TY - GEN

T1 - Machine learning methods in weather forecasts

AU - Volzhina, E.

AU - Mikhailova, E.

AU - Ganshin, A.

N1 - Conference code: 19

PY - 2019

Y1 - 2019

N2 - Growth in computational performance, the amount of accumulated data about the environment and experience of handling such amounts of data leads to an increase in the number of applications of data analysis. Weather forecasting is one of these areas. Weather forecasting uses a variety of data and meteorological models describing the physical processes in the atmosphere. Machine learning algorithms can correct some errors of these models and improve weather forecasts. To improve the temperature forecast, we added to the training data for different models readings from the nearest meteorological stations. This technique proved to be useful for the short-term temperature prediction. We evaluated the results of experiments by the changing of the root-mean-square error. Yandex.Weather service successfully uses the improved model for forecasting. The described technique has already been added to the Yandex.Weather and used on an ongoing basis in production. In addition, we suggest possible directions of this task development.

AB - Growth in computational performance, the amount of accumulated data about the environment and experience of handling such amounts of data leads to an increase in the number of applications of data analysis. Weather forecasting is one of these areas. Weather forecasting uses a variety of data and meteorological models describing the physical processes in the atmosphere. Machine learning algorithms can correct some errors of these models and improve weather forecasts. To improve the temperature forecast, we added to the training data for different models readings from the nearest meteorological stations. This technique proved to be useful for the short-term temperature prediction. We evaluated the results of experiments by the changing of the root-mean-square error. Yandex.Weather service successfully uses the improved model for forecasting. The described technique has already been added to the Yandex.Weather and used on an ongoing basis in production. In addition, we suggest possible directions of this task development.

KW - Gradient boosting

KW - Machine learning

KW - Weather prediction

UR - http://www.scopus.com/inward/record.url?scp=85073351218&partnerID=8YFLogxK

M3 - Conference contribution

T3 - International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM

SP - 391

EP - 398

BT - 19th International Multidisciplinary Scientific Geoconference, SGEM 2019

PB - International Multidisciplinary Scientific Geoconference

CY - Albena

T2 - 19th International Multidisciplinary Scientific Geoconference, SGEM 2019

Y2 - 9 December 2019 through 11 December 2019

ER -