Calculate estimated weather from coefficients
calc_estimated_weather.RdCalculate estimated weather from coefficients
Usage
calc_estimated_weather(
w,
start_date = "2023-04-01",
end_date = "2023-11-30",
lat,
lon,
n_stations = 1:4,
na.rm = FALSE
)Arguments
- w
A
data.tableof weather coefficients, output fromget_weather_coefs`- start_date
a character string of a date value indicating the first date for in the returning data.table Must be in ISO8601 format (YYYY-MM-DD), e.g. “2020-04-26”
- end_date
a character string of a date value indicating the last date for in the returning data.table Must be in ISO8601 format (YYYY-MM-DD), e.g. “2020-04-26”
- lat
numeric, latitude of the query coordinates where weather should be estimated. If missing all stations will be returned
- lon
numeric, longitude of the query coordinates where weather should be estimated. If missing all stations will be returned
- n_stations
integer or vector of integers indicating the number of station/s to return from the closest (1), or 3rd closest (3) or closest five stations (1:5), ect.
- na.rm
logical, remove all weather data from stations with NA rain_fall frequency Not Recommended. We advise the best way is to manually remove weather stations with NAs or correct the weather data. This argument is available if these two options are not available to the user. Default is
FALSE
Value
A data.table output of calculated on
get_weather_coefs with the following columns:
station - Weather station name;
lat - latitude;
lon - longitude;
rh - NA currently not supported see epiphytoolR github issue #14;
yearday - integer, day of the year, see data.table::yday();
wd_rd - numeric, mean wind direction from raw data;
wd_sd_rd - numeric, standard deviation of wind direction from raw data;
ws_rd - numeric, mean wind speed from raw data;
ws_sd_rd - numeric, standard deviation of wind speed from raw data;
rain_freq - numeric, proportional chance of rainfall on this dat 0 - 1
Output can be formatted with format_weather
Examples
set.seed(61)
dat <- data.frame(
station_name = "w_STATION",
lat = -runif(1, 15.5, 28),
lon = runif(1, 115, 150),
state = "SA",
yearday = 1:365,
wd_rw = abs(rnorm(365, 180, 90)),
wd_sd_rw = rnorm(365, 80, 20),
ws_rw = runif(365, 1, 60),
ws_sd_rw = abs(rnorm(365, 10, sd = 5)),
rain_freq = runif(365, 0.05, 0.45)
)
calc_estimated_weather(w = dat,
lat = -25,
lon = 130,
n_stations = 1)
#> Warning: 'max_temp' and 'min_temp' not detected, returning NAs for mean daily 'temp'
#> times station yearday lat lon state wd_rw
#> <POSc> <char> <int> <num> <num> <char> <num>
#> 1: 2023-04-01 00:00:00 w_STATION 91 -19.89674 147.9869 SA 303.05002
#> 2: 2023-04-01 01:00:00 w_STATION 91 -19.89674 147.9869 SA 303.05002
#> 3: 2023-04-01 02:00:00 w_STATION 91 -19.89674 147.9869 SA 303.05002
#> 4: 2023-04-01 03:00:00 w_STATION 91 -19.89674 147.9869 SA 303.05002
#> 5: 2023-04-01 04:00:00 w_STATION 91 -19.89674 147.9869 SA 303.05002
#> ---
#> 5829: 2023-11-29 20:00:00 w_STATION 333 -19.89674 147.9869 SA 247.95358
#> 5830: 2023-11-29 21:00:00 w_STATION 333 -19.89674 147.9869 SA 247.95358
#> 5831: 2023-11-29 22:00:00 w_STATION 333 -19.89674 147.9869 SA 247.95358
#> 5832: 2023-11-29 23:00:00 w_STATION 333 -19.89674 147.9869 SA 247.95358
#> 5833: 2023-11-30 00:00:00 w_STATION 334 -19.89674 147.9869 SA 67.58693
#> wd_sd_rw ws_rw ws_sd_rw rain_freq distance rh rain temp
#> <num> <num> <num> <num> <num> <num> <int> <num>
#> 1: 78.18055 3.682944 10.011768 0.0659163 1933.846 NA 0 NA
#> 2: 78.18055 3.682944 10.011768 0.0659163 1933.846 NA 0 NA
#> 3: 78.18055 3.682944 10.011768 0.0659163 1933.846 NA 0 NA
#> 4: 78.18055 3.682944 10.011768 0.0659163 1933.846 NA 0 NA
#> 5: 78.18055 3.682944 10.011768 0.0659163 1933.846 NA 0 NA
#> ---
#> 5829: 55.64306 56.564044 10.791610 0.2640204 1933.846 NA 0 NA
#> 5830: 55.64306 56.564044 10.791610 0.2640204 1933.846 NA 0 NA
#> 5831: 55.64306 56.564044 10.791610 0.2640204 1933.846 NA 0 NA
#> 5832: 55.64306 56.564044 10.791610 0.2640204 1933.846 NA 0 NA
#> 5833: 73.84805 34.334943 9.999803 0.2522567 1933.846 NA 0 NA
#> ws wd wd_sd YYYY MM DD hh mm
#> <num> <num> <num> <int> <int> <int> <int> <int>
#> 1: 17.04809 303.05002 78.18055 2023 4 1 0 0
#> 2: 17.04809 303.05002 78.18055 2023 4 1 1 0
#> 3: 17.04809 303.05002 78.18055 2023 4 1 2 0
#> 4: 17.04809 303.05002 78.18055 2023 4 1 3 0
#> 5: 17.04809 303.05002 78.18055 2023 4 1 4 0
#> ---
#> 5829: 45.39426 247.95358 55.64306 2023 11 29 20 0
#> 5830: 45.39426 247.95358 55.64306 2023 11 29 21 0
#> 5831: 45.39426 247.95358 55.64306 2023 11 29 22 0
#> 5832: 45.39426 247.95358 55.64306 2023 11 29 23 0
#> 5833: 40.35103 67.58693 73.84805 2023 11 30 0 0