In this document
You will learn the key differences between Long-Term Averages (LTA), Typical Meteorological Year (TMY), and Time Series (TS) data used in solar resource assessment.
Overview
Long-Term Averages (LTA), Typical Meteorological Year (TMY), and Time Series (TS) are the primary data types used to describe solar resource variability and support PV yield analysis or forecasting. For each type, we will provide the core assumptions, a representative visual structure, key limitations, and guidance on typical use cases versus scenarios where that data type is not recommended. This helps you select the most reliable and relevant dataset for your technical or financial assessments.
Long-Term Average (LTA)
LTA data represents weather conditions with a single, smoothly averaged daily curve for each month. For instance, referring to the graph below, LTA data will assume that every day in February across all years appears identical, with no recognition of day-to-day variability or extreme events. This approach visually produces a flat, repetitive profile that overlooks the natural fluctuations in solar irradiance due to clouds or weather changes.
Note: Creating synthetic TMY datasets from long-term average (LTA) data is not considered good practice. This approach fails to capture real year-to-year and day-to-day weather variability, produces artificial variability that does not resemble true meteorological conditions, and can significantly underestimate extremes and risk events. TMY should always be derived from detailed multi-year time series to ensure realistic representation of both typical and atypical patterns.
Limitations:
Fails to capture overcast, rainy, or variable days.
Unsuitable for detailed design, operational or financial assessments.
Not able to capture risks linked to common or rare weather events.
Typical use cases
Initial project prospecting & site screening: Quick assessment of solar resource potential for large areas, identifying promising locations.
Feasibility studies at portfolio scale: Rapidly evaluate dozens or hundreds of sites for broad planning decisions.
Portfolio-level and regional risk/return analysis: Understand long-term trends and average solar resource availability.
Regulatory/benchmarking needs: When only average historical climate values are specified.
Supporting high-level investment or policy analysis: Where day-to-day variability isn’t critical.
Not suitable for
Detailed PV system design or technical due diligence: Misses weather and irradiance extremes.
Operational & financial risk assessment: Cannot estimate variability or risks of low-yield years.
Performance simulation and yield estimation: No realistic representation of daily or interannual fluctuations.
Modeling typical and atypical weather events: Assumes that a single curve represents all weather events in one month.
Typical meteorological year (TMY)
TMY data is constructed by assembling “typical” days or months from historical records, resulting in a set of daily curves that show some variability. The example graph provided below includes days where GHI peaks above 800W/m² or remains below 200W/m². This method simulates weather differences with a level of artificiality, since the data is composed from observations from different years with likely different conditions. Read more about our TMY methodology here.
While TMY files are still widely used for tool compatibility and regulatory requirements, the industry is shifting away from TMY-only studies for financial or technical decisions, due to their failure to reflect actual variability and extremes. TMY datasets are increasingly paired with or replaced by Time Series data, especially for investor-grade and insurance cases.
Limitations:
TMY represents only “typical” conditions: Interannual variability and rare weather extremes are not included.
The most-used hourly data format excludes intra-hourly (sub-hourly) variability and short-term events important for certain analyses.
Much of the original Time Series detail is lost during compression, limiting its suitability for financial or operational risk assessments.
Typical use cases
Early-stage energy yield assessment: Supported by compatibility with simulation tools like PVsyst and SAM.
Bankability studies and preliminary technical-financial modeling.
Pre-feasibility simulation: When low computational requirements or speed are more important than realism.
Creating P50, P75, P90 risk scenarios: Statistical TMY variants are used for different risk-level analyses.
Not suitable for
Analysis of extreme weather risk or multi-year variability.
Operational loss modeling, maintenance, and insurance risk quantification: Misses high-impact, low-frequency weather events.
Studies where real year-to-year performance needs to be replicated.
Time series (TS)
TS data provides a chronologically ordered record for each hour or day, capturing the actual sequence and variability of weather events experienced over multiple years. The result is a dense, highly variable set of daily profiles that authentically represent real meteorological conditions, including both extreme and typical patterns. This can be seen in the graph below, where the Time Series data for all Februarys between years 1994 and 2024 is plotted. The data covers almost all possible values of GHI at any time of the day, and captures the breadth of realistic scenarios.
High-resolution, multi-year TS data at 15-minute or 1-minute steps is quickly becoming the industry standard for all high-value or risk-sensitive project phases. Usage is nearly mandatory for bankability, insurance, or grid connection, and is featured as default in Solargis Evaluate.
Limitations:
Requires access to comprehensive, high-quality datasets.
Demands more computational resources and processing time in simulations.
Typical use cases
Detailed system and financial model simulations: Captures each year, month, day, and hour of up to 30 years.
Bankability, operational resilience, and insurance modeling: Realistic modeling of losses, downtimes, and rare/extreme events.
Performance benchmarking, detailed financial scenarios, and investor/lender technical due diligence.
Simulation of modern PV plant designs: Supports bifacial, storage-integrated, and grid services applications.
Grid integration, ramp rate, and curtailment studies: Where intra-hour, hourly, and interannual variability matter.
Validation/calibration of newer technologies: Bifacial, albedo-sensitive, and hybrid systems.
Not suitable for
Preliminary site screening or rapid portfolio evaluation that doesn’t require variability/resilience checks.
Use with legacy simulation tools that only accept TMY or low-data-volume formats.
Scenarios where computational simplicity is prioritized over fidelity.
Data types comparison summary
Data Type | Description | Variability | Typical Timestep | Common Use Case | Key Risks/Limits |
|---|---|---|---|---|---|
Long-Term Averages (LTA) | Uses annual/monthly/day-averaged values over many years. | None | 60-min, monthly, yearly | High-level feasibility | Ignores every type of weather/event variability |
Typical Meteorological Year (TMY) | Assembles a single year of "typical" months/days based on historic measurements/statistics. | Limited | 60-min | Energy yield estimates | Does not capture extremes and interannual variability |
Time Series (TS) | Uses chronological records of weather for every hour (or finer resolution) for many years. | Complete | 1-min, 15-min, 60-min | Detailed yield simulations, risk assessment | Computationally intensive; requires large datasets |