Meteorological model data

Oveview

Global meteorological models actively simulate the atmosphere's behavior at the global level using mathematical equations and algorithms. Analysts use these models as an essential tool in analyzing solar energy assets because they provide data to characterize the meteorological and environmental conditions required for solar energy applications over both short- and long-term horizons.

For short-term analysis, numerical weather prediction (NWP) models actively generate weather predictions for next few hours and days. Forecasting services utilize these predictions to support real-time operations and immediate decision-making, enabling operators to plan and schedule maintenance activities, manage energy storage, and efficiently balance supply and demand. In this context, these models are typically referred to as operational NWP models.

For long-term analysis, the meteorological models are used to provide reanalysis data—a comprehensive dataset that combines historical observational data into modern global model outputs. Developers use this reanalysis data to offer historical and recent data services, ensuring they have access to accurate and detailed information about past weather conditions. This enables them to make more reliable assessments for solar projects.

Meteorological parameters affecting PV assets

Photovoltaic (PV) assets are influenced by changing site conditions, making meteorological and environmental parameters essential for various purposes.

Meteorological data are critical for simulating the expected performance of photovoltaic systems. Air temperature (TEMP) and wind speed (WS) are key factors in determining the PV cell’s temperature, which directly affects its conversion efficiency. Relative humidity (RH) and precipitable water (PWAT) are necessary for evaluating the spectral response of the cells, while precipitation (PREC) and snow depth water equivalent (SDWE) are used to estimate energy losses caused by environmental factors.

These parameters are also crucial for conducting risk analyses of energy assets. Ultraviolet radiation (UVA, UVB) and thermal cycling driven by extreme air temperatures (TEMP) are important for assessing the risk of accelerated degradation in PV modules. The physical integrity of solar installations is further threatened in areas prone to severe weather, which requires careful characterization using meteorological parameters such as wind gusts (WG) and other relevant data.

In addition to performance and risk considerations, certain meteorological and environmental parameters serve as inputs for various models. Information on ozone content, water vapor (WV), and aerosol optical depth (AOD) is necessary for running semi-empirical solar irradiance models, while Numerical Weather Prediction (NWP)-based solar irradiance is crucial for solar power forecasting. Other parameters, such as those required for ground albedo models or soiling loss estimates, also depend on accurate and comprehensive meteorological data.

Operational NWP data products

Operational NWP (Numerical Weather Prediction) forecasts are the backbone of weather prediction, providing real-time information about the atmospheric conditions for future periods ranging from hours to days ahead. Here’s how the process works:

• Real-time atmospheric data is collected from a wide variety of sources including weather stations, satellites, aircraft, buoys, and radiosondes (weather balloons).

• This raw data is then assimilated into a coherent format suitable for numerical models. The assimilated data is used to initialize the NWP model. Using mathematical equations based on the laws of physics (such as fluid dynamics and thermodynamics), the model simulates the future state of the atmosphere. The Earth’s atmosphere is divided into a three-dimensional grid, and calculations are made for each grid point. The results are produced at regular intervals (e.g., every 6 hours) and for various lead times (e.g., 12 hours, 24 hours, 7 days).

Table legend:

• D+0 means the same day when forecasts are delivered.

• D+n represents predictions for n days after delivery.

• Updates are made available on our servers at 2:30 AM, 8:30 AM, 14:30 PM, and 20:30 PM (all times in UTC).

Reanalysis meteorological data products

Meteorological model reanalysis involves recreating past atmospheric conditions over a long historical period by integrating past observational data with a fixed version of the meteorological model. Here’s how the process works:

• Historical weather data is collected from multiple sources, including surface observations, satellite records, and archived meteorological data. Since historical data can have gaps and inconsistencies, it undergoes rigorous quality control and preprocessing to correct errors and standardize the data. Similar to operational forecasts, this historical data is assimilated into the NWP model. The key difference here is that the same version of the model and assimilation system is used throughout the entire reanalysis period to ensure consistency.

• The meteorological model is run retrospectively using the assimilated historical data, producing a comprehensive dataset that represents the state of the atmosphere over time. This step uses a fixed set of model physics and parameters to avoid inconsistencies. The reanalysis generates long-term datasets that provide continuous, gridded representations of various atmospheric parameters over several decades.

These datasets include key parameters for PV solar energy applications like temperature, wind (speed and direction), relative humidity, precipitation, precipitable water, water equivalent of accumulated snow depth, Ultraviolet radiation, and snow density.

Reanalysis models can also supply historical solar irradiance data, but their results have lower resolution and accuracy compared to satellite-based models.

Table legend:

• D-n represents days before the present time of delivery.

Data post-processing

To achieve optimal results in solar energy applications, certain datasets provided by Numerical Weather Prediction (NWP) models require postprocessing.

Meteorological model outputs undergo post-processing to refine their spatial resolution, enhancing the accuracy and consistency of the datasets. This refinement improves their reliability for long-term analyses and applications in solar energy systems. The original spatial resolution of meteorological data, typically ranging from 0.1 to 0.25 degrees, represents broad geographic regions rather than specific sites. Methods are employed to improve the spatial representation of the data to address this limitation.

Weather models provide not only surface-level temperature data (e.g., at 2 meters above ground) but also vertical temperature profiles across multiple atmospheric layers, from the surface to the top of the atmosphere. By analyzing these profiles, a simplified parameterization of temperature variation with altitude is developed. Generally, temperature decreases with increasing altitude, but temperature inversions—where temperature rises with altitude—are common near the surface, particularly over cold surfaces.

To account for vertical temperature changes, the lapse rate is calculated, representing the rate of temperature change with altitude. This lapse rate varies across time and locations, influenced by weather patterns, local microclimates, and topographic features. By applying the lapse rate, temperature data are spatially downscaled to a finer resolution, ensuring greater accuracy for regions with complex terrain. Using the SRTM-3 Digital Elevation Model, the spatial resolution of air temperature data is refined to 1 km, significantly improving its precision for localized applications.

In some cases, secondary parameters must be derived from the primary data provided by the models. Examples include calculating UVA and UVB radiation, dew point temperature, wet bulb temperature, and accumulated snow depth. These additional parameters are critical for specialized solar energy analyses and applications.

To keep the data updated, the DAY-1 and DAY-2 values are taken from the NWP-based forecasted value. The meteorological data are later updated with data from re-analysed archive data). Meteorological data for period DAY-3 or earlier can be considered definitive.