On the Assimilation of Satellite Sounder Data in Cloudy Skies in Numerical Weather Prediction Models

+ Author Affiliations + Find other works by these authors
  • Corresponding author: WANG Pei
Funds: 

Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201406011), NOAA Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) and GOES-R High Impact Weather (HIW) study programs.

DOI: 10.1007/s13351-016-5114-2

PDF

    Abstract

  • Abstract

    Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed.
    • FullText(HTML)
    • References (63)
  • Ackerman, S. A., K. I. Strabala, W. P. Menzel, et al., 1998:Discriminating clear-sky from clouds with MODIS. J. Geophys. Res., 103, 32141-32157. Bauer, P., P. Lopez, A. Benedetti, et al., 2006a:Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. I:1D-Var. Quart. J. Roy. Meteor. Soc., 132, 2277-2306.
    Bauer, P., P. Lopez, D. Salmond, et al., 2006b:Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. II:4D-Var. Quart. J. Roy. Meteor. Soc., 132, 2307-2332.
    Bauer, P., E. Moreau, F. Chevallier, et al., 2006c:Multiple-scattering microwave radiative transfer for data assimilation applications. Quart. J. Roy. Me-teor. Soc., 132, 1259-1281.
    Bauer, P., A. J. Geer, P. Lopez, et al., 2010:Direct 4D-Var assimilation of all-sky radiances. Part I:Implementation. Quart. J. Roy. Meteor. Soc., 136, 1868-1885.
    Bauer, P., T. Aulign, W. Bell, et al., 2011a:Satellite cloud and precipitation assimilation at operational NWP centres. Quart. J. Roy. Meteor. Soc., 137, 1934-1951.
    Bauer, P., G. Ohring, C. Kummerow, et al., 2011b:As-similating satellite observations of clouds and pre-cipitation into NWP models. Bull. Amer. Meteor. Soc., 92, ES25-ES28.
    Benjamin, S. G., D. Devenyi, S. S. Weygandt, et al., 2004a:An hourly assimilation-forecast cycle:The RUC. Mon. Wea. Rev., 132, 495-518.
    Benjamin, S. G., J. M. Brown, S. S. Weygandt, et al., 2004b:Assimilation of surface cloud, visibility, and current weather observations in the RUC. Preprints for 16th Conference on Numerical Weather Predic-tion, Seattle, Amer. Meteor. Soc., Boston, MA, p1.47.
    Bjerknes, V., 1911:Dynamic Meteorology and Hydrogra-phy. Part II:Kinematics Carnegie Institute. Gibson Bros, New York, USA, 4-6.
    Bormann, N., A. Fouilloux, and W. Bell, 2013:Evalua-tion and assimilation of ATMS data in the ECMWF system. J. Geophys. Res., 118, 12970-12980. Cardinali, C., 2009:Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239-250.
    Chen, Y. D., H. L. Wang, J. Z. Min, et al., 2015:Vari-ational assimilation of cloud liquid/ice water path and its impact on NWP. J. Appl. Meteor. Climatol., 54, 1809-1825.
    Cucurull, L., R. A. Anthes, and L.-L. Tsao, 2014:Ra-dio occultation observations as anchor observations in numerical weather prediction models and associ-ated reduction of bias corrections in microwave and infrared satellite observations. J. Atmos. Oceanic Technol., 31, 20-32.
    Daley, R., 1991:Atmospheric Data Analysis. Cambridge University Press, Cambridge, UK.
    Errico, R. M., P. Bauer, and J.-F. Mahfouf, 2007:Issues regarding the assimilation of cloud and precipitation data. J. Atmos. Sci., 64, 3785-3798.
    Evensen, G., 1994:Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geo-phys. Res., 99, 10143-10162.
    Eyre, J. R., and W. P. Menzel, 1989:Retrieval of cloud parameters from satellite sounder data:A simula-tion study. J. Appl. Meteor., 28, 267-275.
    Garand, L., M. Buehner, S. Heilliette, et al., 2013:Satel-lite radiance assimilation impact in new Canadian ensemble-variational syste. Proceedings of the 2013 EUMETSAT Meteorological Satellite Conference, 16-20 September, 2013, Vienna, Austria, 1-7.
    Geer, A. J., P. Bauer, and P. Lopez, 2008:Lessons learnt from the operational 1D+4D-Var assimila-tion of rain-and cloud-affected SSM/I observations at ECMWF. Quart. J. Roy. Meteor. Soc., 134, 1513-1525.
    Geer, A. J., P. Bauer, and P. Lopez, 2010:Direct 4D-Var assimilation of all-sky radiances. Part II:Assess-ment. Quart. J. Roy. Meteor. Soc., 136, 1886-1905.
    Geer, A. J., and P. Bauer, 2011:Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc., 137, 2024-2037.
    Han, H., J. Li, M. Goldberg, et al., 2015:Microwave sounder cloud detection using a collocated high res-olution imager and its impact on radiance assimila-tion in tropical cyclone forecasts. Mon. Wea. Rev., in press.
    Heilliette, S., and L. Garand, 2007:A practical approach for the assimilation of cloudy infrared radiances and its evaluation using airs simulated observations. Atmos.-Ocean, 45, 211-225.
    Huang, H.-L., and W. L. Smith, 2004:Apperception of clouds in AIRS data. Proc. ECMWF Workshop on Assimilation of High Spectral Resolution Sounder in NWP, June 28-July 1, 2004, 155-169.
    Jones, T. A., and D. J. Stensrud, 2015:Assimilating cloud water path as a function of model cloud mi-crophysics in an idealized simulation. Mon. Wea. Rev., 143, 2052-2081.
    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 sensitiv-ity method. Mon. Wea. Rev., 141, 3331-3342.
    Kalnay, E., 2003:Atmospheric Modeling, Data Assimila-tion and Predictability. Cambridge University Press, Cambridge, UK. 51-72.
    Le Marshall, J., J. Jung, J. Derber, et al., 2005:AIRS hyperspectral data improves Southern Hemisphere forecasts. Aust. Meteor. Mag., 54, 57-60.
    Le Marshall, J., J. Jung, J. Derber, et al., 2006:Improv-ing global analysis and forecasting with AIRS. Bull. Amer. Meteor. Soc., 87, 891-894.
    Li, J., and H.-L. Huang, 1999:Retrieval of atmospheric profiles from satellite sounder measurements by use of the discrepancy principle. Appl. Opt., 38, 916-923.
    Li, J., W. Wolf, W. P. Menzel, et al., 2000:Global sound-ings of the atmosphere from ATOVS measurements:The algorithm and validation. J. Appl. Meteor., 39, 1248-1268.
    Li, J., J. L. Li, E. Weize, et al., 2007:Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances. Geophys. Res. Lett., 34, L16812.
    Li, J., and J. L. Li, 2008:Derivation of global hyper-spectral resolution surface emissivity spectra from advanced infrared sounder radiance measurements. Geophys. Res. Lett., 35, L15807.
    Li, J., C. Y. Liu, H.-L. Huang, et al., 2005:Optimal cloud-clearing for AIRS radiances using MODIS. IEEE Trans. Geosci. Remote Sens., 43, 1266-1278.
    Li, J., and H. Liu, 2009:Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements. Geophys. Res. Lett., 36, L11813.
    Li, J., W. P. Menzel, and A. J. Schreiner, 2001:Vari-ational retrieval of cloud parameters from GOES sounder longwave cloudy radiance measurements. J. Appl. Meteor., 40, 312-330.
    Li, J., W. P. Menzel, F. Y. Sun, et al., 2004:AIRS subpixel cloud characterization using MODIS cloud products. J. Appl. Meteor., 43, 1083-1094.
    Liu, H., and J. Li, 2010:An improvement in forecast-ing rapid intensification of Typhoon Sinlaku (2008) using clear-sky full spatial resolution advanced IR soundings. J. Appl. Meteor. Climatol., 49, 821-287.
    Macpherson, B., B. J. Wright, W. H. Hand, et al., 1996:The impact of MOPS moisture data in the UK Me-teorological Office data assimilation scheme. Mon. Wea. Rev., 124, 1746-1766.
    McNally, A. P., 2002:A note on the occurrence of cloud in meteorologically sensitive areas and the impli-cations for advanced infrared sounders. Quart. J. Roy. Meteor. Soc., 128, 2551-2556.
    McNally, A. P., 2009:The direct assimilation of cloud-affected satellite infrared radiances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 135, 1214-1229.
    McNally, A. P., and P. D. Watts, 2003:A cloud detec-tion algorithm for high-spectral-resolution infrared sounders. Quart. J. Roy. Meteor. Soc., 129, 3411-3423.
    McNally, T., M. Bonavita, and J.-N. Thpaut, 2014:The role of satellite data in the forecasting of Hurricane Sandy. Mon. Wea. Rev., 142, 634-646.
    Moreau, E., P. Lopez, P. Bauer, et al., 2004:Variational retrieval of temperature and humidity profiles using rain rates versus microwave brightness tempera-tures. Quart. J. Roy. Meteor. Soc., 130, 827-852.
    Okamoto, K., and J. C. Derber, 2006:Assimilation of SSM/I radiances in the NCEP global data assimila-tion system. Mon. Wea. Rev., 134, 2612-2631.
    Otkin, J. A., 2010:Clear and cloudy sky infrared bright-ness temperature assimilation using an ensemble Kalman filter. J. Geophys. Res., 115, D19207.
    Pangaud, T., N. Fourrie, V. Guidard, et al., 2009:As-similation of AIRS radiances affected by mid-to low-level clouds. Mon. Wea. Rev., 137, 4276-4292.
    Pavelin, E. G., S. J. English, and J. R. Eyre, 2008:The assimilation of cloud-affected infrared satellite radi-ances for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 134, 737-749.
    Qin, Z. K., X. L. Zou, and F. Z. Weng, 2013:Evalu-ating added benefits of assimilating GOES Imager radiance data in GSI for coastal QPFs. Mon. Wea. Rev., 141, 75-92.
    Reale, O., J. Susskind, R. Rosenberg, et al., 2008:Im-proving forecast skill by assimilation of quality con-trolled AIRS temperature retrievals under partially cloudy conditions. Geophys. Res. Lett., 35, L08809.
    Renshaw, R., and P. N. Francis, 2011:Variational as-similation of cloud fraction in the operational Met Office Unified Model. Quart. J. Roy. Meteor. Soc., 137, 1963-1974.
    Smith, W. L., 1968:An improved method for calculating tropospheric temperature and moisture from satel-lite radiometer measurements. Mon. Wea. Rev., 96, 387-396.
    Smith, W. L., D. K. Zhou, H.-L. Huang, et al., 2004:Extraction of profile information from cloud con-taminated radiances. Proc. ECMWF Workshop on Assimilation of High Spectral Resolution Sounder in NWP, June 28-July 1, 2004, 145-154.
    Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003:Re-trieval of atmospheric and surface parameters from AIRS/AMSU/HSB under cloudy conditions. IEEE Trans. Geosci. Remote Sens., 41, 390-409.
    Vidot, J., 2015:Overview of the status of radiative trans-fer models for satellite data assimilation. Seminar on Use of Satellite Observations in Numerical Weather Prediction, 8-12 Sept. 2014, Shinfield Park, Read-ing, 1-14.
    Wang, P., J. Li, J. L. Li, et al., 2014:Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP. Geo-phys. Res. Lett., 41, 1773-1780.
    Wang, P., J. Li, M. D. Goldberg, et al., 2015:Assimila-tion of thermodynamic information from advanced infrared sounders under partially cloudy skies for regional NWP. J. Geophys. Res., 120, 5469-5484.
    Wei, H. L., P. Yang, J. Li, et al., 2004:Retrieval of semitransparent ice cloud optical thickness from At-mospheric Infrared Sounder (AIRS) measurements. IEEE Trans. Geosci. Remote Sensing, 42, 2254-2267.
    Wylie, D. P., W. P. Menzel, H. M. Woolf, et al., 1994:Four years of global cirrus cloud statistics using HIRS. J. Climate, 7, 1972-1986.
    Zavodsky, B. T., S. H. Chou, G. Jedlovec, et al., 2007:The impact of atmospheric infrared sounder (AIRS) profiles on short-term weather forecasts. Proc. SPIE 6565, Algorithms and Technologies for Multispec-tral, Hyperspectral, and Ultraspectral Imagery XIII, 7 May, 2007, 656561J.
    Zhang, M., M. Zupanski, M.-J. Kim, et al., 2013:Assim-ilating AMSU-A radiances in the TC core area with NOAA Operational HWRF (2011) and a hybrid data assimilation system:Danielle (2010). Mon. Wea. Rev., 141, 3889-3907.
    Zheng, J., J. Li, T. J. Schmit, et al., 2015:The impact of AIRS atmospheric temperature and moisture pro-files on hurricane forecasts:Ike (2008) and Irene (2011). Adv. Atmos. Sci., 32, 319-335.
    Zhu, Y., E. H. Liu, A. Collard, et al., 2015:As-similation of all-sky microwave radiances in the NCEP's global forecast system. American Me-teorological Society 2015, Phoenix, Arizona, 6 January, 2015.[Available online at https://ams.confex.com/ams/95Annual/webprogram/Paper 268340.html].
    Zou, X. L., Z. K. Qin, and F. Z. Weng, 2013:Improved quantitative precipitation forecasts by MHS radi-ance data assimilation with a newly added cloud detection algorithm. Mon. Wea. Rev., 141, 3203-3221.
    • Relative Articles
  • Supplements (0)

  • Cited by

    Periodical cited type(53)

    1. Miranti Indri Hastuti, Ki-Hong Min. Impact of Assimilating GK-2A All-Sky Radiance with a New Observation Error for Summer Precipitation Forecasting. Remote Sensing, 2023, 15(12): 3113. DOI:10.3390/rs15123113
    2. Qian Liu, Hui Xu, Paul R. Houser, et al. Cross-track infrared sounder cloud fraction retrieval using a deep neural network. Computers & Geosciences, 2023, 170: 105268. DOI:10.1016/j.cageo.2022.105268
    3. Kuo Zhou, Lingkun Ran, Libo Zhou, et al. The study of Fengyun4A temperature profile data assimilation in a southwest vortex heavy rainfall case. Atmospheric Research, 2023, 283: 106566. DOI:10.1016/j.atmosres.2022.106566
    4. Wei Huang, Lei Liu, Bin Yang, et al. Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm. Atmospheric Measurement Techniques, 2023, 16(17): 4101. DOI:10.5194/amt-16-4101-2023
    5. Yufen Ma, Juanjuan Liu, Ali Mamtimin, et al. Validation of FY-4A Temperature Profiles by Radiosonde Observations in Taklimakan Desert in China. Remote Sensing, 2023, 15(11): 2925. DOI:10.3390/rs15112925
    6. Christian Borger, Steffen Beirle, Thomas Wagner. A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range. Earth System Science Data, 2023, 15(7): 3023. DOI:10.5194/essd-15-3023-2023
    7. Xusheng Yan, Yaodeng Chen, Gang Ma, et al. A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1. DOI:10.1109/TGRS.2023.3307563
    8. Yufen Ma, Wei Han, Zhenglong Li, et al. Evaluation of CAMEL over the Taklimakan Desert Using Field Observations. Land, 2023, 12(6): 1232. DOI:10.3390/land12061232
    9. Mingyang Zhang, Lifeng Zhang, Bin Zhang, et al. Preliminary evaluation of all-sky radiance assimilation scheme with POD-3DEnVar method. Atmospheric Research, 2022, 275: 106198. DOI:10.1016/j.atmosres.2022.106198
    10. Kuo Zhou, Lingkun Ran, Libo Zhou, et al. The Study of Fengyun4a Temperature Profile Data Assimilation in a Southwest Vortex Heavy Rainfall Case. SSRN Electronic Journal, 2022. DOI:10.2139/ssrn.4145237
    11. Deming Meng, Yaodeng Chen, Jun Li, et al. The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017). Journal of Geophysical Research: Atmospheres, 2022, 127(19) DOI:10.1029/2022JD036910
    12. Lei Zhu, Zhiyong Meng, Yonghui Weng, et al. Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin (2015). Advances in Atmospheric Sciences, 2022, 39(11): 1859. DOI:10.1007/s00376-022-2015-4
    13. Jun Li, Alan J. Geer, Kozo Okamoto, et al. Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives. Advances in Atmospheric Sciences, 2022, 39(1): 9. DOI:10.1007/s00376-021-1088-9
    14. Deming Meng, Pei Wang, Jun Li, et al. New Observation Operators for Cloud Liquid/Ice Water Path From ABI and Their Impact on Assimilation and Hurricane Forecasts. Journal of Geophysical Research: Atmospheres, 2021, 126(10) DOI:10.1029/2020JD034164
    15. Buddhi Prakash Jangid, S. Indira Rani, M. T. Bushair, et al. The impact of hyperspectral radiances in the NCUM 4D-Var data assimilation system. International Journal of Remote Sensing, 2021, 42(16): 6284. DOI:10.1080/01431161.2021.1933244
    16. Deming Meng, Yaodeng Chen, Jun Li, et al. Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction. Monthly Weather Review, 2021. DOI:10.1175/MWR-D-20-0419.1
    17. Dongmei Xu, Zhiquan Liu, Shuiyong Fan, et al. Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China. Advances in Atmospheric Sciences, 2021, 38(4): 661. DOI:10.1007/s00376-020-0219-z
    18. Tianlei Yu, Gang Ma, Feng Lu, et al. Quality Scoring of the Fengyun 4A Clear Sky Radiance Product. Remote Sensing, 2021, 13(18): 3658. DOI:10.3390/rs13183658
    19. Pei Wang, Zhenglong Li, Jun Li, et al. Added-value of GEO-hyperspectral Infrared Radiances for Local Severe Storm Forecasts Using the Hybrid OSSE Method. Advances in Atmospheric Sciences, 2021, 38(8): 1315. DOI:10.1007/s00376-021-0443-1
    20. Feifei Shen, Dongmei Xu, Hong Li, et al. Assimilation of GPM Microwave Imager Radiance data with the WRF hybrid 3DEnVar system for the prediction of Typhoon Chan-hom (2015). Atmospheric Research, 2021, 251: 105422. DOI:10.1016/j.atmosres.2020.105422
    21. Haiqin Chen, Jidong Gao, Yunheng Wang, et al. Radar reflectivity data assimilation method based on background‐dependent hydrometeor retrieval: Comparison with direct assimilation for real cases. Quarterly Journal of the Royal Meteorological Society, 2021, 147(737): 2409. DOI:10.1002/qj.4031
    22. Di Di, Jun Li, Zhenglong Li, et al. Can Current Hyperspectral Infrared Sounders Capture the Small Scale Atmospheric Water Vapor Spatial Variations?. Geophysical Research Letters, 2021, 48(21) DOI:10.1029/2021GL095825
    23. Ruoying Yin, Wei Han, Zhiqiu Gao, et al. The evaluation of FY4A's Geostationary Interferometric Infrared Sounder (GIIRS) long‐wave temperature sounding channels using the GRAPES global 4D‐Var. Quarterly Journal of the Royal Meteorological Society, 2020, 146(728): 1459. DOI:10.1002/qj.3746
    24. Luyao Qin, Yaodeng Chen, Tianlei Yu, et al. Dynamic Channel Selection of Microwave Temperature Sounding Channels under Cloudy Conditions. Remote Sensing, 2020, 12(3): 403. DOI:10.3390/rs12030403
    25. Wanlin Kan, Peiming Dong, Zhiqing Zhang, et al. Development and application of ARMS fast transmittance model for GIIRS data. Journal of Quantitative Spectroscopy and Radiative Transfer, 2020, 251: 107025. DOI:10.1016/j.jqsrt.2020.107025
    26. Xin Li, Xiaolei Zou, Xiaoyong Zhuge, et al. Improved Himawari‐8/AHI Radiance Data Assimilation With a Double Cloud Detection Scheme. Journal of Geophysical Research: Atmospheres, 2020, 125(13) DOI:10.1029/2020JD032631
    27. Weiren Zhu, Lin Zhu, Jun Li, et al. Retrieving Volcanic Ash Top Height through Combined Polar Orbit Active and Geostationary Passive Remote Sensing Data. Remote Sensing, 2020, 12(6): 953. DOI:10.3390/rs12060953
    28. Hejun Xie, Lei Bi, Wei Han, et al. Vertical Inhomogeneity Effect of Frozen Hydrometeor Habits in All‐Sky Passive Microwave Simulations. Journal of Geophysical Research: Atmospheres, 2020, 125(21) DOI:10.1029/2020JD032817
    29. Di Di, Yunheng Xue, Jun Li, et al. Effects of CO2 Changes on Hyperspectral Infrared Radiances and Its Implications on Atmospheric Temperature Profile Retrieval and Data Assimilation in NWP. Remote Sensing, 2020, 12(15): 2401. DOI:10.3390/rs12152401
    30. Zhipeng Xian, Keyi Chen, Jiang Zhu. All‐Sky Assimilation of the MWHS‐2 Observations and Evaluation the Impacts on the Analyses and Forecasts of Binary Typhoons. Journal of Geophysical Research: Atmospheres, 2019, 124(12): 6359. DOI:10.1029/2018JD029658
    31. Jieying He, Haonan Chen. Atmospheric Retrievals and Assessment for Microwave Observations from Chinese FY-3C Satellite during Hurricane Matthew. Remote Sensing, 2019, 11(8): 896. DOI:10.3390/rs11080896
    32. Jiazheng Lu, Tao Feng, Jun Li, et al. Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato. Journal of Geophysical Research: Atmospheres, 2019, 124(6): 3050. DOI:10.1029/2018JD029364
    33. Rucong Yu, Yi Zhang, Jianjie Wang, et al. Recent Progress in Numerical Atmospheric Modeling in China. Advances in Atmospheric Sciences, 2019, 36(9): 938. DOI:10.1007/s00376-019-8203-1
    34. Sihui Fan, Wei Han, Zhiqiu Gao, et al. Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis. Remote Sensing, 2019, 11(22): 2710. DOI:10.3390/rs11222710
    35. Prashant Kumar, Munn V. Shukla. Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone. Journal of Geophysical Research: Atmospheres, 2019, 124(4): 1897. DOI:10.1029/2018JD028827
    36. Jung‐Rim Lee, Jun Li, Zhenglong Li, et al. ABI Water Vapor Radiance Assimilation in a Regional NWP Model by Accounting for the Surface Impact. Earth and Space Science, 2019, 6(9): 1652. DOI:10.1029/2019EA000711
    37. Pei Wang, Jun Li, Zhenglong Li, et al. Impacts of Observation Errors on Hurricane Forecasts When Assimilating Hyperspectral Infrared Sounder Radiances in Partially Cloudy Skies. Journal of Geophysical Research: Atmospheres, 2019, 124(20): 10802. DOI:10.1029/2019JD031029
    38. Deming Meng, Yaodeng Chen, Hongli Wang, et al. The evaluation of EnVar method including hydrometeors analysis variables for assimilating cloud liquid/ice water path on prediction of rainfall events. Atmospheric Research, 2019, 219: 1. DOI:10.1016/j.atmosres.2018.12.017
    39. Xinya Gong, Zhenglong Li, Jun Li, et al. Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements. Journal of Geophysical Research: Atmospheres, 2019, 124(15): 8688. DOI:10.1029/2018JD030019
    40. Yunheng Xue, Jun Li, W. Paul Menzel, et al. Characteristics of Satellite Sampling Errors in Total Precipitable Water from SSMIS, HIRS, and COSMIC Observations. Journal of Geophysical Research: Atmospheres, 2019, 124(13): 6966. DOI:10.1029/2018JD030045
    41. W. Paul Menzel, Timothy J. Schmit, Peng Zhang, et al. Satellite-Based Atmospheric Infrared Sounder Development and Applications. Bulletin of the American Meteorological Society, 2018, 99(3): 583. DOI:10.1175/BAMS-D-16-0293.1
    42. Zhenglong Li, Jun Li, Pei Wang, et al. Value-added Impact of Geostationary Hyperspectral Infrared Sounders on Local Severe Storm Forecasts—via a Quick Regional OSSE. Advances in Atmospheric Sciences, 2018, 35(10): 1217. DOI:10.1007/s00376-018-8036-3
    43. Ronan Fablet, Phi Viet, Redouane Lguensat, et al. Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation. Remote Sensing, 2018, 10(2): 310. DOI:10.3390/rs10020310
    44. Di Di, Jun Li, Wei Han, et al. Enhancing the Fast Radiative Transfer Model for FengYun‐4 GIIRS by Using Local Training Profiles. Journal of Geophysical Research: Atmospheres, 2018, 123(22) DOI:10.1029/2018JD029089
    45. Yanhui Xie, Jiancheng Shi, Shuiyong Fan, et al. Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study. Remote Sensing, 2018, 10(9): 1380. DOI:10.3390/rs10091380
    46. Pei Wang, Jun Li, Bing Lu, et al. Impact of Moisture Information From Advanced Himawari Imager Measurements on Heavy Precipitation Forecasts in a Regional NWP Model. Journal of Geophysical Research: Atmospheres, 2018, 123(11): 6022. DOI:10.1029/2017JD028012
    47. Yanhui Xie, Shuiyong Fan, Min Chen, et al. An Assessment of Satellite Radiance Data Assimilation in RMAPS. Remote Sensing, 2018, 11(1): 54. DOI:10.3390/rs11010054
    48. Jun Li, Zhenglong Li, Pei Wang, et al. An efficient radiative transfer model for hyperspectral IR radiance simulation and applications under cloudy‐sky conditions. Journal of Geophysical Research: Atmospheres, 2017, 122(14): 7600. DOI:10.1002/2016JD026273
    49. Seyedeh Atefeh Mohammadi, Majid Azadi, Morteza Rahmani. Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts. Journal of Meteorological Research, 2017, 31(4): 791. DOI:10.1007/s13351-017-6135-1
    50. Pei Wang, Jun Li, Zhenglong Li, et al. The Impact of Cross‐track Infrared Sounder (CrIS) Cloud‐Cleared Radiances on Hurricane Joaquin (2015) and Matthew (2016) Forecasts. Journal of Geophysical Research: Atmospheres, 2017, 122(24) DOI:10.1002/2017JD027515
    51. Jun Yang, Zhiqing Zhang, Caiying Wei, et al. Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4. Bulletin of the American Meteorological Society, 2017, 98(8): 1637. DOI:10.1175/BAMS-D-16-0065.1
    52. Sid A. Boukabara, Tong Zhu, Hendrik L. Tolman, et al. S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems: A Step Toward Bridging the Gap between Research and Operations. Bulletin of the American Meteorological Society, 2016, 97(12): 2359. DOI:10.1175/BAMS-D-14-00188.1
    53. Tao Feng, Xunjian Xu, Li Li, et al. Heavy Precipitation Numerical Prediction Model Based on Himawari-8 Sounder Data Assimilation: A Case Study. 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), DOI:10.1109/EI247390.2019.9061731

    Other cited types(0)

Catalog

    • Abstract
    • References

    Authors

    About the Journal

    Links

    WeChat

    Domestic Purchase

    京ICP备09060741号-1   Copyright©Journal of Meteorological Research  Contact: jmr@cms1924.org; 86-10-68408571  Total visits: 775149

    Supported by Beijing Renhe Information Technology Co. Ltd.    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return