ISSN 2330-717X

Using Satellite Data To Improve Model’s Interpretation Of Albedo Improves Snowfall Simulations On Tibetan Plateau

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Albedo is a term that represents the ability of Earth’s surface to reflect solar radiation. This is the primary factor of the energy balance between the surface and the atmosphere. When snow falls, albedo changes quickly, as snow is able to reflect most wavelengths of light back into the atmosphere. Naturally, albedo fluctuates often during winter and spring in the Tibetan Plateau, which has a great impact on the surface energy balance and water cycle.

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However, modern weather and climate models that focus on land surfaces have struggled to parametrize albedo, or represent albedo effects as a computer algorithm. Locations that have frequent snow cover fluctuations, like the Tibetan Plateau, often have larger model simulation error than regions that do not experience snowfall and rapid melting.

Satellites continue to provide new data and derived products that provide a better picture of Earth’s radiation budget. With recent improvements to satellite-retrieved albedo data, corresponding author Prof. Yaoming Ma and a group of researchers from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences used satellite spectral albedo data as well as ground observed and simulated snow depth data to develop a scheme that optimizes albedo parameters at the local level using the Noah land surface model. They just published their findings in Advances in Atmospheric Sciences .

The research team found that their improved albedo scheme significantly reduces model albedo overestimation throughout the Tibetan Plateau. Likewise, the new scheme closes the gap between the model-simulated and satellite-retrieved albedo data. Furthermore, the Noah model’s cold air temperature bias has become less prominent, and the model also more accurately reproduces the spatial distribution characteristics of heavy snowfall.

This study breaks through the limitations of the Noah model’s default albedo scheme and provides a reference for improving its physical meteorological parameterization schemes by using remote sensing products, such as satellite data.

Regarding future applications, Prof. Ma remarked, “Whether this scheme is universal in improving the performance of snowfall and snowmelt estimates on the Tibetan plateau, as well as its future applications, still needs further research.”

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Prof. Massimo Menenti with the Aerospace Information Research Institute of Chinese Academy of Sciences, also a coauthor of the study, added, “The scheme combines the best of two worlds. On the one hand the capability of the model to compute, albeit with moderate accuracy, daily snow depth, which is a driver of snow albedo, and daily snow cover, which can be used to determine snow age, another driver of snow albedo. On the other hand, satellite observations provide accurate measurements of snow albedo, so that the parameterization we propose can provide accurate estimates, even though the model estimates of snow albedo and age might not be very accurate.”

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