{nasapower} aims to make it quick and easy to automate downloading
NASA POWER global meteorology,
surface solar energy and climatology data data in your R session as a
tidy data frame for analysis and use in modelling or other purposes
using get_power(). POWER (Prediction Of Worldwide Energy
Resource) data are freely available for download through a web interface
with a spatial resolution of 0.5 x 0.625 degree latitude and longitude
for meteorology and 1 x 1 degree latitude and longitude for solar
parameters with various temporal resolutions depending on the POWER
parameter and community.
The get_power() function has eight possible arguments
and returns a data frame with a metadata header in the current R
session.
Fetch daily “AG” community temperature, relative humidity and precipitation for January 1985 for Kingsthorpe, Queensland, Australia.
library("nasapower")
daily_single_ag <- get_power(
community = "ag",
lonlat = c(151.81, -27.48),
pars = c("RH2M", "T2M", "PRECTOTCORR"),
dates = c("1985-01-01", "1985-01-31"),
temporal_api = "daily"
)
daily_single_ag
#> ────────────────────────────────────────────────────────────────────────────────
#>
#> ── NASA/POWER Source Native Resolution Daily Data ─────────────────────────────
#> Dates (month/day/year): 01/01/1985 through 01/31/1985 in LST
#> Location: latitude -27.48 longitude 151.81
#> elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = 442.77
#> meters
#> The value for missing source data that cannot be computed or is outside of the
#> sources availability range: NA
#> parameter(s):
#> ────────────────────────────────────────────────────────────────────────────────
#> Parameters:
#> RH2M MERRA-2 Relative Humidity at 2 Meters (%) ; T2M MERRA-2 Temperature at 2
#> Meters (C) ; PRECTOTCORR MERRA-2 Precipitation Corrected (mm/day)
#> ────────────────────────────────────────────────────────────────────────────────
#> # A tibble: 31 × 10
#> LON LAT YEAR MM DD DOY YYYYMMDD RH2M T2M PRECTOTCORR
#> <dbl> <dbl> <dbl> <int> <int> <int> <date> <dbl> <dbl> <dbl>
#> 1 152. -27.5 1985 1 1 1 1985-01-01 54.7 24.9 0.9
#> 2 152. -27.5 1985 1 2 2 1985-01-02 42.1 28.6 0.49
#> 3 152. -27.5 1985 1 3 3 1985-01-03 43.5 27.4 0.01
#> 4 152. -27.5 1985 1 4 4 1985-01-04 48.9 24.3 0.05
#> 5 152. -27.5 1985 1 5 5 1985-01-05 55.3 26.5 1.33
#> 6 152. -27.5 1985 1 6 6 1985-01-06 60.3 27.0 4.88
#> 7 152. -27.5 1985 1 7 7 1985-01-07 63.1 27.2 10.7
#> 8 152. -27.5 1985 1 8 8 1985-01-08 70.6 24.9 9.99
#> 9 152. -27.5 1985 1 9 9 1985-01-09 60.0 26.1 2.45
#> 10 152. -27.5 1985 1 10 10 1985-01-10 45.2 27.0 0.48
#> # ℹ 21 more rowsFetch daily “ag” community relative humidity for south east Queensland region.
daily_region_ag <- get_power(
community = "ag",
lonlat = c(150.5, -28.5, 153.5, -25.5),
pars = "RH2M",
dates = c("1985-01-01", "1985-01-02"),
temporal_api = "daily"
)
daily_region_ag
#> ────────────────────────────────────────────────────────────────────────────────
#>
#> ── NASA/POWER Source Native Resolution Daily Data ─────────────────────────────
#> Dates (month/day/year): 01/01/1985 through 01/02/1985 in LST
#> Location: Regional
#> elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = na
#> meters
#> The value for missing source data that cannot be computed or is outside of the
#> sources availability range: NA
#> parameter(s):
#> ────────────────────────────────────────────────────────────────────────────────
#> Parameters:
#> RH2M MERRA-2 Relative Humidity at 2 Meters (%)
#> ────────────────────────────────────────────────────────────────────────────────
#> # A tibble: 70 × 8
#> LAT LON YEAR MM DD DOY YYYYMMDD RH2M
#> <dbl> <dbl> <dbl> <int> <int> <int> <date> <dbl>
#> 1 -28.5 151. 1985 1 1 1 1985-01-01 42.0
#> 2 -28.5 151. 1985 1 1 1 1985-01-01 42.0
#> 3 -28.5 152. 1985 1 1 1 1985-01-01 54.3
#> 4 -28.5 152. 1985 1 1 1 1985-01-01 61.7
#> 5 -28.5 153. 1985 1 1 1 1985-01-01 66.1
#> 6 -28 151. 1985 1 1 1 1985-01-01 44.8
#> 7 -28 151. 1985 1 1 1 1985-01-01 46.8
#> 8 -28 152. 1985 1 1 1 1985-01-01 54.9
#> 9 -28 152. 1985 1 1 1 1985-01-01 57.9
#> 10 -28 153. 1985 1 1 1 1985-01-01 61.7
#> # ℹ 60 more rowsFetch interannual solar cooking parameters for south east Queensland region and create a single data.
interannual_clrsky <- get_power(
community = "re",
lonlat = c(150.5, -28.5, 153.5, -25.5),
dates = c("1984", "1985"),
temporal_api = "monthly",
pars = "CLRSKY_SFC_SW_DWN"
)
interannual_allsky <- get_power(
community = "re",
lonlat = c(150.5, -28.5, 153.5, -25.5),
dates = c("1984", "1985"),
temporal_api = "monthly",
pars = "ALLSKY_SFC_SW_DWN"
)
interannual_list <- list(interannual_clrsky, interannual_allsky)
interannual_re <- do.call("rbind", interannual_list)
interannual_re
#> ────────────────────────────────────────────────────────────────────────────────
#>
#> ── NASA/POWER Source Native Resolution Monthly and Annual ─────────────────────
#> Dates (month/day/year): 01/01/1984 through 12/31/1985 in LST
#> Location: Regional
#> Elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = na
#> meters
#> The value for missing source data that cannot be computed or is outside of the
#> sources availability range: NA
#> Parameter(s):
#> ────────────────────────────────────────────────────────────────────────────────
#> Parameters:
#> CLRSKY_SFC_SW_DWN CERES SYN1deg Clear Sky Surface Shortwave Downward Irradiance
#> (kW-hr/m^2/day)
#> ────────────────────────────────────────────────────────────────────────────────
#> # A tibble: 64 × 17
#> PARAMETER YEAR LAT LON JAN FEB MAR APR MAY JUN JUL AUG
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 CLRSKY_SFC… 1984 -25.5 150. 8.48 7.99 7.05 5.8 4.8 4.32 4.6 5.5
#> 2 CLRSKY_SFC… 1984 -25.5 152. 8.43 7.92 7 5.71 4.74 4.24 4.56 5.46
#> 3 CLRSKY_SFC… 1984 -25.5 152. 8.35 7.85 6.91 5.65 4.7 4.21 4.45 5.27
#> 4 CLRSKY_SFC… 1984 -25.5 154. 8.25 7.75 6.78 5.55 4.56 4.12 4.27 5.22
#> 5 CLRSKY_SFC… 1984 -26.5 150. 8.52 8 7 5.72 4.72 4.16 4.49 5.45
#> 6 CLRSKY_SFC… 1984 -26.5 152. 8.56 7.98 6.95 5.66 4.68 4.15 4.47 5.44
#> 7 CLRSKY_SFC… 1984 -26.5 152. 8.42 7.97 6.91 5.62 4.62 4.13 4.41 5.38
#> 8 CLRSKY_SFC… 1984 -26.5 154. 8.32 7.81 6.76 5.48 4.45 4 4.18 5.18
#> 9 CLRSKY_SFC… 1984 -27.5 150. 8.57 7.96 6.99 5.62 4.63 4.04 4.32 5.39
#> 10 CLRSKY_SFC… 1984 -27.5 152. 8.55 7.97 6.92 5.59 4.55 4.03 4.32 5.33
#> # ℹ 54 more rows
#> # ℹ 5 more variables: SEP <dbl>, OCT <dbl>, NOV <dbl>, DEC <dbl>, ANN <dbl>Climatology data can be retrieved for point or regional areas as
demonstrated previously. Change the temporal_api value to
“climatology” to get these data.
Fetch “ag” climatology for temperature and relative humidity for Kingsthorpe, Queensland, Australia.
climatology_ag <- get_power(
community = "ag",
pars = c("T2M", "RH2M"),
lonlat = c(151.81, -27.48),
temporal_api = "climatology"
)
climatology_ag
#> ────────────────────────────────────────────────────────────────────────────────
#>
#> ── NASA/POWER Source Native Resolution Climatology in LST ──────────────────────
#> 20-year Meteorological and Solar Monthly & Annual Climatologies (January 2001 -
#> December 2020)
#> Location: Latitude -27.48 Longitude 151.81
#> Elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = 442.77
#> meters
#> The value for missing source data that cannot be computed or is outside of the
#> sources availability range: NA
#> Parameter(s):
#> ────────────────────────────────────────────────────────────────────────────────
#> Parameters:
#> RH2M MERRA-2 Relative Humidity at 2 Meters (%) ; T2M MERRA-2 Temperature at 2
#> Meters (C) ; Message(s): ; The requested parameters are retrieved from a
#> pre-computed climatological period (January 2001 - December 2020)
#> ────────────────────────────────────────────────────────────────────────────────
#> # A tibble: 2 × 16
#> LON LAT PARAMETER JAN FEB MAR APR MAY JUN JUL AUG SEP
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 152. -27.5 RH2M 64.7 69.6 71.1 70.5 69.2 75.1 70.4 63.1 59.9
#> 2 152. -27.5 T2M 24.6 23.7 22.0 19 15.1 12.6 11.6 13.1 16.9
#> # ℹ 4 more variables: OCT <dbl>, NOV <dbl>, DEC <dbl>, ANN <dbl>Note the associated metadata in the data frame header are not saved if the data are exported to a file format other than an R data format, e.g., .Rdata, .rda or .rds.
The POWER API offers functionality to get detailed information on any parameter offered or all parameters that are offered for a given community and temporal API. This can be used to find available parameter names and definitions for each community and temporal API.
Fetch the complete available information for the temperature at 2 metres above the Earth’s surface, T2M.
query_parameters(pars = "T2M")
#> $T2M
#> $T2M$temporal
#> HOURLY
#> 1 Temperature at 2 Meters
#> 2 The average air (dry bulb) temperature at 2 meters above the surface of the earth.
#> 3 AG, RE, SB
#> 4 FALSE
#> DAILY
#> 1 Temperature at 2 Meters
#> 2 The average air (dry bulb) temperature at 2 meters above the surface of the earth.
#> 3 AG, RE, SB
#> 4 FALSE
#> MONTHLY
#> 1 Temperature at 2 Meters
#> 2 The average air (dry bulb) temperature at 2 meters above the surface of the earth.
#> 3 AG, RE, SB
#> 4 FALSE
#> CLIMATOLOGY
#> 1 Temperature at 2 Meters
#> 2 The average air (dry bulb) temperature at 2 meters above the surface of the earth.
#> 3 AG, RE, SB
#> 4 FALSE
#>
#> $T2M$type
#> [1] "METEOROLOGY"Fetch complete temporal and community specific attribute information for “T2M” in the “ag” community for the “hourly” temporal API.
query_parameters(pars = "T2M",
community = "ag",
temporal_api = "hourly")
#> $T2M
#> $T2M$type
#> [1] "METEOROLOGY"
#>
#> $T2M$temporal
#> [1] "HOURLY"
#>
#> $T2M$source
#> [1] "SOURCE"
#>
#> $T2M$community
#> [1] "AG"
#>
#> $T2M$calculated
#> [1] FALSE
#>
#> $T2M$inputs
#> NULL
#>
#> $T2M$units
#> [1] "C"
#>
#> $T2M$name
#> [1] "Temperature at 2 Meters"
#>
#> $T2M$definition
#> [1] "The average air (dry bulb) temperature at 2 meters above the surface of the earth."The POWER API endpoints limit queries to prevent overloads due to
repetitive and rapid requests. If you find that the API is throttling
your queries, I suggest that you investigate the use of
limit_rate() from ratelimitr
to create self-limiting functions that will respect the rate limits that
the API has in place. It is best to check the POWER
website for the latest rate limits as they differ between temporal
APIs and may change over time as the project matures.