ESG

climateR 특별판

Tidyverse Korea

1 온도와 강수량¹

# remotes::install_github("mikejohnson51/AOI") # suggested!
# remotes::install_github("mikejohnson51/climateR")

library(AOI)
library(climateR)
library(sf)
library(raster)
library(rasterVis)

korea <- aoi_get(country = "KOR")

plot(korea$geometry)

prcp <-  getTerraClim(korea, param = "prcp", startDate = "2019-01-01")
# chirps <- getCHIRPS(korea, startDate = "2018-01-01", endDate = "2018-01-04" )

p1 <- levelplot(prcp$terraclim_prcp, par.settings = BTCTheme, main = "January 2018; TerraClim", margin = TRUE) +
  layer(sp.lines(as_Spatial(korea), col="red", lwd=2))

# p2 <- levelplot(chirps,  par.settings = BTCTheme, main = "Janaury 1-4, 2018; CHIRPS", layout=c(2, 2)) +
#   layer(sp.lines(as_Spatial(korea), col="white", lwd=3))

AOI::geocode("Republic of Korea")

            request      lat      lon
1 Republic of Korea 36.63839 127.6961

AOI::aoi_get(country = "KOR") %>% 
   AOI::aoi_map(returnMap = TRUE)

1.1 쿠키 모양을 찍는 데 쓰는 모형

AOI는 일종의 Cookie Cutter(쿠키 모양을 찍는 데 쓰는 모형)로 이해할 수 있고 이를 이용하여 대한민국 강수량을 뽑아내보자.

AOI     <- aoi_get(country = "KOR")
(extent <- st_bbox(AOI))

     xmin      ymin      xmax      ymax 
126.11740  34.39005 129.46830  38.61224 

(crs    = st_crs(AOI)$proj4string)

[1] "+proj=longlat +datum=NAD83 +no_defs"

plot(AOI$geometry, border  = "darkred",lwd = 2)
plot(st_as_sfc(extent), add = TRUE,  lwd = 3)

rain <- getTerraClim(aoi_get(country = "KOR"), param = "prcp", startDate = "2018-01-19")
# temp <- getTerraClim(aoi_get(country = "KOR"), param = "tmax", startDate = "2018-01-19")
levelplot(rain$terraclim_prcp)

# levelplot(temp)

param_meta$gridmet

      common.name   call                                     description
1            prcp     pr                            precipitation_amount
2           rhmax   rmax                 daily_maximum_relative_humidity
3           rhmin   rmin                 daily_minimum_relative_humidity
4            shum    sph                    daily_mean_specific_humidity
5            srad   srad       daily_mean_shortwave_radiation_at_surface
6        wind_dir     th                       daily_mean_wind_direction
7            tmin   tmmn                       daily_minimum_temperature
8            tmax   tmmx                       daily_maximum_temperature
9        wind_vel     vs                           daily_mean_wind_speed
10     burn_index     bi                      daily_mean_burning_index_g
11     fmoist_100  fm100                        dead_fuel_moisture_100hr
12    fmoist_1000 fm1000                       dead_fuel_moisture_1000hr
13 energy_release    erc           daily_mean_energy_release_component-g
14         palmer   pdsi        daily_mean_palmer_drought_severity_index
15    pet_alfalfa    etr daily_mean_reference_evapotranspiration_alfalfa
16      pet_grass    pet   daily_mean_reference_evapotranspiration_grass
17            vpd    vpd               daily_mean_vapor_pressure_deficit
   timestep                        units
1     daily                           mm
2     daily                      Percent
3     daily                      Percent
4     daily                        kg/kg
5     daily                        W/m^2
6     daily Degrees Clockwise from north
7     daily                         degK
8     daily                         degK
9     daily                          m/s
10    daily                     Unitless
11    daily                      Percent
12    daily                      Percent
13    daily                     Unitless
14   pentad                     Unitless
15    daily                           mm
16    daily                           mm
17    daily                          kPa

model_meta$maca

            model ensemble scenario
1         BNU-ESM   r1i1p1    rcp45
2        CNRM-CM5   r1i1p1    rcp45
3   CSIRO-Mk3-6-0   r1i1p1    rcp45
4      bcc-csm1-1   r1i1p1    rcp45
5         CanESM2   r1i1p1    rcp45
6      GFDL-ESM2G   r1i1p1    rcp45
7      GFDL-ESM2M   r1i1p1    rcp45
8   HadGEM2-CC365   r1i1p1    rcp45
9   HadGEM2-ES365   r1i1p1    rcp45
10         inmcm4   r1i1p1    rcp45
11         MIROC5   r1i1p1    rcp45
12      MIROC-ESM   r1i1p1    rcp45
13 MIROC-ESM-CHEM   r1i1p1    rcp45
14      MRI-CGCM3   r1i1p1    rcp45
15   IPSL-CM5A-LR   r1i1p1    rcp45
16   IPSL-CM5A-MR   r1i1p1    rcp45
17   IPSL-CM5B-LR   r1i1p1    rcp45
18          CCSM4   r6i1p1    rcp45
19      NorESM1-M   r1i1p1    rcp45
20   bcc-csm1-1-m   r1i1p1    rcp45
21        BNU-ESM   r1i1p1    rcp85
22       CNRM-CM5   r1i1p1    rcp85
23  CSIRO-Mk3-6-0   r1i1p1    rcp85
24     bcc-csm1-1   r1i1p1    rcp85
25        CanESM2   r1i1p1    rcp85
26     GFDL-ESM2G   r1i1p1    rcp85
27     GFDL-ESM2M   r1i1p1    rcp85
28  HadGEM2-CC365   r1i1p1    rcp85
29  HadGEM2-ES365   r1i1p1    rcp85
30         inmcm4   r1i1p1    rcp85
31         MIROC5   r1i1p1    rcp85
32      MIROC-ESM   r1i1p1    rcp85
33 MIROC-ESM-CHEM   r1i1p1    rcp85
34      MRI-CGCM3   r1i1p1    rcp85
35   IPSL-CM5A-LR   r1i1p1    rcp85
36   IPSL-CM5A-MR   r1i1p1    rcp85
37   IPSL-CM5B-LR   r1i1p1    rcp85
38          CCSM4   r6i1p1    rcp85
39      NorESM1-M   r1i1p1    rcp85
40   bcc-csm1-1-m   r1i1p1    rcp85

param_meta$terraclim

     common.name call                                      description units
1            aet  aet                        Actual Evapotranspiration    mm
2  water_deficit  def                           Climatic Water Deficit    mm
3         palmer PDSI                    Palmer Drought Severity Index      
4            pet  pet                     Reference Evapotranspiration    mm
5           prcp  ppt                        Accumulated Precipitation    mm
6              q    q                                           Runoff    mm
7          soilm soil                    Soil Moisture at End of Month    mm
8           srad srad Downward Shortwave Radiation Flux at the Surface  W/m2
9            swe  swe            Snow Water Equivalent at End of Month    mm
10          tmax tmax                          Maximum 2-m Temperature     C
11          tmin tmin                          Minimum 2-m Temperature     C
12            vp  vap                               2-m Vapor Pressure   kPa
13           vpd  vpd                      Mean Vapor Pressure Deficit   kPa
14          wind   ws                               Wind Speed at 10-m   m/s

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1. Mike Johnson, “Gridded Climate Data in R - Introduction to AOI & climateR”↩︎

 

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kwangchun.lee.7@gmail.com