check-MRI-JRA55-do-1-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check JRA55-do 1.3 input4MIPS files\n",
    "see\n",
    "- https://github.com/COSIMA/access-om2/issues/120\n",
    "- https://github.com/COSIMA/access-om2/issues/155\n",
    "- https://github.com/COSIMA/libaccessom2/issues/20\n",
    "- https://github.com/COSIMA/libaccessom2/issues/25\n",
    "- https://github.com/COSIMA/libaccessom2/pull/26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n",
      "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-19.01/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  defaults = yaml.load(f)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import json\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get JRA55-do forcing fields from forcing.json\n",
    "runs = ['input4MIPs_testing_1deg_jra55_iaf_control', 'input4MIPs_testing_1deg_jra55_iaf_test_yearp1_qv56']  # runs to compare\n",
    "# runs = ['input4MIPs_testing_1deg_jra55_iaf_control', 'input4MIPs_testing_1deg_jra55_iaf_test_yearp1_qv56_2_hybrid']  # runs to compare\n",
    "forcing = []\n",
    "for r in runs:\n",
    "    fpath = '/g/data/hh5/tmp/cosima/access-om2/'+r+'/output000/atmosphere/forcing.json'\n",
    "    with open(fpath) as json_file:\n",
    "        forcing.append(json.load(json_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rsds = rsds? True\n",
      "rlds = rlds? True\n",
      "prrn = prra? True\n",
      "prsn = prsn? True\n",
      "psl = psl? True\n",
      "friver = friver? True\n",
      "tas_10m = tas? False\n",
      "/g/data1/ua8/JRA55-do/latest/t_10.1958.nc\n",
      "<xarray.DataArray 'tas_10m' (latitude: 320, longitude: 640)>\n",
      "array([[253.22519, 253.22519, 253.22519, ..., 253.22519, 253.22519, 253.22519],\n",
      "       [253.60388, 253.60388, 253.60388, ..., 253.60388, 253.60388, 253.60388],\n",
      "       [253.98482, 253.98482, 253.98482, ..., 253.98482, 253.98482, 253.98482],\n",
      "       ...,\n",
      "       [238.34068, 238.38274, 238.42615, ..., 238.2284 , 238.26494, 238.3015 ],\n",
      "       [239.43384, 239.48071, 239.5281 , ..., 239.31105, 239.34796, 239.37918],\n",
      "       [240.63419, 240.58076, 240.60344, ..., 240.47102, 240.49231, 240.51193]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * latitude   (latitude) float64 -89.57 -89.01 -88.45 ... 88.45 89.01 89.57\n",
      "  * longitude  (longitude) float64 0.0 0.5625 1.125 1.688 ... 358.3 358.9 359.4\n",
      "    time       datetime64[ns] 1958-12-31T21:00:00\n",
      "Attributes:\n",
      "    units:          K\n",
      "    long_name:      Near-Surface Air Temperature at the 10 meter height\n",
      "    standard_name:  air_temperature\n",
      "/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/tas/gn/v20180412/tas/tas_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_195801010000-195812312230.nc\n",
      "<xarray.DataArray 'tas' (latitude: 320, longitude: 640)>\n",
      "array([[253.22519, 253.22519, 253.22519, ..., 253.22519, 253.22519, 253.22519],\n",
      "       [253.60388, 253.60388, 253.60388, ..., 253.60388, 253.60388, 253.60388],\n",
      "       [253.98482, 253.98482, 253.98482, ..., 253.98482, 253.98482, 253.98482],\n",
      "       ...,\n",
      "       [238.34052, 238.3824 , 238.42607, ..., 238.22841, 238.2652 , 238.30182],\n",
      "       [239.42537, 239.47566, 239.52774, ..., 239.31107, 239.34848, 239.38588],\n",
      "       [240.53802, 240.56953, 240.60283, ..., 240.47105, 240.4927 , 240.51433]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1958-12-31T21:00:00\n",
      "  * latitude   (latitude) float64 -89.57 -89.01 -88.45 ... 88.45 89.01 89.57\n",
      "  * longitude  (longitude) float64 0.0 0.5625 1.125 1.688 ... 358.3 358.9 359.4\n",
      "Attributes:\n",
      "    standard_name:  air_temperature\n",
      "    long_name:      Near-Surface Air Temperature\n",
      "    comment:        near-surface (usually, 2 meter) air temperature\n",
      "    units:          K\n",
      "    cell_methods:   area: mean time: point\n",
      "    cell_measures:  area: areacella\n",
      "    history:        2018-04-11T19:54:22Z altered by CMOR: Treated scalar dime...\n",
      "huss_10m = huss? False\n",
      "/g/data1/ua8/JRA55-do/latest/q_10.1958.nc\n",
      "<xarray.DataArray 'huss_10m' (latitude: 320, longitude: 640)>\n",
      "array([[0.000851, 0.000851, 0.000851, ..., 0.00085 , 0.00085 , 0.00085 ],\n",
      "       [0.000869, 0.000869, 0.000869, ..., 0.00087 , 0.000869, 0.000869],\n",
      "       [0.000907, 0.000907, 0.000907, ..., 0.000907, 0.000907, 0.000907],\n",
      "       ...,\n",
      "       [0.000162, 0.000163, 0.000164, ..., 0.000162, 0.000162, 0.000162],\n",
      "       [0.000177, 0.000178, 0.000179, ..., 0.000177, 0.000177, 0.000177],\n",
      "       [0.000196, 0.000198, 0.000199, ..., 0.000198, 0.000198, 0.000198]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * latitude   (latitude) float64 -89.57 -89.01 -88.45 ... 88.45 89.01 89.57\n",
      "  * longitude  (longitude) float64 0.0 0.5625 1.125 1.688 ... 358.3 358.9 359.4\n",
      "    time       datetime64[ns] 1958-12-31T21:00:00\n",
      "Attributes:\n",
      "    units:          kg/kg\n",
      "    long_name:      Near-Surface Specific Humidity at the 10 meter height\n",
      "    standard_name:  specific_humidity\n",
      "/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/huss/gn/v20180412/huss/huss_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_195801010000-195812312230.nc\n",
      "<xarray.DataArray 'huss' (latitude: 320, longitude: 640)>\n",
      "array([[0.000851, 0.000851, 0.000851, ..., 0.00085 , 0.00085 , 0.00085 ],\n",
      "       [0.000869, 0.000869, 0.000869, ..., 0.00087 , 0.000869, 0.000869],\n",
      "       [0.000907, 0.000907, 0.000907, ..., 0.000907, 0.000907, 0.000907],\n",
      "       ...,\n",
      "       [0.000162, 0.000163, 0.000164, ..., 0.000162, 0.000162, 0.000162],\n",
      "       [0.000178, 0.000178, 0.000179, ..., 0.000177, 0.000177, 0.000177],\n",
      "       [0.000198, 0.000199, 0.000199, ..., 0.000198, 0.000198, 0.000198]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1958-12-31T21:00:00\n",
      "  * latitude   (latitude) float64 -89.57 -89.01 -88.45 ... 88.45 89.01 89.57\n",
      "  * longitude  (longitude) float64 0.0 0.5625 1.125 1.688 ... 358.3 358.9 359.4\n",
      "Attributes:\n",
      "    standard_name:   specific_humidity\n",
      "    long_name:       Near-Surface Specific Humidity\n",
      "    comment:         Near-surface (usually, 2 meter) specific humidity\n",
      "    units:           1\n",
      "    original_units:  1.0\n",
      "    history:         2018-04-11T08:44:18Z altered by CMOR: Converted units fr...\n",
      "    cell_methods:    area: mean time: point\n",
      "    cell_measures:   area: areacella\n",
      "uas_10m = uas? True\n",
      "vas_10m = vas? True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare corresponding fields (assuming they are in the same order in the two forcing.json files)\n",
    "fields = list(zip(forcing[0]['inputs'],forcing[1]['inputs']))\n",
    "for field in fields:\n",
    "    for year in range(1958,1959):\n",
    "        fn0 = field[0]['filename'].replace('{{year}}',str(year)).replace('{{year+1}}',str(year+1))\n",
    "        fn1 = field[1]['filename'].replace('{{year}}',str(year)).replace('{{year+1}}',str(year+1))\n",
    "        da0 = xr.open_dataset(fn0)[field[0]['fieldname']].isel(time=-1)\n",
    "        da1 = xr.open_dataset(fn1)[field[1]['fieldname']].isel(time=-1).rename({'lon': 'longitude','lat': 'latitude'})\n",
    "        try:\n",
    "            da1 = da1.drop('height')\n",
    "        except:\n",
    "            pass\n",
    "        ok = da0.equals(da1)\n",
    "        print(field[0]['fieldname']+' = '+field[1]['fieldname']+'? '+str(ok))\n",
    "        if not ok:\n",
    "            print(fn0)\n",
    "            print(da0)\n",
    "            print(fn1)\n",
    "            print(da1)\n",
    "            diff = da1-da0\n",
    "            plt.figure(figsize=(16,8))\n",
    "            diff.plot()\n",
    "            plt.title(field[0]['fieldname']+' difference')\n",
    "            plt.figure(figsize=(16,8))\n",
    "            plt.hist(np.ma.ravel(diff), bins=500, log=True);\n",
    "            plt.xlabel('Difference')\n",
    "            plt.ylabel('Count');\n",
    "            plt.title('Histogram of '+field[0]['fieldname']+' difference');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check for forcing files that don't match pattern in forcing.json\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hr/rsds/gn/v20180412/rsds/rsds_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010130-201802020000.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hr/rlds/gn/v20180412/rlds/rlds_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010130-201802020000.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hr/prra/gn/v20180412/prra/prra_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010130-201802020000.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hr/prsn/gn/v20180412/prsn/prsn_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010130-201802020000.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/psl/gn/v20180412/psl/psl_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010000-201802012230.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/ocean/day/friver/gn/v20180412/friver/friver_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_20180101-20180301.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/tas/gn/v20180412/tas/tas_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010000-201802012230.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/huss/gn/v20180412/huss/huss_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010000-201802012230.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/uas/gn/v20180412/uas/uas_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010000-201802012230.nc'}\n",
      "{'/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-3/atmos/3hrPt/vas/gn/v20180412/vas/vas_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-3_gn_201801010000-201802012230.nc'}\n"
     ]
    }
   ],
   "source": [
    "for field in fields:\n",
    "    loadedfiles = set(glob.glob(field[1]['filename'].replace('{{year}}','*').replace('{{year+1}}','*')))\n",
    "    allfiles = set(glob.glob(os.path.dirname(field[1]['filename'])+'/*'))\n",
    "    print(allfiles-loadedfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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