--- title: "Argus PV simulator optical simulation" slug: "argus-optical-simulation-overview" updated: 2026-06-17T06:58:21Z published: 2026-06-17T06:58:21Z canonical: "kb.solargis.com/argus-optical-simulation-overview" --- > ## Documentation Index > Fetch the complete documentation index at: https://kb.solargis.com/llms.txt > Use this file to discover all available pages before exploring further. # Argus PV simulator optical simulation **In this document** This document describes the optical simulation stage of the Argus PV simulation engine in Solargis Evaluate. It explains how solar radiation is translated into Global tilted irradiance (GTI) at the module level, accounting for all relevant optical losses. ### Overview The optical simulation is the **first computational stage** of the Argus PV simulation chain, following the collection of [simulation inputs](/v1/docs/argus-simulation-inputs-overview). It takes the site parameters and energy system configuration established in the inputs stage and uses them to calculate the Global tilted irradiance (GTI) on the front and rear surfaces of PV modules — the foundational quantity for all subsequent electrical calculations. This stage is critical because it determines how much solar radiation actually reaches each PV cell under real-world conditions. The Argus optical simulation goes significantly beyond industry-standard approaches by combining **Perez's all-weather sky model** with unbiased **Monte Carlo backward raytracing**, enabling cell-level accuracy in shading and irradiance calculations. The result is a highly detailed representation of direct, diffuse, and reflected radiation reaching each module, with all relevant optical losses applied sequentially. The optical simulation results feed directly into the electrical simulation stage, where GTI values are converted into DC power output. #### Processes included in this stage The following processes are applied sequentially during the Argus PV simulator optical simulation: - Sky irradiance model (Perez all-weather sky model, solar position via PSA model) - Far horizon shading (View Factor model) - Near shading — 3D backward raytracing (unbiased Monte Carlo path tracing) - Soiling losses - Angular reflection losses (Martin and Ruiz model) - Spectral correction (Lee & Panchula model) ![](https://cdn.document360.io/ae2d502f-6c0d-4865-a68e-43ad8da61149/Images/Documentation/image-THAC81IE.png) ## Optical simulation ### Sky irradiance model In Solargis Evaluate, satellite-derived **Global horizontal irradiation** (GHI) and **Direct normal irradiation** (DNI) variables are used to calculate the distribution of diffuse radiance on the sky dome. This is achieved through the implementation of [Perez all-weather sky model](/v1/docs/incident-irradiance). From these inputs, the Sun position and the configuration of the power plant, the theoretical **Global tilted irradiation** (GTI) without any losses is also calculated **Solar position calculation** The solar position is calculated using the [PSA model](https://www.sciencedirect.com/science/article/pii/S0038092X20311488) from the geographical coordinates of the site and date-time information. For full details on the Perez all-weather sky model and how it compares to the simple Perez model used in other Solargis simulators, see [Incident irradiance — Sky irradiance](/v1/docs/incident-irradiance#sky-irradiance). **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GHI_NOSHD - GHI without horizon shading losses - DNI_NOSHD - DNI without horizon shading losses - DIF_NOSHD - DIF without horizon shading losses - GTI_FRONT_NOSHD - Front GTI without shading losses - GTI_REAR_NOSHD - Rear GTI without shading losses ### Far horizon shading Far horizon shading effects are simulated using [View Factor model](https://pvpmc.sandia.gov/pv-research/bifacial-pv-project/bifacial-pv-performance-models/ray-tracing-models-for-backside-irradiance/view-factor-models/). The default horizon from Solargis data is used at the project reference point. For energy systems with segments defined, the far horizon is pre-loaded for each segment’s reference point. Far horizon is defined by azimuth and height and can be [edited](https://kb.solargis.com/docs/horizon-shading#far-horizon-shading)in the Energy system designer. **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GHI_HORIZ_SHD - GHI with horizon shading losses - DNI_HORIZ_SHD - DNI with horizon shading losses - DIF_HORIZ_SHD - DIF with horizon shading losses - GTI_FRONT_HORIZ_SHD - Front GTI with horizon shading losses - GTI_REAR_HORIZ_SHD - Rear GTI with horizon shading losses - GTI_HORIZ_SHD - GTI Front + Rear with horizon shading losses ### Near shading Near shading simulation is the most complex step in the Argus optical simulation. All objects from the energy system design and the surrounding terrain are placed into a 3D calculation scene, in which direct and diffuse light is simulated using **unbiased Monte Carlo backward raytracing**. This approach achieves cell-level shading accuracy for both monofacial and bifacial systems, enabling precise simulation of partial shading without simplifying assumptions. For full details on the unbiased Monte Carlo backward raytracing method, including the four-step process, validation against NREL Bifacial Radiance, and comparison with the View Factor model, see [Incident irradiance — Ray tracing](/v1/docs/incident-irradiance#ray-tracing). **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GTI_FRONT_NEAR_SHD - Front GTI with horizon shading and near shading losses - GTI_REAR_NEAR_SHD - Rear GTI with horizon shading and near shading losses ### Soiling losses [Soiling losses](/v1/docs/soiling-loss) are an essential factor in the energy yield simulation, as they affect the amount of solar radiation that reaches the PV cells. In Solargis Evaluate, these losses are applied to the GTI calculated in the previous step. For bifacial energy systems, the simulation is done separately for the front and rear sides, while 15% of the front side loss value is applied to the rear side. - **Method of application**: Soiling losses are typically applied as average monthly figures or as a single yearly figure. Users have the option to specify these values if needed. For full details on the Solargis soiling model — including the particle accumulation formula, soiling ratio calculation, and validation against 51 sites worldwide — see [Soiling loss model](/v1/docs/soiling-losses). **Setting soiling losses in Solargis Evaluate** Default soiling losses can be adjusted in the [Losses](/v1/docs/cabling-and-system-losses) section of the Energy system designer. Solargis operates a proprietary [soiling model](https://kb.solargis.com/docs/soiling-loss#the-soiling-model) that estimates soiling based on atmospheric pollution at the location. ![](https://cdn.document360.io/ae2d502f-6c0d-4865-a68e-43ad8da61149/Images/Documentation/image-VT6NB6BK.png) **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GTI_FRONT_SOIL - Front GTI with horizon shading, near shading, and pollution losses - GTI_REAR_SOIL - Rear GTI with horizon shading, near shading, and pollution losses ### Angular reflection losses [Angular reflection losses](https://kb.solargis.com/docs/incident-irradiance#angular-losses) occur due to the angle of incidence effects on the surface of PV modules. These losses are significant because they affect the resulting radiation reaching the PV cell. Solargis Evaluate employs the [Martin and Ruiz model](https://www.sciencedirect.com/science/article/abs/pii/S0927024800004086?via%3Dihub) to estimate angular reflection losses. This model uses an angular loss coefficient, which is estimated by Solargis based on the properties of the PV module surface, particularly its soiling. For bifacial energy systems, the simulation is done separately for the front and rear sides. For full details on the Martin and Ruiz model — including the incidence angle dependency curve, surface property factors, and comparison with implementations in PVsyst, SAM, and SolarFarmer — see [Incident irradiance — Angular losses](/v1/docs/incident-irradiance#angular-losses). **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GTI_FRONT_IAM - Front GTI with horizon shading, near shading, pollution and angular losses - GTI_REAR_IAM - Rear GTI with horizon shading, near shading, pollution and angular losses ### Spectral correction [Spectral correction](https://kb.solargis.com/docs/incident-irradiance#spectral-correction) is an essential step in the simulation process, and Solargis Evaluate uses the [Lee & Panchula model](https://ieeexplore.ieee.org/document/7749836) for this purpose. The specific intensity of the spectral responsivity correction depends on two key atmospheric factors: - **Air mass**: This represents the optical path length of sunlight through the Earth's atmosphere. It increases as the Sun's position moves closer to the horizon, affecting the spectral distribution of sunlight. - **Precipitable water content**: This refers to the total amount of water vapor present in a column of the atmosphere. For full details on the Lee & Panchula model — including the role of air mass and precipitable water content, module-type coefficients, and comparison with implementations in Prospect, PVsyst, and SolarFarmer — see [Incident irradiance — Spectral correction](/v1/docs/incident-irradiance#spectral-correction). **Solargis Evaluate data export parameters related to this stage of simulation** Select the following parameters in the [data export](https://kb.solargis.com/docs/project-reports-and-data-exports#generating-data-exports): - GTI_FRONT_SPECTRAL - Front GTI with horizon shading, near shading, pollution, angular and spectral losses - GTI_REAR_SPECTRAL - Rear GTI with horizon shading, near shading, pollution, angular and spectral losses ## Further reading - [A new simplified version of the Perez diffuse irradiance model for tilted surfaces](https://www.sciencedirect.com/science/article/abs/pii/S0038092X87800312?via%3Dihub). Richard Perez, Robert Seals, Pierre Ineichen, Ronald Stewart, and David Menicucci. - [All-weather model for sky luminance distribution—Preliminary configuration and validation](https://www.sciencedirect.com/science/article/abs/pii/0038092X9390017I). R. Perez, R. Seals, and J. Michalsky. - [Updating the PSA sun position algorithm](https://www.sciencedirect.com/science/article/pii/S0038092X20311488). Manuel J. Blanco, Kypros Milidonis, and Aristides M. Bonanos. - [Comparison of ray tracing rendering technique with ground measurements for improved solar radiation modeling](https://pvpmc.sandia.gov/app/uploads/sites/243/2022/10/8-4-Solargis_raytracing_validation_v2.pdf). L. Dvonc, P. Orosi, T. Cebecauer, and B. Schnierer. - [Simple Model for Predicting Time Series Soiling of Photovoltaic Panels](https://www.researchgate.net/publication/333733786_Simple_Model_For_Predicting_Time_Series_Soiling_of_Photovoltaic_Panels). M. Coello and L. Boyle. - [Calculation of the PV modules angular losses under field conditions by means of an analytical model](https://www.sciencedirect.com/science/article/abs/pii/S0927024800004086?via%3Dihub). Martin N. and Ruiz J.M. - [Spectral correction for photovoltaic module performance based on air mass and precipitable water](https://ieeexplore.ieee.org/document/7749836). Lee, M., & Panchula, A. - [Lambert W function for applications in physics](https://www.sciencedirect.com/science/article/abs/pii/S0010465512002366). D.Veberič.