Snow Data from Satellites Improve Temperate Predictions

A graphic from a study led by The University of Texas at Austin shows how snow data from NASA satellites impacts seasonal temperature prediction.

The negative values represented by warm colors indicate regions where temperature predictions improved and show the percentage by which errors were reduced. The graphics show the prediction results made with data from the satellites MODIS and GRACE against a prediction that did not incorporate snow satellite data.

Researchers with The University of Texas at Austin have found that incorporating snow data collected from space into computer climate models can significantly improve seasonal temperature predictions. Snow influences the amount of heat that is absorbed by the ground and the amount of water available for evaporation into the atmosphere, which plays an important role in influencing regional climate.

"Seasonal forecasts are influenced by factors that are significantly more difficult to account for than the variables for daily to weekly weather forecasts or long-term climate change. Between the short and very long time scale there's a seasonal time scale that's a very chaotic system. But there is some evidence that slowly varying surface conditions, like snow cover, will have a signature in the seasonal timescale." said Zong-Liang Yang, a professor at the Jackson School of Geosciences Department of Geological Sciences.

The researchers found that incorporating snow data collected by NASA satellites into climate models improved regional temperature predictions by 5 to 25 percent. These findings are the first to go beyond general associations and break down how much snow can impact the temperature of a region months into the future. The researchers analyzed how data on snow cover and depth collected from two NASA satellites MODIS and GRACE affected temperature predictions of the Northern Hemisphere in a climate model. The study examined seasonal data from 2003 through 2009, so the researchers could compare the model's predictions to recorded temperatures. The model ran predictions within moths of January, February and March each used as starting months. The computer model's temperature improvement changed depending on the region and time, with the biggest improvements happening in regions where ground-based measurements are sparse.

"This correlation between snow and future monsoon has been established for several decades, but here we are developing a predictive framework where you can run the model forward and get a quantity, not just a correlation," Yang said.

In the future the researchers plan to expand their research to predict other climatic factors, such as snowfall and rainfall.

Randal Koster, a scientist at NASA's Goddard Space Flight Center who studies land-atmosphere interactions using computer models, said that the study is an example of how satellites can improve climate forecasts by providing more accurate data to inform the starting conditions of the model.

https://www.sciencedaily.com/releases/2016/12/161206111455.htm