As we all know, the application of artificial intelligence in the field of meteorology has become more and more widespread in recent years. The rapid development of cutting-edge technology, represented by artificial intelligence technology, has also brought about a far-reaching technological revolution. The integration and development of big data, cloud computing, deep learning and other technologies has promoted major changes in meteorological services, the most significant of which is the application of big data in meteorological services, which has led to the explosive growth of meteorological data. According to statistics, more than 500 terabytes of meteorological data are generated globally every year. How to make better use of these data and improve the accuracy of weather forecasting has become an important issue facing meteorological departments.
Humans have mastered most of the technology for the weather
For more than 200 years, weather forecasting has been one of the major scientific problems in the field of meteorology. At present, mankind has mastered most of the technologies of weather forecasting, including from the exploration of atmospheric movement laws to the establishment of numerical models, from the analysis of weather elements to the weather system, from the revelation of complex meteorological changes to the response to extreme weather events. However, there are still many challenges to achieving accurate forecasting.
On the one hand, existing weather models have a high degree of ability to simulate real-world weather changes, but they face significant limitations. On the other hand, with the deepening of people's understanding of the laws of atmospheric motion, people find that it is more and more difficult for models to simulate complex meteorological change processes, especially there are many nonlinear characteristics and complex system interaction processes in atmospheric motion. Therefore, the construction of large-scale, high-efficiency, high-precision and universal numerical weather prediction models has become one of the most important tasks in meteorological research.
There are four major challenges in the field of weather forecasting
Regarding the practical difficulties faced by China's weather forecasting business, Yu Yong, deputy director of the China Meteorological Administration, has pointed out that there are still four major problems in China's weather forecasting
First, there are many factors that affect climate, and the intensity, direction and scope of each factor are different.
Second, there are still physical inconsistencies in numerical prediction models.
Thirdly, satellite observation data and numerical prediction models are still deficient in terms of spatiotemporal resolution and data quality.
Fourth, the development of a new generation of information technology, artificial intelligence, and big data has significantly improved the ability of meteorological data processing, analysis, and mining.
The "Development and Application of Large Models Based on Artificial Intelligence" project is aimed at the four major problems of weather forecasting. The project takes the earth system numerical prediction model as the core, integrates big data technology and deep learning and other artificial intelligence technologies, and carries out research and application demonstration of key technologies such as multi-source heterogeneous data fusion, multi-source data dimensionality reduction and modeling, and efficient processing and analysis of massive meteorological data, so as to further improve China's weather forecasting capabilities.
Large models are developing rapidly and are a great tool for solving problems
Artificial intelligence large model refers to large-scale general intelligence based on deep learning technology to solve complex problems with powerful learning capabilities. According to statistics, more than 500 artificial intelligence models have been released around the world, and these large models have achieved remarkable application results in the fields of weather forecasting, disaster prevention, and climate ** around the world.
The R&D and application of artificial intelligence large model is based on artificial intelligence technology earth system numerical prediction model, efficient machine learning method and large model algorithm and other technologies to improve the ability of numerical prediction model to the law of atmospheric motion and physical processes, and develop high-precision, high-efficiency and high-robust global atmospheric numerical prediction model and efficient machine learning method, so as to achieve refined forecasting and high-quality ability of major weather processes.
The introduction of artificial intelligence large model technology in weather forecasting will improve the ability of a new generation of global atmospheric numerical prediction models and refined forecasting of major weather processes, and provide new ideas and new ways to improve China's meteorological disaster prevention and mitigation capabilities.
How does artificial intelligence accurately ** the weather?
With the continuous development of artificial intelligence technology, the ability to use machine learning and other methods to build weather models is becoming stronger and stronger. Among them, deep learning technology is the use of data-driven methods to build ** models.
Specifically, deep learning refers to taking a large amount of data as input, processing it by complex algorithms, and then outputting results. By training a large number of artificial neural networks, it is possible to determine the weather conditions for a certain period of time in the future based on the mapping relationship between inputs and outputs. It can be said that deep learning is an important branch of machine learning, and it is also a mainstream artificial intelligence algorithm.
The advantage of large AI models lies in their superior ability to learn and adapt. It's like an experienced meteorologist who, after countless practices and summaries, is able to build a detailed model of weather changes in his mind. Similarly, AI models are able to capture subtle trends and potential connections that are difficult to reach by traditional numerical models by continuously learning past and present meteorological data.
For example, the artificial intelligence model can accurately detect sudden precipitation events in local areas. In the past, such small-scale, high-impact weather phenomena have often been a headache for forecasters because they tend to occur suddenly, are short-lived, and have a limited impact that is difficult to capture by traditional large-scale models. However, large AI models are able to capture these small-scale but potentially significant phenomena with their keen insights into data.
In addition to improving the accuracy of the **, the large AI model also shows great potential in terms of processing speed. In the face of emergencies, such as typhoons, torrential rains and other natural disasters, fast and accurate forecasts can buy valuable time for disaster prevention and mitigation. Large AI models are capable of analyzing and analyzing global weather data in minutes, compared to traditional numerical models that take hours or more. This increase in speed is significant for emergency management departments.
China's meteorological department will use artificial intelligence technology to further improve the accuracy of forecasting
In recent years, the accuracy of weather forecasting in China has been significantly improved. In the "Global Climate Change Report" released by the World Meteorological Organization in 2022, China has a significant warming trend in general, among which the "Climate Change Special Monitoring and Evaluation Report" of the China Meteorological Administration shows that the accuracy of China's overall weather forecast in 2016 was 795%, an increase of 83 percentage points.
But meteorological services are also acutely aware that weather forecasting is a scientific problem that is highly dependent on data and models. With the rapid development of China's weather forecasting business, a large amount of meteorological data and observation data have been accumulated, but there are still problems such as short forecast timeliness, low resolution and low accuracy. The "Development and Application of Large Models Based on Artificial Intelligence" project will further improve the accuracy of air forecasting by building large models that meet the needs of multi-model parallel computing capabilities, developing numerical prediction models with higher spatiotemporal resolution and stronger capabilities, and developing artificial intelligence toolsets with high interpretability and stability.
Meteorologywill produce greater economic benefits
Nowadays, the integration of meteorology and all walks of life is getting closer and closer, and "meteorology+" is also giving birth to greater economic benefits. For example, using artificial intelligence technology, we can develop a variety of service products such as intelligent early warning, intelligent grid forecasting, intelligent **, and intelligent decision-making, effectively improving the level of disaster prevention and mitigation and public services; Using artificial intelligence technology, meteorological forecasting can also be combined with the Internet, Internet of Things and other technologies to achieve refined meteorological services; The use of artificial intelligence technology can also provide accurate weather forecasting services for the public and improve the public's ability to prevent disasters and avoid dangers.
In the context of the era of "smart meteorology", the use of artificial intelligence technology to build a more efficient and reliable data processing model and product development mechanism not only improves the accuracy of the first class, but also greatly accelerates the speed of data processing, providing strong technical support for disaster prevention and mitigation. Although there are still some challenges, with the continuous improvement of technology, artificial intelligence large models will play a more important role in the field of weather forecasting in the future.
Artificial intelligence will promote the high-quality development of China's meteorological industry
With the rapid development of artificial intelligence technology, meteorological services are gradually changing from an empirical model based on rules to an intelligent model based on knowledge, which puts forward higher requirements for the accuracy of weather forecasting, and artificial intelligence can play an important role in this regard. For example, the accuracy and refinement of weather forecasting can be effectively improved by using artificial intelligence algorithms for atmospheric composition detection, soil moisture measurement, and rainfall monitoring. By using artificial intelligence algorithms such as machine learning to carry out**, warn and assess the climate, the accuracy of the climate can be improved.
In the future, with the development of big data, cloud computing and artificial intelligence technology, more and more meteorological data will be generated and mined and utilized. The accumulation and accumulation of meteorological data will enable us to better understand the weather laws and weather system characteristics, so as to provide more accurate and refined basic data for weather forecasting, and ultimately promote the high-quality development of China's meteorological undertakings.