A variety of meteorological and atmospheric factors cause solar irradiance predictions and actual values to vary significantly in Kurnool

 Wonderful discussion with Shri SK Vali Sir 

Regarding solar plant forecasting prediction and real data variation reason behind it .

A variety of meteorological and atmospheric factors cause solar irradiance predictions and actual values to vary significantly in Kurnool, especially during the month of October. In India, forecasting during the transition from the monsoon to the post-monsoon season is especially challenging, and error percentages of 20% or higher are not uncommon.

Key meteorological factors for October in Kurnool

Variable cloud cover: October marks the withdrawal of the monsoon from southern India. This transition period is highly dynamic, with unpredictable and rapidly changing cloud formations.

Forecasting limitations: Satellite and numerical weather models have difficulty accurately predicting the formation, movement, and density of these transitional cloud patterns, which leads to discrepancies between predicted and actual irradiance.

Irradiance reduction: Thick, low-level clouds, which are common during this season, can block a significant portion of solar radiation. Studies show that mid- and low-level clouds can cause up to a 40–44% reduction in surface solar radiation.

Elevated aerosol levels: October often follows the monsoon, a time when particulate matter and dust remain suspended in the atmosphere. Aerosols scatter and absorb sunlight, reducing the amount of radiation that reaches the ground.

Prediction complexity: Accurately quantifying and predicting aerosol optical depth (AOD) is complex and a significant source of error for models.

Variable impact: The concentration and type of aerosols vary daily and are difficult to incorporate into standard forecast models, contributing to unpredictable dips in solar irradiance.

Humidity and atmospheric conditions: High humidity and fluctuating atmospheric pressure are common during the post-monsoon period in Andhra Pradesh.

Effect on light: Water vapor in the air affects the scattering and absorption of solar radiation.

Correlation uncertainty: While forecasting models include these parameters, their non-linear and dynamic relationship with irradiance adds complexity and uncertainty to predictions.

Limitations of forecasting models

Modeling challenges: Accurately predicting local atmospheric conditions is inherently difficult due to chaotic weather systems. Standard numerical weather models used for forecasting cannot perfectly capture these localized variations.

Resolution gaps: Satellite-based data and weather models can have spatial and temporal limitations. They may miss localized, rapidly developing phenomena like small, dense cloud patches that cause significant drops in irradiance in specific areas like Kurnool.

Data dependency: Many forecasting models rely on historical weather data and ground-based measurements to calibrate predictions. Gaps in or low-resolution ground-based data can compromise model accuracy. Hybrid models that integrate satellite data, numerical weather prediction, and AI are continuously being developed to improve accuracy