{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Make your own surface data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CTSM needs lots of surface variables to run. \n", "\n", "CTSM technical notes has the official tutorial for creating surface datasets.\n", "\n", "[**CTSM technical notes 1.3.5 Creating Surface data**](https://escomp.github.io/ctsm-docs/versions/release-clm5.0/html/users_guide/using-clm-tools/creating-surface-datasets.html)\n", "\n", "However, it is difficult to use the tool for users that are not familiar to CESM/CTSM. Because in pyclmuapp, we mainly focus on urban climate and the percentage of urban is set to 100%, which means the output are only urban related. Hence, we can easily over-write the urban surface data in the default `surfdata.nc` provided by pyclmuapp, this avoids the complex steps of using the mksurfdata_map tools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "**How to get our own urban surface data of the interest point from CESM provided file**\n", "\n", "Ref: \n", "- [Technical Description of an Urban Parameterization for the Community Land Model (CLMU)](https://www.researchgate.net/publication/261062625_Technical_Description_of_an_Urban_Parameterization_for_the_Community_Land_Model_CLMU)\n", "\n", "- [Table 3 Input data required for the urban model](https://www.researchgate.net/figure/3-Input-data-required-for-the-urban-model_tbl1_261062625)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Workflow\n", "---\n", "\n", "- get the urban surface input from [`mksrf_urban_0.05x0.05_simyr2000.c170724.nc`](https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/lnd/clm2/rawdata/)\n", "\n", "- get the soil parameter for the pervious road in urban from [`mksrf_soitex.10level.c010119.nc`](https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/lnd/clm2/rawdata/)\n", "\n", "- You **can directly** use the provided `surfdata.nc` by pyclmuapp, which is located at \"pyclmuapp/usp/surfdata.nc\". (No need to input)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1e+03 ns, sys: 0 ns, total: 1e+03 ns\n", "Wall time: 3.1 µs\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "No suitable point found in the search range, expanding search range.\n", "Found suitable point at lat: 51.4116, lon: -0.1167\n", "Found suitable point at lat: 51.4116, lon: -0.1167\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset> Size: 9kB\n",
       "Dimensions:                  (lsmlat: 1, lsmlon: 1, nlevsoi: 10, natpft: 15,\n",
       "                              cft: 2, time: 12, lsmpft: 17, numurbl: 3,\n",
       "                              numrad: 2, nglcecp1: 11, nglcec: 10, nlevurb: 10)\n",
       "Coordinates:\n",
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       "  * lsmlon                   (lsmlon) int32 4B 1\n",
       "  * numrad                   (numrad) int32 8B 1 2\n",
       "  * numurbl                  (numurbl) int32 12B 1 2 3\n",
       "  * nlevurb                  (nlevurb) int32 40B 0 1 2 3 4 5 6 7 8 9\n",
       "Dimensions without coordinates: nlevsoi, lsmpft, nglcecp1, nglcec\n",
       "Data variables: (12/77)\n",
       "    mxsoil_color             int32 4B ...\n",
       "    SOIL_COLOR               (lsmlat, lsmlon) int32 4B ...\n",
       "    PCT_SAND                 (nlevsoi, lsmlat, lsmlon) float64 80B 44.75 ... ...\n",
       "    PCT_CLAY                 (nlevsoi, lsmlat, lsmlon) float64 80B 28.61 ... ...\n",
       "    ORGANIC                  (nlevsoi, lsmlat, lsmlon) float64 80B ...\n",
       "    FMAX                     (lsmlat, lsmlon) float64 8B ...\n",
       "    ...                       ...\n",
       "    TK_IMPROAD               (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "    TK_ROOF                  (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "    TK_WALL                  (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "    CV_IMPROAD               (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "    CV_ROOF                  (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "    CV_WALL                  (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n",
       "Attributes: (12/52)\n",
       "    Conventions:                          NCAR-CSM\n",
       "    History_Log:                          created on: 02-14-19 10:18:01\n",
       "    Logname:                              erik\n",
       "    Host:                                 cheyenne3\n",
       "    Source:                               Community Land Model: CLM5\n",
       "    Version:                              release-clm5.0.18/gpfs/fs1/scratch/...\n",
       "    ...                                   ...\n",
       "    Fmax_raw_data_file_name:              mksrf_fmax_3x3min_USGS_c120911.nc\n",
       "    Organic_matter_raw_data_file_name:    mksrf_organic_10level_5x5min_ISRIC-...\n",
       "    zero_out_pft_override:                TRUE\n",
       "    history:                              Thu Feb 14 10:18:15 2019: ncks -A -...\n",
       "    history_of_appended_files:            Thu Feb 14 10:18:15 2019: Appended ...\n",
       "    NCO:                                  netCDF Operators version 4.7.4 (htt...
" ], "text/plain": [ " Size: 9kB\n", "Dimensions: (lsmlat: 1, lsmlon: 1, nlevsoi: 10, natpft: 15,\n", " cft: 2, time: 12, lsmpft: 17, numurbl: 3,\n", " numrad: 2, nglcecp1: 11, nglcec: 10, nlevurb: 10)\n", "Coordinates:\n", " * natpft (natpft) int32 60B 0 1 2 3 4 5 ... 9 10 11 12 13 14\n", " * cft (cft) int32 8B 15 16\n", " * time (time) int32 48B 1 2 3 4 5 6 7 8 9 10 11 12\n", " * lsmlat (lsmlat) int32 4B 1\n", " * lsmlon (lsmlon) int32 4B 1\n", " * numrad (numrad) int32 8B 1 2\n", " * numurbl (numurbl) int32 12B 1 2 3\n", " * nlevurb (nlevurb) int32 40B 0 1 2 3 4 5 6 7 8 9\n", "Dimensions without coordinates: nlevsoi, lsmpft, nglcecp1, nglcec\n", "Data variables: (12/77)\n", " mxsoil_color int32 4B ...\n", " SOIL_COLOR (lsmlat, lsmlon) int32 4B ...\n", " PCT_SAND (nlevsoi, lsmlat, lsmlon) float64 80B 44.75 ... ...\n", " PCT_CLAY (nlevsoi, lsmlat, lsmlon) float64 80B 28.61 ... ...\n", " ORGANIC (nlevsoi, lsmlat, lsmlon) float64 80B ...\n", " FMAX (lsmlat, lsmlon) float64 8B ...\n", " ... ...\n", " TK_IMPROAD (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", " TK_ROOF (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", " TK_WALL (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", " CV_IMPROAD (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", " CV_ROOF (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", " CV_WALL (nlevurb, numurbl, lsmlat, lsmlon) float32 120B ...\n", "Attributes: (12/52)\n", " Conventions: NCAR-CSM\n", " History_Log: created on: 02-14-19 10:18:01\n", " Logname: erik\n", " Host: cheyenne3\n", " Source: Community Land Model: CLM5\n", " Version: release-clm5.0.18/gpfs/fs1/scratch/...\n", " ... ...\n", " Fmax_raw_data_file_name: mksrf_fmax_3x3min_USGS_c120911.nc\n", " Organic_matter_raw_data_file_name: mksrf_organic_10level_5x5min_ISRIC-...\n", " zero_out_pft_override: TRUE\n", " history: Thu Feb 14 10:18:15 2019: ncks -A -...\n", " history_of_appended_files: Thu Feb 14 10:18:15 2019: Appended ...\n", " NCO: netCDF Operators version 4.7.4 (htt..." ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time\n", "from pyclmuapp import get_urban_params\n", "urban = get_urban_params(urban_ds='data/mksrf_urban_0.05x0.05_simyr2000.c170724.nc', # can be a xarray dataset or a path to a netcdf file\n", " soil_ds='data/mksrf_soitex.10level.c010119.nc', # can be a xarray dataset or a path to a netcdf file\n", " # here we use the default vancouverCAN dataset provided by CLM5.0 as template\n", " lat = 51.5116,\n", " lon = -0.1167,\n", " #template='surfdata.nc', \n", " # optional, if not provided, the default surfdata dataset will be used\n", " # the default surfdata dataset is the vancouverCAN dataset provided by CLM5.0 as follows\n", " PTC_URBAN=[0,0,100.0], # percentage of urban land use in each density class, sum should be 100 \n", " # this means that the urban land use is 100% in the MD class\n", " outputname='data/surfdata_london.nc',)\n", "urban # the created surfdata.nc file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[19]], dtype=int32), array([[359.875]]), array([[51.525]]))" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "urban['URBAN_REGION_ID'].values, urban['LONGXY'].values, urban['LATIXY'].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (optional) more to know about the urban parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**fist, let's check the mksrf_urban_0.05x0.05_simyr2000.c170724.nc**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "import xarray as xr" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Region 0: 348 grid cells\n", "Region 1: 74945 grid cells\n", "Region 2: 278922 grid cells\n" ] }, { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# open the downloaded mksrf_urban_0.05x0.05_simyr2000.c120621.nc\n", "global_surf = xr.open_dataset('data/mksrf_urban_0.05x0.05_simyr2000.c120621.nc')\n", "fig = plt.figure(figsize=(12, 3))\n", "for i in range(3):\n", " ax = fig.add_subplot(1, 3, i+1)\n", " global_surf['PCT_URBAN'].sel(density_class=i).where(global_surf['PCT_URBAN'].sel(density_class=i) > 0).plot(ax=ax, add_colorbar=False)\n", " print(f\"Region {i}:\", global_surf['PCT_URBAN'].sel(density_class=i).where(global_surf['PCT_URBAN'].sel(density_class=i) > 0).count().values, \"grid cells\")\n", " ax.set_title(f'Region {i}')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**then check mksrf_soitex.10level.c010119.nc**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "soil = xr.open_dataset('data/mksrf_soitex.10level.c010119.nc')\n", "soil = soil.assign_coords(lat=soil.LAT, lon=soil.LON) # add lat and lon as coordinates\n", "fig = plt.figure(figsize=(4, 3))\n", "soil['MAPUNITS'].plot()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (Optional) here is the step for generate a template. \n", "\n", "This just helps you know how to generate a `surfdata.nc` template with 10 urban layers. \n", "\n", "Refer to [**Section: Create your urban parameters**](#create-your-urban-parameters) for creating your `surfdata.nc`.\n", "\n", "Here we use the Vancouver city urban surface input file privdied by CTSM default case to generate the default surface data. \n", "\n", "We can aslo use the `surfdata.nc` provdied by pyclmuapp as template.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "add the nevlurb to 10" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 1kB\n",
       "Dimensions:          (density_class: 3, numsolar: 2, numrad: 2, nlevurb: 10)\n",
       "Coordinates:\n",
       "    lat              float64 8B 53.47\n",
       "    lon              float64 8B -123.5\n",
       "Dimensions without coordinates: density_class, numsolar, numrad, nlevurb\n",
       "Data variables: (12/31)\n",
       "    LANDMASK         int8 1B ...\n",
       "    LAT              float64 8B ...\n",
       "    LON              float64 8B ...\n",
       "    LATIXY           float64 8B ...\n",
       "    LONGXY           float64 8B ...\n",
       "    PCT_URBAN        (density_class) float64 24B ...\n",
       "    ...               ...\n",
       "    CV_IMPROAD       (nlevurb, density_class) float32 120B ...\n",
       "    NLEV_IMPROAD     (density_class) float32 12B ...\n",
       "    THICK_ROOF       (density_class) float32 12B ...\n",
       "    THICK_WALL       (density_class) float32 12B ...\n",
       "    T_BUILDING_MIN   (density_class) float32 12B ...\n",
       "    T_BUILDING_MAX   (density_class) float32 12B ...\n",
       "Attributes:\n",
       "    date:     Mon Jul 24 10:08:51 MDT 2017\n",
       "    source:   /glade/p/cgd/tss/people/oleson/urban_sfcdata/Feddema_urban_data...\n",
       "    case_id:  Feddema/Jackson region_prop.170724-090103.csv (from urban prope...\n",
       "    title:    Urban parameters for TBD,HD, and MD classes - Dominant - Lamina...
" ], "text/plain": [ " Size: 1kB\n", "Dimensions: (density_class: 3, numsolar: 2, numrad: 2, nlevurb: 10)\n", "Coordinates:\n", " lat float64 8B 53.47\n", " lon float64 8B -123.5\n", "Dimensions without coordinates: density_class, numsolar, numrad, nlevurb\n", "Data variables: (12/31)\n", " LANDMASK int8 1B ...\n", " LAT float64 8B ...\n", " LON float64 8B ...\n", " LATIXY float64 8B ...\n", " LONGXY float64 8B ...\n", " PCT_URBAN (density_class) float64 24B ...\n", " ... ...\n", " CV_IMPROAD (nlevurb, density_class) float32 120B ...\n", " NLEV_IMPROAD (density_class) float32 12B ...\n", " THICK_ROOF (density_class) float32 12B ...\n", " THICK_WALL (density_class) float32 12B ...\n", " T_BUILDING_MIN (density_class) float32 12B ...\n", " T_BUILDING_MAX (density_class) float32 12B ...\n", "Attributes:\n", " date: Mon Jul 24 10:08:51 MDT 2017\n", " source: /glade/p/cgd/tss/people/oleson/urban_sfcdata/Feddema_urban_data...\n", " case_id: Feddema/Jackson region_prop.170724-090103.csv (from urban prope...\n", " title: Urban parameters for TBD,HD, and MD classes - Dominant - Lamina..." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_global = xr.open_dataset('data/mksrf_urban_0.05x0.05_simyr2000.c170724.nc')\n", "ds_global = ds_global.assign_coords(lat=ds_global.LAT, lon=ds_global.LON) # add lat and lon as coordinates\n", "#ds_global.sel(lat=49.5, lon=236.5-360, method='nearest')['REGION_ID'].values # 6\n", "dd = ds_global.sel(lat=53.4808, lon=236.5-360, method='nearest')\n", "dd = dd.sel(region=dd.REGION_ID.values-1)\n", "dd" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array(-123.475), array(53.475), array(6, dtype=int8))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dd['LONGXY'].values, dd['LATIXY'].values, dd['REGION_ID'].values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "ds = xr.open_dataset('data/surfdata_1x1_vancouverCAN_hist_16pfts_Irrig_CMIP6_simyr2000_c190214.nc')\n", "for var in ['TK_IMPROAD', 'TK_ROOF', 'TK_WALL', 'CV_IMPROAD', 'CV_ROOF', 'CV_WALL']:\n", " del ds[var]\n", "ds=ds.assign(nlevurb=(np.array(range(0, 10), dtype=np.int32)))\n", "#ds['nlevurb'].values = np.array(range(1, 11), dtype=np.int32)\n", "ds['LONGXY'].values, ds['LATIXY'].values\n", "for var in ['TK_IMPROAD', 'TK_ROOF', 'TK_WALL', 'CV_IMPROAD', 'CV_ROOF', 'CV_WALL']:\n", " ds = ds.assign({var: (['nlevurb', 'numurbl', 'lsmlat', 'lsmlon'], dd[var].values.reshape(10, 3, 1, 1))})\n", " ds[var].attrs = dd[var].attrs\n", "ds.to_netcdf('data/surfdata_1x1_vancouverCAN_hist_16pfts_Irrig_CMIP6_simyr2000_c190214_10levurb.nc')" ] } ], "metadata": { "kernelspec": { "display_name": "pymet", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 2 }