Retrieves daily weather data from the NASA POWER database and formats it
for use in seir() and epicrop disease models. This function wraps
nasapower::get_power() with predefined parameters optimized for
epidemiological modelling.
Arguments
- lonlat
Numeric vector of coordinates as
c(longitude, latitude). Longitude: -180 to 180 degrees (West/East) Latitude: -90 to 90 degrees (South/North) Example:c(121.255669, 14.167425)for IRRI, Philippines- dates
Character vector of start date and optional end date, or character scalar start date. Format:
"YYYY-MM-DD"(ISO 8601).Length 1: Use with
durationparameterLength 2+: First is start, second is end;
durationparameter ignored
- duration
Optional integer. Number of days to retrieve (inclusive). Only used if
datesis a single start date. Ignored ifdateshas length >= 2. Must be positive (> 0). If not provided anddateshas only one element, an error is raised.
Value
An epicrop.wth object (data.table::data.table() subclass)
containing daily weather data with columns:
| Column | Description | Units |
| YYYYMMDD | Date | ISO 8601 (YYYY-MM-DD) |
| DOY | Day of year | Integer (1-365/366) |
| TEMP | Mean daily temperature | Degrees C |
| TMIN | Minimum daily temperature | Degrees C |
| TMAX | Maximum daily temperature | Degrees C |
| RHUM | Mean daily relative humidity | Percent (0-100) |
| RAIN | Total daily rainfall | Millimeters |
| LAT | Latitude of query location | Degrees (-90 to 90) |
| LON | Longitude of query location | Degrees (-180 to 180) |
Ready for direct use in seir(), bacterial_blight(), leaf_blast(),
and other disease models.
Data Source
Weather data are obtained from NASA's Prediction of Worldwide Energy Resource
(POWER) database, which provides global daily weather data from 1984 onwards
at 0.5-degree spatial resolution. See nasapower::get_power() for details.
POWER data are derived from satellite and reanalysis sources and may differ from local weather station observations. For critical analyses, consider comparing with ground-truth data from your region.
Retrieved Variables
This function automatically retrieves:
T2M: Mean daily temperature (2-meter height)
T2M_MIN: Daily minimum temperature
T2M_MAX: Daily maximum temperature
RH2M: Mean daily relative humidity (2-meter height)
PRECTOTCORR: Daily precipitation (bias-corrected)
All variables are returned in standard units: temperature in degrees C, humidity in %, rainfall in mm.
Date Handling
Two methods for specifying the weather window:
Start and end dates (character vector):
dates = c("2000-06-30", "2000-12-31")Start date + duration (integer):
dates = "2000-06-30", duration = 120
Duration is interpreted as inclusive days: end = start + duration - 1.
For example, duration = 120 starting on June 30 ends on October 27 (120 days
total).
References
NASA POWER project (2019). Agroclimatology, Prediction Of Worldwide Energy Resource. Funded by NASA's Applied Science Program. https://power.larc.nasa.gov/
Sparks, A. H., P. D. Esker, M. Bates, W. Dall'Acqua, Z. Guo, V. Segovia, S. D. Silwal, S. Tolos, and K. A. Garrett. 2008. Ecology and Epidemiology in R: Disease Progress over Time. The Plant Health Instructor. doi:10.1094/PHI-A-2008-0129-02 .
Author
Adam H. Sparks, adamhsparks@gmail.com
Examples
# Example 1: Get weather for a date range
# IRRI Zeigler Experiment Station, wet season 2000
wth_range <- get_wth(
lonlat = c(121.255669, 14.167425),
dates = c("2000-06-30", "2000-12-31")
)
head(wth_range)
#> Key: <YYYYMMDD>
#> YYYYMMDD DOY TEMP TMIN TMAX RHUM RAIN LAT LON
#> <IDat> <int> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2000-06-30 182 26.60 23.94 29.89 85.99 11.94 14.16742 121.2557
#> 2: 2000-07-01 183 25.50 24.26 27.39 90.80 22.45 14.16742 121.2557
#> 3: 2000-07-02 184 26.45 24.07 30.09 85.74 14.87 14.16742 121.2557
#> 4: 2000-07-03 185 25.88 24.82 27.53 93.76 19.77 14.16742 121.2557
#> 5: 2000-07-04 186 26.12 24.97 28.08 91.53 12.15 14.16742 121.2557
#> 6: 2000-07-05 187 26.28 25.51 27.70 91.45 21.90 14.16742 121.2557
nrow(wth_range) # Number of days
#> [1] 185
# Example 2: Get weather by start date and duration
# 120-day window from June 30, 2000
wth_duration <- get_wth(
lonlat = c(121.255669, 14.167425),
dates = "2000-06-30",
duration = 120L
)
nrow(wth_duration) # Should be 120 rows
#> [1] 121
# Example 3: Verify both methods return equivalent data
# (within floating-point precision)
all.equal(wth_range[1:120], wth_duration)
#> [1] "Different number of rows"
# Example 4: Use retrieved weather in disease model
wth <- get_wth(
lonlat = c(121.255669, 14.167425),
dates = "2000-06-30",
duration = 120L
)
# Run bacterial blight model
bb_sim <- bacterial_blight(wth, emergence = "2000-07-01")
head(bb_sim)
#> simday dates sites latent infectious removed senesced rateinf rlex
#> <int> <IDat> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1 2000-07-01 100.0000 0 0 0 1.000000 0 0
#> 2: 2 2000-07-02 108.6875 0 0 0 2.086875 0 0
#> 3: 3 2000-07-03 118.1002 0 0 0 3.267877 0 0
#> 4: 4 2000-07-04 128.2934 0 0 0 4.550811 0 0
#> 5: 5 2000-07-05 139.3254 0 0 0 5.944065 0 0
#> 6: 6 2000-07-06 151.2581 0 0 0 7.456646 0 0
#> rtransfer rremoved rgrowth rsenesced diseased intensity lat lon
#> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0 0 9.68750 1.000000 0 0 14.16742 121.2557
#> 2: 0 0 10.49959 1.086875 0 0 14.16742 121.2557
#> 3: 0 0 11.37416 1.181002 0 0 14.16742 121.2557
#> 4: 0 0 12.31499 1.282934 0 0 14.16742 121.2557
#> 5: 0 0 13.32593 1.393254 0 0 14.16742 121.2557
#> 6: 0 0 14.41084 1.512581 0 0 14.16742 121.2557
plot(bb_sim$simday, bb_sim$intensity, type = "l",
xlab = "Days After Emergence", ylab = "Disease Intensity")
# Example 5: Multiple locations - compare disease risk
locations <- data.frame(
name = c("IRRI_Laguna", "BRRI_Gazipur", "CIMMYT_Mexico"),
lon = c(121.255669, 90.2167, -99.1828),
lat = c(14.167425, 24.0028, 19.1296)
)
# Get weather for each location
wth_list <- lapply(1:nrow(locations), function(i) {
get_wth(
lonlat = c(locations$lon[i], locations$lat[i]),
dates = "2000-06-30",
duration = 120L
)
})
names(wth_list) <- locations$name
# Run leaf blast model at each location
lb_results <- lapply(wth_list, function(w) {
leaf_blast(w, emergence = "2000-07-01")
})
# Compare AUDPC (disease intensity summary)
audpc_comparison <- data.frame(
location = names(lb_results),
AUDPC = sapply(lb_results, function(x) attr(x, "AUDPC"))
)
audpc_comparison
#> # Data frame like object (class data.frame) 2 x 3:
#> │location │AUDPC
#> │<chr> │<dbl>
#> IRRI_Laguna│IRRI_Laguna │39.449
#> BRRI_Gazipur│BRRI_Gazipur │41.452
#> CIMMYT_Mexico│CIMMYT_Mexico│ 0.035
# Example 6: Cross-year comparison (same location, different years)
years <- 2000:2002
wth_by_year <- lapply(years, function(y) {
get_wth(
lonlat = c(121.255669, 14.167425),
dates = paste0(y, "-06-30"),
duration = 120L
)
})
names(wth_by_year) <- as.character(years)
# Run model for each year
bb_by_year <- lapply(wth_by_year, function(w) {
bacterial_blight(w, emergence = unique(w$YYYYMMDD)[1])
})
# Compare disease progression across years
plot_data <- data.table::rbindlist(
lapply(seq_along(bb_by_year), function(i) {
data.frame(
year = names(bb_by_year)[i],
simday = bb_by_year[[i]]$simday,
intensity = bb_by_year[[i]]$intensity
)
})
)
plot(plot_data$simday, plot_data$intensity,
col = as.factor(plot_data$year), pch = 16)
legend("topleft", legend = names(bb_by_year),
col = seq_along(bb_by_year), pch = 16)
# Example 7: Error handling
if (FALSE) { # \dontrun{
# This will fail (invalid coordinates)
get_wth(
lonlat = c(200, 95), # Out of range
dates = "2000-06-30",
duration = 120
)
# Error: Longitude must be in [-180, 180], got 200
# This will fail (invalid date format)
get_wth(
lonlat = c(121.255669, 14.167425),
dates = "30-06-2000", # Wrong format
duration = 120
)
# Error: dates[1] must be an ISO date ('YYYY-MM-DD')
# This will fail (missing date specification)
get_wth(
lonlat = c(121.255669, 14.167425),
dates = "2000-06-30" # No end date or duration
)
# Error: Provide dates as c(start, end) or supply duration
} # }