{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Accessing and analysing a REMO dataset at DKRZ"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://nbviewer.org/github/remo-rcm/pyremo/blob/master/notebooks/remo-dataset.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we want to show some typical data analysis that might be useful to look at after or during a REMO run. Some of the code in this notebook in actually implemented in the `pyremo.analysis` module. However, we will show a little more in detail how we can access and evaluate a REMO dataset here so you can tweak this to your own analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cf\n",
"import numpy as np\n",
"import xarray as xr\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import pyremo as pr\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will do the processing at DKRZ where we reserve a shared node for distributed computing with dask."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"\n",
"client = Client()\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Access and preparation of REMO output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this notebook, we use 30 years of preliminary test results from a REMO run using ERA5 data as lateral boundary forcings. We will only use monthly mean data for this analysis, so let's catch that data from the filesystem:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/1979/e056000m197901.nc',\n",
" '/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/1979/e056000m197902.nc',\n",
" '/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/1979/e056000m197903.nc',\n",
" '/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/1979/e056000m197904.nc',\n",
" '/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/1979/e056000m197905.nc']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import glob\n",
"\n",
"pattern = \"/work/ch0636/remo/tutorial/tutorial_REMO2020/example-dataset/*/e056000m*.nc\"\n",
"filenames = glob.glob(pattern)\n",
"filenames.sort()\n",
"filenames[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will define a slightly different function for opening the monthly dataset that is based on some [xarray discussions on github](https://github.com/pydata/xarray/issues/1385#issuecomment-561920115)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def open_mfdataset(\n",
" files,\n",
" use_cftime=True,\n",
" parallel=True,\n",
" data_vars=\"minimal\",\n",
" chunks={\"time\": 1},\n",
" coords=\"minimal\",\n",
" compat=\"override\",\n",
" drop=None,\n",
" **kwargs,\n",
"):\n",
" \"\"\"optimized function for opening large cf datasets.\n",
"\n",
" based on https://github.com/pydata/xarray/issues/1385#issuecomment-561920115\n",
"\n",
" \"\"\"\n",
"\n",
" def drop_all_coords(ds):\n",
" # ds = ds.drop(drop)\n",
" return ds.reset_coords(drop=True)\n",
"\n",
" ds = xr.open_mfdataset(\n",
" files,\n",
" parallel=parallel,\n",
" decode_times=False,\n",
" combine=\"by_coords\",\n",
" preprocess=drop_all_coords,\n",
" decode_cf=False,\n",
" chunks=chunks,\n",
" data_vars=data_vars,\n",
" coords=\"minimal\",\n",
" compat=\"override\",\n",
" **kwargs,\n",
" )\n",
" return xr.decode_cf(ds, use_cftime=use_cftime)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 15.9 s, sys: 577 ms, total: 16.5 s\n",
"Wall time: 24.9 s\n"
]
}
],
"source": [
"%time ds = open_mfdataset(filenames, parallel=True, chunks='auto')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have our monthly REMO output in one dataset. Please remember, that [xarray uses dask under the hood](http://xarray.pydata.org/en/stable/user-guide/dask.html) (if you have installed it!) which means that we can easily work and analyze large datasets that would normally not fit into our memory (or because we simply don't want all data loaded and slow us down)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For historical reason, the current REMO version sets the day of the month to 0 for monthly means. This is not compatible, e.g., with standard datetime formats. We provided a function to correct the time axis here."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"ds = pr.parse_dates(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we have a reasonable time axis in the dataset that makes analysis much easier:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 416
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds.topo.plot(cmap=\"terrain\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spin up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's always useful to look at the soil temperatures. During the run, the soil has to get into an equilibrium with the atmospheric temperature to *forget* the initial conditions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the following variables:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"temp_vars = [\"TS\", \"TSL\", \"TEMP2\", \"TSN\", \"TD3\", \"TD4\", \"TD5\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will extract those variables from the big dataset and stuff them into a coordinate so that we can easily plot them."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
<xarray.Dataset>\n",
"Dimensions: (lon: 720, lat: 360, time: 1428)\n",
"Coordinates:\n",
" * lon (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * lat (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
" * time (time) object 1901-01-16 00:00:00 ... 2019-12-16 00:00:00\n",
"Data variables:\n",
" tmp (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray>\n",
" stn (time, lat, lon) float64 dask.array<chunksize=(476, 120, 240), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: CF-1.4\n",
" title: CRU TS4.04 Mean Temperature\n",
" institution: Data held at British Atmospheric Data Centre, RAL, UK.\n",
" source: Run ID = 2004151855. Data generated from:tmp.2004011744.dtb\n",
" history: Wed 15 Apr 2020 19:58:33 BST : User ianharris : Program mak...\n",
" references: Information on the data is available at http://badc.nerc.ac...\n",
" comment: Access to these data is available to any registered CEDA user.\n",
" contact: support@ceda.ac.uk
Data held at British Atmospheric Data Centre, RAL, UK.
source :
Run ID = 2004151855. Data generated from:tmp.2004011744.dtb
history :
Wed 15 Apr 2020 19:58:33 BST : User ianharris : Program makegridsauto.for called by update.for
references :
Information on the data is available at http://badc.nerc.ac.uk/data/cru/
comment :
Access to these data is available to any registered CEDA user.
contact :
support@ceda.ac.uk
"
],
"text/plain": [
"\n",
"Dimensions: (lon: 720, lat: 360, time: 1428)\n",
"Coordinates:\n",
" * lon (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * lat (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
" * time (time) object 1901-01-16 00:00:00 ... 2019-12-16 00:00:00\n",
"Data variables:\n",
" tmp (time, lat, lon) float32 dask.array\n",
" stn (time, lat, lon) float64 dask.array\n",
"Attributes:\n",
" Conventions: CF-1.4\n",
" title: CRU TS4.04 Mean Temperature\n",
" institution: Data held at British Atmospheric Data Centre, RAL, UK.\n",
" source: Run ID = 2004151855. Data generated from:tmp.2004011744.dtb\n",
" history: Wed 15 Apr 2020 19:58:33 BST : User ianharris : Program mak...\n",
" references: Information on the data is available at http://badc.nerc.ac...\n",
" comment: Access to these data is available to any registered CEDA user.\n",
" contact: support@ceda.ac.uk"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cru_tmp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will also add a mask for the `CRU` temperature that is used by the regridder. We create a land sea mask simply from valid temperatures here."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
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]
},
"metadata": {
"image/png": {
"height": 573,
"width": 720
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_seasons(\n",
" diff1_, transform=ccrs.PlateCarree(), projection=rotated_pole, extent=extent\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HYRAS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also use even more specialized observational datasets. In this case, we will use the HYRAS dataset which has a resolution of about 5 km. In this case, we should actually regrid the observational dataset to the model grid."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"