地球科学进展 ›› 2025, Vol. 40 ›› Issue (5): 487 -499. doi: 10.11867/j.issn.1001-8166.2025.032

综述与评述 上一篇    下一篇

星载红外高光谱资料同化中晴空判识技术的发展
马刚1(), 黄静1, 巩欣亚2, 尹若莹1, 张华1, 杨宗儒1,3, 龚建东1   
  1. 1.中国气象局地球系统数值预报中心,北京 100081
    2.国家卫星气象中心,北京 100081
    3.河海大学 海洋学院,江苏 南京 210024
  • 收稿日期:2025-02-17 修回日期:2025-04-22 出版日期:2025-05-10
  • 基金资助:
    国家自然科学基金项目(42475170)

Development of Clear Sky Channel Identification Techniques in Satellite Infrared Hyperspectral Data Assimilation

Gang MA1(), Jing HUANG1, Xinya GONG2, Ruoying YIN1, Hua ZHANG1, Zongru YANG1,3, Jiandong GONG1   

  1. 1.Earth System Modeling and Prediction Center, China Meteorological Administration, Beijing 100081, China
    2.National Satellite Meteorological Center, Beijing 100081, China
    3.College of Oceanography, Hohai University, Nanjing 210024, China
  • Received:2025-02-17 Revised:2025-04-22 Online:2025-05-10 Published:2025-07-10
  • About author:MA Gang, research areas include application fields of meteorological satellite data. E-mail:magang@cma.gov.cn
  • Supported by:
    the National Natural Science Foundation of China(42475170)

星载红外高光谱资料在数值预报业务中具有关键作用。鉴于红外光谱辐射易受云层强烈干扰,星载红外高光谱通道的晴空判识已成为资料同化中不可或缺的核心技术环节。以国外HIRS、AIRS、IASI和CrIS等探测器数据以及我国HIRAS和GIIRS资料为研究对象,系统梳理了星载红外大气探测资料应用中晴空像元/通道判识方法的发展脉络,归纳总结了多类晴空判识技术,主要包括:基于单一光谱特征与跨光谱特征的晴空通道判识、基于主成分分析与机器学习的晴空像元判识,以及我国风云卫星红外高光谱资料同化过程中自主研发的晴空通道判识方法。研究表明,三维晴空判识方法通过有效同化云顶以上的红外高光谱晴空资料,已成为提升资料同化效能的关键技术;在高光谱探测器像元尺度上,基于跨光谱资料反演云参数的匹配判识方法,相较于高光谱资料自身三维晴空判识,在多相态云环境中表现出更高的判识精度;此外,该方法还为红外高光谱资料的机器学习晴空像元判识和全天空同化提供了足够精确的先验样本。结合当前研究进展与业务应用现状,针对全天空、全光谱星载红外高光谱资料同化中晴空判识技术面临的挑战,提出以跨光谱匹配算法构建的晴空通道判识样本为基础,进一步发展融合机器学习的三维晴空通道判识方法,这将成为星载红外高光谱资料同化技术的重要发展方向,并为逐步增强其在数值预报中的应用价值提供更有力的技术支撑。

Spaceborne infrared hyperspectral data have become a cornerstone of modern Numerical Weather Prediction (NWP) systems, enabling high-resolution atmospheric profiling and improved forecast accuracy. However, the utility of these data is significantly constrained by cloud interference, as infrared spectral radiation is strongly attenuated or scattered by cloud particles. Consequently, clear-sky identification—specifically, the discrimination of cloud-free pixels and channels—has become an essential preprocessing step in data assimilation, ensuring that only reliable observations are integrated into NWP models. This review provides a systematic overview of the evolution of clear-sky identification methods for spaceborne infrared atmospheric sounding data, covering both foreign and domestic sensor systems. It critically evaluates techniques applied to datasets from prominent foreign missions, such as the High-Resolution Infrared Sounder (HIRS), Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS), alongside domestic advancements using the Hyperspectral Infrared Radiation Sounder (HIRAS) and the Global Infrared Imager and Interferometer Sounder (GIIRS) aboard China’s Fengyun satellites. The methodologies are categorized into three distinct technological frameworks: Spectral Feature-Based Approaches:①Early techniques rely on single-spectral thresholding, flagging channels as clear-sky based on predefined radiance thresholds sensitive to cloud absorption or emission. More advanced variants employ cross-spectral consistency checks, leveraging the spectral dependence of cloud properties across multiple wavelength bands to enhance discrimination accuracy. For example, IASI’s cloud-clearing algorithm combines shortwave and longwave infrared channels to identify consistent clear-sky signatures.②Data-Driven and Machine Learning Techniques: Principal Component Analysis (PCA) has been widely applied to reduce the dimensionality of hyperspectral datasets, enabling the extraction of latent variables that distinguish clear-sky from cloudy conditions. More recently, machine learning models—including random forests, support vector machines, and deep neural networks—have demonstrated superior performance in pixel-level clear-sky classification. These models learn complex nonlinear relationships between spectral features and cloud states, achieving higher precision in heterogeneous cloud environments. For instance, AIRS has adopted neural networks to improve clear-sky identification in regions with thin cirrus clouds.③Domestic Innovations in Assimilation Systems: China’s Fengyun satellite program has developed specialized clear-sky identification schemes tailored to the HIRAS and GIIRS instruments. These methods integrate physical constraints from radiative transfer models with statistical learning, optimizing clear-sky channel selection for regional NWP models over the Tibetan Plateau and monsoon-affected areas. Such innovations have significantly enhanced the utilization of domestic hyperspectral data in operational assimilation systems. The review highlights two transformative technologies:①Three-Dimensional (3D) Clear-Sky Identification. By incorporating vertical atmospheric structure from NWP model forecasts, 3D methods enable the assimilation of clear-sky data above cloud tops, extending the utility of hyperspectral observations in partially cloudy conditions. This approach has been shown to improve upper-tropospheric humidity analysis in Arctic NWP systems.②Cross-Spectral Matching with Cloud Parameter Inversion: At the pixel scale, matching hyperspectral observations with cloud properties derived from complementary sensors (e.g., microwave radiometers or visible imagers) has proven particularly effective in multi-phase cloud environments. Compared to standalone 3D methods, this hybrid approach achieves a 15%~20% improvement in clear-sky identification accuracy over ice-water mixed clouds, as demonstrated in CrIS data applications. The review identifies key challenges in all-sky and full-spectrum assimilation, including the handling of sub-pixel cloud heterogeneity, spectral bias in multi-sensor datasets, and computational scalability for real-time operations. To address these, a novel framework is proposed: a machine learning-enhanced 3D clear-sky identification model, trained on cross-spectral matching datasets. By fusing physical radiative transfer principles with data-driven learning, this approach promises to unlock the full potential of spaceborne infrared hyperspectral data, offering robust technical support for next-generation NWP systems and advancing global weather forecasting capabilities.

中图分类号: 

表1 探测器探测参数
Table 1 Detector detection parameters
[1] LI J, GEER A J, OKAMOTO K, et al. Satellite all-sky infrared radiance assimilation: recent progress and future perspectives[J]. Advances in Atmospheric Sciences202239(1): 9-21.
[2] EYRE J R, BELL W, COTTON J, et al. Assimilation of satellite data in numerical weather prediction. Part II: recent years[J]. Quarterly Journal of the Royal Meteorological Society2022148(743): 521-556.
[3] SMITH W L, WOOLF H M, JACOB W J. A regression method for obtaining real-time temperature and geopotential height profiles from satellite spectrometer measurements and its application to Nimbus 3 “sirs” observations[J]. Monthly Weather Review197098(8): 582-603.
[4] SMITH W L, WOOLF H M, HAYDEN C M, et al. The Tiros-N operational vertical sounder[Z]. Bulletin of the American Meteorological Society, 1979.
[5] CHAHINE M T, PAGANO T S, AUMANN H H, et al. AIRS: improving weather forecasting and providing new data on greenhouse gases[J]. Bulletin of the American Meteorological Society200687(7). DOI:10.1175/BAMS-87-7-911 .
[6] LERNER J A, WEISZ E, KIRCHENGAST G. Temperature and humidity retrieval from simulated Infrared Atmospheric Sounding Interferometer (IASI) measurements[J]. Journal of Geophysical Research: Atmospheres2002107(D14). DOI:10.1029/2001JD900254 .
[7] BLOOM H. The Cross-track Infrared Sounder (CrIS): a sensor for operational meterological remote sensing[C]// IEEE international geoscience & remote sensing symposium. IEEE, 2001. DOI:10.1109/IGARSS.2001.976838 .
[8] QI C L, WU C Q, HU X Q, et al. High spectral Infrared Atmospheric Sounder (HIRAS): system overview and on-orbit performance assessment[J]. IEEE Transactions on Geoscience and Remote Sensing202058(6): 4 335-4 352.
[9] ZHANG C M, QI C L, YANG T H, et al. Evaluation of FY-3E/HIRAS-II radiometric calibration accuracy based on OMB analysis[J]. Remote Sensing202214(13). DOI: 10.3390/rs14133222 .
[10] YANG J, ZHANG Z Q, WEI C Y, et al. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4[J]. Bulletin of the American Meteorological Society201798(8): 1 637-1 658.
[11] MENZEL W P, SCHMIT T J, ZHANG P, et al. Satellite-based atmospheric infrared sounder development and applications[J]. Bulletin of the American Meteorological Society201899(3): 583-603.
[12] CHAHINE M T. Remote sounding of cloudy atmospheres. I. the single cloud layer[J]. Journal of the Atmospheric Sciences197431(1): 233-243.
[13] CHAHINE M T. Remote sounding of cloudy atmospheres. II. multiple cloud formations[J]. Journal of the Atmospheric Sciences197734(5): 744-757.
[14] PHULPIN T, DERRIEN M, BRARD A. A two-dimensional histogram procedure to analyze cloud cover from NOAA satellite high-resolution imagery[J]. Journal of Climate and Applied Meteorology198322(8): 1 332-1 345.
[15] SAUNDERS R W, KRIEBEL K T. An improved method for detecting clear sky and cloudy radiances from AVHRR data[J]. International Journal of Remote Sensing19889(1): 123-150.
[16] STOWE L L, MCCLAIN E P, CAREY R, et al. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data[J]. Advances in Space Research199111(3): 51-54.
[17] GARAND L, WEINMAN J A. A structural-stochastic model for the analysis and synthesis of cloud images[J]. Journal of Climate and Applied Meteorology198625(7): 1 052-1 068.
[18] EBERT E. A pattern recognition technique for distinguishing surface and cloud types in the polar regions[J]. Journal of Climate and Applied Meteorology198726(10): 1 412-1 427.
[19] LEMON R E, BUEDE D E. The Advanced Very High Resolution Radiometer (AVHRR): instrument description and initial results[J]. Journal of Geophysical Research198085(B10): 5 173-5 186.
[20] DERRIEN M, FARKI B, HARANG L, et al. Automatic cloud detection applied to NOAA-11/AVHRR imagery[J]. Remote Sensing of Environment199346(3): 246-267.
[21] LI J, WOLF W W, MENZEL W P, et al. Global soundings of the atmosphere from ATOVS measurements: the algorithm and validation[J]. Journal of Applied Meteorology200039(8): 1 248-1 268.
[22] SMITH W L, PLATT C M R. Comparison of satellite-deduced cloud heights with indications from radiosonde and ground-based laser measurements[J]. Journal of Applied Meteorology197817(12): 1 796-1 802.
[23] MCNALLY A P, WATTS P D. A cloud detection algorithm for high-spectral-resolution infrared sounders[J]. Quarterly Journal of the Royal Meteorological Society2003129(595): 3 411-3 423.
[24] EYRE J R, MENZEL W P. Retrieval of cloud parameters from satellite sounder data: a simulation study[J]. Journal of Applied Meteorology198928(4): 267-275.
[25] YAN X S, CHEN Y D, MA G, et al. A 3-D cloud detection method for FY-4A GIIRS and its application in operational numerical weather prediction system[J]. IEEE Transactions on Geoscience and Remote Sensing2023, 61. DOI: 10.1109/TGRS.2023.3307563 .
[26] HAN B, KANG L S, SONG H Z. A fast cloud detection approach by integration of image segmentation and support vector machine[C]// Advances in neural networks—ISNN 2006. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006: 1 210-1 215.
[27] ZHANG Q, YU Y, ZHANG W M, et al. Cloud detection from FY-4A’s geostationary interferometric infrared sounder using machine learning approaches[J]. Remote Sensing201911(24). DOI:10.3390/rs11243035 .
[28] WYLIE D P, MENZEL W P, WOOLF H M, et al. Four years of global Cirrus cloud statistics using HIRS[J]. Journal of Climate19947(12): 1 972-1 986.
[29] DERBER J C, TREADON R, VANDELST P, et al. Assimilation of advanced sounders at NCEP[Z]. ECMWF2004:57-62.
[30] MCNALLY A P. The direct assimilation of cloud-affected satellite infrared radiances in the ECMWF 4D-Var[J]. Quarterly Journal of the Royal Meteorological Society2009135(642): 1 214-1 229.
[31] LIN L, ZOU X L, WENG F Z. Combining CrIS double CO2 bands for detecting clouds located in different layers of the atmosphere[J]. Journal of Geophysical Research: Atmospheres2017122(3): 1 811-1 827.
[32] HUANG J, MA G, LIU G Q, et al. The evaluation of FY-3E hyperspectral infrared atmospheric sounder-II long-wave temperature sounding channels[J]. Remote Sensing202315(23). DOI:10.3390/rs15235525 .
[33] HUANG J, MA G, LIU G, et al. Direct assimilation of FY-3E hyperspectral infrared atmospheric sounder-II radiance data in CMA-GFS system[J]. Weather and Forecasting2025, under review.
[34] XIA X L, ZOU X L. Development of CO2 band-based cloud emission and scattering indices and their applications to FY-3D hyperspectral infrared atmospheric sounder[J]. Remote Sensing202012(24). DOI:10.3390/rs12244171 .
[35] LI J, MENZEL W P, YANG Z D, et al. High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements[J]. Journal of Applied Meteorology200342(2): 204-226.
[36] LI J, MENZEL W P, SUN F Y, et al. AIRS subpixel cloud characterization using MODIS cloud products[J]. Journal of Applied Meteorology200443(8): 1 083-1 094.
[37] RANSON K J, BHATTI D, CHERVENAK A, et al. MODIS instrument performance and calibration[J]. IEEE Transactions on Geoscience and Remote Sensing200240(3): 754-760.
[38] WANG L K, TREMBLAY D A, HAN Y, et al. Geolocation assessment for CrIS sensor data records[J]. Journal of Geophysical Research: Atmospheres2013118(22). DOI: 10.1002/2013JD020376 .
[39] WANG L K, TREMBLAY D, ZHANG B, et al. Fast and accurate collocation of the visible infrared imaging radiometer suite measurements with cross-track infrared sounder[J]. Remote Sensing20168(1). DOI: 10.3390/rs8010076 .
[40] WANG P, LI J, LI J L, et al. Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP[J]. Geophysical Research Letters201441(5): 1 773-1 780.
[41] ERESMAA R. Imager-assisted cloud detection for assimilation of Infrared Atmospheric Sounding Interferometer radiances[J]. Quarterly Journal of the Royal Meteorological Society2014140(684): 2 342-2 352.
[42] HUANG H L, SMITH W. Apperception of clouds in AIRS data[Z]. ECMWF2004: 155-170.
[43] YIN R, HAN W, GAO Z, et al. The evaluation of FY4A’s Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channels using the GRAPES global 4D-Var[M]. John Wiley & Sons, Ltd., 2020. DOI:10.1002/qj.3746 .
[44] 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]. Journal of Meteorological Research201731(4): 708-719.
[45] MATRICARDI M, MCNALLY T. The direct assimilation of IASI short wave principal component scores into the ECMWF NWP model[Z]. EUMETSAT Contract 2011. No. EUM/CO/07/4600000475/PS.
[46] WANG Liwen, MA Gang, XU Daosheng, et al. Impact of a new three-dimensional cloud detection method of FY4A GIIRS in the CMA-GFS[J].Weather and Forecasting2025. DOI:10.1175/WAF-O-24-0087.1 .
[47] HAN H, LEE S, IM J, et al. Detection of convective initiation using meteorological imager onboard communication, ocean, and meteorological satellite based on machine learning approaches[J]. Remote Sensing20157(7): 9 184-9 204.
[48] SHAO Z F, PAN Y, DIAO C Y, et al. Cloud detection in remote sensing images based on multiscale features-convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing201957(6): 4 062-4 076.
[49] YANG J Y, GUO J H, YUE H J, et al. CDnet: CNN-based cloud detection for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing201957(8): 6 195-6 211.
[50] LIU Q, XU H, SHA D X, et al. Hyperspectral infrared sounder cloud detection using deep neural network model[J]. IEEE Geoscience and Remote Sensing Letters2020, 19. DOI:10.1109/LGRS.2020.3023683 .
[51] SHI X H, FAN Y L, SUN L, et al. Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine learning application[J]. Scientific Reports202414(1). DOI:10.1038/s41598-024-78889-z .
[52] SHI H X, YU Y, ZHANG W M, et al. Cloud detection from a hyperspectral infrared atmospheric sounder using a machine-learning model[C]// 2021 international conference on Computer Information Science and Artificial Intelligence (CISAI). Kunming, China: IEEE, 2021: 107-116.
[53] GEER A J, BAUER P, LOPEZ P. Direct 4D-var assimilation of all-sky radiances. Part II: assessment[J]. Quarterly Journal of the Royal Meteorological Society2010136(652): 1 886-1 905.
[54] OKAMOTO K, ISHIBASHI T, OKABE I, et al. Extension of all-sky radiance assimilation to hyperspectral infrared sounders[J]. Quarterly Journal of the Royal Meteorological Society2024150(765): 5 472-5 497.
[55] GEER A J, MIGLIORINI S, MATRICARDI M. All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud[J]. Atmospheric Measurement Techniques201912(9): 4 903-4 929.
[56] MATRICARDI M. The inclusion of aerosols and clouds in RTIASI, the ECMWF fast radiative transfer model for the infrared atmospheric sounding interferometer[M]. ECMWF Technical Memoranda2005.
[57] BAUER P, GEER A J, LOPEZ P, et al. Direct 4D-var assimilation of all-sky radiances. Part I: implementation[J]. Quarterly Journal of the Royal Meteorological Society2010136(652): 1 868-1 885.
[58] OKAMOTO K, MCNALLY A P, BELL W. Progress towards the assimilation of all-sky infrared radiances: an evaluation of cloud effects[J]. Quarterly Journal of the Royal Meteorological Society2014140(682): 1 603-1 614.
[59] SUSSKIND J, JOINER J, CHAHINE M T. Determination of temperature and moisture profiles in a cloudy atmosphere using AIRS/AMSU[C]// High spectral resolution infrared remote sensing for Earth’s weather and climate studies. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993: 149-161.
[60] SUSSKIND J, BARNET C, BLAISDELL J. Determination of atmospheric and surface parameters from simulated AIRS/AMSU/HSB sounding data: retrieval and cloud clearing methodology[J]. Advances in Space Research199821(3): 369-384.
[61] SUSSKIND J, BARNET C D, BLAISDELL J M. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds[J]. IEEE Transactions on Geoscience and Remote Sensing200341(2): 390-409.
[62] SUSSKIND J, BARNET C, BLAISDELL J, et al. Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover[J]. Journal of Geophysical Research: Atmospheres2006111(D9). DOI:10.1029/2005JD006272 .
[63] SMITH W L. An improved method for calculating tropospheric temperature and moisture from satellite radiometer measurements[J]. Monthly Weather Review196896(6): 387-396.
[64] DONG Chaohua, LI Jun, ZHANG Peng, et al. Principle and application of satellite hyperspectral infrared atmospheric remote sensing[M]. Beijing: Science Press, 2013.
董超华, 李俊, 张鹏, 等. 卫星高光谱红外大气遥感原理和应用[M]. 北京: 科学出版社, 2013.
[65] LIU H, COLLARD A, DERBER J. Variational cloud‐clearing with CrIS data at NCEP[R]. New Orleans, LA: AMS Annual Meeting, 2016.
[66] LI J, LIU C Y, HUANG H L, et al. Optimal cloud-clearing for AIRS radiances using MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing200543(6): 1 266-1 278.
[67] WANG P, LI J, LI Z L, et al. Impacts of observation errors on hurricane forecasts when assimilating hyperspectral infrared sounder radiances in partially cloudy skies[J]. Journal of Geophysical Research: Atmospheres2019124(20): 10 802-10 813.
[68] WANG P, LI J, LI Z L, et al. The impact of Cross-track Infrared Sounder (CrIS) cloud-cleared radiances on hurricane Joaquin (2015) and Matthew (2016) forecasts[J]. Journal of Geophysical Research: Atmospheres2017122(24): 13 201-13 218.
[69] GOLDBERG M D, KING T S, WOLF W W, et al. Using MODIS with AIRS to develop an operational cloud-cleared radiance product[C]// Multispectral and hyperspectral remote sensing instruments and applications II. Honolulu, USA: SPIE, 2005.
[70] MADDY E S, KING T S, SUN H B, et al. Improved soundings using collocated imager and sounder data from MetOp-A[C]// Imaging and applied optics. Toronto: OSA, 2011. DOI:10.1364/HISE.2011.HTuA3 .
[71] LIU H X, COLLARD A, DERBER J. Comparison among three CrIS Cloud-Clearing Radiance (CCR) products & allsky SEVIRI radiance assimilation at NCEP[Z/OL]. 2017. [2024-10-20]. .
[72] REALE O, MCGRATH-SPANGLER E L, MCCARTY W, et al. Impact of adaptively thinned AIRS cloud-cleared radiances on tropical cyclone representation in a global data assimilation and forecast system[J]. Weather and Forecasting201833(4): 909-931.
[73] GONG X, LI J, LI Z, et al. Cloud-cleared radiance of geostationary hyperspectral infrared sounder based on collocated image[C]// Proceeding of 19th annual meeting of the Asia Oceania Geosciences Society (AOGS). Singapore, 2022.
[74] GONG X Y, LI Z L, LI J, et al. Cloud-cleared radiances from collocated observations of hyperspectral IR sounder and advanced imager onboard the same geostationary platform[J]. IEEE Transactions on Geoscience and Remote Sensing2024, 6. DOI:10.1109/TGRS.2024.3458093 .
[75] DI D, LI J, LI Z L, et al. Enhancing clear radiance generation for geostationary hyperspectral infrared sounder using high temporal resolution information[J]. Geophysical Research Letters202451(2). DOI:10.1029/2023GL107194 .
[76] MATRICARDI M. A principal component based version of the RTTOV fast radiative transfer model[J]. Quarterly Journal of the Royal Meteorological Society2010136(652): 1 823-1 835.
[77] BERMUDO F, ROUSSEAU S, PEQUIGNOT E, et al. IASI-NG program: a new generation of Infrared Atmospheric Sounding Interferometer[C]// 2014 IEEE geoscience and remote sensing symposium. Quebec City, Canada: IEEE, 2014: 1 373-1 376.
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