Numerical models are fundamental tools in atmospheric and environmental forecasting, and their accuracy depends on the quality of data and model formulation. There are certain limitations associated with these models that must be acknowledged to understand their capabilities and boundaries.
The causes of limitations in numerical models can be divided into three main categories:
- Initial Data:
Numerical models rely directly on initial data collected from ground observation stations, satellites, radars, and other sources. Some deficiencies may appear in this data, such as insufficient distribution and coverage of observation stations across different geographic regions, leading to gaps in information. Additionally, the number of daily observations may be inadequate to capture rapid atmospheric changes. Furthermore, technical challenges such as equipment malfunctions or communication outages can result in incomplete or erroneous data. These factors are part of the practical reality of data collection and require ongoing monitoring and improvement. - Numerical Model:
Numerical models depend on mathematical equations representing physical processes in the atmosphere, which cannot be fully and precisely formulated due to the complexity of these processes. This involves necessary simplifications in representing terrain, where the model may not accurately capture fine details such as mountains and valleys, affecting wind flow and thermal distribution. Models also face difficulties in estimating large-scale precipitation processes, especially when relying on approximate parameters that may not accurately reflect real conditions, such as the detailed interactions between clouds and rainfall or heat and moisture exchanges between the earth’s surface and the atmosphere. The accuracy of boundary data, especially in global models, also impacts simulation results. - Predictive Limitations:
Even with high quality initial data and advanced numerical models, the accuracy of forecasts naturally decreases as the simulation period increases. This limitation is an expected aspect that must be considered when using numerical predictions.