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Added-value of GEO-hyperspectral Infrared Radiances for Local Severe Storm Forecasts Using the Hybrid OSSE Method

Abstract

High spectral resolution (or hyperspectral) infrared (IR) sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction (NWP) models. In contrast, imagers on geostationary (GEO) satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems, such as rapidly developing local severe storms (LSS). A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature, moisture, and wind profiles that have both high vertical resolution and high temporal/spatial resolutions. In this work, the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment (OSSE) method. A hybrid OSSE is distinctively different from the traditional OSSE in that, (a) only future sensors are simulated from the nature run and (b) the forecasts can be evaluated using real observations. This avoids simulating the complicated observation characteristics of the current systems (but not the new proposed system) and allows the impact to be assessed against real observations. The Cross-track Infrared Sounder (CrIS) full spectral resolution (FSR) is assumed to be onboard a GEO for the impact studies, and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5 (ERA5) with the hyperspectral IR all-sky radiative transfer model (HIRTM). The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment. Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data. The impact studies show improved atmospheric temperature, moisture, and precipitation forecasts, along with some improvements in the wind forecasts. An added-value, consisting of an overall 5% Root Mean Square Error (RMSE) reduction, was found when a GEO CrIS-FSR is used in replacement of LEO ones indicating the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.

摘要

地球静止轨道卫星上的高光谱红外大气探测仪能够为数值天气预报提供高频次三维大气温度、湿度和风廓线信息,对于监测和预报高影响天气系统(例如快速发展的局地强风暴系统)至关重要。本文提出了混合观测系统模拟试验(OSSE)方法,并结合静止卫星上的高光谱红外大气探测仪数据进行模拟和影响试验。与传统的OSSE相比,混合OSSE只需要对未来卫星观测系统进行模拟,既避免了模拟现有观测系统的复杂性,又可以利用实际的观测数据对预报结果进行验证和评估。采用欧洲中期天气预报中心的ERA5再分析数据作为高时空分辨率大气场景来模拟静止气象卫星上的高光谱红外大气探测仪观测结果,通过对两个典型的强风暴天气过程进行模拟和影响试验,发现在现有观测系统的基础上加入静止卫星高光谱红外大气探测仪的观测数据可以进一步改善大气温度、湿度、风场、降水等预报,总体上降低5%左右的均方根误差。这表明基于静止卫星的高光谱红外大气探测仪观测,能够通过数据同化改善强风暴天气系统的预报。

References

  • Adam, S., A. Behrendt, T. Schwitalla, E. Hammann, and V. Wulfmeyer, 2016: First assimilation of temperature lidar data into an NWP model: Impact on the simulation of the temperature field, inversion strength and PBL depth. Quart. J. Roy. Meteor. Soc., 142, 2882–2896, https://doi.org/10.1002/qj.2875.

    Google Scholar 

  • Atlas, R., 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation. J. Meteor. Soc. Japan, 75, 111–130, https://doi.org/10.2151/jmss1965.75.1B_111.

    Google Scholar 

  • Atlas, R., L. Bucci, B. Annane, R. Hoffman, and S. Murillo, 2015: Observing system simulation experiments to assess the potential impact of new observing systems on hurricane forecasting. Marine Technology Society Journal, 49, 140–148, https://doi.org/10.4031/MTSJ.49.6.3.

    Google Scholar 

  • Bachmann, K., C. Keil, and M. Weissmann, 2018: Impact of radar data assimilation and orography on predictability of deep convection. Quart. J. Roy. Meteor. Soc., 145, 117–130, https://doi.org/10.1002/qj.3412.

    Google Scholar 

  • Balogh, W., and T. Kurino, 2020: The world meteorological organization and space-based observations for weather, climate, water and related environmental services. Space Capacity Building in the XXI Century, S. Ferretti, Ed., Springer, 223–232.

  • Bauer, P., and Coauthors, 2011: Satellite cloud and precipitation assimilation at operational NWP centres. Quart. J. Roy. Meteor. Soc., 137, 1934–1951, https://doi.org/10.1002/qj.905.

    Google Scholar 

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The rapid refresh. Mon. Wea. Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1.

    Google Scholar 

  • Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009.

    Google Scholar 

  • Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 2033–2056, https://doi.org/10.1175/1520-0469(2002)059<2033:IOPAWW>2.0.CO;2.

    Google Scholar 

  • Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239–250, https://doi.org/10.1002/qj.366.

    Google Scholar 

  • Chen, Y., Y. Han, P. Van Delst, and F. Z. Weng, 2010: On water vapor Jacobian in fast radiative transfer model. J. Geophys. Res., 115, D12303, https://doi.org/10.1029/2009JD013379.

    Google Scholar 

  • Chen, Y., Y. Han, and F. Z. Weng, 2012: Comparison of two transmittance algorithms in the community radiative transfer model: Application to AVHRR. J. Geophys. Res., 117, D06206, https://doi.org/10.1029/2011JD016656.

    Google Scholar 

  • Chen, Y., Y. Han, and F. Z. Weng, 2013: Detection of earth-rotation Doppler shift from suomi national polar-orbiting partnership cross-track infrared sounder. Appl. Opt., 52, 6250–6257, https://doi.org/10.1364/AO.52.006250.

    Google Scholar 

  • Cucurull, L., R. A. Anthes, and L.-L. Tsao, 2014: Radio occultation observations as anchor observations in numerical weather prediction models and associated reduction of bias corrections in microwave and infrared satellite observations. J. Atmos. Oceanic Technol., 31, 20–32, https://doi.org/10.1175/JTECH-D-13-00059.1.

    Google Scholar 

  • Eicker, A., L. Jensen, V. Wöhnke, H. Dobslaw, A. Kvas, T. Mayer-Gürr, and D. Robert, 2020: Daily GRACE satellite data evaluate short-term hydro-meteorological fluxes from global atmospheric reanalyses. Scientific Reports, 10, 4504, https://doi.org/10.1038/s41598-020-61166-0.

    Google Scholar 

  • Errico, R. M., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78, 2577–2592, https://doi.org/10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2.

    Google Scholar 

  • Errico, R. M., T. Vukicevic, P. Courtier, J. Derber, and J. F. Louis, 1993a: Workshop on adjoint applications in dynamic meteorology 23–28 August 1992, pacific grove, California. Bull. Amer. Meteor. Soc., 74, 845–847.

    Google Scholar 

  • Errico, R. M., T. VukiĆEviĆ, and K. Raeder, 1993b: Examination of the accuracy of a tangent linear model. Tellus A: Dynamic Meteorology and Oceanography, 45, 462–477, https://doi.org/10.3402/tellusa.v45i5.15046.

    Google Scholar 

  • Garand, L., M. Buehner, S. Heilliette, S. R. Macpherson, and A. Beaulne, 2013: Satellite radiance assimilation impact in new Canadian ensemble-variational system. Proc. EUMETSAT Meteorological Satellite Conf., Vienna, Austria, EUMETSAT.

    Google Scholar 

  • Geer, A. J., and Coauthors, 2018: All-sky satellite data assimilation at operational weather forecasting centres. Quart. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202.

    Google Scholar 

  • Graham, R. J., S. R. Anderson, and M. J. Bader, 2000: The relative utility of current observation systems to global-scale NWP forecasts. Quart. J. Roy. Meteor. Soc., 126, 2435–2460, https://doi.org/10.1002/qj.49712656805.

    Google Scholar 

  • Han, H., J. Li, M. Goldberg, P. Wang, J. L. Li, Z. L. Li, B.-J. Sohn, and J. Li, 2016a: Microwave sounder cloud detection using a collocated high-resolution imager and its impact on radiance assimilation in tropical cyclone forecasts. Mon. Wea. Rev., 144, 3927–3959, https://doi.org/10.1175/MWR-D-15-0300.1.

    Google Scholar 

  • Han, Y., P. van Delst, Q. H. Liu, F. Z. Weng, B. H. Yan, R. Treadon, and J. Derber, 2006b: JCSDA community radiative transfer model (CRTM): Version 1. NOAA Tech. Rep. 122.

  • Hersbach, H., and D. Dee, 2017: ERA5 reanalysis is in production. ECMWF Newsletter 147, ECMWF.

  • Hilton, F., N. C. Atkinson, S. J. English, and J. R. Eyre, 2009: Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments. Quart. J. Roy. Meteor. Soc., 135, 495–505, https://doi.org/10.1002/qj.379.

    Google Scholar 

  • Hoffman, R. N., and R. Atlas, 2016: Future observing system simulation experiments. Bull. Amer. Meteor. Soc., 97, 1601–1616, https://doi.org/10.1175/BAMS-D-15-00200.1.

    Google Scholar 

  • Hooker, J., G. Duveiller, and A. Cescatti, 2018: A global dataset of air temperature derived from satellite remote sensing and weather stations. Scientific Data, 5, 180246, https://doi.org/10.1038/sdata.2018.246.

    Google Scholar 

  • Hu, M., G. Q. Ge, C. H. Zhou, D. Stark, H. Shao, K. Newman, J. Beck, and X. Zhang, 2018: Gridpoint Statistical Interpolation (GSI): User’s guide version 3.7. Development Testbed Center.

  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

  • Jones, T. A., S. Koch, and Z. L. Li, 2017: Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts. Atmospheric Research, 186, 9–25, https://doi.org/10.1016/j.atmosres.2016.11.004.

    Google Scholar 

  • Joo, S., J. Eyre, and R. Marriott, 2013: The impact of MetOp and other satellite data within the met office global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev., 141, 3331–3342, https://doi.org/10.1175/MWR-D-12-00232.1.

    Google Scholar 

  • Kalinga, O. A., and T. Y. Gan, 2010: Estimation of rainfall from infrared-microwave satellite data for basin-scale hydrologic modelling. Hydrological Processes, 24, 2068–2086, https://doi.org/10.1002/hyp.7626.

    Google Scholar 

  • Kazumori, M., 2018: Assimilation of Himawari-8 clear sky radiance data in JMA’s global and mesoscale NWP systems. J. Meteor. Soc. Japan, 96B, 173–192, https://doi.org/10.2151/jmsj.2018-037.

    Google Scholar 

  • Lee, J.-R., J. Li, Z. L. Li, P. Wang, and J. L. Li, 2018: ABI water vapor radiance assimilation in a regional NWP model by accounting for the surface impact. Earth and Space Science, 6, 1652–1666, https://doi.org/10.1029/2019EA000711.

    Google Scholar 

  • Li, J., J. L. Li, J. Otkin, T. J. Schmit, and C.-Y. Liu, 2011: Warning information in a preconvection environment from the geostationary advanced infrared sounding system - A simulation study using the IHOP case. J. Appl. Meteor. Climatol., 50, 776–783, https://doi.org/10.1175/2010JAMC2441.1.

    Google Scholar 

  • Li, J., C.-Y. Liu, P. Zhang, and T. J. Schmit, 2012: Applications of full spatial resolution space-based advanced infrared soundings in the preconvection environment. Wea. Forecasting, 27, 515–524, https://doi.org/10.1175/WAF-D-10-05057.1.

    Google Scholar 

  • Li, J., P. Wang, H. J. Han, J. L. Li, and J. Zheng, 2016: On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models. Journal of Meteorological Research, 30, 169–182, https://doi.org/10.1007/s13351-016-5114-2.

    Google Scholar 

  • Li, J., Z. L. Li, P. Wang, T. J. Schmit, W. G. Bai, and R. Atlas, 2017: An efficient radiative transfer model for hyperspectral IR radiance simulation and applications under cloudy — sky conditions. J. Geophys. Res., 122, 7600–7613, https://doi.org/10.1002/2016JD026273.

    Google Scholar 

  • Li, J. L., J. Li, C. Velden, P. Wang, T. J. Schmit, and J. Sippel, 2020: Impact of rapid-scan-based dynamical information from GOES-16 on HWRF hurricane forecasts. J. Geophys. Res., 125, e2019JD031647, https://doi.org/10.1029/2019JD031647.

    Google Scholar 

  • Li, Z. L., and Coauthors, 2018: Value-added impact of geostationary hyperspectral infrared sounders on local severe storm forecasts — via a quick regional OSSE. Advances in Atmospheric Sciences, 35, 1217–1230, https://doi.org/10.1007/s00376-018-8036-3.

    Google Scholar 

  • Lin, H. D., S. S. Weygandt, A. H. N. Lim, M. Hu, J. M. Brown, and S. G. Benjamin, 2017: Radiance preprocessing for assimilation in the hourly updating rapid refresh mesoscale model: A study using AIRS data. Wea. Forecasting, 32, 1781–1800, https://doi.org/10.1175/WAF-D-17-0028.1.

    Google Scholar 

  • Lopez, P., and P. Bauer, 2007: “1D+4DVAR” assimilation of NCEP stage-IV radar and gauge hourly precipitation data at ECMWF. Mon. Wea. Rev., 135, 2506–2524, https://doi.org/10.1175/MWR3409.1.

    Google Scholar 

  • Ma, Z., Z. Li, J. Li, T. J. Schmit, L. Cucurull, R. Atlas, and B. Sun, 2020: Enhance low level temperature and moisture profiles through combining NUCAPS, ABI and surface data. Submitted to Earth and Space Sciences.

  • Ma, Z. Z., E. S. Maddy, B. L. Zhang, T. Zhu, and S. A. Boukabara, 2017: Impact assessment of Himawari-8, AHI data assimilation in NCEP GDAS/GFS with GSI. J. Atmos. Oceanic Technol., 34, 797–815, https://doi.org/10.1175/JTECH-D-16-0136.1.

    Google Scholar 

  • Menzel, W. P., T. J. Schmit, P. Zhang, and J. Li, 2018: Satellite-based atmospheric infrared sounder development and applications. Bull. Amer. Meteor. Soc., 2018, 99, 583–603, https://doi.org/10.1175/BAMS-D-16-0293.1.

    Google Scholar 

  • Okamoto, K., and Coauthors, 2020: Assessment of the potential impact of a hyperspectral infrared sounder on the Himawari follow-on geostationary satellite. SOLA, 16, 162–168, https://doi.org/10.2151/sola.2020-028.

    Google Scholar 

  • Pangaud, T., N. Fourrie, V. Guidard, M. Dahoui, and F. Rabier, 2009: Assimilation of AIRS radiances affected by mid- to low-level clouds. Mon. Wea. Rev., 137, 4276–4292, https://doi.org/10.1175/2009MWR3020.1.

    Google Scholar 

  • Pavelin, E. G., S. J. English, and J. R. Eyre, 2008: The assimilation of cloud-affected infrared satellite radiances for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 134, 737–749, https://doi.org/10.1002/qj.243.

    Google Scholar 

  • Reen, B. P., and R. E. Dumais, 2018: Assimilation of aircraft observations in high-resolution mesoscale modeling. Advances in Meteorology, 2018, 8912943, https://doi.org/10.1155/2018/8912943.

    Google Scholar 

  • Schmit, T. J., and Coauthors, 2019: Legacy atmospheric profiles and derived products from GOES-16: Validation and applications. Earth and Space Science, 6, 1730–1748, https://doi.org/10.1029/2019EA000729.

    Google Scholar 

  • Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation advanced baseline imager on GOES-R. Bull. Amer. Meteor. Soc., 86, 1079–1096, https://doi.org/10.1175/BAMS-86-8-1079.

    Google Scholar 

  • Schmit, T. T., J. Li, S. A. Ackerman, and J. J. Gurka, 2009: High-spectral-and high-temporal-resolution infrared measurements from geostationary orbit. J. Atmos. Oceanic Technol., 26, 2273–2292, https://doi.org/10.1175/2009JTECHA1248.1.

    Google Scholar 

  • Seo, D. J., and J. P. Breidenbach, 2002: Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J. Hydrometeorology, 3, 93–111, https://doi.org/10.1175/1525-7541(2002)003<0093:RTCOSN>2.0.CO;2.

    Google Scholar 

  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 1427–1440, https://doi.org/10.1175/BAMS-D-13-00245.1.

    Google Scholar 

  • Stettner, D., C. Velden, R. Rabin, S. Wanzong, J. Daniels, and W. Bresky, 2019: Development of enhanced vortex-scale atmospheric motion vectors for hurricane applications. Remote Sensing, 11, 1981, https://doi.org/10.3390/rs11171981.

    Google Scholar 

  • Stith, J. L., and Coauthors, 2018: 100 years of progress in atmospheric observing systems. Meteor. Monogr., 59, 2.1–2.55, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0006.1.

    Google Scholar 

  • Taylor, J. K., H. E. Revercomb, and D. C. Tobin, 2018: An analysis and correction of polarization induced calibration errors for the cross-track infrared sounder (CrIS) sensor. Proc. Light, Energy and the Environment 2018, Washington, DC, Optical Society of America.

  • Wang, P., J. Li, J. L. Li, Z. L. Li, T. J. Schmit, and W. G. Bai, 2014: Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP. Geophys. Res. Lett., 41, 1773–1780, https://doi.org/10.1002/2013GL059067.

    Google Scholar 

  • Wang, P., J. Li, Z. L. Li, A. H. N. Lim, J. L. Li, T. J. Schmit, and M. D. Goldberg, 2017: The impact of cross — track infrared sounder (CrIS) cloud — cleared radiances on hurricane Joaquin (2015) and Matthew (2016) forecasts. J. Geophys. Res., 122, 13 201–13 218, https://doi.org/10.1002/2017JD027515.

    Google Scholar 

  • Wang, P., J. Li, Z. Li, A. H. N. Lim, J. Li, and M. D. Goldberg, 2019: Impacts of observation errors on hurricane forecasts when assimilating hyperspectral infrared sounder radiances in partially cloudy skies. J. Geophys. Res., 124, 10 802–10 813. https://doi.org/10.1029/2019JD031029

    Google Scholar 

  • Wang, P., J. Li, and T. J. Schmit, 2020: The impact of low latency satellite sounder observations on local severe storm forecasts in regional NWP. Sensors, 20(3), 650. https://doi.org/10.3390/s20030650

    Google Scholar 

  • Xue, Y. H., J. Li, Z. L. Li, R. Y. Lu, M. M. Gunshor, S. L. Moeller, D. Di, and T. J. Schmit, 2020a: Assessment of upper tropospheric water vapor monthly variation in reanalyses with near-global homogenized 6.5-µm radiances from geostationary satellites. J. Geophys. Res., 125, e2020JD032695, https://doi.org/10.1029/2020JD032695.

    Google Scholar 

  • Xue, Y. H., J. Li, Z. L. Li, M. M. Gunshor, and T. J. Schmit, 2020b: Evaluation of the diurnal variation of upper tropospheric humidity in reanalysis using homogenized observed radiances from international geostationary weather satellites. Remote Sensing, 12, 1628, https://doi.org/10.3390/rs12101628.

    Google Scholar 

  • Yang, J., Z. Q. Zhang, C. Y. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteor. Soc., 98, 1637–1658, https://doi.org/10.1175/BAMS-D-16-0065.1.

    Google Scholar 

  • Yin, R. Y., W. Han, Z. Q. Gao, and D. Di, 2020: The evaluation of FY4A’s geostationary interferometric infrared sounder (GIIRS) long-wave temperature sounding channels using the GRAPES global 4D-Var. Qourt. J. Roy. Meteor. Soc., 146, 1459–1476, https://doi.org/10.1002/qj.3746.

    Google Scholar 

  • Zheng, J., J. Li, T. J. Schmit, J. L. Li, and Z. Q. Liu, 2015: The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011). Advances in Atmospheric Sciences, 32, 319–335, https://doi.org/10.1007/s00376-014-3162-z.

    Google Scholar 

  • Zhou, L. H., M. Divakarla, X. P. Liu, A. Layns, and M. Goldberg, 2019: An overview of the science performances and calibration/validation of joint polar satellite system operational products. Remote Sensing, 11, 698, https://doi.org/10.3390/rs11060698.

    Google Scholar 

  • Zhu, Y. Q., J. Derber, A. Collard, D. Dee, R. Treadon, G. Gayno, and J. A. Jung, 2014: Enhanced radiance bias correction in the National Centers for Environmental Prediction’s Grid-point Statistical Interpolation data assimilation system. Quart. J. Roy. Meteor. Soc., 240, 1479–1492, https://doi.org/10.1002/qj.2233.

    Google Scholar 

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Acknowledgements

This work is supported by the NOAA GeoXO program (NA15NES4320001). The view, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration’s or U.S. government’s position, policy, or decision. Thanks to the JCSDA (Joint Center for Satellite Data Assimilation) for providing the “S4” supercomputer (Supercomputer for Satellite Simulations and Data Assimilation) physically located at SSEC at the University of Wisconsin-Madison as the main computational resource for this research study.

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Article Highlights

• The added-value from a GEO-hyperspectral IR sounder is studied by using a hybrid OSSE method.

• The hybrid OSSE system can be used to evaluate the simulated GEO CrIS-FSR data by verifying the simulated LEO CrIS-FSR compared to the real CrIS-FSR.

• The assimilation of GEO-hyperspectral IR data improves atmospheric temperature, moisture, wind, and precipitation forecasts.

• An overall 5% RMSE reduction was found from using a GEO hyperspectral IR sounder on the atmospheric variables.

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Wang, P., Li, Z., Li, J. et al. Added-value of GEO-hyperspectral Infrared Radiances for Local Severe Storm Forecasts Using the Hybrid OSSE Method. Adv. Atmos. Sci. 38, 1315–1333 (2021). https://doi.org/10.1007/s00376-021-0443-1

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Key words

  • GEO hyperspectral IR
  • hybrid OSSE
  • satellite data assimilation

关键词

  • 静止卫星
  • 高光谱红外探测仪
  • 混合OSSE试验
  • 数据同化