Downloading Kansas Mesonet data

Introduction

The Kansas Mesonet is a statewide network of automated weather stations operated by Kansas State University. It provides sub-hourly meteorological data — including air temperature, relative humidity, precipitation, solar radiation, wind, and soil measurements — for more than 60 stations across Kansas.

This vignette walks through the workflow for discovering stations, browsing the variable catalog, downloading data, and reading cached files using the preMetabolizer Mesonet functions. We use Konza Prairie, a long-term ecological research (LTER) site in northeastern Kansas, as our example station.

Note: Functions that contact the Mesonet API require an internet connection. Set PREMETABOLIZER_RUN_VIGNETTES=true to run the API chunks; they are skipped by default during package installation and checks.

library(preMetabolizer)
library(dplyr)
library(ggplot2)

Caching downloaded data

Repeated calls to the Kansas Mesonet API download the same data on every run. This vignette saves each result to a local cache directory the first time it is downloaded and reloads from disk on subsequent runs. The cache lives in tools::R_user_dir("preMetabolizer", which = "cache"), a platform-appropriate, user-specific directory that persists across sessions. Each data-fetching chunk below checks for a cached .rds file, downloads and saves on the first run, and reloads from disk on all subsequent runs.

Explore the variable catalog

ks_meso_vars() retrieves and parses the current Kansas Mesonet variable metadata, including CSV headings, variable names, units, and descriptions.

cache_file <- file.path(cache_dir, "ks_meso_vars.rds")
if (!file.exists(cache_file)) {
  mesonet_vars <- ks_meso_vars()
  saveRDS(mesonet_vars, cache_file)
} else {
  mesonet_vars <- readRDS(cache_file)
}

Search the catalog to find variables of interest:

mesonet_vars |>
  filter(grepl("temp|precip|solar|humidity", desc, ignore.case = TRUE)) |>
  select(var, tidy_name, units, desc)

Discover stations

ks_meso_stations() retrieves metadata for all Mesonet stations, including location, network affiliation, and whether the station supports FW13 fire weather reports.

cache_file <- file.path(cache_dir, "ks_meso_stations.rds")
if (!file.exists(cache_file)) {
  stations <- ks_meso_stations()
  saveRDS(stations, cache_file)
} else {
  stations <- readRDS(cache_file)
}

glimpse(stations)

Filter to find Konza Prairie and confirm the exact station name used by the API:

konza <- stations |>
  filter(grepl("Konza", station_name, ignore.case = TRUE))

konza

The station name recognized by the API is "Konza Prairie".

Check station activity

ks_meso_station_activity() shows the observation intervals available at each station and the date range of archived data. This is useful for verifying that a station has data for your study period and identifying the finest available temporal resolution.

cache_file <- file.path(cache_dir, "ks_meso_station_activity.rds")
if (!file.exists(cache_file)) {
  activity <- ks_meso_station_activity()
  saveRDS(activity, cache_file)
} else {
  activity <- readRDS(cache_file)
}

activity |>
  filter(station_name == "Konza Prairie")

Check the most recent observation

ks_meso_most_recent() returns the timestamp of the latest ingested observation for every station at a given interval, which is helpful for monitoring live data pipelines.

cache_file <- file.path(cache_dir, "ks_meso_most_recent_hour.rds")
if (!file.exists(cache_file)) {
  recent <- ks_meso_most_recent(interval = "hour")
  saveRDS(recent, cache_file)
} else {
  recent <- readRDS(cache_file)
}

recent |>
  filter(station_name == "Konza Prairie")

Retrieve time-series data

ks_meso_time_series() retrieves Mesonet data for one or more stations and returns a tibble directly. Large date ranges are split into chunks automatically to stay within the API record limit.

Here we retrieve hourly data for Konza Prairie for the 2024 calendar year, requesting air temperature, relative humidity, precipitation, and surface pressure.

cache_file <- file.path(cache_dir, "ks_meso_konza_hourly_2024.rds")
if (!file.exists(cache_file)) {
  konza_hourly <- ks_meso_time_series(
    stations   = "Konza Prairie",
    start_date = "2024-01-01",
    end_date   = "2024-12-31",
    interval   = "hour",
    vars       = c("TEMP2MAVG", "RELHUM2MAVG", "PRECIP", "PRESSUREAVG")
  )
  saveRDS(konza_hourly, cache_file)
} else {
  konza_hourly <- readRDS(cache_file)
}

glimpse(konza_hourly)

Example workflow: monthly precipitation

With the data in hand, standard dplyr and ggplot2 workflows apply. The example below summarizes monthly precipitation totals and daily temperature ranges.

monthly_precip <- konza_hourly |>
  mutate(month = lubridate::floor_date(timestamp, "month")) |>
  group_by(month) |>
  summarise(
    precip_mm    = sum(precip, na.rm = TRUE),
    temp_mean_C  = mean(temp2_mavg, na.rm = TRUE),
    .groups      = "drop"
  )

ggplot(monthly_precip, aes(month, precip_mm)) +
  geom_col(fill = "#4575b4") +
  labs(
    x = NULL,
    y = "Precipitation (mm)",
    title = "Monthly precipitation — Konza Prairie 2024"
  ) +
  theme_bw()

FW13 fire weather data

ks_meso_fw13() retrieves fire weather records in the standard FW13 format used by USDA Forest Service fire behavior modeling tools.

cache_file <- file.path(cache_dir, "ks_meso_fw13_konza_2024-04.rds")
if (!file.exists(cache_file)) {
  fw13_records <- ks_meso_fw13(
    station    = "Konza Prairie",
    start_date = "2024-04-01",
    end_date   = "2024-04-30"
  )
  saveRDS(fw13_records, cache_file)
} else {
  fw13_records <- readRDS(cache_file)
}

head(fw13_records, 3)