Gathering and summarizing weather data

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')

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)]

##   year avg_maxt avg_mint rain_sum
## 1 2017    15.79     3.72   814.04
## 2 2018    14.01     2.81  1401.30
## 3 2019    13.75     2.17   998.94
## 4 2020    15.47     3.11   644.34

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)