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Related Experiment Video

Updated: Aug 3, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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PM_2.5 extended-range forecast based on MJO and S2S using LightGBM.

Zhongqi Yu¹, Jinghui Ma², Yuanhao Qu¹

¹Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai 200030, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China.

The Science of the Total Environment

|April 8, 2023

Summary

The Madden-Julian Oscillation (MJO) significantly enhances extended-range PM2.5 forecasting in Shanghai. Incorporating MJO data improves prediction accuracy and reduces errors for air pollution outlooks.

Keywords:

Extended-range forecast LightGBM MJO PM(2.5)S2S

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Area of Science:

Atmospheric Science
Environmental Science
Data Science

Background:

Extended-range forecasting of fine particulate matter (PM2.5) is crucial for public health and environmental management.
Traditional models often struggle with long-term prediction accuracy due to complex meteorological influences.
The Madden-Julian Oscillation (MJO) is a major source of subseasonal variability in the tropics, potentially impacting mid-latitude weather patterns.

Purpose of the Study:

To develop and evaluate an extended-range PM2.5 prediction model for Shanghai.
To assess the impact of incorporating Madden-Julian Oscillation (MJO) data on PM2.5 forecasting skill.
To investigate the MJO's influence on meteorological conditions conducive to air pollution in Eastern China.

Main Methods:

Developed an extended-range PM2.5 prediction model using the LightGBM algorithm.
Utilized historical PM2.5 data, meteorological observations, Subseasonal-to-Seasonal (S2S) forecasts, and MJO monitoring data.
Employed advanced regression analysis to examine the MJO's impact on atmospheric variables.

Main Results:

The MJO significantly improved the predictive skill of the extended-range PM2.5 forecast model.
MJO indexes (RMM1 and RMM2) were the most influential meteorological predictors.
Incorporating MJO data enhanced correlation coefficients (0.31-0.56) and reduced RMSEs (23.2-28.7 μg/m³) for 11-40 day forecasts, particularly for 16-40 days.

Conclusions:

The MJO plays a critical role in modulating weather patterns affecting air pollution in Eastern China.
MJO indexes (RMM1, RMM2) influence geopotential height and trough positioning 45 days in advance, favoring pollutant transport.
The findings highlight the utility of MJO and S2S data for improving subseasonal air pollution outlooks.