Method to determine snow albedo values in the ultraviolet for radiative transfer modeling

Area of Science:

• Atmospheric Science
• Remote Sensing
• Radiative Transfer

Background:

• UV global irradiance modeling typically achieves +/-5% accuracy under cloudless conditions when atmospheric and surface parameters are known.
• Snow-covered surfaces present a significant challenge, as regional albedo is often unavailable and UV irradiance can vary by up to 50% due to snow cover.

Purpose of the Study:

• To develop a method for determining regional albedo values under snow-covered conditions.
• To improve the accuracy of UV global irradiance modeling by incorporating dynamic snow albedo.

Main Methods:

• A parameterization method was developed using snow depth and snow age, routinely monitored by weather services.
• A multiple linear regression algorithm was created by correlating snow data with UV snow-albedo values, derived from fitting modeled and measured UV irradiances at an alpine site.

Main Results:

• The developed algorithm yields regional snow albedo values ranging from 0.18 to 0.5.
• Implementing this algorithm significantly enhances the accuracy of modeled UV irradiances for varying snow conditions.
• Using actual albedo values derived from the algorithm halves the average deviations between measured and modeled UV global irradiances compared to using a constant average snow albedo.

Conclusions:

• The developed parameterization provides a reliable method for estimating regional snow albedo.
• Accurate snow albedo estimation is crucial for precise UV irradiance modeling, especially in snowy environments.
• The regression constants require site-specific adaptation for broader application of the method.