极轨卫星微波水汽大气运动矢量反演

IF 11.4 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Zongru Yang , Xuezhi Bai , Gang Ma , Peng Zhang , Yangtian Yan , Chunhong Zhou
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引用次数: 0

摘要

大气运动矢量(amv)是数值天气预报中最重要的数据源之一,但目前实际运行的风产品还不能满足预报要求。这项研究解决了卫星风反演的一个基本观测缺口。传统的极轨卫星检索仅限于高纬度地区,而地球静止AMV产品仅限于中低纬度地区。在产生的间隙区域,目前只有早轨Metop红外amv提供有限的覆盖范围。本文介绍了一种基于光流的大气运动矢量反演方法,该方法利用NOAA-20/21先进技术微波测深仪(ATMS) 183.31 GHz微波水汽通道亮度温度的时空匹配,实现了具有晴空像素的高垂直分辨率风反演。在固定的5°× 5°特征跟踪区域(5°FTR)下,风速偏差范围为0.16 ~ 0.64 m·s−1,均方根误差(RMSE)范围为3.45 ~ 3.81 m·s−1,风向偏差始终被约束在27.4°以下。整体精度达到现有产品的误差水平。在极端风速条件下,提出了低速区3°FTR、高风速区10°FTR的混合尺度FTR优化模型。它可以将可探测风速范围从45 m·s−1扩大到70 m·s−1,每个通道的样本量增加10%以上。3°FTR的RMSE降低了0.5 m·s−1,而10°FTR在500和450 hPa时的角度偏差和标准偏差(STD)都降低了1.5°。各通道混合区域的RMSE保持在3.47 ~ 3.79 m·s−1之间,相关系数提高约10%,风向偏差与固定FTR基本一致。这种混合尺度跟踪策略能够有效地平衡空间分辨率和统计可靠性,为极轨微波AMV检索提供了一种新的技术范式。由此产生的下午轨道微波amv为NWP同化系统提供了一种新的风数据源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microwave water vapor atmospheric motion vectors retrieval from polar-orbiting satellites
Atmospheric motion vectors (AMVs) constitute one of the most critical data sources assimilated in numerical weather prediction (NWP), yet current operational wind products fall short to meet forecast requirements. This study addresses a fundamental observational gap in satellite wind retrievals. Traditional polar-orbiting satellite retrievals are limited to high latitudes, and geostationary AMV products are restricted to mid-low latitudes. In the resulting gap regions, only morning-orbit Metop infrared AMVs currently provide limited coverage. This study introduces an optical flow-based atmospheric motion vector retrieval method employing spatiotemporal matching of 183.31 GHz microwave water vapor channel brightness temperatures from NOAA-20/21 Advanced Technology Microwave Sounders (ATMS), enabling highly vertically resolved wind retrievals with clear-sky pixels. Using a fixed 5° × 5° feature tracking regions (5° FTR), the wind speed bias ranges from 0.16 to 0.64 m·s−1, the root mean square error (RMSE) ranges from 3.45 to 3.81 m·s−1, and the wind direction bias was consistently constrained below 27.4°. The overall accuracy achieves the error levels of existing products.
For extremely wind speed conditions, a hybrid-scale FTR optimization model, 3° FTR for slow wind speed region and 10° FTR for those high wind speed region, is also proposed. It can expand the detectable wind speed range from 45 m·s−1 to 70 m·s−1 with a sample size increase of over 10 % per channel. The RMSE for 3° FTR reduces by 0.5 m·s−1, while the 10° FTR achieves a 1.5° reduction in both angular deviations and their standard deviation (STD) at 500 and 450 hPa. For all the hybrid regions in all channels, the RMSE remains within 3.47–3.79 m·s−1, the correlation coefficient is enhanced by about 10 % and the wind direction bias is almost the same as that of the fixed FTR. This hybrid-scale tracking strategy can effectively balance spatial resolution and statistical reliability, and thus provides a new technical paradigm for polar-orbiting microwave AMV retrieval. The resulting afternoon-orbit microwave AMVs deliver a novel wind data source for NWP assimilation systems.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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