synoptReg is an open source package for computing synoptic climate classifications and spatial regionalizations of environmental data. Package website: lemuscanovas.github.io/synoptreg/
The interaction between the troposphere and the environment is well known, and how this can condition human activities on some occasions. These situations can occur with an anticyclonic block that causes an increase in the concentration of NO2; or when a low-pressure area causes abundant rainfall over urban areas, etc. This package is intended to be a roadmap for territorial management on a regional scale and to anticipate the management of adverse situations for the population, due to pollution, as well as extreme weather conditions. In short,
synoptReg allows to:
Compute an objective synoptic classification to obtain the main atmospheric patterns, the so-called weather types, of a given region. Two approaches are provided:
Circulation-To-Environment: First establishes the main circulation types for a long time series and then characterises an environmental variable (i.e. precipitation,NO2,O3,…) based on the previous circulation types.
Environment-To-Circulation: First categorises the environmental variable (e.g. precipitation, temperature,…) and then characterises the synoptic patterns prevailing under specific environmental conditions (e.g. days with elevated temperatures, torrential rainfall events).
Several methods are implemented: PCA-based, Lamb and SOM.
Represent the impact of each weather type on an environmental variable (continuous): precipitation, temperature, pollutants, …
Define a categorical regionalization of this environmental variable. Each region will be independent and with specific characteristics.
Interested in learning how to use
synoptReg? Visit the package website and read the articles:
Using synoptReg for research publication? Please cite it! I’m an early career scientist and every citation matters.
Lemus-Canovas, M., Lopez-Bustins, J.A., Martin-Vide, J., Royé, D., 2019. synoptReg: An R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environmental Modelling & Software, Vol. 118,114-119pp, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2019.04.006
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