README.md
In hongyuanjia/epwshiftr: Create Future 'EnergyPlus' Weather Files using 'CMIP6' Data
epwshiftr 
Create future EnergyPlus Weather files using CMIP6 data
How to cite
To cite epwshiftr in publications use:
Jia, Hongyuan, Chong, Adrian, Ning, Baisong, 2023.
Epwshiftr: incorporating open data of climate change prediction into building performance simulation for future adaptation and mitigation,
in: Proceedings of Building Simulation 2023: 18th Conference of IBPSA, Building Simulation.
Presented at the Building Simulation 2023, IBPSA, Shanghai, China, pp. 3201–3207.
https://doi.org/10.26868/25222708.2023.1612
A BibTeX entry for LaTeX users is:
@inproceedings{jia2023epwshiftr,
title = {Epwshiftr: Incorporating Open Data of Climate Change Prediction into Building Performance Simulation for Future Adaptation and Mitigation},
shorttitle = {Epwshiftr},
booktitle = {Proceedings of {{Building Simulation}} 2023: 18th {{Conference}} of {{IBPSA}}},
author = {Jia, Hongyuan and Chong, Adrian and Ning, Baisong},
year = {2023},
series = {Building {{Simulation}}},
volume = {18},
pages = {3201--3207},
publisher = {{IBPSA}},
address = {{Shanghai, China}},
doi = {10.26868/25222708.2023.1612}
}
Installation
You can install the latest stable release of epwshiftr from
CRAN.
install.packages("epwshiftr")
Alternatively, you can install the development version from GitHub.
install.packages("epwshiftr",
repos = c(
ideaslab = "https://ideas-lab-nus.r-universe.dev",
cran = "https://cran.r-project.org"
)
)
Get started
Build CMIP6 output file index
- The first step is to build CMIP6 experiment output file index based
on queries using ESGF search RESTful API
# 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>
Manage CMIP6 output files
-
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] |
Extract CMIP6 output data
- With previous step, now we can match coordinates of an EPW in the
CMIP6 output file
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 323
- Once we get the matched coordinates, we can extract corresponding
data related to input EPW file using
extract_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 |
Morphing EPW weather variables
- With all necessary information extracted above, now we can perform
morphing on out EPW
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 longwave radiation'. Skip.
#> Morphing 'global horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave radiation'. Skip.
#> Morphing 'diffuse horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave radiation'. Skip.
#> Morphing 'direct normal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave 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 |
knitr::kable(head(morphed$rh))
| 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 |
Create future EPW files
- Once we get the morphed data using
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"
Author
Hongyuan Jia and Adrian Chong
License
-
epwshiftr
epwshiftr is released under the terms of MIT License.
Copyright © 2019-2024 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.
Disclaimer
CMIP6 model data is licensed under a Creative Commons Attribution-ShareAlike
4.0 International License.
Consult Terms of Use for terms of use
governing CMIP6 output, including citation requirements and proper
acknowledgment. Further information about each GCM output data, including some
limitations, can be found via the further_info_url (recorded as a global
attribute in the NetCDF file) and at EC-Earth. The
data producers and data providers make no warranty, either express or implied,
including, but not limited to, warranties of merchantability and fitness for a
particular purpose. All liabilities arising from the supply of the information
(including any liability arising in negligence) are excluded to the fullest
extent permitted by law.
Contribute
If you encounter a clear bug or have questions about the usage, please file an
issue with a minimal reproducible example on
GitHub
If you have a solution for an existing bug or an implementation for a missing
feature, please send a pull request and let us review.
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.
hongyuanjia/epwshiftr documentation built on March 14, 2024, 9:17 a.m.
R Package Documentation
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epwshiftr
Create future EnergyPlus Weather files using CMIP6 data
How to cite
To cite epwshiftr in publications use:
Jia, Hongyuan, Chong, Adrian, Ning, Baisong, 2023.
Epwshiftr: incorporating open data of climate change prediction into building performance simulation for future adaptation and mitigation,
in: Proceedings of Building Simulation 2023: 18th Conference of IBPSA, Building Simulation.
Presented at the Building Simulation 2023, IBPSA, Shanghai, China, pp. 3201–3207.
https://doi.org/10.26868/25222708.2023.1612
A BibTeX entry for LaTeX users is:
@inproceedings{jia2023epwshiftr,
title = {Epwshiftr: Incorporating Open Data of Climate Change Prediction into Building Performance Simulation for Future Adaptation and Mitigation},
shorttitle = {Epwshiftr},
booktitle = {Proceedings of {{Building Simulation}} 2023: 18th {{Conference}} of {{IBPSA}}},
author = {Jia, Hongyuan and Chong, Adrian and Ning, Baisong},
year = {2023},
series = {Building {{Simulation}}},
volume = {18},
pages = {3201--3207},
publisher = {{IBPSA}},
address = {{Shanghai, China}},
doi = {10.26868/25222708.2023.1612}
}
Installation
You can install the latest stable release of epwshiftr from CRAN.
install.packages("epwshiftr")
Alternatively, you can install the development version from GitHub.
install.packages("epwshiftr",
repos = c(
ideaslab = "https://ideas-lab-nus.r-universe.dev",
cran = "https://cran.r-project.org"
)
)
Get started
Build CMIP6 output file index
- The first step is to build CMIP6 experiment output file index based on queries using ESGF search RESTful API
# 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>
Manage CMIP6 output files
-
You have to download CMIP6 output file by yourself using your preferable methods or tools. The download url can be found in the
file_urlcolumn 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] |
Extract CMIP6 output data
- With previous step, now we can match coordinates of an EPW in the CMIP6 output file
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 323
- Once we get the matched coordinates, we can extract corresponding
data related to input EPW file using
extract_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 |
Morphing EPW weather variables
- With all necessary information extracted above, now we can perform morphing on out EPW
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 longwave radiation'. Skip.
#> Morphing 'global horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave radiation'. Skip.
#> Morphing 'diffuse horizontal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave radiation'. Skip.
#> Morphing 'direct normal radiation'...
#> WARNING: Input does not contain any data of 'surface downwelling shortwave 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 |
knitr::kable(head(morphed$rh))
| 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 |
Create future EPW files
- Once we get the morphed data using
morphing_epw(), we can now create future EPW files usingfuture_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"
Author
Hongyuan Jia and Adrian Chong
License
-
epwshiftr
epwshiftr is released under the terms of MIT License.
Copyright © 2019-2024 Hongyuan Jia and Adrian Chong
-
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