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Disaggregate Daily Weather Data to Hourly Leaf Wetness Duration

Source: R/leaf_wetness.R
daily_to_hourly_lwd.Rd

Takes a daily data frame containing temperature extrema and average relative humidity, reconstructs the hourly parameters internally, and computes the hourly leaf wetness presence along with its daily operational hour sum.

Usage

daily_to_hourly_lwd(
  data,
  date_col,
  t_min_col,
  t_max_col,
  rh_daily_col,
  lat_col,
  rh_threshold = 85
)

Arguments

data

A data frame containing the daily weather records.

date_col

Unquoted name of the column containing the Date object.

t_min_col

Unquoted name of the column containing the current day's minimum temperature (°C).

t_max_col

Unquoted name of the column containing the current day's maximum temperature (°C).

rh_daily_col

Unquoted name of the column containing the daily average relative humidity (%).

lat_col

Unquoted name of the column containing the latitude (decimal degrees).

rh_threshold

A single numeric value indicating the RH percentage above which leaf wetness is assumed to occur. Default is 85% based on optimized regional validations.

Value

A tibble (data frame) expanded to hourly resolution (24 rows per original daily row) with four columns: datetime (POSIXct), temperature_hourly (°C), rh_hourly (\ leaf_wetness_hourly (binary 0/1), and lwd_daily_sum (total wet hours in that day).

Examples

# Sample daily dataset matching your exact input structure with 5 continuous days
daily_series <- tibble::tibble(
  date = as.Date("2026-06-01") + 0:4,
  lat = rep(-27.3, 5),
  tmin = c(12.0, 13.5, 11.0, 10.5, 14.0),
  tmax = c(22.0, 24.5, 21.0, 19.5, 23.0),
  rh_mean = c(80, 75, 85, 90, 70)
)

daily_to_hourly_lwd(daily_series, date, tmin, tmax, rh_mean, lat, rh_threshold = 85)
#> # A tibble: 120 × 5
#>    datetime            temperature_hourly rh_hourly leaf_wetness_hourly
#>    <dttm>                           <dbl>     <dbl>               <dbl>
#>  1 2026-06-01 00:00:00               14.7      92.8                   1
#>  2 2026-06-01 01:00:00               14.2      95.5                   1
#>  3 2026-06-01 02:00:00               13.9      97.9                   1
#>  4 2026-06-01 03:00:00               13.5      99.9                   1
#>  5 2026-06-01 04:00:00               13.3     100                     1
#>  6 2026-06-01 05:00:00               13.1     100                     1
#>  7 2026-06-01 06:00:00               12.9     100                     1
#>  8 2026-06-01 07:00:00               12.7     100                     1
#>  9 2026-06-01 08:00:00               12.6     100                     1
#> 10 2026-06-01 09:00:00               13.1     100                     1
#> # ℹ 110 more rows
#> # ℹ 1 more variable: lwd_daily_sum <dbl>