Create future EnergyPlus Weather files using CMIP6 data
You can install the latest stable release of epwshiftr from CRAN.
Alternatively, you can install the development version from GitHub.
# set directory to store files
options(epwshiftr.dir = tempdir())
options(epwshiftr.verbose = TRUE)
# get CMIP6 data nodes
(nodes <- get_data_node())
#> data_node status
#> <char> <char>
#> 1: aims3.llnl.gov UP
#> 2: cmip.bcc.cma.cn UP
#> 3: cmip.dess.tsinghua.edu.cn UP
#> 4: cmip.fio.org.cn UP
#> 5: crd-esgf-drc.ec.gc.ca UP
#> 6: data.meteo.unican.es UP
#> 7: dataserver.nccs.nasa.gov UP
#> 8: dist.nmlab.snu.ac.kr UP
#> 9: dpesgf03.nccs.nasa.gov UP
#> 10: esg-cccr.tropmet.res.in UP
#> 11: esg-dn1.ru.ac.th UP
#> 12: esg-dn2.nsc.liu.se UP
#> 13: esg.camscma.cn UP
#> 14: esg.lasg.ac.cn UP
#> 15: esg.pik-potsdam.de UP
#> 16: esgf-data.ucar.edu UP
#> 17: esgf-data1.ceda.ac.uk UP
#> 18: esgf-data1.diasjp.net UP
#> 19: esgf-data1.llnl.gov UP
#> 20: esgf-data2.ceda.ac.uk UP
#> 21: esgf-data2.diasjp.net UP
#> 22: esgf-data2.llnl.gov UP
#> 23: esgf-data3.ceda.ac.uk UP
#> 24: esgf-data3.diasjp.net UP
#> 25: esgf-dev.bsc.es UP
#> 26: esgf-nimscmip6.apcc21.org UP
#> 27: esgf-node.cmcc.it UP
#> 28: esgf-node2.cmcc.it UP
#> 29: esgf.anl.gov UP
#> 30: esgf.apcc21.org UP
#> 31: esgf.dwd.de UP
#> 32: esgf.nci.org.au UP
#> 33: esgf.rcec.sinica.edu.tw UP
#> 34: esgf2.dkrz.de UP
#> 35: noresg.nird.sigma2.no UP
#> 36: vesg.ipsl.upmc.fr UP
#> 37: 145.100.59.180.surf-hosted.nl DOWN
#> 38: acdisc.gesdisc.eosdis.nasa.gov DOWN
#> 39: cordexesg.dmi.dk DOWN
#> 40: esg-dn1.nsc.liu.se DOWN
#> 41: esg1.umr-cnrm.fr DOWN
#> 42: esgdata.gfdl.noaa.gov DOWN
#> 43: esgf-cnr.hpc.cineca.it DOWN
#> 44: esgf-ictp.hpc.cineca.it DOWN
#> 45: esgf.bsc.es DOWN
#> 46: esgf.ichec.ie DOWN
#> 47: esgf1.dkrz.de DOWN
#> 48: esgf3.dkrz.de DOWN
#> 49: gpm1.gesdisc.eosdis.nasa.gov DOWN
#> data_node status
# create a CMIP6 output file index
idx <- init_cmip6_index(
# only consider ScenarioMIP activity
activity = "ScenarioMIP",
# specify dry-bulb temperature and relative humidity
variable = c("tas", "hurs"),
# specify report frequent
frequency = "day",
# specify experiment name
experiment = c("ssp585"),
# specify GCM name
source = "AWI-CM-1-1-MR",
# specify variant,
variant = "r1i1p1f1",
# specify years of interest
years = c(2050, 2080),
# save to data dictionary
save = TRUE
)
#> Querying CMIP6 Dataset Information
#> Querying CMIP6 File Information [Attempt 1]
#> Checking if data is complete
#> Data file index saved to '/tmp/RtmpDtbJVc/cmip6_index.csv'
# the index has been automatically saved into directory specified using
# `epwshiftr.dir` option and can be reloaded
idx <- load_cmip6_index()
str(head(idx))
#> Classes 'data.table' and 'data.frame': 6 obs. of 23 variables:
#> $ file_id : chr "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529.hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f"| __truncated__ "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529.hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f"| __truncated__ "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529.hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f"| __truncated__ "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.tas.gn.v20190529.tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_"| __truncated__ ...
#> $ dataset_id : chr "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529|esgf3.dkrz.de" "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529|esgf3.dkrz.de" "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.hurs.gn.v20190529|esgf3.dkrz.de" "CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp585.r1i1p1f1.day.tas.gn.v20190529|esgf3.dkrz.de" ...
#> $ mip_era : chr "CMIP6" "CMIP6" "CMIP6" "CMIP6" ...
#> $ activity_drs : chr "ScenarioMIP" "ScenarioMIP" "ScenarioMIP" "ScenarioMIP" ...
#> $ institution_id : chr "AWI" "AWI" "AWI" "AWI" ...
#> $ source_id : chr "AWI-CM-1-1-MR" "AWI-CM-1-1-MR" "AWI-CM-1-1-MR" "AWI-CM-1-1-MR" ...
#> $ experiment_id : chr "ssp585" "ssp585" "ssp585" "ssp585" ...
#> $ member_id : chr "r1i1p1f1" "r1i1p1f1" "r1i1p1f1" "r1i1p1f1" ...
#> $ table_id : chr "day" "day" "day" "day" ...
#> $ frequency : chr "day" "day" "day" "day" ...
#> $ grid_label : chr "gn" "gn" "gn" "gn" ...
#> $ version : chr "20190529" "20190529" "20190529" "20190529" ...
#> $ nominal_resolution: chr "100 km" "100 km" "100 km" "100 km" ...
#> $ variable_id : chr "hurs" "hurs" "hurs" "tas" ...
#> $ variable_long_name: chr "Near-Surface Relative Humidity" "Near-Surface Relative Humidity" "Near-Surface Relative Humidity" "Near-Surface Air Temperature" ...
#> $ variable_units : chr "%" "%" "%" "K" ...
#> $ datetime_start : POSIXct, format: "2049-01-01" "2050-01-01" ...
#> $ datetime_end : POSIXct, format: "2049-12-31" "2050-12-31" ...
#> $ file_size : int 91761231 91729347 91727399 82292505 82268546 82149654
#> $ data_node : chr "esgf3.dkrz.de" "esgf3.dkrz.de" "esgf3.dkrz.de" "esgf3.dkrz.de" ...
#> $ file_url : chr "http://esgf3.dkrz.de/thredds/fileServer/cmip6/ScenarioMIP/AWI/AWI-CM-1-1-MR/ssp585/r1i1p1f1/day/hurs/gn/v201905"| __truncated__ "http://esgf3.dkrz.de/thredds/fileServer/cmip6/ScenarioMIP/AWI/AWI-CM-1-1-MR/ssp585/r1i1p1f1/day/hurs/gn/v201905"| __truncated__ "http://esgf3.dkrz.de/thredds/fileServer/cmip6/ScenarioMIP/AWI/AWI-CM-1-1-MR/ssp585/r1i1p1f1/day/hurs/gn/v201905"| __truncated__ "http://esgf3.dkrz.de/thredds/fileServer/cmip6/ScenarioMIP/AWI/AWI-CM-1-1-MR/ssp585/r1i1p1f1/day/tas/gn/v2019052"| __truncated__ ...
#> $ dataset_pid : chr "hdl:21.14100/89501ae0-2fec-307b-bf68-552ea4d504a0" "hdl:21.14100/89501ae0-2fec-307b-bf68-552ea4d504a0" "hdl:21.14100/89501ae0-2fec-307b-bf68-552ea4d504a0" "hdl:21.14100/a336f13f-a4d3-3b57-a45a-8f27f0ba01b8" ...
#> $ tracking_id : chr "hdl:21.14100/f46077ee-ae81-4932-81af-d61394446ea3" "hdl:21.14100/a476933a-0f14-4d4c-b62d-0bf08e3471fd" "hdl:21.14100/3c3c98f8-d56e-4d8d-8ba7-1a9e541e6018" "hdl:21.14100/8503efb4-6509-4728-b95c-7203bd214a77" ...
#> - attr(*, ".internal.selfref")=<externalptr>You have to download CMIP6 output file by yourself using your preferable methods or tools. The download url can be found in the file_url column in the index.
After you have downloaded CMIP6 output files of interest, you can use suumary_database() to get a summary on files downloaded against the CMIP6 output file index.
This step is necessary as it map the loaded files against index so that epwshiftr knows which case is complete and can be used for the next step.
# Summary downloaded file by GCM and variable, use the latest downloaded file if
# multiple matches are detected and save matched information into the index file
sm <- summary_database(tempdir(), by = c("source", "variable"), mult = "latest", update = TRUE)
#> 24 NetCDF files found.
#> Data file index updated and saved to '/tmp/RtmpDtbJVc/cmip6_index.csv'
knitr::kable(sm)| variable_id | source_id | datetime_start | datetime_end | file_num | file_size | dl_num | dl_percent | dl_size |
|---|---|---|---|---|---|---|---|---|
| hurs | AWI-CM-1-1-MR | 2049-01-01 12:00:00 | 2081-12-31 12:00:00 | 6 | 551 [Mbytes] | 6 | 100 [%] | 548 [Mbytes] |
| tas | AWI-CM-1-1-MR | 2049-01-01 12:00:00 | 2081-12-31 12:00:00 | 6 | 493 [Mbytes] | 6 | 100 [%] | 484 [Mbytes] |
epw <- file.path(eplusr::eplus_config(8.8)$dir, "WeatherData/USA_CA_San.Francisco.Intl.AP.724940_TMY3.epw")
# match any coordinates with absolute distance less than 1 degree
coord <- match_coord(epw, threshold = list(lon = 1, lat = 1), max_num = 1)
#> Start to match coordinates...
class(coord)
#> [1] "epw_cmip6_coord"
names(coord)
#> [1] "epw" "meta" "coord"
coord$meta
#> $city
#> [1] "San Francisco Intl Ap"
#>
#> $state_province
#> [1] "CA"
#>
#> $country
#> [1] "USA"
#>
#> $latitude
#> [1] 37.62
#>
#> $longitude
#> [1] -122.4
coord$coord[, .(file_path, coord)]
#> file_path
#> <char>
#> 1: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20490101-20491231.nc
#> 2: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20500101-20501231.nc
#> 3: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20510101-20511231.nc
#> 4: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20490101-20491231.nc
#> 5: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20500101-20501231.nc
#> 6: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20510101-20511231.nc
#> 7: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20790101-20791231.nc
#> 8: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20800101-20801231.nc
#> 9: /tmp/RtmpDtbJVc/hurs_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20810101-20811231.nc
#> 10: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20790101-20791231.nc
#> 11: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20800101-20801231.nc
#> 12: /tmp/RtmpDtbJVc/tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20810101-20811231.nc
#> coord
#> <list>
#> 1: <list>
#> 2: <list>
#> 3: <list>
#> 4: <list>
#> 5: <list>
#> 6: <list>
#> 7: <list>
#> 8: <list>
#> 9: <list>
#> 10: <list>
#> 11: <list>
#> 12: <list>
str(coord$coord$coord[[1]])
#> List of 2
#> $ lat:List of 4
#> ..$ index: int 1
#> ..$ value: num 36.9
#> ..$ dis : num -0.685
#> ..$ which: int 136
#> $ lon:List of 4
#> ..$ index: int 1
#> ..$ value: num 302
#> ..$ dis : num -0.525
#> ..$ which: int 323extract_data()data <- extract_data(coord, years = c(2050, 2080))
#> Start to extract CMIP6 data according to matched coordinates...
class(data)
#> [1] "epw_cmip6_data"
names(data)
#> [1] "epw" "meta" "data"
knitr::kable(head(data$data))| activity_drs | institution_id | source_id | experiment_id | member_id | table_id | datetime | lat | lon | variable | description | units | value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-01 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 57.04578 |
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-02 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 66.95392 |
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-03 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 71.37276 |
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-04 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 82.09089 |
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-05 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 65.37158 |
| ScenarioMIP | AWI | AWI-CM-1-1-MR | ssp585 | r1i1p1f1 | day | 2050-01-06 20:00:00 | 36.93492 | 301.875 | hurs | Near-Surface Relative Humidity | % | 78.18507 |
morphed <- morphing_epw(data)
#> Morphing 'dry bulb temperature'...
#> Morphing 'relative humidity'...
#> Morphing 'dew point temperature'...
#> Morphing 'atmospheric pressure'...
#> WARNING: Input does not contain any data of 'sea level pressure'. Skip.
#> Morphing 'horizontal infrared radiation from the sky'...
#> WARNING: Input does not contain any data of 'surface downwelling longware radiation'. Skip.
#> Morphing 'global horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortware radiation'. Skip.
#> Morphing 'diffuse horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortware radiation'. Skip.
#> Morphing 'direct normal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortware radiation'. Skip.
#> Morphing 'wind speed'...
#> WARNING: Input does not contain any data of 'near-surface wind speed'. Skip.
#> Morphing 'total sky cover'...
#> WARNING: Input does not contain any data of 'total cloud area fraction for the whole atmospheric column'. Skip.
#> Morphing 'opaque sky cover'...
#> WARNING: Input does not contain any data of 'total cloud area fraction for the whole atmospheric column'. Skip.
class(morphed)
#> [1] "epw_cmip6_morphed"
names(morphed)
#> [1] "epw" "tdb" "tdew" "rh"
#> [5] "p" "hor_ir" "glob_rad" "norm_rad"
#> [9] "diff_rad" "wind" "total_cover" "opaque_cover"
knitr::kable(head(morphed$tdb))| activity_drs | experiment_id | institution_id | source_id | member_id | table_id | lon | lat | interval | datetime | year | month | day | hour | minute | dry_bulb_temperature | delta | alpha |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 01:00:00 | 1999 | 1 | 1 | 1 | 0 | 13.056525 | 7.808153 | 1.813406 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 02:00:00 | 1999 | 1 | 1 | 2 | 0 | 13.056525 | 7.808153 | 1.813406 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 03:00:00 | 1999 | 1 | 1 | 3 | 0 | 12.149822 | 7.808153 | 1.813406 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 04:00:00 | 1999 | 1 | 1 | 4 | 0 | 11.061778 | 7.808153 | 1.813406 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 05:00:00 | 1999 | 1 | 1 | 5 | 0 | 7.978987 | 7.808153 | 1.813406 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 06:00:00 | 1999 | 1 | 1 | 6 | 0 | 7.978987 | 7.808153 | 1.813406 |
| activity_drs | experiment_id | institution_id | source_id | member_id | table_id | lon | lat | interval | datetime | year | month | day | hour | minute | relative_humidity | delta | alpha |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 01:00:00 | 1999 | 1 | 1 | 1 | 0 | 75.94106 | -12.70029 | 0.8437895 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 02:00:00 | 1999 | 1 | 1 | 2 | 0 | 75.94106 | -12.70029 | 0.8437895 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 03:00:00 | 1999 | 1 | 1 | 3 | 0 | 75.09727 | -12.70029 | 0.8437895 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 04:00:00 | 1999 | 1 | 1 | 4 | 0 | 78.47243 | -12.70029 | 0.8437895 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 05:00:00 | 1999 | 1 | 1 | 5 | 0 | 81.84758 | -12.70029 | 0.8437895 |
| ScenarioMIP | ssp585 | AWI | AWI-CM-1-1-MR | r1i1p1f1 | day | 301.875 | 36.93492 | 2050 | 2017-01-01 06:00:00 | 1999 | 1 | 1 | 6 | 0 | 81.84758 | -12.70029 | 0.8437895 |
morphing_epw(), we can now create future EPW files using future_epw()# create future EPWs grouped by GCM, experiment ID, interval (year)
epws <- future_epw(morphed, by = c("source", "experiment", "interval"),
dir = tempdir(), separate = TRUE, overwrite = TRUE
)
#> Warning: Empty morphed data found for variables listed below. Original data from EPW will be used:
#> [1]: Atmospheric pressure
#> [2]: Horizontal infrared radiation intensity from sky
#> [3]: Global horizontal radiation
#> [4]: Direct normal radiation
#> [5]: Diffuse horizontal radiation
#> [6]: Wind speed
#> [7]: Total sky cover
#> [8]: Opaque sky cover
#> ── Info ──────────────────────────────────────────────────────────────────
#> Data period #1 has been replaced with input data.
#>
#> Name StartDayOfWeek StartDay EndDay
#> 1: Data Sunday 1/ 1 12/31
#> ──────────────────────────────────────────────────────────────────────────
#> Replace the existing EPW file located at /tmp/RtmpDtbJVc/AWI-CM-1-1-MR/ssp585/2050/USA_CA_San.Francisco.Intl.AP.724940_TMY3.AWI-CM-1-1-MR.ssp585.2050.epw.
#> ── Info ──────────────────────────────────────────────────────────────────
#> Data period #1 has been replaced with input data.
#>
#> Name StartDayOfWeek StartDay EndDay
#> 1: Data Sunday 1/ 1 12/31
#> ──────────────────────────────────────────────────────────────────────────
#> Replace the existing EPW file located at /tmp/RtmpDtbJVc/AWI-CM-1-1-MR/ssp585/2080/USA_CA_San.Francisco.Intl.AP.724940_TMY3.AWI-CM-1-1-MR.ssp585.2080.epw.
epws
#> [[1]]
#> ══ EnergyPlus Weather File ═══════════════════════════════════════════════
#> [Location ]: San Francisco Intl Ap, CA, USA
#> {N 37°37'}, {W 122°24'}, {UTC-08:00}
#> [Elevation]: 2m above see level
#> [Data Src ]: TMY3
#> [WMO Stat ]: 724940
#> [Leap Year]: No
#> [Interval ]: 60 mins
#>
#> ── Data Periods ──────────────────────────────────────────────────────────
#> Name StartDayOfWeek StartDay EndDay
#> 1: Data Sunday 1/ 1 12/31
#>
#> ──────────────────────────────────────────────────────────────────────────
#>
#> [[2]]
#> ══ EnergyPlus Weather File ═══════════════════════════════════════════════
#> [Location ]: San Francisco Intl Ap, CA, USA
#> {N 37°37'}, {W 122°24'}, {UTC-08:00}
#> [Elevation]: 2m above see level
#> [Data Src ]: TMY3
#> [WMO Stat ]: 724940
#> [Leap Year]: No
#> [Interval ]: 60 mins
#>
#> ── Data Periods ──────────────────────────────────────────────────────────
#> Name StartDayOfWeek StartDay EndDay
#> 1: Data Sunday 1/ 1 12/31
#>
#> ──────────────────────────────────────────────────────────────────────────
sapply(epws, function (epw) epw$path())
#> [1] "/tmp/RtmpDtbJVc/AWI-CM-1-1-MR/ssp585/2050/USA_CA_San.Francisco.Intl.AP.724940_TMY3.AWI-CM-1-1-MR.ssp585.2050.epw"
#> [2] "/tmp/RtmpDtbJVc/AWI-CM-1-1-MR/ssp585/2080/USA_CA_San.Francisco.Intl.AP.724940_TMY3.AWI-CM-1-1-MR.ssp585.2080.epw"Hongyuan Jia and Adrian Chong
epwshiftr
epwshiftr is released under the terms of MIT License.
Copyright © 2019-2020 Hongyuan Jia and Adrian Chong
CMIP6 data
To enable modeling groups and others who support CMIP6 to demonstrate its impact (and secure ongoing funding), you are required to cite and acknowledge those who have made CMIP6 possible. You also must abide by any licensing restrictions, which are recorded in each file as a global attribute (named “license”).
Please carefully read and adhere to the CMIP6 Terms of Use.
Please note that the ‘epwshiftr’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.