image: This workflow involves three main steps: (a) data integration, which processes previously developed SV data and other multisource data; (b) model building, which employs automated ML methods to develop an optimal, self-iterating model, and (c) model deployment, which fine-tunes the model using dynamically updated data from the past two years and retrieves hourly PM10 concentrations for the most recent day.
Credit: ©Science China Press
This study is led by Prof. Huizheng Che and Dr. Ke Gui from the State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences. Sand and dust storms pose an ongoing threat to environment and public health. PM10, the primary pollutant of sandstorms, is directly detected by sparse ground-based stations, which fail to capture the incursion process of large-scale events. While satellite spectral products integrating with ancillary data provide an indirect method, real-time PM10 monitoring remains constrained by delays in meteorological data inputs. "Satellite-based PM10 retrieval limited to coarse daily scale, and most studies focus on reconstructing historical datasets rather than tracking real-time PM10 levels.” Huizheng says.
Huizheng and Ke, together with lab member Xutao Zhang, sought to build a real-time surface PM10 retrieval (RT-SPMR) framework powered by interpretable automated machine learning with dynamic updates. The framework comprises three core modules (see Figure 1 below) and uniquely integrates the team’s custom-developed surface visibility dataset as a key input. These ingenuities enable the RT-SPMR to provide real-time gridded PM10 data across China, with a gapless coverage of spatial resolution of 6.25 km, which temporally updates every hour (see Figure 2 below).
The team found that the RT-SPMR model demonstrates robust generalization and stability, achieving higher daily retrieval accuracy than previous studies, as confirmed through cross-validation and rolling iterative validation experiments. "These performance tests highlight the readiness of RT-SPMR for operational deployment." Ke says.
During a severe sandstorm event that began on March 14, 2021, in northern China, the RT-SPMR showcased exceptional performance in real-time tracking of the fine-scale evolution of dust intrusion. It successfully captured the dynamic PM10 variations in areas beyond the reach of geostationary satellite imagery and ground observation networks (see Figure 3 below).
"This new framework overcomes the limitations of current satellite-based PM10 monitoring. We remain committed to advancing our models by incorporating more detailed information to enhance retrieval accuracy. Our goal is to produce even more reliable datasets, providing robust support for atmospheric environmental monitoring." Ke says.
The seamless real-time PM10 data products generated by RT-SPMR are expected to provide more accurate initial field information for dust storm forecasting models, improving prediction accuracy. This research also supports the “virtual network” objective outlined in the WMO’s Scientific and Implementation Plan: 2021-2025 for the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS). Additionally, it offers a "China solution" for building refined monitoring systems in other countries frequently affected by dust storms.
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See the article:
Real-time mapping of gapless 24-hour surface PM10 in China
https://doi.org/10.1093/nsr/nwae446
Journal
National Science Review