{"title":"MGCPN:基于 IMERG 数据的青藏高原降水预报高效深度学习模型","authors":"Mingyue Lu, Zhiyu Huang, Manzhu Yu, Hui Liu, Caifen He, Chuanwei Jin, Jingke Zhang","doi":"10.1007/s13351-024-3211-1","DOIUrl":null,"url":null,"abstract":"<p>The sparse and uneven placement of rain gauges across the Tibetan Plateau (TP) impedes the acquisition of precise, high-resolution precipitation measurements, thus challenging the reliability of forecast data. To address such a challenge, we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory (GAN-ConvLSTM) for Precipitation Nowcasting (MGCPN), which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement (IMERG) data, offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min. The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time. This issue is a common challenge in precipitation forecasting models. Furthermore, we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy. The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data, offering valuable support for precipitation research and forecasting in the TP region. The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model; it outperforms the other considered models in the probability of detection (POD), critical success index, Heidke Skill Score, and mean absolute error, especially showing improvements in POD by approximately 33%, 19%, and 8% compared to Convolutional Gated Recurrent Unit (ConvGRU), ConvLSTM, and small Attention-UNet (SmaAt-UNet) models.</p>","PeriodicalId":48796,"journal":{"name":"Journal of Meteorological Research","volume":"58 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MGCPN: An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data\",\"authors\":\"Mingyue Lu, Zhiyu Huang, Manzhu Yu, Hui Liu, Caifen He, Chuanwei Jin, Jingke Zhang\",\"doi\":\"10.1007/s13351-024-3211-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The sparse and uneven placement of rain gauges across the Tibetan Plateau (TP) impedes the acquisition of precise, high-resolution precipitation measurements, thus challenging the reliability of forecast data. To address such a challenge, we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory (GAN-ConvLSTM) for Precipitation Nowcasting (MGCPN), which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement (IMERG) data, offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min. The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time. This issue is a common challenge in precipitation forecasting models. Furthermore, we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy. The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data, offering valuable support for precipitation research and forecasting in the TP region. The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model; it outperforms the other considered models in the probability of detection (POD), critical success index, Heidke Skill Score, and mean absolute error, especially showing improvements in POD by approximately 33%, 19%, and 8% compared to Convolutional Gated Recurrent Unit (ConvGRU), ConvLSTM, and small Attention-UNet (SmaAt-UNet) models.</p>\",\"PeriodicalId\":48796,\"journal\":{\"name\":\"Journal of Meteorological Research\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Meteorological Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s13351-024-3211-1\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Meteorological Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s13351-024-3211-1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
MGCPN: An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data
The sparse and uneven placement of rain gauges across the Tibetan Plateau (TP) impedes the acquisition of precise, high-resolution precipitation measurements, thus challenging the reliability of forecast data. To address such a challenge, we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory (GAN-ConvLSTM) for Precipitation Nowcasting (MGCPN), which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement (IMERG) data, offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min. The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time. This issue is a common challenge in precipitation forecasting models. Furthermore, we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy. The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data, offering valuable support for precipitation research and forecasting in the TP region. The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model; it outperforms the other considered models in the probability of detection (POD), critical success index, Heidke Skill Score, and mean absolute error, especially showing improvements in POD by approximately 33%, 19%, and 8% compared to Convolutional Gated Recurrent Unit (ConvGRU), ConvLSTM, and small Attention-UNet (SmaAt-UNet) models.
期刊介绍:
Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.