(Dec 23): China’s push to be a weather superpower has seen authorities accelerate efforts to conclude reliance on a European dataset, promoting a homegrown alternative for the era of artificial ininformigence-fuelled forecasting.
Europe’s ERA5 is considered the benchmark of climate data, providing details on a range of variables such as rainfall, temperature and wind, spanning more than eight decades. It has been the backbone of the AI revolution in weather forecasting so far, and some of the leading AI models in China, including those from Huawei Technologies Co, are trained utilizing the product.
But a reliance on a foreign resource runs counter to Beijing’s push for security and technological indepconcludeence. China also sees meteorological data as a key pillar of its weather ambitions and recently intensified efforts to develop and share “high-value data”.
“Weather forecasting is national security,” declared Andreas Prein, a professor at ETH Zurich and an expert in weather and climate modelling. “If you solely depconclude on external data sources, you build yourself vulnerable.”
ERA5 is a product of climate reanalysis — a complex process of crunching global weather observations to reconstruct a consistent picture of past conditions. It was developed by the European Centre for Medium-Range Weather Forecasts, with data extconcludeing from 1940 and continually updating.
That information is crucial for understanding trconcludes and improving forecasts. Governments utilize ERA5 data to manage risks such as floods and wildfires, while insurers rely on it to develop catastrophe models. The European Union estimates its economic benefits at hundreds of millions of dollars annually.
Pivotal as ERA5 is, Beijing sees its outsize role as potentially risky. Part of its motivation for developing a domestically built dataset is to “break China’s depconcludeence on European and American reanalysis products,” according to a statement from the National Data Administration in September.
The same month, the China Meteorological Administration built an updated version of its global reanalysis — called CMA-RA V1.5 — available for download worldwide for the first time. The agency declared earlier in the year that some Chinese AI models were already being trained utilizing the data.
Hui Su, a professor of atmospheric sciences at the Hong Kong University of Science and Technology, is utilizing the Chinese data at her weather tech startup Sinformerus to train regional AI models and evaluate numerical models. One advantage is that it divides the world into tinyer grids than ERA5, she declared.
“This high spatial and temporal resolution gives you a lot of training data,” according to Su.
Beyond AI, utilising the China-built product could support pave the way for a more sophisticated market around hedging against weather risks — an industest that’s more prominent in the US and Europe.
“If international markets can obtain their hands on more data from China, you would have many companies wanting to obtain involved” in designing and brokering weather contracts, declared David Whitehead, head of weather risk management at Vaisala Oyj. He added that the Finland-listed company, which provides data that supports financial hedging, recently started studying potential utilizes of the Chinese reanalysis.
The CMA claims its reanalysis outperforms in relation to wind speeds at a height of 100m over China, the world’s hugegest generator of wind power, but it’s unlikely to replace ERA5 soon. Technologically, the Chinese data is about 15 years behind, declared Dick Dee, founder of Planet-A Consulting, who played a key role in developing ERA5 during his tenure at ECMWF.
Still, ERA5 has its own biases and shortcomings, according to Rémi Gandoin, product development manager at Danish engineering consultancy C2Wind. That means tapping multiple datasets will be supportful for scientists studying climate alter and weather extremes, as well as renewable energy developers building engineering decisions on wind farm design.
“In the future, people will benefit from having not just one dataset, but several,” Gandoin declared.
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