ANALYSIS OF THE PARALLAX CHARACTERISTICS OF GEOSTATIONARY-ORBITING MICROWAVE SOUNDER
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摘要: 现有的极轨微波仪器有着较强的穿云能力,空间分辨率较低,故目前而言,国内外鲜有针对微波探测器的视差分析研究,但若未来在静止轨道上搭载空间分辨率较高的静止轨道微波探测器,则微波视场内的视差问题不容忽视。针对未来发展的静止轨道卫星微波探测器,利用辐射传输模式CRTM模拟该类仪器的亮温,并分析其受视场内云影响所产生的视场偏差,结果表明空间分辨率越高,视差问题越明显;天顶角越大,云顶高度越高,视差越大。除此之外,以台风和高云的个例对实际情况中微波探测仪内视差误差进行了展示,说明静止轨道微波仪器尽管有较高的时间分辨率,但是因为视差的存在,仍然会对台风预警的准确性产生影响。Abstract: The existing polar-orbit microwave instruments have strong cloud penetrating ability and low spatial resolution, resulting in little research on the parallax issue of microwave detectors. However, if the microwave sounder with high spatial resolution can be carried onboard the geostationary orbit in the future, its effect on the microwave field of view cannot be ignored any more. In this paper, we use the Community Radiative Transfer Model (CRTM) to simulate the brightness temperature (BT) of geostationary satellite microwave detectors, and analyze the BT deviation caused by the cloud in the field of view. The results are shown as follows. The higher the spatial resolution is, the more obvious the parallax will be. The parallax increases with the increase of the zenith angle or the cloud-top height. In addition, taking typhoons and high clouds as examples, this paper presents the parallax of microwave detectors in the actual situation, which suggests that although the geostationary orbit microwave instrument has a high time resolution, the existence of the parallax will still affect the accuracy of typhoon warning.
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Key words:
- geostationary orbit /
- microwave sounder /
- parallax
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图 4 同图 3,但为通道16
图 5 同图 3,但为通道18
表 1 ATMS仪器通道设置情况介绍
通道 编号 中心频率/GHz 星下点分辨率/km 温度探测通道 1 23.80(低频窗区通道) 75 2 31.40(低频窗区通道) 75 3 50.30(低频窗区通道) 32 4 51.76 32 5 52.80 32 6 53.59±0.12 32 7 54.40 32 8 54.94 32 9 55.50 32 10 f0=57.29 32 11 f0±0.32±0.22 32 12 f0±0.32±0.05 32 13 f0±0.32±0.02 32 14 f0±0.32±0.01 32 15 f0±0.32±0.01 32 16 88.20(高频窗区通道) 32 17 165.50(高频窗区通道) 16 湿度探测通道 18 183.31±7.00 16 19 183.31±4.50 16 20 183.31±3.00 16 21 183.31±1.80 16 22 183.31±1.00 16 -
[1] LI W W, ZHANG F, SHI Y N, et al. An efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres[J]. Optics Express, 2020, 28: 25 730-25 749. [2] 邱金桓, 郑斯平, 黄其荣, 等. 北京地区对流层中上部云和气溶胶的激光雷达探测[J]. 大气科学, 2003, 27(1): 1-7. [3] SALLY A M, JAMES H M, THOMAS P A, et al. Effect of clouds on the calculated vertical distribution of shortwave absorption in the tropics[J]. J Geophy Res, 2008, 113: D18203. [4] YANG J, ZHANG Z Q, WEI C Y, et al. Introducing the new generation of Chinese geostationary weather satellites-FengYun 4 (FY-4)[J]. Bull Amer Meteor Soc, 2017, 98(8): 1 637-1 658. [5] SCHMIT T J, LI J, ACKERMAN S, et al. High-Spectral- and High-Temporal-Resolution Infrared Measurements from Geostationary Orbit [J]. J Atmos and Oceanic Tech, 2009, 26(11): 2 273-2 292. [6] 董佩明, 刘健文, 刘桂青, 等. ATMS卫星资料的同化应用及与AMSUA/MHS的比较研究[J]. 热带气象学报, 2014, 30(4): 623-632. [7] 卢乃锰, 谷松岩. 静止轨道微波大气探测的技术现状与发展展望[J]. 气象科技进展, 2016, 6(1): 120-123. [8] PINORI S, BAORDO F, MEDAGLIA C M, et al. On the potential of sub-mm passive MW observations from geostationary satellites to retrieve heavy precipitation over the Mediterranean Area[J]. Advances in Geosciences, 2006, 7: 387-394. [9] ZHANG Z. Introduction of FY-4 product and ground application system[R]. Application of New Detection Technique and Data in Heavy Rain Seminar, 2011. [10] 李晓坤, 王淦泉, 陈桂林. 风云四号气象卫星扫描成像仪——可见光通道星敏感[J]. 科学技术与工程, 2007(6): 993-996, 1003. [11] YANG J, China's FengYun meteorological satellite programs[R]. 6th GOES users' conference. Madison, WI, 2011. [12] 杨忠东, 卢乃锰, 施进明, 等. 风云三号卫星有效载荷与地面应用系统概述[J]. 气象科技进展, 2013, 3(4): 6-12. [13] 袁金南, 蒙伟光, 张艳霞, 等. AMSU-A温度反演及其对南海热带低压暖心的分析[J]. 热带气象学报, 2013, 29(6): 889-898. [14] 王新, 方翔, 邱红, 等. 应用AMSU B微波资料分析0509号Matsa台风水汽场分布特征[J]. 气象, 2009, 35(12): 30-36. [15] CHEN Y, WENG F Z, HAN Y, et al. Validation of the community radiative transfer model by using CloudSat data[J]. J Geophy Res Atmos, 2008, 113: D00A03. [16] LIANG X M, ALEXANDER I, YURY K. Implementation of the Community Radiative Transfer Model (CRTM) in Advanced Clear-Sky Processor for Oceans (ACSPO) and validation against nighttime AVHRR radiances[J]. J Geophy Res Atmos, 2009, 114: D06112. [17] MIN M, WU C Q, LI C, et al. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series[J]. J Meteor Res, 2017, 31(4): 708-719. [18] MIN M, LI J, WANG F, et al. Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms[J]. Remote Sensing of Environment, 2020, 239(11): 111616. [19] 李五生, 王洪庆, 吴琼, 等. 静止卫星云图的云位置偏差及其几何校正[J]. 北京大学学报(自然科学版), 2012, 48(5): 732-736. [20] 赵敏, 张华, 王海波, 等. 2000-2018年东亚地区云顶高度的时空变化特征[J]. 气候变化研究进展, 2020, 16(5): 591-599.