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The Ultimate List of Typical Meteorological Year (TMY) Do’s and Don’ts

When it comes to analysing and designing energy systems, a  TMY (Typical Meteorological Year) plays a crucial role. It represents a synthesised set of weather data that helps engineers and researchers assess the performance and reliability of renewable energy systems. This article presents the ultimate list of do’s and don’ts when working with TMY data.


1. Understand the Purpose: Before using TMY data, it is essential to grasp its purpose. TMY datasets are created by combining multiple years of observed weather data to form a typical representative year. Familiarise yourself with the specific objectives of the TMY dataset you are working with to ensure accurate analysis.

2. Verify Data Quality: TMY datasets are not immune to errors or inconsistencies. Perform a thorough quality check to ensure the data is reliable and suitable for your purposes. Check for outliers, missing data, and discrepancies that could affect the integrity of your analysis.

3. Utilise Multiple TIMEs: To gain a comprehensive understanding of the long-term weather patterns, it is beneficial to use multiple TMY datasets. Analysing multiple TIMEs from different sources or locations helps mitigate the biases inherent in a single dataset and provides a more robust representation of weather conditions.

4. Validate Results: Cross-validation is a crucial step in TMY analysis. Comparing the simulated results using TMY data with actual measured data can help validate the accuracy of the TMY dataset. This process ensures the reliability of the TMY dataset and provides confidence in the outcomes of your analysis.


1. Overgeneralize Findings: TMY datasets represent typical weather patterns, but they may not accurately capture extreme events or unusual weather phenomena. Avoid overgeneralizing the findings based solely on TMY data. Consider additional data sources or conduct further analysis if extreme events are of concern.

2. Neglect Temporal Resolution: TMY datasets often provide hourly or sub-hourly weather data. Neglecting the temporal resolution can lead to inaccurate energy simulations or inappropriate system sizing. Pay attention to time-of-day variations, diurnal cycles, and seasonal patterns when utilising TMY data for energy system analysis.

3. Ignore Local Influences: TMY datasets are synthesised from data collected at specific locations. While they provide a representative picture, they may not reflect the microclimates or local weather conditions of your project site. Consider local influences such as topography, urban heat island effect, or coastal influences when interpreting TMY data.

4. Disregard Updates: Weather patterns can change over time due to various factors, including climate change. TMY datasets have a specific time range, and their accuracy may diminish as they become outdated. Stay informed about updates or new versions of TMY datasets to ensure you are working with the most recent and reliable data available.