Improving Forecast Accuracy For Extreme Cold In East Asia

Severe cold outbreaks in East Asia have long presented major challenges, with far-reaching disruption to transport, power supplies, and everyday life. The uncertainty of these events, bringing rapidly dropping temperatures and heavy snowfall, renders it challenging for governments and industries to anticipate in advance. Although present subseasonal-to-seasonal (S2S) forecasting systems make informative predictions, their reliability falls off rapidly after two weeks, rendering long-term preparedness problematic.

A new method for enhancing the precision of extreme cold event forecasts has been highlighted in a recent study published in Atmospheric and Oceanic Science Letters. The study, led by a group of researchers, used the European Centre for Medium-Range Weather Forecasts (ECMWF) model and ERA5 reanalysis data to assess the predictive skill of extreme cold events in East Asia. The results indicate that although the ensemble mean of the ECMWF model has difficulties in predicting beyond two weeks, some ensemble members have exceptionally high predictive skill, providing new promise for enhancing long-range forecasts.

Unlocking High-Skill Members in Forecasting

The ECMWF model works on the basis of an ensemble forecasting system, i.e., it conducts several simulations—ensemble members—to consider various possibilities. The typical practice is to use the mean of these simulations, or ensemble mean, to make predictions. But for severe cold events, the ensemble mean tends to fail to represent the sudden drops in temperature and extreme minimum temperatures above the two-week mark.

The research discovered that certain ensemble members repeatedly showed greater forecasting ability, correctly forecasting surface air temperature change and the severity of cold events. These high-skill members performed better because they could forecast atmospheric circulation patterns over Eurasia, especially sea level pressure and the 500-hPa geopotential height. These atmospheric predictors are significant in determining the formation and motion of cold air masses and thus are important predictors of extreme weather.

Xinli Liu, author of the study, highlighted the significance of such high-skill members, explaining, "Among the ensemble members, at least 10% were always high-skill members offering valuable insights." This implies that even among a large group of ensemble members, there are some who always make correct predictions, which identifies a new frontier for improving weather forecasts.

Improving Forecast Accuracy With AI and Historical Analogs

Given the significance of high-skill ensemble members, the study suggests that future research should focus on identifying these members more efficiently. One proposed method is using historical analogs—comparing current weather patterns with similar past events—to determine which ensemble members are likely to be more accurate. Another promising approach is leveraging artificial intelligence (AI) to analyze vast amounts of weather data and identify high-skill members in real-time.

Jingzhi Su, the author responsible for this study, emphasized the possible advantages of ensemble forecasting optimization, saying, "In ensemble forecasting, properly increasing the number of ensemble members and assigning greater weights to high-skill members will enhance the forecasting accuracy and credibility." By assigning greater weights to high-skill members in the final forecast, scientists hope to improve prediction reliability substantially.

Implications for Disaster Preparedness and Climate Research

Enhanced predictions of extreme cold occurrences can bring extensive advantages to East Asia. These occurrences are likely to result in transport disruptions, electricity outages, and heightened health risks, especially among the elderly and the weak. Better long-term forecasts would allow governments, enterprises, and societies to proactively prepare by, for instance, guaranteeing energy supplies, making early warnings, and laying down emergency response plans.

The findings of the study also help inform wider climate research. Extreme cold events are driven by big-picture atmospheric patterns, such as the polar vortex and Siberian high. By refining forecasting models, scientists can learn more about these intricate climate processes and potentially drive further breakthroughs in climate science.

For More Accurate Extreme Weather Forecasts

While predictions of extreme cold conditions more than two weeks into the future are still challenging, this research is a major breakthrough in improving forecasting methods. By recognizing high-skill members of an ensemble and combining AI-powered analysis, meteorologists can enhance the accuracy of long-range weather forecasts.

As climate variability persists to introduce unforeseen weather patterns, the development of forecasting technology will be key to reducing the effects of extreme cold episodes. The merge of historical records, machine learning, and best-performing ensemble forecasting systems holds significant hope for the future, making East Asia—and the planet—more capable of anticipating the threats that extreme weather can bring.