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Construction and validation of summer drought prediction model in Hubei Province based on machine learning algorithms
WANG Yajun, LUO Juying, CHENG Liehai, LI Wei
Journal of Arid Meteorology, 2024 42 (5): 670
In order to construct an accurate drought prediction model, it is very important to select predictors with physical significance and adopt efficient prediction methods. Compared to the traditional prediction methods, more efficient and reliable machine learning algorithms have been more widely used in climate prediction. This study is based on the monthly meteorological element data of 70 national meteorological stations in Hubei Province from 1960 to 2022, as well as the atmospheric circulation and sea temperature indices provided by the National Climate Center and the National Oceanic and Atmospheric Administration (NOAA). The standardized precipitation evapotranspiration index was used to. . .
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Analysis of two meteorological drought events in Guizhou Province and establishment of drought prediction model based on machine learning
WANG Yuetong, HE Dongpo, LI Zhongyan, WANG Shuo, CHEN Zaoyang
Journal of Arid Meteorology, 2024 42 (5): 682
Comparative analysis of different drought events occurring in Guizhou Province during the flood season (from June to September) is significant for improving the short-term climate prediction techniques. Based on the precipitation data from 84 meteorological stations in Guizhou Province, the spatial and temporal evolution characteristics of two severe drought events in Guizhou Province during the period of 1981—2023 were characterized statistically, and the causes of the two drought events were revealed using reanalyzed data, and the differences were compared. At the same time, combined with 130 climate indexes of the National Climate Center and machine learning method, the drought event in G. . .
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Machine learning flood early warning model for small and medium watersheds in arid and semi-arid regions and its application
SU Hongmei, ZHANG Nan, RAN Xinmin, KANG Chao
Journal of Arid Meteorology, 2024 42 (5): 693
The accuracy of flood forecasting in small and medium-sized basins in arid and semi-arid regions needs to be improved, primarily due to a limited understanding of critical factors such as topographic features, critical rainfall, time of concentration, and recurrence intervals. In this paper, Xiahe County, Gansu Province, which is located in the semi-arid region, and Yongchang County, Gansu Province, which is located in the arid region, are selected as the research objects. Field investigations were carried out on flood-related factors across 34 small basins in 86 riverine villages of Xiahe County (2015) and 240 cross-sections in 395 riverine villages of Yongchang County (2014). Flood charact. . .
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Fusion model of wind power prediction based on multiple machine learning algorithms
HAN Zifen, CHEN Ning, FAN Yi, XIE Zhihua, SHA Sha
Journal of Arid Meteorology, 2024 42 (5): 718
The accurate prediction of wind power is of great significance for the dispatching department to adjust power generation planning in a timely manner. Machine learning is one of the main methods of wind power prediction at present. However, how to select reliable and effective single model from numerous machine learning algorithms, and how to fit and associate different models are the key and difficult points of multi-model combination prediction methods. Based on the wind power and wind tower wind speed data of Jiuquan Gandong Wind Power Electric Station of China Genneral Nuclear Power Corporation from January 1, 2020 to December 31, 2020, and the characteristics of various typical machine l. . .
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Advances in convolutional neural networks and their applications in atmospheric science
MA Minjin, CHEN Ran, CAO Yidan, ZHANG Xingyu, LI Yuebin
Journal of Arid Meteorology, 2024 42 (5): 733
With the advancement of computer technology and big data, convolutional neural networks of the deep learning have become the mainstream technology for processing large-scale data with grid structure, especially in the field of computer vision. Convolutional neural networks have also been gradually applied in the field of atmospheric science to process multi-angle and multi-scale meteorological data. This paper reviews the progress of convolutional neural networks and their applications in atmospheric science, the conclusions are as following. Through the optimization of network depth and width and magnitude compression, the accuracy and efficiency of convolutional neural networks have been s. . .
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Retrieval of net primary productivity in grassland of Inner Mongolia based on FY-3D/MERSI2 data
LI Bin, SUN Xiaolong, LU Shiqing, WANG Yuchen, XIA Ningyue, HAN Fang
Journal of Arid Meteorology, 2024 42 (5): 743
Net Primary Productivity (NPP) is not only the main parameter for estimating carbon sequestration and oxygen release in ecosystems and measuring terrestrial carbon cycle, but also the main index for evaluating ecosystem health. In view of the limited application of domestic satellites in remote sensing monitoring of grassland NPP, a set of NPP inversion model of grassland NPP in Inner Mongolia was constructed based on FY-3D/MERSI2 data. The NPP of grassland under clear sky condition in Inner Mongolia was obtained by using a strict cloud detection algorithm, which was driven by remote sensing data products and CLDAS meteorological assimilation data, combined with light energy utilization mode. . .
