The "Outline for High-Quality Meteorological Development (2022-2035)" issued by the State Council clearly states that it is necessary to "forecast major weather processes one month in advance", and this It is inseparable from sub-seasonal climate prediction technology of more than 15 days.
Sub-seasonal climate prediction focuses on climate anomalies in the next 15 to 60 days, which can provide important support for production arrangements in agriculture, water conservancy, energy and other fields.
Compared with short- and medium-term weather forecasts with a validity period of less than two weeks, sub-seasonal climate forecasts have greater uncertainty. It not only needs to consider the initial value problem, but also consider the impact of boundary forcing. The prediction sources are more complex and the prediction skills are less. Therefore, sub-seasonal climate prediction has always been called the "predictability desert". Due to its complexity, even the performance of large AI models on time scales has not been able to surpass traditional models for a long time.
In order to solve this problem, the Shanghai Institute of Science and Intelligence (referred to as SIRI), Fudan University, and the National Climate Center of the China Meteorological Administration jointly developed the "Fuxi" sub-seasonal climate prediction model (FuXi-S2S), which for the first time surpassed traditional numerical forecasting The model benchmark - the S2S model of the European Center for Medium-Range Weather Forecasts (ECMWF).
Recently, the paper entitled "A Machine Learning Model that Outperforms Conventional Global Subseasonal Forecast Models" was published in the authoritative international comprehensive journal "Nature Communications".
As a machine learning model, the "Fuxi" sub-seasonal climate prediction model contains relatively comprehensive variables:
5 upper-altitude atmospheric variables in 13 pressure layersThis forecast information is crucial for agricultural planning, resource management, disaster preparedness, and protection against extreme weather events such as heat waves, droughts, cold waves, and floods.
The "Fuxi" large-scale subseasonal climate prediction model has achieved two key technological innovations:
Introduced the air-sea interaction process, especially the tropical atmospheric intraseasonal oscillation (MJO), the most important sub-seasonal factor. The source of predictability is incorporated into the model;Illustration: Overview of process architecture. (Source: Paper)
The MJO is a periodic atmospheric circulation pattern that affects areas from the tropics to mid- and high-latitudes. Forecasting the MJO can help meteorologists and climatologists more accurately understand and predict precipitation patterns, storm activity, temperature changes, and the occurrence of extreme weather events such as droughts and floods in the weeks to months ahead.The "Fuxi" large sub-seasonal climate prediction model has effectively improved the prediction skill of the MJO, reaching 36 days, which significantly exceeds the 30-day duration of the ECMWF's S2S model.
Illustration: Real-time multivariate Madden of the ensemble mean between ECMWF subseasonal-to-seasonal (S2S) forecasts (blue) and FuXi-S2S forecasts (red) using all test data from 2017 to 2021 - Julian Oscillation (MJO) (RMM) Bivariate Correlation (COR) for comparison. (Source: paper)
In addition, the "Fuxi" sub-seasonal climate prediction large model can also identify potential information leading to extreme events by constructing a saliency map. This ability is useful in predicting extreme rainfall during the 2022 Pakistan floods. aspect has been verified.The specific process is to first define a loss function, such as the average precipitation anomaly percentage in Pakistan marked by the green box in the figure below, keep the model parameters fixed, and then solve the gradient through backpropagation and finally output the gradient of the input image pixels to reflect the input The positive and negative correlation effects of meteorological elements on the precipitation anomaly percentage in Pakistan.
With the powerful prediction capabilities and precursor signal identification capabilities of the "Fuxi" sub-seasonal climate prediction model, effective tools and strategies can be provided to deal with extreme weather events.
The above is the detailed content of Nature sub-journal, Shanghai Institute of Technology, Fudan University, and China Meteorological Administration develop sub-seasonal AI large model 'Fuxi' to break through the 'predictability desert'. For more information, please follow other related articles on the PHP Chinese website!