Construction of predictive models of meteorological parameters of the atmospheric surface layer

N. A. Soltaganov, V. S. Sherstnev, A. I. Sherstneva, I. A. Botygin, V. A. Krutikov

Research output: Contribution to journalConference articlepeer-review


This paper considers some approaches to building a regression model and a seasonal autoregressive (moving average) integrated model using the Python programming language. The additive regression model was created by using Facebook's Prophet library. The seasonal integrated autoregressive model was created by using the StatsModels library. We developed a prognostic time series of the monthly precipitation sum for the next 2 years. Program experiments were conducted by using data acquired on a Tomsk station (station synoptic index 29430) with an observation period from 1996 to 2016. An interactive environment called Jupiter Notebook was used for the initial data processing, mathematical calculations, and graph plotting. The environment in question is a graphical web-interface for Python which expands the idea of console approach for interactive computing. The model prediction accuracy was assessed by finding the absolute and average absolute errors. The maximum values of the studied time series could not be predicted.

Original languageEnglish
Article number012027
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 17 Dec 2018
EventInternational Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems, ENVIROMIS 2018 - Tomsk, Russian Federation
Duration: 5 Jul 201811 Jul 2018

ASJC Scopus subject areas

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

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