Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitatio...Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitation Measurement Core Observatory for GR assessment is well-established, current methodologies are inherently limited. Focusing on three GRs in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)—strategically selected for their high overlapping coverage(>65%) and distinct from single GR or less dense coverage studies—this work introduces key refinements by integrating innovative enhancements into the volume-matching method(VMM), reflecting a systematic approach to mitigating potential error sources. Specifically, we integrate: 1) a novel frequency correction method that adapts to DPR-observed precipitation phase and type, replacing assumption-based polynomial fitting;and 2) a precise beam time-difference matching approach(accuracy < 1 s) to minimize temporal mismatch errors, which improves upon coarser time averaging methods. Furthermore, we developed statistically robust, optimized threshold criteria based on systematic sensitivity analyses using 11 quality control factors, including precipitation type, bright band effects, and attenuation correction limitations. These criteria establish an enhanced protocol designed to minimize errors arising from instrumental, frequency, and scanning differences. Application of this enhanced methodology to the GBA GRs(2021–2023) yielded a significantly improved matching accuracy(correlation coefficient, CC: 0.90–0.95;standard deviation,STD: 1.2–1.6 dB). A unique contribution of this work is the quantitative estimation of historical calibration errors and operational stability, which was achieved by linking VMM results with operational GR calibration and maintenance records. This analysis revealed decreasing STD trends and identified specific calibration-related events, such as an underestimation of approximately 2.43 dB for the Shenzhen radar following calibration in 2023. Consequently, the refined methodology provides a robust framework for ongoing GR network monitoring and offers a validated pathway for authenticating China's Fengyun-3G(FY-3G) satellite precipitation measurement radar(PMR) data.展开更多
激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算...激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算法的最新发展及其在生态与地学领域的应用。首先,LiDAR的硬件呈现出多样化、高精度的发展态势,特别是近些年无人驾驶技术的成熟极大丰富了近地面LiDAR平台的类型;其次,深度学习、同步定位与地图构建SLAM(Simultaneous Localization And Mapping)、大模型等人工智能技术的发展极大推动了LiDAR算法的进步,使得点云配准、点云分割与分类、点云与多源数据融合等算法不断推陈出新;最后,本文详述了LiDAR在内陆地形测绘、海洋测绘、地质灾害监测、森林结构测量、树木枝干结构网络、3D辐射传输及场景重建、森林微气候模拟、智慧农业、生物多样性、城市与建筑,以及行星测量11个生态与地学分支领域的应用。未来,随着硬件、算法、及LiDAR大数据的进一步发展,LiDAR将继续推动生态与地学的研究,并有望在更多领域发挥重要作用。展开更多
基金National Key Research and Development Program of China (2023YFB3905801)。
文摘Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitation Measurement Core Observatory for GR assessment is well-established, current methodologies are inherently limited. Focusing on three GRs in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)—strategically selected for their high overlapping coverage(>65%) and distinct from single GR or less dense coverage studies—this work introduces key refinements by integrating innovative enhancements into the volume-matching method(VMM), reflecting a systematic approach to mitigating potential error sources. Specifically, we integrate: 1) a novel frequency correction method that adapts to DPR-observed precipitation phase and type, replacing assumption-based polynomial fitting;and 2) a precise beam time-difference matching approach(accuracy < 1 s) to minimize temporal mismatch errors, which improves upon coarser time averaging methods. Furthermore, we developed statistically robust, optimized threshold criteria based on systematic sensitivity analyses using 11 quality control factors, including precipitation type, bright band effects, and attenuation correction limitations. These criteria establish an enhanced protocol designed to minimize errors arising from instrumental, frequency, and scanning differences. Application of this enhanced methodology to the GBA GRs(2021–2023) yielded a significantly improved matching accuracy(correlation coefficient, CC: 0.90–0.95;standard deviation,STD: 1.2–1.6 dB). A unique contribution of this work is the quantitative estimation of historical calibration errors and operational stability, which was achieved by linking VMM results with operational GR calibration and maintenance records. This analysis revealed decreasing STD trends and identified specific calibration-related events, such as an underestimation of approximately 2.43 dB for the Shenzhen radar following calibration in 2023. Consequently, the refined methodology provides a robust framework for ongoing GR network monitoring and offers a validated pathway for authenticating China's Fengyun-3G(FY-3G) satellite precipitation measurement radar(PMR) data.
文摘激光雷达LiDAR(Light Detection and Ranging)能够精准地还原被测物体的3D结构,是遥感领域最具革新性的技术之一。近几十年来,LiDAR技术取得了快速的发展,并极大地推动了生态与地学领域的相关研究。本文系统回顾并展望了LiDAR硬件和算法的最新发展及其在生态与地学领域的应用。首先,LiDAR的硬件呈现出多样化、高精度的发展态势,特别是近些年无人驾驶技术的成熟极大丰富了近地面LiDAR平台的类型;其次,深度学习、同步定位与地图构建SLAM(Simultaneous Localization And Mapping)、大模型等人工智能技术的发展极大推动了LiDAR算法的进步,使得点云配准、点云分割与分类、点云与多源数据融合等算法不断推陈出新;最后,本文详述了LiDAR在内陆地形测绘、海洋测绘、地质灾害监测、森林结构测量、树木枝干结构网络、3D辐射传输及场景重建、森林微气候模拟、智慧农业、生物多样性、城市与建筑,以及行星测量11个生态与地学分支领域的应用。未来,随着硬件、算法、及LiDAR大数据的进一步发展,LiDAR将继续推动生态与地学的研究,并有望在更多领域发挥重要作用。
