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Huawei Cloud and Shenzhen Meteorological Bureau to Introduce Advanced AI Model

Huawei Cloud and Shenzhen Meteorological Bureau to Introduce Advanced AI Model

Huawei Cloud and the Meteorological Bureau of Shenzhen Municipality have introduced their regional and advanced AI weather forecasting model. The “Zhiji” Regional Model in Chinese stands out as one of the earliest of its kind, offering a swift generation of five-day forecasts with pinpoint accuracy within a 3 km range. Introduced during an event on March 23 in Shenzhen to commemorate World Meteorological Day 2024, this model signifies a fusion of AI technology, Chinese culture, and meteorological expertise.

Utilizing Huawei Cloud’s Pangu-Weather Model and meticulously trained on top-tier regional datasets, Zhiji provides comprehensive forecasts for Shenzhen and nearby areas. It improves spatial resolution by far more finer than conventional global models. Its predictions encompass crucial meteorological factors such as temperature, precipitation, and wind speed, promising enhanced precision and preparedness in weather forecasting.

Advancing Weather Forecasting Technology

During its recent trial phase, Zhiji accurately predicted various cold-temperature periods. The team aims to bolster the model and enhance its capability to deliver precise precipitation forecasts.

For decades, the Meteorological Bureau of Shenzhen Municipality has pioneered improved weather services and bolstered disaster preparedness. According to Lan Hongping, Deputy Director of the Meteorological Bureau, weather forecasting is pivotal in disaster prevention and mitigation. Leveraging AI as a key tool, the bureau aims to refine its forecasting and warning services, thereby better serving the residents of Shenzhen and contributing to the city’s sustainable development.

President of Huawei Cloud Marketing Dept William Dong emphasized the significance of the regional AI weather model’s launch for Shenzhen. This development signifies a new era in accurate, localized weather forecasts facilitated by AI technology. Dong highlighted the broader implications of this scientific advancement, particularly in light of Huawei Cloud’s previous publication of the Pangu-Weather Model in the esteemed scientific journal Nature. Additionally, Huawei Cloud remains committed to innovating AI weather forecasting methods to address the increasing global challenges of extreme weather events.

Transformative Impact of AI in Weather Prediction

The World Meteorological Organization reports nearly 12,000 weather, climate, and water-related disasters occurred from 1970 to 2021, causing over two million deaths and $US 4.3 trillion in economic damages. Early warnings are crucial in mitigating casualties and economic losses, with precise weather predictions enhancing effectiveness. AI technology, capable of processing vast data volumes and identifying patterns, is key to improving weather forecasting accuracy and accelerating prediction times. Meteorological bureaus worldwide are integrating AI models developed by technology firms into their forecasting methodologies.

The introduction of the regional model marks another milestone for Pangu-Weather. Huawei Cloud’s Pangu-Weather Model garnered recognition with a publication in the esteemed science journal Nature in July 2023. Subsequently, it became publicly accessible on the European Center for Medium-Range Weather Forecasts (ECMWF) website in August. Acknowledged as one of China’s top 10 scientific achievements in 2023, the Pangu-Weather Model continues to expand its reach. Huawei Cloud’s collaboration with the Thai Meteorological Department in December 2023 underscores its commitment to further advancing weather forecasting capabilities globally.

Huawei Cloud and Shenzhen Meteorological Bureau’s Collaborative Efforts

With the monsoon season looming over Southern China, Huawei Cloud and the Meteorological Bureau of Shenzhen Municipality are gearing up for collaborative efforts. Their primary objective is rigorously verifying and comprehensively evaluating the Zhiji model’s performance during this critical period. Additionally, the partners are committed to ongoing model enhancements, ensuring its efficacy in providing invaluable insights for weather forecasters. Through this concerted endeavor, they aim to bolster the accuracy and reliability of Zhiji, further empowering meteorological services in the region.

FAQs

1. What is the Zhiji Regional AI Weather Forecasting Model?

The Zhiji model is an advanced AI-powered weather forecasting system jointly developed by Huawei Cloud and the Meteorological Bureau of Shenzhen Municipality. It offers precise five-day forecasts within a 3 km range, incorporating various meteorological elements like temperature, precipitation, and wind speed.

2. How accurate is the Zhiji model in predicting weather events?

During its trial phase, Zhiji accurately forecasted multiple cold-temperature periods. To ensure reliable weather forecasts, ongoing efforts are focused on further enhancing its precision, particularly in predicting precipitation.

3. What distinguishes Zhiji from other weather forecasting models?

Zhiji stands out for its localized accuracy. It leverages AI technology to provide detailed forecasts specifically tailored to the Shenzhen region and its neighboring areas. Its spatial resolution of 3 km offers a finer granularity than traditional global models, resulting in more precise predictions.

4. How does Zhiji contribute to disaster preparedness and mitigation?

Accurate weather forecasting plays a crucial role in disaster prevention and mitigation efforts. By providing timely and precise weather predictions, Zhiji enables authorities to issue early warnings, thereby reducing casualties and minimizing economic losses associated with weather-related disasters.

5. What are the plans for Zhiji’s development?

Huawei Cloud and the Meteorological Bureau of Shenzhen Municipality are committed to continuously enhancing the Zhiji model. Plans include comprehensive evaluation and verification during the upcoming monsoon season and ongoing refinement to improve its forecasting capabilities and usefulness for weather forecasters.

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