SCIENCE CHINA Earth Sciences, Volume 61 , Issue 7 : 957-972(2018) https://doi.org/10.1007/s11430-017-9150-0

Simulation of FY-2D infrared brightness temperature and sensitivity analysis to the errors of WRF simulated cloud variables

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  • ReceivedJun 16, 2017
  • AcceptedNov 24, 2017
  • PublishedMar 9, 2018


This study simulated FY-2D satellite infrared brightness images based on the WRF and RTTOV models. The effects of prediction errors in WRF micro- and macroscale cloud variables on FY-2D infrared brightness temperature accuracy were analyzed. The principle findings were as follows. In the T+0–48 h simulation time, the root mean square errors of the simulated brightness temperatures were within the range 10–27 K, i.e., better than the range of 20–40 K achieved previously. In the T+0–24 h simulation time, the correlation coefficients between the simulated and measured brightness temperatures for all four channels were >0.5. The simulation performance of water channel IR3 was stable and the best. The four types of cloud microphysical scheme considered all showed that the simulated values of brightness temperature in clouds were too high and that the distributions of cloud systems were incomplete, especially in typhoon areas. The performance of the THOM scheme was considered best, followed in descending order by the WSM6, WDM6, and LIN schemes. Compared with observed values, the maximum deviation appeared in the range 253–273 K for all schemes. On the microscale, the snow water mixing ratio of the THOM scheme was much bigger than that of the other schemes. Improving the production efficiency or increasing the availability of solid water in the cloud microphysical scheme would provide slight benefit for brightness temperature simulations. On the macroscale, the cloud amount obtained by the scheme used in this study was small. Improving the diagnostic scheme for cloud amount, especially high-level cloud, could improve the accuracy of brightness temperature simulations. These results could provide an intuitive reference for forecasters and constitute technical support for the creation of simulated brightness temperature images for the FY-4 satellite.

Funded by

This work was supported jointly by the major special projects of the Information System Bureau

Ministry of Central Military Commission of Equipment Development(Grant,No.,GFZX0402180102)

National Natural Science Foundation of China(Grant,No.,U1533131)


This work was supported jointly by the Major Special Projects of the Information System Bureau, the Special Proget of Earth Observation with High Resolution (Grant No. GFZX0402180102) and the National Natural Science Foundation of China (Grant No. U1533131).


[1] Cao X Q, Song J Q, Zhang W M, Huang Q B. 2014. Application of multi-source satellite observations in the global 4D-VAR system (in Chinese). B Surv Map, S1: 102–107. Google Scholar

[2] Chevallier F, Bauer P, Kelly G, Jakob C, McNally T. Model clouds over oceans as seen from space: Comparison with HIRS/2 and MSU radiances. J Clim, 2001, 14: 4216-4229 CrossRef Google Scholar

[3] Chevallier F, Kelly G. Model clouds as seen from space: Comparison with geostationary imagery in the 11-μm window channel. Mon Weather Rev, 2002, 130: 712-722 CrossRef Google Scholar

[4] Ding W Y. 2011. Numerical simulation of FY-2 TBB based on Grapes mesoscale model. In: Branch of the 28th Annual of Chinese Meteorological Society: Quantitative Application and Numerical Analysis of the Fengyun Satellite (in Chinese). Beijing: Chinese Meteorological Society. 217–223. Google Scholar

[5] Ding W Y, Wan Q L. 2008. The simulation of typhnoon Chanchu infrared channels brightness temperature (in Chinese). Chin J Atmos Sci, 32: 572–580. Google Scholar

[6] Fang Z Y. 2014. The evolution of meteorological satellites and the insight from it (in Chinese). Adv Meteorol Sci Technol, 4: 27–34. Google Scholar

[7] Han Y, Delst P V, Liu Q, Weng F, Yan B, Treadon R, Derber J. 2005. User’s Guide to the JCSDA Community Radiative Transfer Model (Beta Version). Google Scholar

[8] He M Y, Shi H Q. 2002. Some issues associated with geostationary meteorological satellite data processing (in Chinese). J PLA Univ Sci Technol, 3: 75–78. Google Scholar

[9] Hocking J, Rayer P, Rundle D, Saunders R, Matricardi M, Geer A, Brunel P, Vidot J. 2013. RTTOV v11 Users Guide. Google Scholar

[10] Keil C, Tafferner A, Mannstein H, Schättler U. Evaluating high-resolution model forecasts of European winter storms by use satellite and radar observation. Weather Forecast, 2003, 18: 732-747 CrossRef Google Scholar

[11] Li J, Han Z G, Chen H B, Zhao Z L, Wu H Y. 2011. Detection of heavy fog events over north China plain by using the geostationary satellite data (in Chinese). Remot Sens Technol Appl, 26: 186–195. Google Scholar

[12] Lim K S S, Hong S Y. Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon Weather Rev, 2010, 138: 1587-1612 CrossRef ADS Google Scholar

[13] Lin Y L, Farley R D, Orville H D. Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol, 1983, 22: 1065-1092 CrossRef Google Scholar

[14] Liu S S, Dong P M, Han W, Zhang W J. 2012. Simulative study of satellite microwave observations for Typhoon Luosha using RTTOV and CRTM and the comparison (in Chinese). Acta Meteor Sin, 70: 585–597. Google Scholar

[15] Ma G, Fang Z Y, Zhang F Y. 2001. The impact of cloud parameters on the simulated errors in RTTOV5 (in Chinese). J Appl Meteor Sci, 12: 385–392. Google Scholar

[16] Ma G, Qiu C J, Li G Q, Zhang F Y. 2005. Study of simulation on radiance from infrared and water vapor channel of FY2B by a fast forward model-RTTOV7 (in Chinese). J Infrared Milli Wave, 24: 37–40. Google Scholar

[17] Morcrette J J. Evaluation of model-generated cloudiness: Satellite-observed and model-generated diurnal variability of brightness temperature. Mon Weather Rev, 1991, 119: 1205-1224 CrossRef Google Scholar

[18] Qian Z A, Liu M, Yi Y H. 1992. Comparison tests of parameterization schemes for cloud cover over Asia and the effect of cloud cover (in Chinese). Acta Meteorol Sin, 50: 50–59. Google Scholar

[19] Razagui A, Bouchouicha K, Bachari N E I. 2011. Cloud type identification algorithm to simulate MSG infrared radiance using the Radiative Transfer Model RTTOV and ALADIN forecasting output. Revue des Energies Renouvelables, 14: 601–612. Google Scholar

[20] Rutledge S A, Hobbs P V. 1984. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J Atmos Sci, 41: 2949–2972. Google Scholar

[21] Sheng P X, Mao J T, Li J G, Ge Z M, Zhang A C, Sang J G, Pan N X, Zhang H S. 2009. Atmospheric Physics (in Chinese). Peking: Peking University Press. 290–307. Google Scholar

[22] Shi X K, Liu J W, Li Y D, Tian H, Liu X P. Improved SAL method and its application to verifying regional soil moisture forecasting. Sci China Earth Sci, 2014, 57: 2657-2670 CrossRef Google Scholar

[23] Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Powers J G. 2008. A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR. Google Scholar

[24] Thompson G, Field P R, Rasmussen R M, Hall W D. Explicit Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon Weather Rev, 2008, 136: 5095-5115 CrossRef ADS Google Scholar

[25] Thompson G, Rasmussen R M, Manning K. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon Weather Rev, 2004, 132: 519-542 CrossRef Google Scholar

[26] Wang H, Yin J F, Wang D H. 2014. Comparative analysis of single-moment and double-moment microphysics schemes on a local heavy rainfall in south China (in Chinese). Plateau Meteorol, 33: 1341–1351. Google Scholar

[27] Wang X J, Ma H. 2011. Progress of application of the Weather Research and Forecast (WRF) model in China (in Chinese). Adv Earth Sci, 26: 1191–1199. Google Scholar

[28] Weng F, Liu Q. Satellite data assimilation in numerical weather prediction models. Part I: Forward radiative transfer and jacobian modeling in cloudy atmospheres. J Atmos Sci, 2003, 60: 2633-2646 CrossRef Google Scholar

[29] Xu K M, Randall D A. Explicit simulation of cumulus ensembles with the GATE phase III data: Comparison with observations. J Atmos Sci, 1996, 53: 3710-3736 CrossRef Google Scholar

[30] Xue J S. 2009. Scientific issues and perspective of assimilation of meteorological satellite data (in Chinese). Acta Meteor Sin, 67: 903–911. Google Scholar

[31] Yang J, Chen B J, Yin Y. 2011. Cloud Precipitation Physics (in Chinese). Beijing: Meteorological Press. 1–15. Google Scholar

[32] Zhang X H, Duan Y H. 2014. Simulation of brightness temperature in infrared channel of FY-2F and bias analysis (in Chinese). Meteorol Mon, 40: 1066–1075. Google Scholar

[33] Zheng X H, Xu G Q, Wei R Q. 2013. Introducing and influence testing of the new cloud fraction scheme in the GRAPES (in Chinese). Meteorol Mon, 39: 57–66. Google Scholar

[34] Zhu P J, Chen M, Tao Z Y, Wang H Q. 2002. The numerical simulation of typhoon cloud (in Chinese). Universitatis Pekinensis Acta Sci Nat, 38: 358–363. Google Scholar

  • Figure 1

    Simulation area (shaded area represents terrain height).

  • Figure 2

    Distributions of simulated ((e)–(h)) and observed ((a)–(d)) brightness temperatures at T+24 h of the T3 test.

  • Figure 3

    Evolutions of ME (a), RMSE (b), and R (c) of the simulated brightness temperatures of the T3 test and the observed brightness temperature.

  • Figure 4

    Distributions of simulated brightness temperature of channel IR1 at T+24 h: (a) T1, (b) T2, and (c) T4 test (Blue rectangles indicate the frontal area (top) and the typhoon area (bottom)).

  • Figure 5

    Evolutions of ME, RMSE, R, and FRAC of simulated brightness temperatures by channel IR1 of the T1–T4 tests and the observed brightness temperatures. (a) ME, (b) RMSE, (c) R, (d) FRAC (≥273 and 253–273 K; Frontal), (e) FRAC (≥273 and 253–273 K; Typhoon).

  • Figure 6

    Vertical profiles of areal mean stratified cloud cover and total water mass mixing ratio in frontal and typhoon areas at T+24 h of the T1–T4 tests. (a) Total water mass mixing ratio in frontal area; (b) total water mass mixing ratio in typhoon area; (c) cloud cover in frontal area; (d) cloud cover in typhoon area.

  • Figure 7

    Evolutions of (a) ME difference, (b) RMSE difference of channel IR1 in the typhoon area (Difference: T7–T3 (pink line); T8–T3 (red line); T9–T4 (blue line); T10–T4 (green line)).

  • Figure 8

    Evolutions of ME, RMSE, R, SLOP, and FRAC of simulated brightness temperatures in the typhoon area with different cloud cover amount. (a) ME, (b) RMSE, (c) R, (d) SLOP, (e) FRAC (≥273 K), (f) FRAC (253–273 K), and (g) FRAC(<253 K).

  • Figure 9

    Distribution of simulated brightness temperatures of channel IR1 channel at T+24 h. (a) Cloud cover increased by 20% in the cloud area and (b) cloud cover set equal to 10 in the cloud area.

  • Table 1   Experimental schemes adopted in the study


    Cloud microphysical scheme









  • Table 2   Test schemes


    Cloud microphysical scheme

    In-cloud solid water content threshold (g m−3)

    Changes of in-cloud solid water content


    THOM scheme







    Increase 20%


    Decrease 20%


    WDM6 scheme


    Increase 20%


    Decrease 20%

  • Table 3   Test scheme


    Cloud microphysical scheme

    3-D cloud change value



    Decrease 10%


    Increase 10%


    Increase 20%


    Increase 30%


    Equivalently 10

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