Relying exclusively on geostationary infrared satellite data for tropical cyclone center fixing presents several limitations:
Data Quality and Resolution: Geostationary IR data, while high in temporal resolution, may lack the spatial resolution needed to accurately identify the TC center, especially in weak systems or those with poorly defined structures. This limitation can be addressed by integrating higher-resolution data from polar-orbiting satellites or using advanced imaging techniques that enhance the spatial resolution of IR data.
Sensitivity to Atmospheric Conditions: IR satellite data can be affected by atmospheric conditions such as cloud cover, which may obscure the TC center. To mitigate this, a hybrid approach that combines IR data with other observational data, such as radar or surface observations, could provide a clearer picture of the TC structure.
Limited Information on Wind Patterns: IR data primarily provides information on temperature and cloud patterns, which may not fully capture the wind field dynamics essential for accurate center fixing. Incorporating data from scatterometers or atmospheric motion vectors could provide critical insights into the wind patterns associated with TCs.
Dependence on Satellite Availability: Geostationary satellites may not always provide continuous coverage, especially in regions with frequent cloud cover or during extreme weather events. Developing algorithms that can intelligently interpolate or extrapolate TC center positions based on historical data and trends could help address this issue.
Model Biases: The model may inherit biases from the training data, particularly if certain TC types or structures are underrepresented. Regularly updating the training dataset with new TC cases and ensuring a diverse representation of storm types can help reduce these biases.