Abstract
Different methods for the statistical correction of the forecasts of surface parameters using the COSMO-Ru13-ENA model with the lead time up to 117 hours are considered. The methods include the systematic correction using the data from recent observations at a weather station, the correction based on special neural networks as well as different combinations of these two techniques. The study presents the estimates of the results of applying the analyzed correction methods to the forecasts of surface air temperature, dew point, and wind speed modulus based on the independent sample for 2018 with the total volume of 2.34 × 107 forecasts. The correction method based on neural networks reduces forecast errors even at the points where meteorological observations have not been carried out.
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Acknowledgements
The author thanks the specialists of Hydrometcenter of Russia: D.V. Blinov for assistance in the work with the COSMO-Ru model archives, V.A. Gordin, G.S. Rivin, and I.A. Rozinkina for fruitful discussions as well as the anonymous reviewer for useful remarks.
Funding
The paper was prepared in the framework of the “Higher School of Economics” Academic Fund Program in 2018–2019 (project 18-05-0011), in 2020–2021 (project 20-04-021), and in the framework of governmental support of the leading universities of the Russian Federation “5-100.”
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Russian Text ©The Author(s), 2020, published in Meteorologiya i Gidrologiya, 2020, No. 3, pp. 5–20.
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Bykov, F.L. Statistical Correction of the COSMO Model Weather Forecasts Based on Neural Networks. Russ. Meteorol. Hydrol. 45, 141–152 (2020). https://doi.org/10.3103/S1068373920030012
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DOI: https://doi.org/10.3103/S1068373920030012