Predictions by the European weather model (ECMWF) and the American weather model (GFS) can differ due to several factors:
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Model Design and Structure: The two models are built on different mathematical frameworks and assumptions about the atmosphere. This includes different grid resolutions, physics schemes, and numerical methods. The ECMWF tends to have finer resolution and uses sophisticated data assimilation techniques, which can lead to differences in how they represent atmospheric processes.
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Data Assimilation: Each model incorporates observational data differently. The type, amount, and timing of data used can impact model forecasts. The ECMWF is known for its advanced data assimilation techniques, which can lead to more accurate initial conditions, thus affecting subsequent forecasts.
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Physical Parameterizations: Both models use parameterizations (simplified representations) of complex atmospheric processes, such as cloud formation, precipitation, and radiation. Differences in how these processes are modeled can lead to varying outcomes.
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Initial Conditions: Weather predictions are highly sensitive to initial conditions. Even small discrepancies in the initial state of the atmosphere can lead to significantly different outcomes over time. The two models may use different sets of initial conditions based on varying data sources or model cycles.
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Forecasting Horizon: The skill of both models can vary depending on how far into the future the forecast is made. They may perform better or worse at different time scales, which could lead to divergence in their predictions.
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Ensemble Forecasting: Both models employ ensemble forecasting techniques, running multiple simulations with slightly varied initial conditions to assess uncertainty. Differences in the number and variety of ensemble members can lead to differing predictions.
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Physical Constants and Constants Used: The choice of certain constants or parameters within the models can also cause variations, as different modelers might prefer different values based on empirical evidence or theoretical reasons.
These factors contribute to differences in model outputs, and often meteorologists compare the forecasts from both models to form a more comprehensive view of forthcoming weather. In practice, forecasters may utilize insights from both models, along with additional data, to arrive at a more reliable forecast.