Skip Navigation Links
Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal


Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email:

Study on Quantitative Prediction Scheme of Aircraft Icing Based on Random Forest Algorithm

Journal of Environmental Accounting and Management 11(3) (2023) 329--339 | DOI:10.5890/JEAM.2023.09.006

Pan Pan$^1$, Ming Xue$^1$, Ying Zhang$^1$, Zhangsong Ni$^1$, Zixu Wang$^2$

$^1$ Chengdu Fluid Dynamics Innovation Center, Chengdu 610072, China

$^2$ China Aerodynamics Research and Development Center, Mianyang 621000, China

Download Full Text PDF



In this paper, \replaced[id=Reviewer1,comment=5]{a new aircraft icing prediction scheme is proposed to obtain the aircraft icing shape from common meteorological parameter.}{we propose a new aircraft icing prediction scheme to obtain the aircraft icing shape from common meteorological parameter.} Machine learning modeling is used to establish the mapping between meteorological parameters and in-cloud microphysical parameters based on a random forest algorithm. The outputs of machine learning model, median volume diameter (MVD) and liquid water content (LWC), are utilized as input parameters to simulate ice accretion for a specific airfoil, and the final icing shape is determined. The present work \replaced{shows}{showes} that in-cloud microphysical parameters might have some relationship with common meteorological parameters, and random forest \replaced{shows}{show} better performance in prediction of in-cloud microphysical parameters. The research work has brought about a quantitative prediction scheme of aircraft icing that shows high engineering practical value in route planning, aviation meteorological warning and airworthiness certification, etc.


  1. [1] Brunton, S. L., Proctor, J. L., and Kutz, J.N. (2016), Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proceedings of the national academy of sciences, 113(15), 3932-3937.
  2. [2] Cao, Y., Tan, W., and Wu, Z. (2018), Aircraft icing: An ongoing threat to aviation safety. Aerospace Science and Technology, 75, 353-385.
  3. [3] Gent, R.W., Dart, N.P., and Cansdale, J.T. (2000), Aircraft icing. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 358(1776), 2873-2911.
  4. [4] Carriere, J.M., Alquier, S., Le Bot, C., and Moulin, E. (1997), Statistical verification of forecast icing risk indices. Meteorological Applications: A journal of forecasting, practical applications, Training Techniques and Modelling, 4(2), 115-130.
  5. [5] Thompson, G., Bruintjes, R.T., Brown, B.G., and Hage, F. (1997), Intercomparison of in-flight icing algorithms. Part I: WISP94 real-time icing prediction and evaluation program, Weather and Forecasting, 12(4), 878-889.
  6. [6] Brown, B.G., Thompson, G., Bruintjes, R.T., Bullock, R., and Kane, T. (1997), Intercomparison of in-flight icing algorithms. Part II: Statistical verification results, Weather and Forecasting, 12(4), 890-914.
  7. [7] Cornell, D., Donahue, C.A., and Keith, C. (1995), A comparison of aircraft icing forecast models, Air Force Combat Climatology Center Scott AFB IL.
  8. [8] Zhan, P. (2020), Review of Measuring Instruments for Water Droplet Sizing in Icing Clouds.Measurement and Control Technology, 39(06), 1-7. Doi:10.19708/j.ckjs.2020.04.217(in Chinese).
  9. [9] Merino, A., García-Ortega, E., Fernández-González, S., Díaz-Fernández, J., Quitián-Hernández, L., Martín, M.L., Lopez, L., Marcos,J.L., Valero, F., abd Sánchez, J.L. (2019), Aircraft icing: in-cloud measurements and sensitivity to physical parameterizations, Geophysical Research Letters, 46(20), 11559-11567.
  10. [10] Thompson, G., Politovich, M.K., and Rasmussen, R.M. (2017), A numerical weather model's ability to predict characteristics of aircraft icing environments, Weather and Forecasting, 32(1), 207-221.
  11. [11] Bernstein, B.C., McDonough, F., Politovich, M.K., Brown, B.G., Ratvasky, T.P., Miller, D.R., and Cunning, G. (2005), Current icing potential: Algorithm description and comparison with aircraft observations, Journal of Applied Meteorology and Climatology, 44(7), 969-986.
  12. [12] Zhan, P.(2021), Review on the system of icing facilities in NASA. Aeronautical Science and Technology, 32(05), 1-6. Doi:10.19452/j.issn1007-5453.2021.05.001(in Chinese)
  13. [13] Li, S., Qin, J., He, M., and Paoli, R. (2020), Fast evaluation of aircraft icing severity using machine learning based on XGBoost, Aerospace, 7(4), 36.
  14. [14] Wang, Q., and Chai, C. (2021), Prediction model of aircraft icing based on deep neural network, Transactions of Nanjing University of Aeronautics and Astronautics, 38(4), 535-544.
  15. [15] Ding, D., Qian, W., and Wang, Q.(2022), Aircraft icing classification using optimized probabilistic neural networks, Acta Aerodynamica Sinica, 40(5), 100-109.
  16. [16] Wright, W. (2005), Validation results for LEWICE 3.0, In 43rd AIAA Aerospace Sciences Meeting and Exhibit (p. 1243).
  17. [17] Wright, W. (2008), User's manual for LEWICE version 3.2 (No. E-15537).