go_cmip6

README

goCMIP6

A research project repository for downscaling and bias-correction of CMIP6 data.

Pre-requisites

Install gcc

sudo dnf install gcc
gcc --version

Install NCO

# For RedHat/Fedora based distros
# This particular build was done in CentOS 8.3.2011

# Install nco (which installs netcdf, hdf5 and some other packages as dependencies)
sudo dnf install epel-release

# Need to enable powertools for some dependencies of nco
sudo dnf -y install dnf-plugins-core
sudo dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
sudo dnf config-manager --set-enabled powertools

# Install
sudo dnf install nco

# Need to install netcdf-devel separately
sudo dnf install netcdf-devel

# Check that netcdf has NetCDF-4/HDF5 enabled
nc-config --has-nc4

Install CDO

# Install cdo
# Download the latest tar package from here https://code.mpimet.mpg.de/projects/cdo/files
tar -xzvf cdo-*
cd cdo-*
./configure --with-netcdf=/usr --with-hdf5=/usr # Build with NetCDF4 support
sudo make
sudo make install

cdo --version

# The downloading of NetCDF files from ESGF will be handled through an existing binary here https://github.com/ESGF/sproket

Workflow

Subsetting and Downscaling NetCDF files

# wrapper.sh is a wrapper job that triggers everything else
# Three parameters can be customized in the job

declare -a model=("CanESM5" "NorESM2-LM" "IPSL-CM6A-LR" "EC-Earth3" "ACCESS-CM2")
declare -a experiment=("historical" "ssp245")
declare -a variable=("pr" "tasmax" "tasmin")

for mdl in "${model[@]}"; do
	for exp in "${experiment[@]}"; do
		for var in "${variable[@]}"; do
			python getnc.py $var $exp $mdl
			./sproket -config params.json # a1
			while [ -e *.part ]; do # a2
				:
			done
		done
	done
done

Splitting NetCDF

// After files have been downloaded, subsetted, downscaled and moved to ncfiles/ directory
// Call gosplit.go to split into grid-wise independent files using Goroutines to feed into bias-correction process
// Here's the primary chunk of code that does the splitting

	for i := 0; i < vr.Len(); i++ { // Loop across 1st dim (lat)
		for j := 0; j < len(vr.Index(i).Interface().([][]float32)); j++ { // Loop across 2nd dim (lon)
			wg.Add(1) // Send a signal to the workgroup that an iteration has initiated
			f := path + strconv.Itoa(i) + "_" + strconv.Itoa(j) + ".txt"
			go writeData(sem, vr.Index(i).Interface().([][]float32)[j], f, &wg) // Write 3rd dim to file (time)
		}
		wg.Wait() // Stop "main" from exiting till all goroutines finish
	}

Bias-correction and Creation of new NetCDF files

# Bias-correction done in mbcn.R
# Grid-wise data is fed into MBC algorithm in R iteratively
# Bias-correction is done using moving windows of 30-years
# Bias-corrected grids replace the raw grid data to save disk space

 mbc.n <- MBCn(o.m, h.m, f.m, # Observation, historical and future matrix
               iter = 100, # Iterations
              trace = 0.05, # Trace precipitation
              ratio.seq=c(T,F,F), # Prec is a ratio variable
              ties = 'first',
              jitter.factor=0.0001, # Jitter ties
              silent = T)
return(mbc.n)

# New NetCDF files created in createnc.R

To Do

1. Test for one-file and multi-file scenario (for ex. CanESM5 vs NorESM2-LM)
2. Build email alerts within the job to update progress.

References

1. Sproket binary

2. NCO library

3. CDO library

4. MBC package and corresponding Research Article