Input Data
helios is an R package that calculates heating and
cooling degrees based on high temporal and spatial resolution of climate
data (hourly and 12km resolution) and population data (yearly and 12km
resolution). In this tutorial, we introduce how to use
helios to support GCAM-USA, where we use climate data over
CONUS from WRF or those with the same format and spatial resolution.
Data are available on NERSC if users have access. Table 1 shows more details on the climate and
population data supported by helios.
* This method was developed for the GCAM-USA version as part of the Integrated Multisector, Multiscale Modeling (IM3) project.
Table 1: Description of required input data in this tutorial.
| Specification | WRF Climate Data | Population Data |
|---|---|---|
| Input Format | NetCDF File | CSV File |
| Spatial Resolution | WRF Resolution: 12 x 12 km | WRF Resolution: 12 x 12 km |
| Temporal Resolution | Hourly | Annual |
| Required Variable | Temperature T2 (K) | Population (thousand capita) |
| Cluster Location | (NERSC) /global/cfs/cdirs/m2702/gsharing/tgw-wrf-conus | (NERSC) /global/cfs/cdirs/m2702/gcamusa/hddcdd/pop_1km |
| Full Dataset Download | N/A | (Pre-processed) https://zenodo.org/record/3756179#.Y5fLK3bMKUl |
| Example Dataset | helios::example_wrf_usa_ncdf |
helios::example_pop_usa_csv |
| More Information | https://www.mmm.ucar.edu/models/wrf | https://www.mdpi.com/2071-1050/12/8/3374 |
Please note that helios can process multiple climate
NetCDF files with one population CSV file for each run. However,
parallelizing grouped climate data can improve calculation efficiency
for large dataset.
Workflow
helios includes 6 major steps in the workflow for CONUS
heating- and cooling-degrees calculation (Figure
1),
- Step 1: Process climate NetCDF at 12-km resolution.
- Step 2: Calculate weighted population.
- Step 3: Calculate heating and cooling degree-hours at grid resolution.
- Step 4: Aggregate heating and cooling degree-hours by CONUS and building energy demand sector (This can be used as input to GCAM-USA).
- Step 5: Calculate heating and cooling degree-days at monthly and annual scale for CONUS.
- Step 6: Post-process for diagnostic and convert to GCAM required format.
*Note: Dispatch segments are defined as 24 day and night segments in each month of the year (e.g., Jan-day, Jan-night, Feb-day, Feb-night, etc), plus a “super-peak” segment that has the top 10% of loads within a year for each grid region. Dispatch segments are specifically used in GCAM-USA.
Figure 1: Helios workflow for CONUS heating and cooling degree-days for GCAM-USA.
Method
helios calculates different units of heating and cooling
degrees for different temporal scale. Table 2
shows more details of the method
Table 2: Methods for calculating heating and cooling degrees at different temporal scales.
| Temporal Scale | Spatial Scale | Sector | Unit | Method |
|---|---|---|---|---|
| Dispatch Segment | U.S. States | Building Thermal Service* | Degree-hours | 1. Calculate heating degree-hours (HDH) and cooling degree-hours
(CDH) as the difference between hourly temperature and comfortable
temperature (65F). where, is the hourly temperature at a grid cell; is the comfortable temperature (usually 65F for USA). 2. Multiply the weighted population with degree-days within the same grid cell. 3. Aggregate hourly HDH and CDH by dispatch segment, U.S. States, and building energy demand sectors. |
| Monthly | U.S. States | N/A | Degree-days | 1. Calculate daily mean temperature as the average of minimum and
maximum daily temperature. 2. Calculate heating degree-days (HDD < 0) and cooling degree-days (CDD > 0) as the difference between mean temperature and comfortable temperature (65F). 3. Aggregate daily HDD (HDD < 0) and CDD (CDD > 0) by monthly, respectively. |
| Annual | U.S. States | N/A | Degree-days | 1.Aggregate monthly HDD (HDD < 0) and CDD (CDD > 0) by year, respectively. |
* Building thermal service includes: commercial heating, commercial cooling, residential heating, and residential cooling.
Examples
Example climate and population data
Due to large size of the climate data (NetCDF file),
helios provide spatially subset of example data for
demonstration purpose.
-
Climate NetCDF File:
helios::example_wrf_usa_ncdfsubsets an approximately [-119.86,-118.18,37.99, 39.30] longitude and latitude box within the U.S. (12-km resolution) from the WRF hourly data. Data time ranges from 2020-01-01 01:00:00 to 2020-01-08 00:00:00. -
Population CSV File:
helios::example_pop_usa_csvis population for 2020, which is geo-referenced to WRF grid cells.
Read climate and population data
Users can use helios functions to read climate and
population data and convert to tabular table with latitudes and
longitudes.
-
helios::read_ncdfaccepts NetCDF format -
helios::read_populationaccepts NetCDF and CSV formats
# example data: WRF hourly climate at 12-km resolution
path_to_climate_ncdf <- helios::pkg_example('wrfout_d01_2020-01-01_01%3A00%3A00_sub.nc')
temperature <- helios::read_ncdf(ncdf = path_to_climate_ncdf,
model = 'wrf',
var = 'T2',
time_periods = 2020)## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"| 2020-01-01_01:00:00 | 2020-01-01_02:00:00 | 2020-01-01_03:00:00 | 2020-01-01_04:00:00 | 2020-01-01_05:00:00 | 2020-01-01_06:00:00 | 2020-01-01_07:00:00 | 2020-01-01_08:00:00 | 2020-01-01_09:00:00 | 2020-01-01_10:00:00 | 2020-01-01_11:00:00 | 2020-01-01_12:00:00 | 2020-01-01_13:00:00 | 2020-01-01_14:00:00 | 2020-01-01_15:00:00 | 2020-01-01_16:00:00 | 2020-01-01_17:00:00 | 2020-01-01_18:00:00 | 2020-01-01_19:00:00 | 2020-01-01_20:00:00 | 2020-01-01_21:00:00 | 2020-01-01_22:00:00 | 2020-01-01_23:00:00 | 2020-01-02_00:00:00 | 2020-01-02_01:00:00 | 2020-01-02_02:00:00 | 2020-01-02_03:00:00 | 2020-01-02_04:00:00 | 2020-01-02_05:00:00 | 2020-01-02_06:00:00 | 2020-01-02_07:00:00 | 2020-01-02_08:00:00 | 2020-01-02_09:00:00 | 2020-01-02_10:00:00 | 2020-01-02_11:00:00 | 2020-01-02_12:00:00 | 2020-01-02_13:00:00 | 2020-01-02_14:00:00 | 2020-01-02_15:00:00 | 2020-01-02_16:00:00 | 2020-01-02_17:00:00 | 2020-01-02_18:00:00 | 2020-01-02_19:00:00 | 2020-01-02_20:00:00 | 2020-01-02_21:00:00 | 2020-01-02_22:00:00 | 2020-01-02_23:00:00 | 2020-01-03_00:00:00 | 2020-01-03_01:00:00 | 2020-01-03_02:00:00 | 2020-01-03_03:00:00 | 2020-01-03_04:00:00 | 2020-01-03_05:00:00 | 2020-01-03_06:00:00 | 2020-01-03_07:00:00 | 2020-01-03_08:00:00 | 2020-01-03_09:00:00 | 2020-01-03_10:00:00 | 2020-01-03_11:00:00 | 2020-01-03_12:00:00 | 2020-01-03_13:00:00 | 2020-01-03_14:00:00 | 2020-01-03_15:00:00 | 2020-01-03_16:00:00 | 2020-01-03_17:00:00 | 2020-01-03_18:00:00 | 2020-01-03_19:00:00 | 2020-01-03_20:00:00 | 2020-01-03_21:00:00 | 2020-01-03_22:00:00 | 2020-01-03_23:00:00 | 2020-01-04_00:00:00 | 2020-01-04_01:00:00 | 2020-01-04_02:00:00 | 2020-01-04_03:00:00 | 2020-01-04_04:00:00 | 2020-01-04_05:00:00 | 2020-01-04_06:00:00 | 2020-01-04_07:00:00 | 2020-01-04_08:00:00 | 2020-01-04_09:00:00 | 2020-01-04_10:00:00 | 2020-01-04_11:00:00 | 2020-01-04_12:00:00 | 2020-01-04_13:00:00 | 2020-01-04_14:00:00 | 2020-01-04_15:00:00 | 2020-01-04_16:00:00 | 2020-01-04_17:00:00 | 2020-01-04_18:00:00 | 2020-01-04_19:00:00 | 2020-01-04_20:00:00 | 2020-01-04_21:00:00 | 2020-01-04_22:00:00 | 2020-01-04_23:00:00 | 2020-01-05_00:00:00 | 2020-01-05_01:00:00 | 2020-01-05_02:00:00 | 2020-01-05_03:00:00 | 2020-01-05_04:00:00 | 2020-01-05_05:00:00 | 2020-01-05_06:00:00 | 2020-01-05_07:00:00 | 2020-01-05_08:00:00 | 2020-01-05_09:00:00 | 2020-01-05_10:00:00 | 2020-01-05_11:00:00 | 2020-01-05_12:00:00 | 2020-01-05_13:00:00 | 2020-01-05_14:00:00 | 2020-01-05_15:00:00 | 2020-01-05_16:00:00 | 2020-01-05_17:00:00 | 2020-01-05_18:00:00 | 2020-01-05_19:00:00 | 2020-01-05_20:00:00 | 2020-01-05_21:00:00 | 2020-01-05_22:00:00 | 2020-01-05_23:00:00 | 2020-01-06_00:00:00 | 2020-01-06_01:00:00 | 2020-01-06_02:00:00 | 2020-01-06_03:00:00 | 2020-01-06_04:00:00 | 2020-01-06_05:00:00 | 2020-01-06_06:00:00 | 2020-01-06_07:00:00 | 2020-01-06_08:00:00 | 2020-01-06_09:00:00 | 2020-01-06_10:00:00 | 2020-01-06_11:00:00 | 2020-01-06_12:00:00 | 2020-01-06_13:00:00 | 2020-01-06_14:00:00 | 2020-01-06_15:00:00 | 2020-01-06_16:00:00 | 2020-01-06_17:00:00 | 2020-01-06_18:00:00 | 2020-01-06_19:00:00 | 2020-01-06_20:00:00 | 2020-01-06_21:00:00 | 2020-01-06_22:00:00 | 2020-01-06_23:00:00 | 2020-01-07_00:00:00 | 2020-01-07_01:00:00 | 2020-01-07_02:00:00 | 2020-01-07_03:00:00 | 2020-01-07_04:00:00 | 2020-01-07_05:00:00 | 2020-01-07_06:00:00 | 2020-01-07_07:00:00 | 2020-01-07_08:00:00 | 2020-01-07_09:00:00 | 2020-01-07_10:00:00 | 2020-01-07_11:00:00 | 2020-01-07_12:00:00 | 2020-01-07_13:00:00 | 2020-01-07_14:00:00 | 2020-01-07_15:00:00 | 2020-01-07_16:00:00 | 2020-01-07_17:00:00 | 2020-01-07_18:00:00 | 2020-01-07_19:00:00 | 2020-01-07_20:00:00 | 2020-01-07_21:00:00 | 2020-01-07_22:00:00 | 2020-01-07_23:00:00 | 2020-01-08_00:00:00 | lat | lon |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 276.5300 | 275.6490 | 275.3997 | 276.2654 | 276.3188 | 275.3131 | 275.1793 | 275.1678 | 275.4477 | 274.6594 | 275.0215 | 274.5066 | 274.0973 | 273.4333 | 273.1917 | 273.2197 | 273.4683 | 274.2319 | 274.4253 | 274.5829 | 274.6111 | 274.7595 | 274.4582 | 274.1265 | 272.4343 | 272.1081 | 272.4213 | 272.4012 | 272.5107 | 272.1806 | 271.8319 | 271.9175 | 271.8053 | 271.9059 | 272.2191 | 272.6064 | 272.7346 | 272.7979 | 272.9968 | 273.6599 | 274.5406 | 275.3864 | 276.0518 | 276.6354 | 276.6277 | 276.1523 | 275.4861 | 274.7047 | 274.3335 | 274.1952 | 274.1892 | 274.2293 | 273.9428 | 274.4261 | 274.1110 | 274.0109 | 274.4982 | 273.9119 | 272.9631 | 273.0461 | 273.4452 | 273.7902 | 273.9785 | 274.4789 | 275.4077 | 275.9487 | 276.2252 | 276.3383 | 276.2659 | 276.1937 | 275.8185 | 275.2528 | 273.9643 | 273.6454 | 274.1072 | 274.0459 | 273.9443 | 273.5988 | 273.7031 | 273.8700 | 274.9154 | 274.6056 | 274.0661 | 273.6829 | 273.3907 | 272.8367 | 272.8774 | 273.5490 | 274.2466 | 274.6936 | 275.6333 | 276.2151 | 276.7635 | 276.6874 | 276.6274 | 276.0013 | 275.3850 | 274.8209 | 275.0423 | 275.4397 | 275.5713 | 275.5521 | 275.4211 | 275.0807 | 275.4684 | 275.2352 | 274.5827 | 274.8870 | 274.7747 | 274.5350 | 274.5713 | 275.0025 | 275.7573 | 276.5378 | 276.8432 | 278.0032 | 278.5198 | 278.0701 | 277.0977 | 275.7569 | 277.0160 | 276.0139 | 275.3785 | 274.4422 | 275.7768 | 277.2616 | 276.1501 | 276.7281 | 276.7648 | 276.8806 | 276.5486 | 276.3167 | 275.7559 | 275.4862 | 275.3027 | 275.8199 | 276.3640 | 277.2781 | 278.2332 | 278.1565 | 277.5609 | 277.0736 | 276.5292 | 276.0348 | 275.3117 | 275.4704 | 275.4094 | 275.2180 | 274.9099 | 274.4805 | 274.4451 | 274.3776 | 274.1708 | 273.1686 | 272.9023 | 272.9958 | 273.0955 | 272.8990 | 272.7543 | 273.0616 | 274.1968 | 275.6386 | 276.4114 | 276.2007 | 276.0780 | 276.0026 | 275.6364 | 274.7948 | 39.04633 | -119.8572 |
| 277.5141 | 276.6511 | 276.4965 | 276.4675 | 276.6721 | 276.1877 | 275.8394 | 275.2877 | 275.8579 | 276.3694 | 275.4083 | 275.0790 | 274.4619 | 274.3538 | 274.1082 | 273.8329 | 274.4260 | 274.9395 | 275.4879 | 276.1248 | 276.6231 | 276.6717 | 276.1452 | 274.9381 | 272.6784 | 272.2010 | 273.0173 | 273.0609 | 272.9960 | 272.9965 | 272.8297 | 272.6760 | 272.5325 | 272.5701 | 272.7784 | 272.8768 | 272.8011 | 272.7975 | 272.5889 | 273.0603 | 274.5363 | 275.9919 | 276.7416 | 277.4496 | 277.6798 | 277.4659 | 276.7717 | 275.5746 | 274.4180 | 274.3222 | 274.6838 | 275.0549 | 275.0276 | 275.1476 | 275.1376 | 274.3068 | 273.8412 | 273.4651 | 273.8712 | 274.4512 | 274.6930 | 274.3943 | 274.4009 | 275.2033 | 276.1421 | 276.6801 | 276.6435 | 276.9244 | 277.2086 | 277.0903 | 276.7776 | 276.0005 | 274.5606 | 274.5313 | 274.6798 | 274.5377 | 273.9607 | 273.9301 | 274.4709 | 274.2786 | 273.9256 | 273.8497 | 274.0740 | 274.1812 | 274.0009 | 273.9956 | 273.8887 | 274.3809 | 274.8143 | 275.8507 | 276.7177 | 277.8681 | 278.6509 | 278.1268 | 277.3544 | 276.0275 | 275.1082 | 275.3980 | 275.6841 | 275.8140 | 275.6926 | 275.6695 | 276.1281 | 275.8368 | 275.8124 | 276.5654 | 275.9763 | 275.9733 | 276.0538 | 276.0152 | 275.9520 | 276.3412 | 277.4697 | 278.1335 | 278.1875 | 278.8406 | 279.7073 | 280.8174 | 278.4150 | 277.9463 | 278.1956 | 276.6802 | 275.5489 | 275.3153 | 276.3322 | 277.3853 | 278.4218 | 276.2948 | 276.6752 | 276.6145 | 276.5230 | 276.4247 | 276.5587 | 277.3104 | 276.9005 | 276.7920 | 277.1361 | 278.5967 | 279.8486 | 280.3044 | 280.2184 | 279.5405 | 278.8965 | 278.0336 | 276.8735 | 276.4886 | 276.0832 | 275.4787 | 275.3219 | 274.7709 | 274.3778 | 274.4568 | 274.0514 | 274.2767 | 274.4294 | 274.5095 | 274.7887 | 274.8346 | 273.6943 | 273.4651 | 274.9919 | 276.2297 | 277.4387 | 278.1524 | 277.9485 | 278.0944 | 277.7947 | 276.7672 | 39.07250 | -119.7211 |
| 277.4338 | 277.3451 | 276.8208 | 276.5629 | 276.3838 | 276.4206 | 275.5637 | 275.1495 | 275.4079 | 274.7087 | 274.0557 | 273.5301 | 273.8975 | 273.4431 | 273.0273 | 273.4006 | 274.5069 | 274.8414 | 275.4318 | 275.9485 | 276.2454 | 276.2336 | 275.5822 | 273.6338 | 271.9279 | 271.1920 | 272.5292 | 272.1657 | 271.9913 | 272.0365 | 271.9078 | 271.7598 | 271.4124 | 271.0777 | 271.3087 | 271.5481 | 271.5678 | 271.3572 | 271.3090 | 272.3175 | 274.1086 | 275.4430 | 276.2410 | 276.8939 | 277.0539 | 276.9896 | 276.1978 | 274.2848 | 273.4598 | 274.0288 | 274.0605 | 273.9382 | 274.0850 | 273.9896 | 274.0732 | 274.1925 | 273.2381 | 272.9239 | 273.3215 | 273.4763 | 273.3856 | 273.2394 | 273.3382 | 274.1041 | 275.5028 | 276.5359 | 276.6447 | 277.0298 | 277.1918 | 277.0930 | 276.5129 | 275.3564 | 274.2963 | 274.2750 | 274.3716 | 274.0932 | 273.6718 | 273.6484 | 273.8635 | 274.0537 | 274.0068 | 274.0025 | 274.0842 | 273.7983 | 273.9827 | 273.6584 | 273.4251 | 273.7102 | 274.6863 | 275.9803 | 277.0750 | 277.8293 | 277.6727 | 277.1702 | 276.3065 | 275.4015 | 274.7246 | 274.9166 | 275.4112 | 275.4323 | 275.4433 | 275.3102 | 275.6197 | 275.5220 | 276.0020 | 276.0228 | 275.6092 | 276.1223 | 276.1828 | 276.0825 | 276.1298 | 276.4129 | 277.2372 | 277.8974 | 278.2938 | 279.2236 | 280.4651 | 280.3109 | 277.7477 | 276.8280 | 277.2137 | 276.6455 | 275.9772 | 276.2234 | 277.0934 | 276.7909 | 276.9845 | 276.3396 | 276.0008 | 274.9017 | 275.1274 | 275.2122 | 276.7889 | 276.7823 | 276.7444 | 276.7426 | 276.7131 | 277.6767 | 278.2821 | 278.5796 | 278.0051 | 277.5120 | 276.4938 | 275.7630 | 274.8125 | 274.4836 | 273.6418 | 273.3192 | 273.6409 | 273.2992 | 272.8663 | 272.8728 | 272.5224 | 273.2383 | 273.6937 | 273.6470 | 273.6143 | 273.2316 | 272.8201 | 273.4341 | 274.4675 | 275.2092 | 275.9472 | 276.5947 | 276.3076 | 276.2460 | 275.9823 | 274.6244 | 39.09851 | -119.5850 |
| 276.1456 | 276.6089 | 276.2645 | 276.0904 | 275.8828 | 275.7302 | 274.7753 | 274.7005 | 273.7611 | 273.7706 | 272.9093 | 272.3963 | 272.1255 | 272.2478 | 271.9475 | 272.5542 | 274.0496 | 274.5827 | 275.0481 | 275.5301 | 275.7291 | 275.5295 | 274.6008 | 272.9986 | 271.7695 | 271.6637 | 272.1436 | 272.0763 | 271.8557 | 271.7850 | 271.6707 | 271.1430 | 270.9333 | 270.8156 | 271.0215 | 271.3642 | 271.0447 | 270.7107 | 270.6114 | 271.6955 | 273.4649 | 274.1854 | 274.5420 | 275.1171 | 275.2093 | 275.2749 | 274.5500 | 272.9525 | 272.8267 | 273.6593 | 273.4767 | 273.5377 | 274.1450 | 274.1204 | 273.8391 | 273.2641 | 272.1939 | 271.6876 | 272.1254 | 272.5572 | 272.4587 | 272.2798 | 272.2192 | 273.6588 | 274.7536 | 275.2897 | 275.3186 | 275.5145 | 275.6787 | 275.7152 | 275.5137 | 274.8559 | 274.0413 | 273.9953 | 273.8416 | 273.4784 | 273.3740 | 273.5189 | 273.5863 | 273.5869 | 273.4971 | 273.4205 | 273.4194 | 273.2730 | 273.0462 | 272.6932 | 272.8140 | 273.1826 | 274.1861 | 274.7333 | 275.1110 | 275.4164 | 275.3711 | 275.1987 | 275.0210 | 274.9586 | 274.4376 | 274.8642 | 275.4652 | 275.4933 | 275.6212 | 275.0191 | 274.6194 | 274.8094 | 275.2186 | 274.6300 | 274.7623 | 275.7848 | 275.7439 | 275.8395 | 276.2976 | 275.9301 | 276.6202 | 277.2888 | 277.6465 | 277.9364 | 279.3745 | 279.6475 | 277.0350 | 275.2559 | 276.3464 | 275.9424 | 275.2310 | 275.6198 | 276.4088 | 275.8312 | 275.9510 | 276.2417 | 275.5269 | 275.2292 | 274.7899 | 274.2027 | 275.4911 | 275.3581 | 275.3609 | 275.5811 | 276.0458 | 276.4993 | 276.5338 | 276.0800 | 276.0114 | 276.2060 | 275.8844 | 275.0990 | 274.5387 | 273.9092 | 273.1283 | 273.0601 | 273.2600 | 273.2537 | 273.1850 | 273.0403 | 272.8943 | 272.4073 | 272.2921 | 272.3653 | 272.7559 | 272.3257 | 271.8370 | 272.3258 | 273.9063 | 274.6393 | 275.2941 | 275.8652 | 275.6672 | 275.2633 | 274.9958 | 273.8153 | 39.12438 | -119.4488 |
| 277.5574 | 277.1606 | 276.3387 | 277.1970 | 277.0124 | 275.9257 | 276.3840 | 275.5539 | 274.9964 | 274.6070 | 274.0705 | 273.9977 | 273.1750 | 273.4080 | 273.4267 | 273.8522 | 275.1465 | 276.0978 | 276.7463 | 277.2297 | 277.4701 | 277.1470 | 275.7167 | 274.0936 | 272.8493 | 272.8544 | 273.2525 | 273.1451 | 272.9022 | 272.9698 | 273.0800 | 272.5929 | 272.2767 | 272.2982 | 272.5597 | 272.4359 | 271.6062 | 270.9430 | 270.6098 | 271.0472 | 271.7952 | 272.1649 | 272.4696 | 272.6081 | 272.6893 | 272.9430 | 272.9328 | 271.5771 | 271.0288 | 271.1708 | 270.9561 | 270.6268 | 270.5807 | 270.9570 | 271.0493 | 270.8074 | 270.0400 | 269.8872 | 270.3819 | 270.8673 | 270.8623 | 270.4846 | 269.9919 | 270.7245 | 272.0373 | 273.0488 | 273.8262 | 274.4178 | 275.0051 | 275.3361 | 275.0424 | 274.2755 | 273.6784 | 273.6925 | 273.5287 | 273.1665 | 272.9369 | 272.8120 | 272.8887 | 273.1729 | 272.8366 | 272.4920 | 272.5561 | 272.4862 | 272.1553 | 271.7241 | 271.3921 | 271.7446 | 272.7508 | 273.7265 | 273.9555 | 274.3626 | 274.6337 | 274.8167 | 274.4074 | 273.8510 | 273.3102 | 273.4758 | 273.6809 | 273.6964 | 274.5382 | 275.0141 | 274.5121 | 273.6469 | 273.8307 | 273.7944 | 274.4795 | 274.8350 | 274.8187 | 274.9169 | 275.3838 | 275.9378 | 276.7586 | 277.0553 | 277.4254 | 277.9214 | 278.7803 | 278.3560 | 278.0323 | 277.2965 | 276.8568 | 276.2577 | 276.1409 | 275.6672 | 275.8644 | 274.5813 | 275.4075 | 276.5349 | 275.9372 | 275.4072 | 275.1977 | 275.1949 | 275.7876 | 275.3369 | 275.3577 | 275.3927 | 276.1418 | 276.8679 | 276.9182 | 276.6274 | 276.6988 | 276.9125 | 277.0086 | 276.4327 | 275.7457 | 275.1416 | 275.0683 | 274.9829 | 274.9680 | 274.3209 | 274.1369 | 273.8516 | 273.6762 | 273.2323 | 273.0890 | 273.2127 | 273.2136 | 272.7556 | 272.5639 | 273.4417 | 275.1006 | 275.9895 | 276.8320 | 277.0040 | 277.6437 | 277.6513 | 276.7641 | 275.6492 | 39.15008 | -119.3124 |
| 276.6362 | 276.4886 | 276.3837 | 277.1292 | 277.5344 | 277.0761 | 277.1561 | 276.3749 | 276.5429 | 275.9415 | 275.3298 | 275.2082 | 274.4792 | 274.6838 | 274.3546 | 274.9135 | 276.1785 | 277.0599 | 278.5672 | 279.1985 | 279.7790 | 279.8557 | 279.1856 | 277.3450 | 275.2015 | 274.3091 | 274.2802 | 274.1402 | 273.9168 | 274.1143 | 274.3040 | 274.0294 | 273.7521 | 273.5859 | 273.3768 | 272.4434 | 271.5200 | 271.4015 | 271.3495 | 271.4819 | 271.6245 | 272.0958 | 272.8312 | 273.4870 | 274.1862 | 273.8660 | 273.3923 | 272.2512 | 271.2083 | 270.5250 | 270.6730 | 270.4539 | 270.3042 | 270.0452 | 269.8442 | 269.7107 | 269.2830 | 269.1165 | 269.3252 | 269.4943 | 269.7540 | 269.7180 | 269.7172 | 270.2898 | 270.9779 | 272.1650 | 273.4691 | 274.5907 | 275.5173 | 275.9783 | 275.8399 | 274.5063 | 273.7303 | 273.4798 | 273.3531 | 273.1584 | 272.9585 | 272.2281 | 272.5441 | 272.9510 | 272.7820 | 272.3863 | 271.9530 | 271.8704 | 271.8293 | 271.1426 | 270.5533 | 271.1616 | 272.7609 | 274.1341 | 274.7611 | 275.6989 | 276.2628 | 276.3867 | 275.6536 | 273.8239 | 272.9583 | 272.9818 | 273.0891 | 273.0826 | 273.9201 | 274.4724 | 274.5622 | 274.4401 | 274.4632 | 273.7911 | 273.3518 | 273.1611 | 273.4160 | 273.7315 | 274.1462 | 274.9530 | 276.1523 | 276.9564 | 277.5360 | 277.9356 | 279.0069 | 279.2903 | 279.2244 | 278.2482 | 276.9459 | 277.0415 | 276.1982 | 276.9527 | 277.3260 | 275.6922 | 275.0930 | 275.5206 | 275.5900 | 275.7919 | 275.5002 | 275.6112 | 275.2867 | 275.4781 | 276.0514 | 276.0233 | 276.8167 | 277.9309 | 278.8385 | 279.0706 | 279.1826 | 279.2397 | 279.1642 | 277.8856 | 277.2441 | 277.3467 | 276.5665 | 275.9716 | 275.8318 | 276.3854 | 276.1721 | 275.6964 | 274.9010 | 274.0056 | 274.2306 | 274.1200 | 273.8763 | 273.6025 | 273.0713 | 273.8589 | 275.5552 | 277.2826 | 277.5575 | 278.2564 | 278.5472 | 279.1851 | 279.0857 | 277.4437 | 39.17563 | -119.1760 |
| 276.2963 | 276.6121 | 276.2541 | 276.8140 | 276.6290 | 276.9632 | 276.5995 | 276.6709 | 276.0365 | 276.1100 | 275.2489 | 274.7777 | 273.9265 | 274.3494 | 274.4456 | 274.8008 | 276.1694 | 277.3511 | 278.7174 | 279.6349 | 280.0047 | 280.0694 | 279.4225 | 277.7127 | 275.6732 | 274.8535 | 274.1331 | 273.7986 | 273.4214 | 273.5546 | 273.8021 | 273.3945 | 273.0227 | 272.9539 | 272.6657 | 271.6361 | 271.8269 | 271.5540 | 271.5613 | 271.6844 | 272.0829 | 272.5649 | 273.0470 | 273.5804 | 274.1452 | 274.0459 | 273.6214 | 272.1320 | 271.2590 | 270.6784 | 270.2289 | 270.3208 | 270.5920 | 270.0812 | 269.7522 | 269.5021 | 269.7072 | 270.0575 | 269.9568 | 269.7922 | 269.6241 | 269.6262 | 269.7404 | 270.2112 | 270.9497 | 271.6595 | 273.2072 | 274.6865 | 275.6277 | 275.9276 | 275.9669 | 274.9907 | 273.9891 | 273.7141 | 273.4460 | 273.1572 | 273.0465 | 272.5171 | 272.5580 | 272.5341 | 272.3735 | 272.1211 | 271.8971 | 271.7020 | 271.4621 | 271.2866 | 270.7540 | 271.1844 | 272.5172 | 274.0189 | 275.0211 | 275.8758 | 276.8126 | 276.7267 | 275.9888 | 273.9326 | 272.7770 | 272.6212 | 272.4384 | 272.6735 | 273.5116 | 274.4514 | 274.4038 | 274.5047 | 274.7335 | 273.9295 | 273.2982 | 273.4264 | 273.7062 | 273.9243 | 273.8176 | 274.5712 | 275.9433 | 276.9070 | 277.3366 | 277.8286 | 279.2896 | 279.5755 | 279.7286 | 278.7706 | 277.3856 | 277.4157 | 276.2388 | 276.1969 | 278.2142 | 277.4652 | 274.4645 | 275.0847 | 275.4574 | 276.1287 | 276.6242 | 275.9026 | 275.5157 | 275.7967 | 275.7573 | 276.8502 | 277.3469 | 277.9167 | 278.5349 | 278.7742 | 278.9344 | 278.9853 | 278.9520 | 278.1722 | 277.3676 | 276.3581 | 275.8946 | 275.8795 | 275.7693 | 275.3715 | 274.9284 | 275.0105 | 274.4992 | 274.5377 | 274.8480 | 274.6298 | 273.9529 | 273.4902 | 273.1270 | 273.5658 | 274.8658 | 277.0008 | 277.3558 | 278.1297 | 278.3037 | 278.4222 | 278.5540 | 277.4633 | 39.20103 | -119.0394 |
| 276.4379 | 276.8933 | 276.3231 | 277.0167 | 276.6582 | 277.1019 | 276.3730 | 277.1574 | 275.9712 | 276.2787 | 275.8211 | 275.0320 | 274.1204 | 274.3667 | 274.8317 | 275.1378 | 276.1341 | 277.2547 | 278.3507 | 279.4286 | 279.7080 | 279.9799 | 279.5220 | 278.0905 | 275.9507 | 275.5046 | 275.0261 | 274.4559 | 273.4582 | 273.1310 | 273.2357 | 272.9489 | 272.5832 | 272.7144 | 272.4545 | 271.6514 | 271.9803 | 271.6405 | 271.3302 | 271.5115 | 271.7768 | 272.2100 | 272.7142 | 272.9068 | 273.5273 | 274.1446 | 274.1678 | 272.6223 | 271.9172 | 271.2124 | 271.2262 | 271.2961 | 271.0692 | 270.6841 | 270.2622 | 269.9147 | 269.8617 | 270.1465 | 269.7456 | 269.6786 | 269.5692 | 269.5484 | 269.3379 | 269.6216 | 269.8783 | 271.0949 | 272.6474 | 273.9501 | 275.0127 | 275.4498 | 275.6512 | 274.6992 | 273.4510 | 273.1892 | 272.9397 | 272.8247 | 272.5860 | 272.4595 | 272.4893 | 272.3055 | 272.0563 | 271.7452 | 271.4321 | 271.1940 | 271.0047 | 270.7677 | 270.2565 | 270.5186 | 271.8470 | 273.5811 | 274.2863 | 274.8889 | 275.6752 | 275.8793 | 275.5082 | 273.6963 | 272.9932 | 272.6205 | 272.4362 | 272.9503 | 273.9677 | 275.0739 | 274.7610 | 274.4230 | 274.7618 | 273.9453 | 273.2867 | 273.5633 | 273.7634 | 273.9848 | 274.2432 | 274.9843 | 275.9037 | 276.7125 | 277.1406 | 277.4142 | 278.4420 | 278.5792 | 278.4283 | 278.1028 | 277.7296 | 278.1638 | 276.6209 | 275.8324 | 276.9992 | 276.8207 | 274.6136 | 276.1826 | 275.8783 | 276.6017 | 276.2555 | 275.6486 | 275.3207 | 275.5059 | 275.5987 | 276.5565 | 277.1692 | 277.7535 | 277.9238 | 278.3163 | 278.3680 | 278.4569 | 278.4628 | 277.4140 | 276.6695 | 276.5728 | 275.8547 | 275.5077 | 275.1812 | 274.7299 | 274.7419 | 274.7781 | 274.3143 | 274.2157 | 274.5494 | 274.4983 | 274.0629 | 273.4755 | 273.0145 | 273.2112 | 274.5266 | 276.0755 | 276.7802 | 277.3343 | 277.4138 | 277.7350 | 277.7577 | 276.7287 | 39.22628 | -118.9028 |
| 277.1167 | 277.0620 | 276.7695 | 277.0444 | 276.7961 | 277.3731 | 276.5037 | 276.9947 | 276.0431 | 276.5621 | 276.0296 | 274.5945 | 274.3220 | 274.4857 | 274.7293 | 274.6924 | 275.9088 | 277.2278 | 278.5821 | 279.5623 | 280.1396 | 280.4926 | 280.0145 | 278.7488 | 276.4471 | 276.2380 | 275.7897 | 275.2980 | 274.1073 | 273.6223 | 273.5239 | 273.3415 | 273.1165 | 273.0954 | 272.8290 | 272.2591 | 272.4035 | 272.3036 | 272.1997 | 272.0399 | 272.4974 | 272.8785 | 273.6735 | 273.8044 | 273.8700 | 273.9838 | 274.3726 | 273.3272 | 272.4763 | 272.1916 | 271.8719 | 271.6389 | 271.5988 | 271.4278 | 271.2833 | 271.1048 | 270.7739 | 270.7245 | 270.6742 | 270.6526 | 270.7431 | 270.4582 | 270.0779 | 270.5023 | 270.5863 | 270.8885 | 271.9069 | 273.2590 | 274.6607 | 275.6931 | 276.1277 | 275.7058 | 274.6136 | 274.1273 | 273.8513 | 273.6761 | 273.2264 | 273.0975 | 273.0107 | 272.9329 | 272.6424 | 272.2372 | 271.8620 | 271.6221 | 271.4166 | 271.1098 | 270.5905 | 270.9195 | 271.9250 | 273.6678 | 274.0158 | 274.7234 | 275.8867 | 275.9608 | 275.4776 | 273.9846 | 273.5322 | 272.7969 | 272.8705 | 273.5278 | 274.0771 | 274.6804 | 274.9090 | 274.7454 | 274.6946 | 273.9354 | 273.5759 | 273.5834 | 273.7254 | 273.9752 | 274.1607 | 274.8441 | 275.7167 | 276.6594 | 277.2279 | 277.1647 | 277.9352 | 278.3896 | 279.0757 | 279.0119 | 278.2924 | 278.1585 | 276.8021 | 276.3124 | 277.3176 | 275.3893 | 275.5115 | 275.7507 | 275.3885 | 276.4304 | 275.7621 | 275.1663 | 274.5465 | 275.0132 | 275.5580 | 276.1945 | 276.7315 | 277.7132 | 277.8204 | 278.4711 | 278.9777 | 279.1440 | 279.1247 | 278.5225 | 277.6826 | 276.7433 | 276.0372 | 275.8724 | 275.5506 | 274.9448 | 274.7335 | 274.6502 | 274.5014 | 274.1311 | 274.9364 | 274.6064 | 273.6851 | 273.5462 | 273.3927 | 273.4180 | 274.6096 | 275.9864 | 277.0000 | 277.5066 | 278.1661 | 278.6003 | 278.5086 | 277.5951 | 39.25137 | -118.7661 |
| 277.2552 | 277.3832 | 277.1455 | 276.9172 | 276.7751 | 277.0384 | 276.7365 | 276.4151 | 276.2578 | 276.1789 | 275.7653 | 274.8423 | 274.3571 | 273.8749 | 274.2749 | 274.5484 | 276.5393 | 277.5399 | 278.4653 | 279.5308 | 280.3997 | 280.8011 | 280.4313 | 279.1475 | 276.7415 | 276.4488 | 276.1162 | 275.7077 | 274.6182 | 273.9958 | 273.6461 | 273.2680 | 273.1040 | 273.0687 | 273.0963 | 272.9098 | 273.1409 | 272.8647 | 272.7283 | 272.6208 | 272.7043 | 273.0067 | 273.4982 | 274.3363 | 274.7366 | 275.0040 | 274.9586 | 273.7363 | 273.2126 | 272.8445 | 272.5539 | 272.3567 | 272.3333 | 272.2702 | 271.9602 | 271.6708 | 271.3074 | 271.1045 | 270.9192 | 270.8766 | 271.0182 | 270.8376 | 270.6374 | 270.8886 | 270.6212 | 270.9253 | 272.0952 | 273.6277 | 275.1916 | 276.2575 | 276.6827 | 276.1170 | 275.0607 | 274.4793 | 274.2458 | 273.9888 | 273.5739 | 273.5854 | 273.3496 | 273.0491 | 272.4329 | 272.2160 | 271.9569 | 271.7962 | 271.6453 | 271.4247 | 271.3578 | 271.4896 | 272.1818 | 273.7663 | 274.3321 | 275.4829 | 276.4153 | 276.4406 | 275.9979 | 274.6804 | 273.9972 | 273.4796 | 273.7449 | 274.2661 | 274.6345 | 274.6942 | 275.0323 | 275.2444 | 275.1318 | 274.8805 | 274.7650 | 274.3426 | 274.0858 | 274.4111 | 274.7545 | 275.2231 | 276.1569 | 277.2388 | 277.9303 | 277.5831 | 278.5941 | 279.3911 | 280.0901 | 279.9145 | 279.3365 | 278.8214 | 277.5703 | 276.6717 | 277.0266 | 275.4164 | 276.2921 | 276.4781 | 275.6971 | 276.0178 | 275.6737 | 275.3726 | 274.1363 | 274.1401 | 274.4621 | 275.4663 | 276.1554 | 277.5941 | 277.5861 | 278.1026 | 279.4164 | 279.5989 | 279.4934 | 279.2510 | 278.0062 | 276.8961 | 276.1905 | 275.8318 | 275.7746 | 275.3496 | 275.3437 | 275.1834 | 274.7393 | 274.3904 | 274.6051 | 274.3980 | 273.7921 | 273.1648 | 273.2536 | 273.4294 | 274.6315 | 276.1435 | 277.3985 | 278.1933 | 278.9048 | 279.1982 | 279.0648 | 278.0566 | 39.27631 | -118.6293 |
| Note: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| This only shows the first 10 lines of the example data. |
# example data: population of 2020 at same 12-km resolution as WRF
path_to_population <- helios::pkg_example('population_conus_ssp2_2020wrf_wgs84.csv')
population <- helios::read_population(file = path_to_population,
time_periods = 2020)| lat | lon | year | value |
|---|---|---|---|
| 21.04686 | -120.9716 | 2020 | 0 |
| 21.07325 | -120.8631 | 2020 | 0 |
| 21.09951 | -120.7545 | 2020 | 0 |
| 21.12567 | -120.6458 | 2020 | 0 |
| 21.15170 | -120.5371 | 2020 | 0 |
| 21.17762 | -120.4284 | 2020 | 0 |
| 21.20343 | -120.3196 | 2020 | 0 |
| 21.22913 | -120.2107 | 2020 | 0 |
| 21.25471 | -120.1017 | 2020 | 0 |
| 21.28017 | -119.9927 | 2020 | 0 |
| Note: | |||
| This only shows the first 10 lines of the example data. |
Calculate heating and cooling degrees
The following example specifies the arguments for processing WRF
dataset. More information for the arguments can be found in
(helios::hdcd Reference)[https://jgcri.github.io/helios/reference/hdcd.html].
-
ncdf_var = 'T2'specifies climate variable to extract from the netCDF, which is temperature in this case. -
model = 'wrf'specifies the model used to produce the climate forcing data, which is WRF model in this case. -
model_timestep = 'hourly'indicates the temporal resolution of the climate forcing data, which is hourly in this case. -
spatial = 'gcam_us49'specifies we are only calculating the cooling and heating degrees within 49 states of USA. This will produce outputs that can be used forGCAM-USA. For tutorial of usingheliosfor global 32 regions, please check out GCAM-regions tutorial page. -
time_periods = 2020specifies the time period 2020 to include since the example data only covers 7 days in 2020. -
dispatch_segment = TRUEasks the model to output heating and cooling degree-hours by segment when the input data is hourly. In addition, these two arguments need to be specified asmodel_timestep = 'hourly'andspatial = 'gcam_us49', because the currently only GCAM-USA supports dispatch segment.
library(helios)
# example data: WRF hourly climate at 12-km resolution
path_to_climate_ncdf <- helios::pkg_example('wrfout_d01_2020-01-01_01%3A00%3A00_sub.nc')
# example data: population of 2020 at same 12-km resolution as WRF
path_to_population <- helios::pkg_example('population_conus_ssp2_2020wrf_wgs84.csv')
# Calculate heating and coolong degrees for CONUS (e.g., part of USA in the example)
hdcd_usa <- helios::hdcd(ncdf = path_to_climate_ncdf,
ncdf_var = 'T2',
model = 'wrf',
model_timestep = 'hourly',
population = path_to_population,
spatial = 'gcam_us49',
time_periods = 2020,
dispatch_segment = TRUE,
reference_temp_F = 65,
folder = file.path(getwd(), 'output'),
diagnostics = F,
xml = F,
name_append = '',
save = T)## [1] "Starting function process_hdcd..."
## [1] "Processing files provided: /Users/runner/work/_temp/Library/helios/extras/wrfout_d01_2020-01-01_01%3A00%3A00_sub.nc"
## [1] "........................................."
## [1] "Running hdcd for file: /Users/runner/work/_temp/Library/helios/extras/wrfout_d01_2020-01-01_01%3A00%3A00_sub.nc"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named west_east BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named south_north BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"
## [1] "vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named Time BUT this dimension HAS NO DIMVAR! Code will probably fail at this point"## [1] "Starting population weighting ..."
## [1] "Completed population weighting."
## [1] "Processing hdcd completed for file: /Users/runner/work/_temp/Library/helios/extras/wrfout_d01_2020-01-01_01%3A00%3A00_sub.nc"
## [1] "File saved as : /Users/runner/work/helios/helios/vignettes/output/hdcd_wrf_2020_2020_gcam.csv"
## [1] "File saved as : /Users/runner/work/helios/helios/vignettes/output/hdcd_wrf_2020_2020_monthly.csv"
## [1] "File saved as : /Users/runner/work/helios/helios/vignettes/output/hdcd_wrf_2020_2020_annual.csv"
## [1] "process_hdcd completed."The output is a list containing three tables. Please note the different units (degree-hours or degree-days) for different outputs.
-
hdcd_usa$hdcd_comb_gcam: Heating and cooling degree-hours by dispatch segment for CONUS and by building thermal service. GCAM/GCAM-USA required format. -
hdcd_usa$hdcd_comb_monthly: Heating and cooling degree-days by month for CONUS (part of USA in the example). -
hdcd_usa$hdcd_comb_annual: Heating and cooling degree-days by year for CONUS (part of USA in the example).
1. By GCAM-USA Dispatch Segment and Building Thermal Service
# Heating and cooling degree-hours by dispatch segment and by building thermal service for CONUS
hdcd_segment <- hdcd_usa$hdcd_comb_gcam| region | subRegion | year | segment | gcam.consumer | nodeInput | building.node.input | thermal.building.service.input | unit | value |
|---|---|---|---|---|---|---|---|---|---|
| USA | CA | 2020 | Dec_day | comm | comm | comm_building | comm heating Dec_day | Fahrenheit degree-hours | -59.35209 |
| USA | CA | 2020 | Dec_day | resid | resid | resid_building | resid heating Dec_day | Fahrenheit degree-hours | -59.35209 |
| USA | CA | 2020 | Dec_night | comm | comm | comm_building | comm heating Dec_night | Fahrenheit degree-hours | -214.87109 |
| USA | CA | 2020 | Dec_night | resid | resid | resid_building | resid heating Dec_night | Fahrenheit degree-hours | -214.87109 |
| USA | CA | 2020 | Jan_day | comm | comm | comm_building | comm heating Jan_day | Fahrenheit degree-hours | -1757.70399 |
| USA | CA | 2020 | Jan_day | resid | resid | resid_building | resid heating Jan_day | Fahrenheit degree-hours | -1757.70399 |
| USA | CA | 2020 | Jan_night | comm | comm | comm_building | comm heating Jan_night | Fahrenheit degree-hours | -3240.22847 |
| USA | CA | 2020 | Jan_night | resid | resid | resid_building | resid heating Jan_night | Fahrenheit degree-hours | -3240.22847 |
| USA | NV | 2020 | Dec_day | comm | comm | comm_building | comm heating Dec_day | Fahrenheit degree-hours | -52.61460 |
| USA | NV | 2020 | Dec_day | resid | resid | resid_building | resid heating Dec_day | Fahrenheit degree-hours | -52.61460 |
| USA | NV | 2020 | Dec_night | comm | comm | comm_building | comm heating Dec_night | Fahrenheit degree-hours | -196.23341 |
| USA | NV | 2020 | Dec_night | resid | resid | resid_building | resid heating Dec_night | Fahrenheit degree-hours | -196.23341 |
| USA | NV | 2020 | Jan_day | comm | comm | comm_building | comm heating Jan_day | Fahrenheit degree-hours | -1596.74080 |
| USA | NV | 2020 | Jan_day | resid | resid | resid_building | resid heating Jan_day | Fahrenheit degree-hours | -1596.74080 |
| USA | NV | 2020 | Jan_night | comm | comm | comm_building | comm heating Jan_night | Fahrenheit degree-hours | -2986.69928 |
| USA | NV | 2020 | Jan_night | resid | resid | resid_building | resid heating Jan_night | Fahrenheit degree-hours | -2986.69928 |
2. By Month
# Heating and cooling degree-days at monthly scale for CONUS
hdcd_monthly <- hdcd_usa$hdcd_comb_monthly| region | subRegion | year | month | HDCD | unit | value |
|---|---|---|---|---|---|---|
| USA | CA | 2020 | 01 | CD | Fahrenheit degree-days | 0.0000 |
| USA | CA | 2020 | 01 | HD | Fahrenheit degree-days | -250.3510 |
| USA | NV | 2020 | 01 | CD | Fahrenheit degree-days | 0.0000 |
| USA | NV | 2020 | 01 | HD | Fahrenheit degree-days | -222.5875 |
3. By Year
# Heating and cooling degree-days at annual scale for CONUS
hdcd_annual <- hdcd_usa$hdcd_comb_annual| region | subRegion | year | HDCD | unit | value |
|---|---|---|---|---|---|
| USA | CA | 2020 | CD | Fahrenheit degree-days | 0.0000 |
| USA | CA | 2020 | HD | Fahrenheit degree-days | -250.3510 |
| USA | NV | 2020 | CD | Fahrenheit degree-days | 0.0000 |
| USA | NV | 2020 | HD | Fahrenheit degree-days | -222.5875 |
NOTE: Please note that we use a spatial subset of WRF input climate NetCDF data as an example, which only covers 7 days. To get heating and cooling degrees for a complete year, users need to run climate NetCDF files using
heliosthat cover the length of a year. For example, if each climate NetCDF is for 7-days, then user need to include about 52 climate NetCDF to cover a whole year of climate data. Then, users can usehelios::diagnosticsto create plots to check heating and cooling degree behaviors throughout a year.
Diagnostics
After getting the data table for heating and cooling degrees, users can perform diagnostics and compare with NOAA observations if data covers the U.S. The diagnostics create the following plots at segment and monthly time step:
- Heating and cooling degrees for each year
- Comparison of heating and cooling degrees among all years
Users can also use helios::diagnostics to visualize any
heating and cooling degree data as long as they include those columns in
the monthly data or the segment data: [subRegion, year, month/segment,
HDCD, value]
1. By Month
Let’s run helios::diagnostics to the example heating and
cooling degree-days by month from 2020 to 2050.
| subRegion | year | month | HDCD | value |
|---|---|---|---|---|
| AL | 2020 | 1 | HD | -554.602736 |
| AL | 2020 | 2 | CD | 3.119958 |
| AL | 2020 | 2 | HD | -720.422022 |
| AL | 2020 | 3 | CD | 6.460285 |
| AL | 2020 | 3 | HD | -377.592511 |
| AL | 2020 | 4 | CD | 52.836382 |
| AL | 2020 | 4 | HD | -121.201889 |
| AL | 2020 | 5 | CD | 272.250722 |
| AL | 2020 | 5 | HD | -6.784828 |
| AL | 2020 | 6 | CD | 480.749542 |
| Note: | ||||
| This only shows the first 10 lines of the example data. |
If the data coverage is too short (e.g., less than 6 months),
diagnostics will not create plot by default. Users can change the
constraint on numbers of months in the data using argument
min_diagnostic_months to enable diagnostics even with short
data coverage. The followings show how to use diagnostics with example
outputs of heating and cooling degrees at both segment and monthly time
step.
# Perform diagnostic on monthly data
helios::diagnostics(hdcd_monthly = helios::example_hdcd_monthly_usa,
min_diagnostic_months = 6,
folder = file.path(getwd(), 'output'),
name_append = 'monthly')
Figure 2: Diagnostics for monthly heating and cooling degree-days from 2020 to 2050.
2. By GCAM-USA Dispatch Segment
Let’s run helios::diagnostics to the example heating and
cooling degree-hours by dispatch segment from 2020 to 2050.
| subRegion | year | segment | value | HDCD |
|---|---|---|---|---|
| AL | 2020 | Apr_day | 1658.28538 | CD |
| AL | 2020 | Apr_day | -927.71234 | HD |
| AL | 2020 | Apr_night | 118.58054 | CD |
| AL | 2020 | Apr_night | -2939.63478 | HD |
| AL | 2020 | Aug_day | 9653.28363 | CD |
| AL | 2020 | Aug_night | 4062.64718 | CD |
| AL | 2020 | Dec_day | 117.39468 | CD |
| AL | 2020 | Dec_day | -4717.31780 | HD |
| AL | 2020 | Dec_night | 1.09673 | CD |
| AL | 2020 | Dec_night | -10669.04390 | HD |
| Note: | ||||
| This only shows the first 10 lines of the example data. |
# Perform diagnostic on segment data
helios::diagnostics(hdcd_segment = helios::example_hdcd_segment_usa,
min_diagnostic_months = 6,
folder = file.path(getwd(), 'output'),
name_append = 'segment')
Figure 3: Diagnostics for heating and cooling degree-hours by dispatch segment from 2020 to 2050.
XML file for GCAM-USA
Finally, for GCAM users, helios can save the outputs by
building thermal service to XML format, which is a required format for
GCAM inputs. There are two ways to save helios output to
XML file:
- Set
xml = TRUEin thehelios::hdcdfunction will automatically save the XML file to the output folder. - Use
helios::save_xmlfunction.
helios::save_xml(hdcd_gcam = hdcd_segment,
folder = file.path(getwd(), 'output'),
name_append = 'wrf')## [1] "Starting function save_xml..."
## [1] "File saved as : /Users/runner/work/helios/helios/vignettes/output/hdcd_wrf_xml.csv"
## [1] "File saved as : /Users/runner/work/helios/helios/vignettes/output/hdcd_wrf.xml"
## [1] "save_xml complete."