Highly-precise climate simulation with multi-scale and resolution.
Methodology
The dataset is simulated using WRF4.3 by ERA5 (3 domains) and GCM (4 domains) forcing in NSCC HPC, as shown below.
Output files are in daily netCDF format with equal area projection at three domains (12.5km: Southeast Asia; 2.5km: Southern Malay Peninsula; 500m: Singapore).
Variables include temperature, precipitation, wind speed, relative humidity, and solar radiation.
Each daily file has eight timesteps (0, 3, 6, 9, 12, 15, 18, 21; or 3, 6, 9, 12, 15, 18, 21, 0).
- Due to occasional restarts, one or more files may contain names of the same day.
- Seven timesteps in the last *_00:00:00 file are the same as the first *_03:00:00 file.
- We can merge the first timestep of our target *_00:00:00 file with the first seven timesteps of the *_03:00:00 file using Xarray.
Extracting Data
The following Python (v3.8) script can be used to extract variables from the wrfout files, named according to its respective year. It will take about 14 minutes for each variable, in each year. Using the Xarray library is highly recommended.
import xarray as xr #v2022.3.0
import salem
import datetime
# Extract WRF variables from NAS; ensure the NAS is mounted.
# Download the code to your local machine and run in a local directory.
for yr in range(1981,2020):
print(yr)
dirwrf='/mnt/y/WRF_3domain_fERA5/d02/'+str(yr)+'/'
ds=xr.open_mfdataset(dirwrf+'wrfout_d02_*',concat_dim='Time',combine='nested')
# odir is the local dir for saving out data, './' means present folder
odir='./'
# RAINNC is the var name, you can change this to your requirement;
# the whole output vars is in wrfout.vars_list.txt
ds.RAINNC.to_netcdf(odir+'RAINNC.'+str(yr)+'.nc')
# you can also use salem.deacc to de-accumulate the variables(RAINC RAINNC)
# df=ds.RAINNC.salem.deacc(as_rate=False)
# df.to_netcdf(odir+'RAINNC.'+str(yr)+'.nc')
time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print(time)
Experiments
Efforts are underway to develop future simulations and key model outputs are listed here.
Rainfall | Temperature | Winds | Specific humidity | |
---|---|---|---|---|
Domain (Resolution) | Great Southeast Asia (62.5 km), Southeast Asia (12.5 km), Southern Malay Peninsula (2.5 km), Singapore (500 m) | |||
Models | ERA5, MPI-ESM1-2-LR, EC-Earth3-Veg, FGOALS-f3-L, CESM2-WACCM, ACCESS-CM2, CanESM5 | |||
Temporal resolution | ERA5 (3 hourly at 45 vertical levels and surface level), GCMs (6 hourly at 35 vertical levels and hourly at surface level ) | |||
Scenarios | Historical, SSP126, SSP245, SSP585 | |||
Finished | ERA5 (1981-2020), MPI-ESM1-2-LR HIST(1981-2014) SSP585 (2015-2100) SSP126(2015-2100) SSP126(d01-d03:2015-2100) | |||
Ongoing | MPI-ESM1-2-LR SSP245 (d04:2015-2100) | |||
Planned | MPI-ESM1-2-LR SSP245 |