In the past four years, members of the project team have carried out a series of monitoring studies on extreme weather and climate events using the newly developed Chinese and European satellites, and achieved the following main results.
1.Day and night continuous monitoring of the heavy sand and dust storm on March 15, 2021 in Beijing.
By comprehensively utilizing multiple channel data from visible, short and middle infrared, split window channel and water vapor channel from Chinese new generation Geostationary satellite FY-4A. The Beijing “3.15” heavy sand and dust storm transport path has been continuously monitored day and night. Ground based observation combined with satellite data were applied to retrieve dust intensity index. The dust sources tracking by using HYSPLIT method shown that there were two main dust sources: one is the northwest of Mongolia, and the other is the Hexi Corridor-Hetao, inner Mongolia, China accounted for the storm.
Geostationary satellite monitoring shows the dust weather from March 14 to 16 lasted for nearly 40 hours, transporting more than 4500km affecting a big area including the North China, Northeast China, the Korean Peninsula and Japan and neighboring regions, causing serious air pollution.
Figure 1 showed the situation of the heavy sand and dust storm in Beijing on March 15, 2021
2. Lightning activity characteristics in China based on the FY-4 satellite Lightning Mapping Imager.
Characteristics of lightning activity was analyzed based on data recorded by the Lightning Mapping Imager (LMI) onboard the Chinese new generation of Geostationary satellite FY-4A during 2019-2021.
Figure 2 showed the spatial distribution of lightning activity over northern and southern coverage of the FY-4A LMI.
Figure 3 showed the daily variation of different type of lightning detection data from the FY-4A LMI in eastern China, western Australia, and the Tibetan Plateau.
3. A new mechanism of forming ozone mini holes/highs over north China Plain in winter.
We investigated ozone mini holes/highs events over the North China Plain in winter during 1979-2019. The analysis showed that most ozone mini holes/highs events conform to the mechanism A and B and two typical weather change processes accompanying with these events: rapid cooling weather processes accompanies with ozone minihighs, while abnormal rapid warming weather processes accompanies with ozone miniholes. However, we also found a significant proportion of anomalous events do not conform to this rule: rapid cooling processes accompanies with ozone minihighs, while rapid warming processes accompanies with ozone minihighs. Behind these abnormal phenomena, there may exists a new ozone mini holes/highs forming mechanism: rapid cooling weather processes accompanies with ozone miniholes, while abnormal warming weather processes accompanies with ozone minihighs. The new mechanism may be related to the land and sea position of the North China Plain in the east and its landform features in the west.
4.Deep learning for daily spatiotemporally continuity of satellite surface soil Moisture and relationship with precipitation
The pattern relationship between SM and precipitation is explored, because precipitation is the major force of daily surface SM. Therefore, the major pattern of SM should reflect that of precipitation and present the similar pattern. SVD is conducted between SM and precipitation during May-September of 2015–2020 and shown in Fig.4, Fig.5 .
From the pattern relationship between precipitation and SM, it is found that the satellite SM products could not correctly reflect the major variation of SM patterns associated with rainfall over eastern China. The Conv model using limited number of stations (43) over eastern China produces SM with reasonable major patterns that similar to the major rainfall force. Thus, correction based on ground-based data is needed for the satellite products before its application in a specific region for climate research based on daily SM products, or unreliable results could be achieved. More ground-based data is needed for further studies, which can improve the performance of the Conv model.
5.Deep Learning for Near-Surface Air Temperature Estimation from FY-4A Satellite Data
Near-surface air temperature is a crucial weather parameter that significantly impacts human health and is widely utilized in numerical weather forecasting and climate prediction studies. We conducted a deep learning Transformer-based neural network (TaNet) for near-surface air temperature estimation. TaNet automatically extracts information from imageries captured by China's new-generation geostationary meteorological satellite FengYun-4A and generates grid near-surface air temperature data in near real-time.
Extensive experiments conducted using the state-of-the-art operational reanalysis product ERA5 and meteorological station observations as benchmark standards demonstrate the effectiveness and superiority of TaNet. It achieves an impressive Pearson's correlation coefficient (CC) of 0.990 with ERA5 and 0.959 with station observations, outperforming the other products, such as CFSv2, CRA, and U-Net, on root mean square error (RMSE) and CC metrics. TaNet reduces the RMSE of CFSv2, CRA, and U-Net by a margin of 10.551% (2.594 °C vs. 2.900 °C ), 2.261% (2.594 °C vs. 2.654 °C ), and 5.535% (2.594 °C vs. 2.746 °C ), respectively, using station observations as the benchmark.
