Quality Control and Homogenization: Before generating TMY data, rigorous quality control measures are applied to the raw weather data. This process involves identifying and correcting errors, removing outliers, and addressing inconsistencies in the data. Homogenization techniques are employed to account for changes in instrumentation, station location, and observing practices that may have occurred over time, ensuring the data’s consistency and reliability.

Data Gap Filling: In some cases, historical weather data may have gaps or missing values due to equipment malfunctions or data recording issues. To create a complete and continuous TMY dataset, data gap filling techniques are utilised. These methods involve using statistical models or interpolation to estimate missing values based on neighbouring observations.

Selection Criteria: Not all years of historical weather data are suitable for TMY data generation. Specific selection criteria are applied to identify the most representative meteorological year for a given location. Factors considered include the length of data record, data quality, and representativeness of the selected year compared to the long-term climatology.

Synthesizing TMY Data: Once the representative meteorological year is identified, statistical methods are employed to synthesise the TMY dataset. These methods involve creating synthetic weather data that closely match the statistical properties of the observed historical data. The resulting TMY data set represents a typical year of weather conditions for the specific location, allowing for reliable analyses and predictions.

Meteorological Stations: Meteorological Year (TMY) data primarily comes from specialised meteorological stations strategically located worldwide. These stations are equipped with instruments to measure various weather parameters continuously, providing a wealth of historical and real-time data. 

Long-Term Observations: TMY data is derived from long-term observations conducted over several years, typically spanning three decades. These observations ensure that the data represent the typical weather patterns and variations for a specific location, making them valuable for designing and evaluating energy-related systems.

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