MGCPN:基于 IMERG 数据的青藏高原降水预报高效深度学习模型

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Mingyue Lu, Zhiyu Huang, Manzhu Yu, Hui Liu, Caifen He, Chuanwei Jin, Jingke Zhang
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引用次数: 0

摘要

青藏高原(TP)的雨量计分布稀疏且不均匀,妨碍了精确、高分辨率降水测量的获取,从而对预报数据的可靠性提出了挑战。为了应对这一挑战,我们引入了一种名为 "多源生成对抗网络-卷积长短期记忆(GAN-ConvLSTM)降水预报(MGCPN)"的模型,该模型利用全球降水测量综合多卫星检索(IMERG)数据产品,提供未来 30 至 300 分钟的高时空分辨率降水预报。我们的研究结果证实,MGCPN 模型的实施成功地解决了随着预报时间的延长而经常出现的降水结果被低估和模糊的问题。这个问题是降水预报模型面临的共同挑战。此外,我们还使用了多源时空数据集和综合地理要素进行训练和预测,以提高模型的准确性。该模型展示了其利用 IMERG 数据生成精确降水预报的能力,为 TP 地区的降水研究和预报提供了宝贵的支持。研究得出的度量结果进一步强调了 MGCPN 模型的显著优势;它在检测概率 (POD)、临界成功指数、Heidke 技能分数和平均绝对误差方面均优于其他考虑过的模型,尤其是在 POD 方面,与卷积门控循环单元 (ConvGRU)、ConvLSTM 和小注意-UNet (SmaAt-UNet) 模型相比,分别提高了约 33%、19% 和 8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Journal of Meteorological Research
Journal of Meteorological Research METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
6.20
自引率
6.20%
发文量
54
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.
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