7B. Precipitation Distribution
Reference:
Ahn, M.-S., P. A. Ullrich, P. J. Gleckler, J. Lee, A. C. Ordonez, and A. G. Pendergrass, 2023: Evaluating Precipitation Distributions at Regional Scales: A Benchmarking Framework and Application to CMIP5 and CMIP6. Geoscientific Model Development, 16, 3927–3951, doi: 10.5194/gmd-16-3927-2023
Running the code
[1]:
%%bash
precip_distribution_driver.py --help
usage: precip_distribution_driver.py [-h] [--parameters PARAMETERS]
[--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]
[--mip MIP] [--mod MOD] [--var VAR]
[--frq FRQ] [--modpath MODPATH]
[--results_dir RESULTS_DIR]
[--case_id CASE_ID] [--prd PRD [PRD ...]]
[--fac FAC] [--res RES [RES ...]]
[--ref REF] [--ref_dir REF_DIR]
[--exp EXP] [--ver VER] [--cmec]
[--no_cmec]
options:
-h, --help show this help message and exit
--parameters PARAMETERS, -p PARAMETERS
--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]
Path to other user-defined parameter file. (default:
None)
--mip MIP cmip5, cmip6 or other mip (default: None)
--mod MOD model (default: None)
--var VAR pr or other variable (default: None)
--frq FRQ day, 3hr or other frequency (default: None)
--modpath MODPATH data directory path (default: None)
--results_dir RESULTS_DIR
results directory path (default: None)
--case_id CASE_ID case_id with date (default: None)
--prd PRD [PRD ...] start- and end-year for analysis (e.g., 1985 2004)
(default: None)
--fac FAC factor to make unit of [mm/day] (default: None)
--res RES [RES ...] list of target horizontal resolution [degree] for
interporation (lon, lat) (default: None)
--ref REF reference data (default: None)
--ref_dir REF_DIR reference directory path (default: None)
--exp EXP e.g., historical or amip (default: None)
--ver VER version (default: None)
--cmec Use to save CMEC format metrics JSON (default: False)
--no_cmec Do not save CMEC format metrics JSON (default: False)
Prepare to run
[2]:
from user_choices import demo_data_directory, demo_output_directory
Input data files are daily pr time series.
[3]:
import os
input_data_ref = demo_data_directory + '/obs4MIPs_PCMDI_daily/NASA-JPL/GPCP-1-3/day/pr/gn/latest/' + 'pr_day_GPCP-1-3_PCMDI_gn_19961002-20170101.nc'
input_data_ref_dir = os.path.dirname(input_data_ref)
input_data_ref_file = os.path.basename(input_data_ref)
input_data_model = demo_data_directory + '/CMIP5_demo_timeseries/historical/atmos/day/pr/' + 'pr_day_GISS-E2-H_historical_r6i1p1_20000101-20051231.nc'
input_data_model_dir = os.path.dirname(input_data_model)
input_data_model_file = os.path.basename(input_data_model)
output_dir = demo_output_directory + '/precip_distribution/'
Run for the reference dataset (GPCP)
Below process takes about 24 minutes
[4]:
!precip_distribution_driver.py \
--mip obs \
--mod GPCP \
--exp historical \
--var pr \
--frq day \
--modpath $input_data_ref_dir \
--mod $input_data_ref_file \
--results_dir $output_dir \
--case_id "pr_distribution_GPCP" \
--prd 2000 2001 \
--fac 86400 \
--res 2 2 \
--ref GPCP-1-3 \
--ref_dir $input_data_ref_dir
demo_data_tmp/obs4MIPs_PCMDI_daily/NASA-JPL/GPCP-1-3/day/pr/gn/latest
pr_day_GPCP-1-3_PCMDI_gn_19961002-20170101.nc
[2000, 2001]
[2, 2]
Ref: GPCP-1-3
demo_output_tmp/precip_distribution/
demo_output_tmp/precip_distribution/
demo_output_tmp/precip_distribution/
['demo_data_tmp/obs4MIPs_PCMDI_daily/NASA-JPL/GPCP-1-3/day/pr/gn/latest/pr_day_GPCP-1-3_PCMDI_gn_19961002-20170101.nc']
dat, cal: GPCP-1-3 gregorian
Completed regridding (via Regrid_xr) from (366, 180, 360) to (366, 90, 180)
2000 (366, 90, 180)
Completed regridding (via Regrid_xr) from (365, 180, 360) to (365, 90, 180)
2001 (731, 90, 180)
im, mon: 0 ANN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 1 MAM
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 2 JJA
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 3 SON
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 4 DJF
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 5 JAN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 6 FEB
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 7 MAR
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 8 APR
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 9 MAY
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 10 JUN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 11 JUL
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 12 AUG
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 13 SEP
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 14 OCT
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 15 NOV
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 16 DEC
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
d.shape: (17, 146, 90, 180)
type d: <class 'xarray.core.dataarray.DataArray'>
d.shape: (17, 146, 90, 180)
type d: <class 'xarray.core.dataarray.DataArray'>
(2, 17, 146, 12)
dat: GPCP-1-3 ref: GPCP-1-3
Completed domain metrics
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
(2, 17, 146, 36)
dat: GPCP-1-3 ref: GPCP-1-3
Completed clustering domain metrics
Downloading file 'IPCC-WGI-reference-regions-v4.zip' from 'https://github.com/regionmask/regionmask/raw/v0.12.1/data/IPCC-WGI-reference-regions-v4.zip' to '/Users/lee1043/Library/Caches/regionmask/v0.12.1'.
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:179: FutureWarning: Passing 'lon_name' and 'lat_name' was deprecated in v0.10.0. Please pass the coordinates directly, e.g., `mask*(ds[lon_name], ds[lat_name])`.
warnings.warn(
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:179: FutureWarning: Passing 'lon_name' and 'lat_name' was deprecated in v0.10.0. Please pass the coordinates directly, e.g., `mask*(ds[lon_name], ds[lat_name])`.
warnings.warn(
ddom.shape: (2, 17, 146, 62)
dat: GPCP-1-3 ref: GPCP-1-3
Completed AR6 domain metrics
INFO::2025-09-09 14:53::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:12,744 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:12,744 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GPCP-1-3.json
INFO::2025-09-09 14:53::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:22,401 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:22,401 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GPCP-1-3.json
INFO::2025-09-09 14:53::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:32,217 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GPCP-1-3.json
2025-09-09 14:53:32,217 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GPCP-1-3.json
Completed metrics from precipitation frequency and amount distributions
im, mon: 0 ANN
dat, mon, dmon.shape: GPCP-1-3 ANN (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (365, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (365, 90, 180) is used
im, mon: 1 MAM
dat, mon, dmon.shape: GPCP-1-3 MAM (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (92, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
im, mon: 2 JJA
dat, mon, dmon.shape: GPCP-1-3 JJA (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (92, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
im, mon: 3 SON
dat, mon, dmon.shape: GPCP-1-3 SON (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (91, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (91, 90, 180) is used
im, mon: 4 DJF
dat, mon, dmon.shape: GPCP-1-3 DJF (640, 90, 180)
2000 (91, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (91, 90, 180), but (90, 90, 180) is used
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:334: RuntimeWarning: All-NaN slice encountered
ndm = np.nanmedian(cfy, axis=0) # ignore years with zero precip
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:335: RuntimeWarning: All-NaN slice encountered
prdyfracm = np.nanmedian(prdyfracyr, axis=0)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:336: RuntimeWarning: All-NaN slice encountered
sdiim = np.nanmedian(sdiiyr, axis=0)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:345: RuntimeWarning: All-NaN slice encountered
pfracm = np.nanmedian(pfracyr, axis=0)
im, mon: 5 JAN
dat, mon, dmon.shape: GPCP-1-3 JAN (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 6 FEB
dat, mon, dmon.shape: GPCP-1-3 FEB (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (28, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (28, 90, 180) is used
im, mon: 7 MAR
dat, mon, dmon.shape: GPCP-1-3 MAR (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 8 APR
dat, mon, dmon.shape: GPCP-1-3 APR (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 9 MAY
dat, mon, dmon.shape: GPCP-1-3 MAY (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 10 JUN
dat, mon, dmon.shape: GPCP-1-3 JUN (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 11 JUL
dat, mon, dmon.shape: GPCP-1-3 JUL (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 12 AUG
dat, mon, dmon.shape: GPCP-1-3 AUG (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 13 SEP
dat, mon, dmon.shape: GPCP-1-3 SEP (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 14 OCT
dat, mon, dmon.shape: GPCP-1-3 OCT (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 15 NOV
dat, mon, dmon.shape: GPCP-1-3 NOV (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 16 DEC
dat, mon, dmon.shape: GPCP-1-3 DEC (731, 90, 180)
2000 (366, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (366, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
Completed domain median
Completed domain median
Completed domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
Completed AR6 domain median
Completed AR6 domain median
Completed AR6 domain median
INFO::2025-09-09 14:54::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:26,219 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:26,219 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GPCP-1-3.json
INFO::2025-09-09 14:54::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:36,217 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:36,217 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GPCP-1-3.json
INFO::2025-09-09 14:54::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:45,983 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GPCP-1-3.json
2025-09-09 14:54:45,983 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GPCP-1-3.json
Completed metrics from precipitation cumulative distributions
Generated output files:
[5]:
!ls {demo_output_directory}/precip_distribution/*GPCP*
demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_cumfrac_metrics_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_cumfrac_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_frq.amt_domain3C_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_frq.amt_domainAR6_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_frq.amt_domain_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GPCP-1-3.json
demo_output_tmp/precip_distribution/dist_frq.amt_metrics_regrid.180x90_GPCP-1-3.nc
demo_output_tmp/precip_distribution/dist_frq.amt_regrid.180x90_GPCP-1-3.nc
Run for a model
Below process takes about 22 minutes
[6]:
!precip_distribution_driver.py \
--mip cmip5 \
--mod GISS-E2-H \
--exp historical \
--var pr \
--frq day \
--modpath $input_data_model_dir \
--mod $input_data_model_file \
--results_dir $output_dir \
--case_id "pr_distribution_GISS-E2-H" \
--prd 2000 2001 \
--fac 86400 \
--res 2 2 \
--ref GPCP-1-3 \
--ref_dir $output_dir # Need the interim results from the previous run
demo_data_tmp/CMIP5_demo_timeseries/historical/atmos/day/pr
pr_day_GISS-E2-H_historical_r6i1p1_20000101-20051231.nc
[2000, 2001]
[2, 2]
Ref: GPCP-1-3
demo_output_tmp/precip_distribution/
demo_output_tmp/precip_distribution/
demo_output_tmp/precip_distribution/
['demo_data_tmp/CMIP5_demo_timeseries/historical/atmos/day/pr/pr_day_GISS-E2-H_historical_r6i1p1_20000101-20051231.nc']
dat, cal: GISS-E2-H.r6i1p1 365_day
Completed regridding (via Regrid_xr) from (365, 90, 144) to (365, 90, 180)
2000 (365, 90, 180)
Completed regridding (via Regrid_xr) from (365, 90, 144) to (365, 90, 180)
2001 (730, 90, 180)
im, mon: 0 ANN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 1 MAM
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 2 JJA
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 3 SON
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 4 DJF
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 5 JAN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 6 FEB
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 7 MAR
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 8 APR
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 9 MAY
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 10 JUN
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 11 JUL
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 12 AUG
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 13 SEP
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 14 OCT
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 15 NOV
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
im, mon: 16 DEC
start MakeDists
completed MakeDists
start CalcRainMetrics for each grid point
completed CalcRainMetrics for each grid point
d.shape: (17, 146, 90, 180)
type d: <class 'xarray.core.dataarray.DataArray'>
d.shape: (17, 146, 90, 180)
type d: <class 'xarray.core.dataarray.DataArray'>
(2, 17, 146, 12)
dat: GISS-E2-H.r6i1p1 ref: GPCP-1-3
ref_dir: demo_output_tmp/precip_distribution/ file: dist_frq.amt_domain_regrid.180x90_GPCP-1-3.nc
2025-09-09 15:33:37,614 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:33:37,614 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Completed domain metrics
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
(2, 17, 146, 36)
dat: GISS-E2-H.r6i1p1 ref: GPCP-1-3
2025-09-09 15:34:43,591 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:34:43,591 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:34:43,612 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:34:43,612 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Completed clustering domain metrics
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:179: FutureWarning: Passing 'lon_name' and 'lat_name' was deprecated in v0.10.0. Please pass the coordinates directly, e.g., `mask*(ds[lon_name], ds[lat_name])`.
warnings.warn(
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:179: FutureWarning: Passing 'lon_name' and 'lat_name' was deprecated in v0.10.0. Please pass the coordinates directly, e.g., `mask*(ds[lon_name], ds[lat_name])`.
warnings.warn(
ddom.shape: (2, 17, 146, 62)
dat: GISS-E2-H.r6i1p1 ref: GPCP-1-3
2025-09-09 15:38:23,156 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:38:23,156 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:38:23,195 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 15:38:23,195 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Completed AR6 domain metrics
INFO::2025-09-09 15:38::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:38:56,536 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:38:56,536 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain_regrid.180x90_GISS-E2-H.r6i1p1.json
INFO::2025-09-09 15:39::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:39:06,149 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:39:06,149 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domain3C_regrid.180x90_GISS-E2-H.r6i1p1.json
INFO::2025-09-09 15:39::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:39:16,031 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:39:16,031 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_frq.amt_metrics_domainAR6_regrid.180x90_GISS-E2-H.r6i1p1.json
Completed metrics from precipitation frequency and amount distributions
im, mon: 0 ANN
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 ANN (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (365, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (365, 90, 180) is used
im, mon: 1 MAM
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 MAM (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
im, mon: 2 JJA
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 JJA (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (92, 90, 180) is used
im, mon: 3 SON
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 SON (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (91, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (91, 90, 180) is used
im, mon: 4 DJF
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 DJF (640, 90, 180)
2000 (91, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (91, 90, 180), but (90, 90, 180) is used
im, mon: 5 JAN
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 JAN (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 6 FEB
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 FEB (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (28, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (28, 90, 180) is used
im, mon: 7 MAR
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 MAR (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 8 APR
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 APR (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 9 MAY
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 MAY (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 10 JUN
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 JUN (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 11 JUL
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 JUL (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 12 AUG
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 AUG (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 13 SEP
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 SEP (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 14 OCT
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 OCT (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
im, mon: 15 NOV
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 NOV (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (30, 90, 180) is used
im, mon: 16 DEC
dat, mon, dmon.shape: GISS-E2-H.r6i1p1 DEC (730, 90, 180)
2000 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2000 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
2001 (365, 90, 180)
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/pcmdi_metrics/precip_distribution/lib/lib_precip_distribution.py:1884: RuntimeWarning: divide by zero encountered in divide
sdii = ptot / prdays_gt_1mm # Zhang et al. (2011)
2001 pfrac.shape is (365, 90, 180), but (31, 90, 180) is used
Completed domain median
Completed domain median
Completed domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
<regionmask.Regions 'Heavy/Moderate/Light precipitating regions'>
overlap: None
Regions:
0 HR Heavy precipitating region
1 MR Moderate precipitating region
2 LR Light precipitating region
[3 regions]
/Users/lee1043/mambaforge/envs/pmp_devel_20250908/lib/python3.10/site-packages/regionmask/core/mask.py:444: UserWarning: Detected overlapping regions. As of v0.11.0 these are correctly taken into account. Note, however, that a different mask is returned than with older versions of regionmask. To suppress this warning, set `overlap=True` (to restore the old, incorrect, behaviour, set `overlap=False`).
warnings.warn(
Completed clustering domain median
Completed AR6 domain median
Completed AR6 domain median
Completed AR6 domain median
INFO::2025-09-09 15:40::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:07,094 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:07,094 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median_regrid.180x90_GISS-E2-H.r6i1p1.json
INFO::2025-09-09 15:40::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:16,569 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:16,569 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GISS-E2-H.r6i1p1.json
INFO::2025-09-09 15:40::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:26,169 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GISS-E2-H.r6i1p1.json
2025-09-09 15:40:26,169 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/precip_distribution/dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GISS-E2-H.r6i1p1.json
Completed metrics from precipitation cumulative distributions
Result
[7]:
!ls {output_dir}
dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_cumfrac_metrics_domain.median.3C_regrid.180x90_GPCP-1-3.json
dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_cumfrac_metrics_domain.median.AR6_regrid.180x90_GPCP-1-3.json
dist_cumfrac_metrics_domain.median_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_cumfrac_metrics_domain.median_regrid.180x90_GPCP-1-3.json
dist_cumfrac_metrics_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_cumfrac_metrics_regrid.180x90_GPCP-1-3.nc
dist_cumfrac_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_cumfrac_regrid.180x90_GPCP-1-3.nc
dist_frq.amt_domain3C_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_frq.amt_domain3C_regrid.180x90_GPCP-1-3.nc
dist_frq.amt_domainAR6_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_frq.amt_domainAR6_regrid.180x90_GPCP-1-3.nc
dist_frq.amt_domain_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_frq.amt_domain_regrid.180x90_GPCP-1-3.nc
dist_frq.amt_metrics_domain3C_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_frq.amt_metrics_domain3C_regrid.180x90_GPCP-1-3.json
dist_frq.amt_metrics_domainAR6_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_frq.amt_metrics_domainAR6_regrid.180x90_GPCP-1-3.json
dist_frq.amt_metrics_domain_regrid.180x90_GISS-E2-H.r6i1p1.json
dist_frq.amt_metrics_domain_regrid.180x90_GPCP-1-3.json
dist_frq.amt_metrics_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_frq.amt_metrics_regrid.180x90_GPCP-1-3.nc
dist_frq.amt_regrid.180x90_GISS-E2-H.r6i1p1.nc
dist_frq.amt_regrid.180x90_GPCP-1-3.nc
Diagnostics
NetCDF files
[8]:
import xarray as xr
import matplotlib.pyplot as plt
[9]:
data_var = "unevenness"
#data_var = "prdyfrac"
#data_var = "sdii"
[10]:
obs_file = f"{output_dir}/dist_cumfrac_metrics_regrid.180x90_GPCP-1-3.nc"
model_file = f"{output_dir}/dist_cumfrac_metrics_regrid.180x90_GISS-E2-H.r6i1p1.nc"
ds_obs = xr.open_dataset(obs_file)
ds_model = xr.open_dataset(model_file)
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(18, 5))
ds_obs[data_var].sel(month=0).plot(ax=ax0)
ax0.set_title("GPCP-1-3 ANN")
ds_model[data_var].sel(month=0).plot(label="GISS-E2-H ANN", ax=ax1)
ax1.set_title("GISS-E2-H ANN")
[10]:
Text(0.5, 1.0, 'GISS-E2-H ANN')
Metrics
JSON files
[11]:
json_file = f"{output_dir}/dist_frq.amt_metrics_domain_regrid.180x90_GISS-E2-H.r6i1p1.json"
[12]:
# print json file content
import json
with open(json_file) as data_file:
data = json.load(data_file)
print(json.dumps(data, indent=4))
{
"RESULTS": {
"GISS-E2-H.r6i1p1": {
"amtP10": {
"Land_30N50N": {
"ANN": 0.1316342164059363,
"CalendarMonths": {
"1": 0.1316342164059363,
"2": 0.1316342164059363,
"3": 0.1316342164059363,
"4": 0.1316342164059363,
"5": 0.1316342164059363,
"6": 0.1316342164059363,
"7": 0.1316342164059363,
"8": 0.1316342164059363,
"9": 0.1316342164059363,
"10": 0.1316342164059363,
"11": 0.1316342164059363,
"12": 0.1316342164059363
},
"DJF": 0.1279104316973477,
"JJA": 0.1316342164059363,
"MAM": 0.1316342164059363,
"SON": 0.1316342164059363
},
"Land_30S30N": {
"ANN": 0.12411966905381082,
"CalendarMonths": {
"1": 0.12411966905381082,
"2": 0.12411966905381082,
"3": 0.12411966905381082,
"4": 0.12411966905381082,
"5": 0.12411966905381082,
"6": 0.12411966905381082,
"7": 0.12411966905381082,
"8": 0.12411966905381082,
"9": 0.12411966905381082,
"10": 0.12411966905381082,
"11": 0.12411966905381082,
"12": 0.12411966905381082
},
"DJF": 0.12548614646939915,
"JJA": 0.12411966905381082,
"MAM": 0.12411966905381082,
"SON": 0.12411966905381082
},
"Land_50S30S": {
"ANN": 0.15497831537923712,
"CalendarMonths": {
"1": 0.15497831537923712,
"2": 0.15497831537923712,
"3": 0.15497831537923712,
"4": 0.15497831537923712,
"5": 0.15497831537923712,
"6": 0.15497831537923712,
"7": 0.15497831537923712,
"8": 0.15497831537923712,
"9": 0.15497831537923712,
"10": 0.15497831537923712,
"11": 0.15497831537923712,
"12": 0.15497831537923712
},
"DJF": 0.15517793605435462,
"JJA": 0.15497831537923712,
"MAM": 0.15497831537923712,
"SON": 0.15497831537923712
},
"Land_50S50N": {
"ANN": 0.12766375551158676,
"CalendarMonths": {
"1": 0.12766375551158676,
"2": 0.12766375551158676,
"3": 0.12766375551158676,
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