kriging_quantile_true: Generate spatial interpolated estimates by ordinary kriging...
In jieyu97/QCwind: Quality control procedures for citizen science wind observations

For the input official wind speed data with locations, first find their quantiles from 0.01 to 1 with step interval of 0.01 (100 in total), then for quantiles at each level using ordinary kriging to interpolate estimated quantiles at other locations.

kriging_quantile_true(
  official.windspeed,
  datetime.sequence,
  official.longitude,
  official.latitude,
  split.season = TRUE
)

`official.windspeed`	a data.frame of official/reference wind speed observations, each column represents an individual station, each row represents a timestamp.
`datetime.sequence`	the date time stamps/sequence of the data `official.windspeed`, should indicate the row names of `official.windspeed`.
`official.longitude`	a numeric vector, contains longitude information of official stations, should relate to the column names (station IDs) of `official.windspeed`.
`official.latitude`	a numeric vector, contains latitude information of official stations, should relate to the column names (station IDs) of `official.windspeed`.
`split.season`	a logical, if `TRUE` we perform the spatial interpolation by six different periods (winter/summer/transition season, day/night time) respectively; The six periods are determined by months and hours in a day: winter - 12,1,2,3; summer - 6,7,8,9; trans (transition periods between winter and summer) - 4,5,10,11; day - 7:00~18:00; night - 19:00~6:00; 6 periods: winter_night, winter_day, trans_night, trans_day, summer_night, summer_day.

a large list that contains the estimated quantiles at different locations of each period. It has 6 sublists, each one represent a certain period; Each sublist includes estimated quantiles from 0.01 to 1 at thousands of new locations.

jieyu97/QCwind documentation built on June 18, 2021, 3:37 a.m.