain. Therefore, modern forecasting systems often generate ensembles of plausible forecasts rather than a single definitive answer.

This approach provides a range of probabilities, which can be more beneficial than a confident but incorrect prediction. However, real-time weather forecasting goes beyond mere speed. It requires a continuous cycle of collecting new observations, assessing their quality, synchronizing them in time, running the model, and delivering an updated forecast within minutes.

This rapid updating is crucial for industries that cannot afford to rely on outdated or inaccurate weather data. For instance, a delivery drone should be aware of potential precipitation, icing, or dangerous wind before it reaches a specific location. An airport might need to adjust runway operations as a thunderstorm develops nearby.

A solar operator must anticipate a fast-moving cloud front to prevent sudden drops in energy generation. Logistics and mobility platforms can reroute vehicles before flooding or severe rain disrupts road networks, and outdoor events, emergency services, agriculture, and energy trading can all benefit from timely and accurate weather information.

While AI weather models can forecast quicker and with less computational power compared to traditional numerical models, they still require an accurate atmospheric analysis as their starting point. If this analysis is several hours old, even fast AI inference cannot compensate for the outdated initial state.