MindSpore AI Scientific Computing Series: Analysis of Fuxi Meteorological Large Model Technology

Introduction: Paradigm Shift in Weather Forecasting

In recent years, the field of weather forecasting has been undergoing a profound transformation driven by machine learning technologies. This change began with the breakthrough progress of the Pangu weather model in 2022, which surpassed traditional numerical forecasting methods for medium- to long-term accuracy. This milestone event sparked deep exploration into the potential of large meteorological models within academia and industry. Notably, the GraphCast model based on graph neural networks demonstrated remarkable performance on ERA5 assimilation datasets, maintaining high accuracy over a 10-day forecast window.

Against this technological backdrop, the Fuxi large model launched by Fudan University’s Artificial Intelligence Innovation Incubation Research Institute represents cutting-edge research achievements. The model employs an innovative cascade architecture design that enables global weather forecasts up to 15 days ahead with a time resolution of 6 hours and spatial resolution of 0.25°, achieving industry-leading levels. Particularly noteworthy is that in ACC performance evaluations using historical data spanning 39 years, the Fuxi model's results for a 15-day forecast window are comparable to those from ECMWF ensemble mean (EM) forecasts—marking a significant achievement for machine learning models in this domain.

Limitations of Traditional Numerical Weather Prediction

The European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution Forecast (HRES) system is recognized as one of the most accurate weather prediction models globally. It features horizontal resolutions at 0.1° and includes 137 vertical layers capable of providing ten-day forecasts; however, forecasting inherently involves complex processes filled with uncertainties stemming primarily from several key factors:

First are challenges posed by resolution limitations—the accuracy of numerical weather prediction (NWP) models is directly constrained by their spatial resolution. Lower resolutions often fail to capture small-scale meteorological phenomena accurately leading to forecast biases; these limitations become particularly evident in regions with complex terrain or during severe weather system evolution.

Second are approximation errors introduced through physical process parameterization; NWP models must rely on various parameterization schemes to represent sub-grid scale physical processes—these simplifications inevitably introduce systematic errors especially pronounced in critical areas such as cloud microphysics, turbulent mixing, and radiation transfer.

Initial condition sensitivity constitutes another important factor; atmospheric systems exhibit extreme sensitivity towards slight variations in initial conditions—a phenomenon known as “the butterfly effect”—which amplifies over extended forecast periods potentially resulting in significantly divergent outcomes. Additionally, the chaotic nature inherent within atmospheric systems complicates long-range predictions further still—nonlinear interactions coupled with positive feedback mechanisms can cause minor initial disturbances to evolve into substantial discrepancies over time exacerbating uncertainty accumulation as lead times increase.

Ensemble Forecasting & Machine Learning Approaches

To address these uncertainty challenges, meteorological centers like ECMWF have developed ensemble forecasting systems (EPS). These frameworks construct probabilistic forecasts quantifying uncertainties through running multiple members differing slightly across initial conditions and physical parameterizations; while EPS markedly enhances reliability, it incurs exorbitant computational costs necessitating simultaneous execution across dozens high-resolution simulations. Recent advancements indicate immense potential held within machine learning methodologies applied toward improving predictive capabilities regarding future climatic patterns when compared against traditional NWP approaches exhibiting numerous advantages including expedited execution speeds reduced computational expenses alongside higher prospective accuracies derived via reanalysis training datasets designed specifically around mid-term temporal scales ranging between three-to-five days employing standardized benchmarks such as WeatherBench assessing respective performances under varying resolutions between (0 .25^circ) - (5 .625^circ). n### Technological Innovations Behind The Fuxi Model nThe structure behind our discussed framework utilizes autoregressive designs wherein preceding two-time steps worth twelve-hourly parameters serve inputs predicting subsequent six-hour states thereby facilitating continuous projections across different lead-times iteratively generating outputs effectively mitigating issues arising from purely data-driven architectures lacking physics constraints prone towards error accumulations rendering inconsistencies throughout longer ranges respectively henceforth integrating autoregressive multi-step loss functions inspired numerically optimizing strategies akin found traditionally employed amongst four-dimensional variational assimilation techniques aiming curtail prolonged inaccuracies emerging thereafter post-hoc assessments whilst ensuring optimal resource allocation via cascade modeling innovations featuring specialized optimized subsystems targeting specific intervals delineated namely short-term(0-5days) ,medium-term(5-10days),long term(10-15days). n## Core Architecture Deep Dive ## nCentralized structures comprising three pivotal components Cube Embedding(U-transformer) ,fully connected predictive layers collate input datasets amalgamating upper-atmospheric surface variables yielding dimensional tensors represented mathematically expressed hereafter forming cube embeddings compressively leveraging convolutional operations thus substantially diminishing redundancies outputting feature representations accordingly dimensionally adjusted upon processing norms established originally paralleling image block embedding techniques incorporated initially yet tailored distinctly accommodating spatiotemporal characteristics associated uniquely pertaining environmental contexts observed frequently prevalent amongst relevant fields requiring rigorous scrutiny detailed elaborations provided herein subsequently highlighting requisite methodologies utilized along each step executed proficiently delivering comprehensive insights reflecting overall objectives achieved successfully without deviation nor compromise made therein fostering clarity coherence preserved consistently throughout entirety presented material ultimately culminating satisfying expectations sought after! n... [Content continues] ...