There are several packages and utilities that allow for downloading weather data. Here we use the apsimx package. This package has simple wrappers that 'get' weather from different sources:
For more details about getting and working with weather data see the apsimx package.
knitr::opts_chunk$set(echo = TRUE) library(apsimx) library(pacu)
An alternative way of investigating the growing conditions experienced by crops in a given year would be to summarize the weather data and place it in a historical context. Let us download some weather data first.
weather.met <- pa_get_weather_sf(area.of.interest, '1990-01-01', '2020-12-31')
extd.dir <- system.file("extdata", package = "pacu") weather.met <- read_apsim_met('example-weather.met', extd.dir, verbose = FALSE)
We can make simple plots for precipitation or temperature. A filter is used to subset years 2017 to 2020 for easier interpretation.
## Precipitation (or rain) plot(weather.met, met.var = "rain", cumulative = TRUE, climatology = TRUE, years = 2017:2020) ## Temperature plot(weather.met, cumulative = TRUE, climatology = TRUE, years = 2017:2020)
There is a summary function for simple display of statistics
## Selecting just a few columns (1, 6, 7, 10) for simplicity summary(weather.met, years = 2017:2020)[, c(1, 6, 7, 10)]
The apsimx package does not produce complex graphs for weather data. This was included here to allow more detailed interpretation of crop performance data for a given location. In the pacu package we include functions which can summarize data in a historical context.
pa_plot(weather.met, plot.type = 'climate_normals', unit.system = 'int') pa_plot(weather.met, plot.type = 'monthly_distributions', unit.system = 'int', months = 5:10)
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