MERRA-2 subset for evaluation of renewables with merra2ools R-package: 1980-2020 hourly, 0.5° lat x 0.625° lon global grid

Renewable variable energy resources (VER) - solar and wind energy are
becoming increasingly important sources of electricity worldwide.
Assessing the potential and the reliability of the resources requires
long-term historical data. Directly measured solar radiation and wind
speed are limited to locations of weather stations, and even when
available, the observations are not directly suitable for the evaluation
of VERs potential (as an example, the wind speed is rarely
measured at wind turbines heights).  Reanalysis data
based on satellite imagery and Earth system models, such as MERRA-2 offer
a broad set of long-term time series on a global grid. 
`merra2ools` is a preprocessed subset of MERRA-2 variables and a software
(R-package) designed for quick estimation of hourly output of solar
photovoltaics and wind turbines. The grid of the MERRA-2 dataset has
0.625° step length along longitude (- 180° to 180°) and 0.5° along
latitude (- 90° to 90°), making 576 x 361 grid or 207936 locations. The
subset of the hourly data covers the period from 1980-Jan-01 00:30 UTC to
2020-Jan-31 23:30 UTC. It includes eight variables: wind speed at 10- and
50-meters height (W10M and W50M), wind direction (WDIR), the atmospheric
temperature at 10 meters height (T10M), surface incoming shortwave flux
(SWGDN), surface albedo (ALBEDO), bias-corrected total precipitation
(PRECTOTCORR), and air density at the surface (RHOA). The dataset’s key
variables are date-time in Coordinated Universal Time timezone (UTC) and
location identifiers (locid). In total, the subset has 290,357,084,160
data points (362,946,355,200 including the key variables). To reduce the
dataset’s memory footprint (~3TB uncompressed), the original MERRA-2
variables have been rounded, scaled, and stored as integers in highly
compressed data format with high speed full random access (`fst` package
for R). The resulting dataset is saved in separate files by months (41
years x 12 months, 492 data-files in total). Additionally, some summary
statistics such as mean values of each variable by month and location ID,
annual spatial correlations with the nearest neighbors have been
calculated for wind speed and solar irradiance and added to the dataset.