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基于数值天气预报(NWP)数据的对流层小尺度延迟估计的评估与验证

Assessment and Validation of Small-Scale Tropospheric Delay Estimations Based on NWP Data.

作者信息

Håkegård Jan Erik, Ouassou Mohammed, Sokolova Nadezda, Morrison Aiden

机构信息

SINTEF Digital, Strindveien 4, 7032 Trondheim, Norway.

Norwegian Mapping Authorities, Kartverksveien 21, 3507 Hønefoss, Norway.

出版信息

Sensors (Basel). 2024 Oct 12;24(20):6579. doi: 10.3390/s24206579.

DOI:10.3390/s24206579
PMID:39460059
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11510907/
Abstract

This paper investigates the applicability of the Numerical Weather Prediction (NWP) data for characterizing the gradient of zenith wet delay in horizontal direction observed on short baselines over larger territories. A three-year period of data for an area covering Scandinavia and Finland is analyzed, and maximum gradients during the considered period are identified. To assess the quality of the NWP-based estimates, results for a smaller region are compared with the estimates obtained using Global Navigation Satellite System (GNSS) measurements processed by the GipsyX/RTGx software package (version 2.1) from a cluster of GNSS reference stations. Additionally, the NWP data from 7 to 9 August 2023 covering a period that includes a storm with high rain intensities over Southern Norway leading to sustained flooding are processed and analyzed to assess if the gradient of zenith wet delay in the horizontal direction increases significantly during such events. The results show that maximum gradients in the range of 40-50 mm/km are detected. When comparing NWP-based estimates to GNSS-based estimates, the tropospheric delays show a very strong correlation. The tropospheric gradients, however, show a weak correlation, probably due to the uncertainty in the NWP data exceeding the gradient values. The data captured during the storm show that while the tropospheric delay increases significantly it is difficult to see increases in the gradient of zenith wet delay in the horizontal direction using this data source and resolution.

摘要

本文研究了数值天气预报(NWP)数据在表征较大区域内短基线水平方向天顶湿延迟梯度方面的适用性。分析了覆盖斯堪的纳维亚和芬兰地区的三年数据,并确定了所考虑时间段内的最大梯度。为了评估基于NWP的估计质量,将一个较小区域的结果与使用GipsyX/RTGx软件包(版本2.1)处理的全球导航卫星系统(GNSS)测量数据从一组GNSS参考站获得的估计结果进行了比较。此外,还处理和分析了2023年8月7日至9日的NWP数据,该时间段包括挪威南部一场暴雨引发持续洪水的风暴,以评估在此类事件期间水平方向天顶湿延迟梯度是否显著增加。结果表明,检测到的最大梯度范围为40 - 50毫米/公里。将基于NWP的估计与基于GNSS的估计进行比较时,对流层延迟显示出非常强的相关性。然而,对流层梯度显示出较弱的相关性,这可能是由于NWP数据中的不确定性超过了梯度值。风暴期间捕获的数据表明,虽然对流层延迟显著增加,但使用此数据源和分辨率很难看出水平方向天顶湿延迟梯度的增加。

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本文引用的文献

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A Machine Learning-Based Tropospheric Prediction Approach for High-Precision Real-Time GNSS Positioning.一种基于机器学习的高精度实时全球导航卫星系统定位对流层预测方法。
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Analysis of Spatial Decorrelation of Small-Scale Tropospheric Delay Using High-Resolution NWP Data.利用高分辨率 NWP 数据分析小尺度对流层延迟的空间去相关。
Sensors (Basel). 2023 Jan 21;23(3):1237. doi: 10.3390/s23031237.
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The quiet revolution of numerical weather prediction.数值天气预报的悄然变革。
Nature. 2015 Sep 3;525(7567):47-55. doi: 10.1038/nature14956.