--- title: "Snow loss model" slug: "snow-losses" description: "Explore Solargis' innovative approach to modeling snow-related energy losses in photovoltaic systems, utilizing global meteorological and satellite data." tags: ["snow"] updated: 2025-09-25T08:29:46Z published: 2025-09-25T08:29:46Z --- > ## 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. # Snow loss model **In this document** We will introduce a comprehensive Solargis approach to modeling snow-related energy losses in photovoltaic (PV) systems, highlighting the challenges and methodologies used to estimate these losses. It provides an overview of how global meteorological models and satellite data are utilized to predict snow impacts on PV power production. ## Overview Snowfall significantly affects photovoltaic systems by blocking solar irradiation, leading to **power production losses**. This phenomenon is particularly challenging in regions with frequent snowfall, such as parts of Europe, North America, and Asia. Snow on PV systems is challenging to mitigate, so effective forecasting and planning are crucial to manage impacts like reporting to grid operators or purchasing balancing power. Modeling snow losses involves using **global meteorological models** like ERA5 and Solargis satellite data. These models provide valuable insights into **snowfall patterns** and their **effects on PV systems**. However, challenges arise from data resolution, validation, and the integration of meteorological data with PV system models. This document discusses these challenges and presents a methodology for estimating snow losses, including the categorization of snow events and the extrapolation of models to global scales. ## Modeling snow losses in PV systems ### Introduction to snow losses Snow on PV modules causes significant power production losses due to blocked solar irradiation. This is an undesirable phenomenon, especially in regions where snowfall is frequent and prolonged. Manual or robotic cleaning is often impractical, and in cases where the snow has frozen onto the modules, it becomes virtually impossible. The only effective way to mitigate these impacts is through accurate forecasting and planning, which can include reporting to grid operators, purchasing balancing power, or even investing in PV power in snow-prone areas. ### The Snow loss model This model utilizes data from the ERA5 global meteorological system and Solargis satellite-derived solar radiation data. - **ERA5**and **ERA5-Land** provide hourly meteorological variables such as fresh snow depth and air temperature. - Solargis complements this with **high-resolution global tilted irradiation** (GTI) data, which is essential for estimating PV power production and plays a direct role in accelerating snow melting. #### Data sources and resolution ERA5 ModelSolargis Data The **ERA5** and **ERA5-Land** models provide hourly meteorological data with spatial resolutions of approximately 31 and 9 kilometers, respectively. This resolution is considered coarse and may not accurately capture local conditions such as microclimates or specific topographic features that influence snow accumulation and melting processes. Solargis **satellite-model solar radiation data** offers detailed solar radiation information necessary for calculating expected PV power production without snow losses. While the document does not specify the exact resolution of Solargis data, it is typically available at a higher spatial resolution compared to ERA5, often around **1-2 kilometers**, depending on the specific product and application. ## Model description and parameters A streamlined version of the dynamic Snow loss model is used, with input parameters optimized for effective utilization of ERA5 outputs. The model incorporates meteorological data, including fresh snow depth water equivalent from ERA5 and air temperature from [enhanced](https://kb.solargis.com/docs/meteorological-model-data#data-post-processing)ERA5-Land, as well as global tilted irradiation derived from a high-resolution satellite model. Snow loss calculations are performed using **15-minute Time** **Series**, ensuring a detailed temporal resolution. **The model incorporates several key empirically derived factors**: The meteorological model-specific snow settling coefficient, surface temperature of the snow/module system, thermal melting coefficient, and tilt-induced snow removal speedup factor and thermal GTI coefficient. Additionally, the panel effective tilt/inclination is considered to account for snow shedding dynamics. These parameters are crucial for accurately simulating snow coverage and its impact on PV system performance. ### **Challenges and limitations** Data ResolutionIntegration with PV Models Global models like ERA5 have **coarse resolutions**, which may not accurately capture local conditions such as microclimates or specific topographic features. As a result, snow loss in PV systems can be **misrepresented**, affecting performance estimates and leading to potential discrepancies between modeled and actual energy production. Combining meteorological data with PV system models requires expertise in both fields. This integration is crucial for accurately predicting snow impacts on PV power production. The snow losses are calculated for the full 15-minute Time Series and in the Solargis Evaluate is integrated as 12 average monthly losses values. Snow loss calculation time during the simulation depends on the number of segments in the energy system. More segments result in longer estimation computations. More details about the Snow loss model development and the results of its validation were presented at the 40th [EU PVSEC 2023](https://userarea.eupvsec.org/proceedings/EU-PVSEC-2023/4AO.9.2/), Lisbon, Portugal. ### Snow model validation The Solargis snow loss model has been validated using **ground-based PV production data from 27 sites in the USA and Europe**, with results demonstrating the model’s ability to categorize and quantify snow-related energy losses. Details of the validation process, including methodology, error analysis, and key findings, are provided in the Snow loss model [validation document](/v1/docs/validation-of-the-snow-loss-model). #### Comparison with other software The treatment of snow losses is comparable across various solar simulation software: | Software | Parameter name | Notes | | --- | --- | --- | | Solargis Prospect | Losses due to snow | Monthly effect of snow cover on PV modules | | Solargis Evaluate | Snow losses | Reduction of energy generation due to snow blocking the surface of PV modules, the behavior of PV system is like PV system unavailability. | | PVsyst | Soiling loss factor | Considered monthly as a part of the [soiling](https://www.pvsyst.com/help/project-design/array-and-system-losses/soiling-loss.html)loss factor. | | SAM (NREL) | DC snow losses | Calculates a loss caused by the snow that applies to the subarray’s gross DC [power output](https://www.nrel.gov/docs/fy18osti/67399.pdf) (page 67). | | SolarFarmer (DNV) | Soiling | Included in the [soiling](https://mysoftware.dnv.com/download/public/renewables/solarfarmer/manuals/latest/CalcRef/Irradiance/SoilingEffect.html)parameter (monthly) | ## Usage in the Solargis platform At Solargis, the snow losses model is included in the [Evaluate PV simulation](/v1/docs/solargis-evaluate-simulation-chain) chain when simulating the PV energy yield and for estimation of snow losses in the Energy system designer’s [losses](https://kb.solargis.com/docs/cabling-and-system-losses#environmental-losses)section. ## Further reading - [Instrumentation for Evaluating PV System Performance Losses from Snow](https://www.nrel.gov/docs/fy09osti/45380.pdf). B. Marion, J. Rodriguez, and J. Pruett. - [Photovoltaics and snow: An update from two winters of measurements in the SIERRA](https://www.researchgate.net/publication/261042016_Photovoltaics_and_snow_An_update_from_two_winters_of_measurements_in_the_SIERRA). T. Townsend and L. Powers. - [Dynamic Snow Loss Model in PVSim: Modeling Impact of Snow on PV Production](https://gregorykimball.files.wordpress.com/2018/07/mewcpec1079_0614162840_snow_2018.pdf). D. Gun, M. Anderson, G. Kimball, and B. Bourne.