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
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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	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	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	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	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	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	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	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	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	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	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	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	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	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	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

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CMIP6 data

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Please carefully read and adhere to the CMIP6 Terms of Use.

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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.

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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.

epwshiftr

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Build CMIP6 output file index

Manage CMIP6 output files

Extract CMIP6 output data

Morphing EPW weather variables

Create future EPW files

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