This package contains the front end code for both the Regional Analysis Tools (RAT) and Plan2Adapt (P2A). Configuration is quite onerous and developing/deploying using Docker is highly recommended.

• Apache with mod_perl
• Perl modules: File::Slurp Geo::Proj4 Language::Functional Text::CSV_XS

Build and then run the app using the provided Dockerfile. Requires that the genimage basemaps be mounted a /data/basemaps and the netCDF data mounted at /data/nc.

git clone https://github.com/pacificclimate/scenarios
cd scenarios
sudo docker build -t pcic/scenarios .
sudo docker run -d -v /datasets/data5/projects/rat/data/nc:/data/nc -p <external_port>:80 --name scenarios pcic/scenarios

If you want to override any of the config files:

-v /full/path/to/new/gcminfo.csv:/usr/local/lib/scenarios/gcminfo.csv
-v /full/path/to/new/genimage.cfg:/usr/local/etc/genimage.cfg

Or for development, override the code directory:

-v /full/path/to/scenarios/code/dir:/usr/local/lib/scenarios

Deploying CMIP5 data on the RAT/P2A is a work in progress.

• Determine data format requirements
• Sort through existing code directories, convert to Git and release on GitHub
• Adapt existing data processing pipeline for CMIP5 data
• Create reproducible deployment guide Created README and Docker deployment guide
• Replace existing data processing pipeline to accomodate derived variable generation from daily data rather than monthly. This will involve writing scripts that accomplish:
  • Determine valid CMIP5 model sets PyClimate Filters module
  • Convert CF1.6 CMIP5 metadata standards to PCIC CMIP3 metadata standard CFmeta python package
  • Generate derived variables based on daily data: tas, gdd, hdd, fdd, pas PyClimate variables module
  • Create climatologies of all input variables: tasmin, tasmax, pr, tas, gdd, hdd, fdd, pas
  • Translate metadata into RAT/P2A format
    • Variable renaming
    • dimension permutations/reordering
  • Assemble files into RAT/P2A compatible 'scenarios' files gen_rat_scenarios.py
  • Script generation of gcminfo.csv file from new data

The involves applying elevation adjustments to previously bias correted data. The general procedure involves:

GCM (10k, daily)
  |
  devirve tas, gdd, hdd, fdd, pas # code exists for GCM data, modify gen_rat_derivied_climos.py for BCCAQ data
  |
Degree Days (10k, daily)
  |
  create climatologies # accomplished by gen_rat_derivied_climos.py as well
  |
Degree Days (10k, past abs/future abs, climo)
  |
  calculate anomalies # gen_rat_anomalies.py
  |
Degree Days (10k, past abs/future delta, climo)
  |
  add future delta to ClimateBC 400m # see below
  |
Degree Days (400m, past abs/future abs, climo)

ClimateBC files are located here:

ls /storage/data/climate/ClimateBC/ClimateBC_400/netcdf/climatologies/hist
dg05  dg18  dl00  dl18  nffd  pass  pr  tas  tasmax  tasmin

For example, here is the historical precip file:

/storage/data/climate/ClimateBC/ClimateBC_400/netcdf/climatologies/hist/pr/climatebc/run1/climatebc-hist-pr-run1-1961-1990.nc

The BCCAQ data needs to be remapped to the ClimateBC resolution, then add/multiplied to the historical file based on what variable is being transformed.

cdo remapnn,climate_bc_file file_to_remap.nc remapped.nc
cdo add climate_bc_file.nc remapped.nc out.nc

Add new future period layers to ncWMS instance.

Reconfigure P2A frontend for new layers. This may or may not need doing.... The map is initialized here, and the map definition is here

The P2A ensemble is definied by selecting particular model/run combinations in [Helpers.pm](https://github.com/pacificclimate/scenarios/blob/5d714e7c38a8b02f8407de9c9b01ef80304aaaa9/CICS/Scenario/Helpers.pm#L404). Altering the existing ensemble may be easiest.

The ensemble is selected on the front end here, but the i value given to sset is dynamically determined by the length of the amount of data entries in the exptdata list generated here