Hourly weather sample: twelve cities, year 2019 — merra2

NASA MERRA-2 reanalysis extracted for twelve cities across every populated continent and both hemispheres: Beijing, Cape Town, Dakar, Delhi, Helsinki, Honolulu, Jakarta, Lima, Lisbon, Reykjavik, Sydney, and Tokyo. Used by the README and the visualization vignette (vignette("visualization")) to demonstrate mapping datetimes onto calendars, calendar heatmaps and wall calendars, profiles/ribbons/duration curves, and recasting across resolutions — and, through locid, shared with the geoscales documentation as the time half of "the same data on time and geo scales".

Usage

merra2_cities

Format

A data.frame with 105,120 rows (12 cities x 8,760 hours) and 11 columns:

city: City name.
locid: Integer id of the MERRA-2 grid cell the city falls in (the join key to the MERRA-2 grid, e.g. via the merra2ools package's locid table — the bridge to the space dimension).
datetime: POSIXct, UTC; hourly instants of 2019 stamped at 30 minutes past the hour (the MERRA-2 hourly-mean convention).
T10M: Air temperature at 10 m, degrees C.
W10M, W50M: Wind speed at 10 m / 50 m, m/s.
WDIR: Wind direction, degrees.
SWGDN: Surface incoming shortwave irradiance, W/m2.
ALBEDO: Surface albedo, fraction.
PRECTOTCORR: Bias-corrected total precipitation.
RHOA: Air density, kg/m3.

Source

NASA MERRA-2 reanalysis, Global Modeling and Assimilation Office (GMAO) — a public-domain dataset; extracted with the merra2ools package (https://github.com/optimal2050/merra2ools); subset built by data-raw/merra2_cities.R.

Examples

head(merra2_cities)
#>      city  locid            datetime T10M W10M W50M WDIR SWGDN ALBEDO
#> 1 Beijing 150235 2019-01-01 00:30:00  -10  4.9  6.8  140    79   0.18
#> 2 Beijing 150235 2019-01-01 01:30:00   -9  5.9  7.0  150   211   0.16
#> 3 Beijing 150235 2019-01-01 02:30:00   -6  6.2  7.1  160   331   0.15
#> 4 Beijing 150235 2019-01-01 03:30:00   -3  5.9  6.9  160   411   0.14
#> 5 Beijing 150235 2019-01-01 04:30:00   -2  5.7  6.8  160   444   0.14
#> 6 Beijing 150235 2019-01-01 05:30:00   -1  5.4  6.4  160   416   0.14
#>   PRECTOTCORR RHOA
#> 1           0 1.34
#> 2           0 1.34
#> 3           0 1.32
#> 4           0 1.31
#> 5           0 1.30
#> 6           0 1.30
with(subset(merra2_cities, city == "Reykjavik"), range(W50M))
#> [1]  0.1 22.6