Optional Inputs: res_spat - Spatial resolution. How many pixels (n x n) to download in each direction res_temp - What temporal averaging: 1 day (1d), 6 day (6d), 1 month(1m), ,... Monthly climatology (1mNy), Annual climatology (12mNy) Possible products to download are: dt_analysis, l2p_flags, quality_level, satellite_zenith_angle, sea_ice_fraction, sea_ice_fraction_dtime_from_sst, sea_surface_temperature, sses_bias, sses_count, sses_standard_deviation, sst_count, sst_dtime, sst_mean, sst_standard_deviation, wind_speed, wind_speed_dtime_from_sst
pr_match_GHRSST(
df,
pr,
res_spat = 1,
res_temp = "1d",
parallel = FALSE,
ncore = NULL
)dataframe containing latitude, longitude and Date
products from list above, single or as a list
Number of spatial pixels to average over
Temporal resolution of satellite data to use
Should the analysis run using parallel processing
If parallel = TRUE package will use all available cores, apart from 2 which will be left for system processes and multitasking. If you wish to specify how many cores the package should use, set ncore. Otherwise, leave it as NULL.
df with product output attached
df <- tail(pr_get_DataLocs("CPR") %>%
dplyr::arrange(Date), 5)
pr = c("sea_surface_temperature", "quality_level", "sst_mean", "sst_standard_deviation")
sstout <- pr_match_GHRSST(df, pr, res_spat = 10, res_temp = "6d")