Fusing Multisource A-Train Satellites and Reanalysis Data for a Comprehensive Deep Convective System Dataset

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-04 DOI:10.1109/JSTARS.2024.3491160

Xiaoyu Hu;Lang Zhang;Jinming Ge;Qingyu Mu;Meihua Wang;Bochun Liu;Jiajing Du;Zihang Han;Leyi Wang;Hui Wang;Ruilin Zhou

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引用次数: 0

Abstract

Deep convective systems (DCSs) play a crucial role in global water cycles, energy distribution, and extreme weather events. This study aims to enhance the understanding of DCSs by creating a comprehensive dataset through the fusion of multisource A-Train satellite observations and reanalysis data. Fusing data from multiple active and passive sensors allows us to capture detailed vertical profiles and life stages of DCSs, overcoming the limitations of polar-orbiting satellites in tracking convection over time. Comparison with convection tracking data from geostationary satellites confirmed the reliability of lifecycle determination. Reanalysis data are included for each observed DCS samples, incorporating environmental conditions and aerosol data up to 36 h before the DCS occurrence. Using the fused dataset, we examined the radiative properties of DCSs across different stages of their lifecycle and the influence of environmental factors and aerosols on them. Radiative heating rates showed distinct variations, with mature stages exhibiting the highest shortwave heating and longwave cooling rates due to denser and higher cloud tops. Our findings reveal the role of humidity in increasing cloud top heights and the influence of vertical wind shear on convection development. Additionally, aerosol impacts were notable during the mature and dissipating stages, with higher concentrations linked to increased cloud top heights and lower temperatures. This comprehensive dataset advances the understanding of DCS dynamics, aiding in the improvement of predictive models for severe weather.

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融合多源 A-Train 卫星和再分析数据，建立综合深度对流系统数据集

深对流系统（DCS）在全球水循环、能量分布和极端天气事件中发挥着至关重要的作用。本研究旨在通过融合多源 A-Train 卫星观测数据和再分析数据，创建一个综合数据集，从而加深对深对流系统的了解。通过融合多个主动和被动传感器的数据，我们可以捕捉到 DCS 的详细垂直剖面和生命阶段，克服了极轨卫星在长时间跟踪对流方面的局限性。与地球静止卫星对流跟踪数据的比较证实了生命周期测定的可靠性。每个观测到的 DCS 样本都包含再分析数据，其中包括 DCS 发生前 36 小时的环境条件和气溶胶数据。利用融合数据集，我们研究了DCS在其生命周期不同阶段的辐射特性，以及环境因素和气溶胶对它们的影响。辐射加热率呈现出明显的变化，成熟阶段的短波加热率和长波冷却率最高，原因是云顶更密、更高。我们的研究结果揭示了湿度在增加云顶高度方面的作用，以及垂直风切变对对流发展的影响。此外，气溶胶在成熟和消散阶段的影响也很明显，浓度越高，云顶高度越高，温度越低。这一全面的数据集促进了对 DCS 动力学的了解，有助于改进恶劣天气的预测模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.