Post-processing correction is an effective way to
improve the model forecasting result. Especially, the machine learning methods have
played increasingly important roles in recent years. Taking the meteorological
observational data in a period of two years as the reference, the maximum and
minimum temperature predictions of Shenyang station from the European Center
for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid
forecasts are objectively corrected by using wavelet analysis, sliding training
and other technologies. The evaluation results show that the sliding training
time window of the maximum temperature is smaller than that of the minimum
temperature, and their difference is the largest in August, with a difference
of 2.6 days. The objective correction product of maximum temperature shows a
good performance in spring, while that of minimum temperature performs well throughout
the whole year, with an accuracy improvement of 97% to 186%. The correction
effect in the central plains is better than in the regions with complex
terrain. As for the national intelligent grid forecasts, the objective
correction products have shown positive skills in predicting the maximum
temperatures in spring (the skill-score reaches 0.59) and in predicting the
minimum temperature at most times of the year (the skill-score reaches 0.68).
References
[1]
Bauer, P., Thorpe, A. and Brunet, G. (2015) The Quiet Revolution of Numerical Weather Prediction. Nature, 525, Article No. 47. https://doi.org/10.1038/nature14956
[2]
Gregory, D., Morcrette, J.-J., Jakob, C., Beljaars, A.C.M. and Stockdale, T. (2000) Revision of Convection, Radiation and Cloud Schemes in the ECMWF Integrated Forecasting System. Quarterly Journal of the Royal Meteorological Society, 126, 1685-1710. https://doi.org/10.1002/qj.49712656607
[3]
Bonavita, M., Hólm, E., Isaksen, L. and Fisher, M. (2016) The Evolution of the ECMWF Hybrid Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 142, 287-303. https://doi.org/10.1002/qj.2652
[4]
Tibaldi, S., Palmer, T.N., Branković, Č. and Cubasch, U. (1990) Extended-Range Predictions with ECMWF Models: Influence of Horizontal Resolution on Systematic Error and Forecast Skill. Quarterly Journal of the Royal Meteorological Society, 116, 835-866. https://doi.org/10.1002/qj.49711649404
[5]
Kaymaz, B. (2005) Hazards and Their Impact on Human. 29. IMISE (International Movement for Interdisciplinary Study of Estrangement) Conference, Paris, 4-9 July 2005.
[6]
Krishnamurti, T.N., Kishtawal, C.M., LaRow, T.E., et al. (1999) Improved Weather and Seasonal Climate Forecasts from Multimodel Super Ensemble. Science, 285, 1548-1550. https://doi.org/10.1126/science.285.5433.1548
[7]
Krishnamurti, T. N., Kumar, V., Simon, A., Bhardwaj, A., Ghosh, T., and Ross, R. (2016) A Review of Multimodel Superensemble Forecasting for Weather, Seasonal Climate, and Hurricanes. Reviews of Geophysics, 54, 336-377. https://doi.org/10.1002/2015RG000513
[8]
Cai, N.H. and Yu, J.W. (2019) Temperature Forecasting Method Based on Numerical Model Bias Analysis. Transactions of Atmospheric Sciences, 42, 864-873. (In Chinese)
[9]
Oshima, N., Kato, H. and Kadokura, S. (2022) An Application of Statistical Downscaling to Estimate Surface Air Temperature in Japan. Journal of Geophysical Research: Atmospheres, 107, ACL 14-1-ACL 14-10. https://doi.org/10.1029/2001JD000762
[10]
LeMone, M.A., Ikeda, K., Grossman, R.L. and Rotach, M.W. (2003) Horizontal Variability of 2-m Temperature at Night during CASES-97. Journal of the Atmospheric Sciences, 60, 2431-2449. https://doi.org/10.1175/1520-0469(2003)060<2431:HVOMTA>2.0.CO;2
[11]
Charabi, Y. and Al-Yahyai, S. (2015) Evaluation of Ensemble NWP Models for Dynamical Downscaling of Air Temperature over Complex Topography in a Hot Climate: A Case Study from the Sultanate of Oman. Atmósfera, 28, 261-269. https://doi.org/10.20937/ATM.2015.28.04.05
[12]
Zurada, J.M. (1992) Introduction to Artificial Neural Systems. West Publishing Company, St Paul.
[13]
Gers, F.A., Schmidhuber, J. and Cummins F. (2000) Learning of Forget: Continual Prediction with LSTM. Neural Computation, 12, 2451-2471. https://doi.org/10.1162/089976600300015015
[14]
Krizhevsky, A., Sutskever, I. and IIinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Network. In: Pereira, F., Burges, C.J., Bottou, L. and Weinberger, K.Q., Eds., Advance in Neural Information Processing Systems 25, Curran Associates, Red Hook, 1097-1105.
[15]
Ustaoglu, B., Cigizoglu, H.K. and Karaca, M. (2008) Forecast of Daily Mean, Maximum and Minimum Temperature Time Series by Three Artificial Neural Network Methods. Meteorological Applications, 15, 431-445. https://doi.org/10.1002/met.83
[16]
Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J. and Duque, N. (2016) Rainfall Prediction: A Deep Learning Approach. In: Martínez-álvarez, F., Troncoso, A., Quintián, H. and Corchado, E., Eds., Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science, Vol. 9648, Springer, Cham, 151-162. https://doi.org/10.1007/978-3-319-32034-2_13
[17]
Men, X.L., Jiao, R.L., Wang, D., et al. (2019) A Temperature Correction Method for Multi-Model Ensemble Forecast in North China Based on Machine Learning. Climatic and Environmental Research, 24, 116-124. (In Chinese)
[18]
Chang, J.M.-H., Yun, F.L., Lau, S.P.-W. and Wong, W.-K. (2021) Development of Fine-Scale Spatiotemporal Temperature Forecast Model with Urban Climatology and Geomorphometry in Hong Kong. Urban Climate, 37, Article ID: 100816. https://doi.org/10.1016/j.uclim.2021.100816
[19]
Chen, Y.W., Huang, X.M., Li, Y., et al. (2020) Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products. Journal of Applied Meteorological Science, 31, 494-503. (In Chinese)
[20]
Li, C., Li, Ｍ.H., Zhou, K., et al. (2022) Application of KNN Approach in Improvement of Temperature Forecast in Zhejiang. Meteorological and Environmental Sciences, 45, 81-89. (In Chinese)
