{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Performing NCGR on a full forecast field\n", "\n", "This notebook walks you through how to use the `NCGR` package to perform non-homogenous Gaussian regression (NCGR) on a full field of ice-free date or freeze-up date forecasts using the `NCGR.ncgr_fullfield` module. For this specific example, we'll be calibrating a freeze-up date forecast. This option is **plug-and-play**, and allows the user to provide a forecast, training hindcasts, and training observations in NetCDF files. An output netCDF file is produced that contains a calibrated probabilistic ice-free date/freeze-up date forecast.\n", "\n", "Outline\n", "-----------\n", "* The first section of this notebook, **Working Script**, offers a copy and pasteable section of code that performs NCGR on a forecast ice-free date or freeze-up date field.\n", "\n", "* The second section section of the notebook, **Detailed explanation**, goes through step-by-step and provides insights on each piece of code. \n", "\n", "* The third section of the notebook, **Plotting**, shows how to load the output NetCDF file created in the previous section (it's pre-made so you don't have to run the previous section), and create a figure showing a three-category forecast for early, near-normal, and late ice-free date/freeze-up date forecast.\n", "\n", "*Hint: You can hold \"shift+tab\" inside the parenthesis of any function in the notebook to see its help/docstring.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working Script" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "from NCGR import ncgr, sitdates\n", "\n", "# input filenames\n", "hc_netcdf = './Data/fud_hc_1981_2019_im10.nc' \n", "obs_netcdf = './Data/fud_obs_1981_2019_im10.nc' \n", "fcst_netcdf = './Data/fud_fcst_2020_im10.nc' \n", "clim_netcdf = './Data/fud_clim_2011_2019_im10.nc' \n", "\n", "# output filename (this usually doesn't exist yet)\n", "out_netcdf = './Data/fud_fcst_ncgr_2020_im10.nc'\n", "\n", "# Dictionary defining the relevant variables/dimensions for hc_netcdf and fcst_netcdf\n", "model_dict = ({'event_vn' : 'fud', # variable name for the ice-free date or freeze-up date variable\n", " 'time_vn' : 'time'}, # variable name for the time coordinate\n", " {'time_dn' : 'time', # dimension name for the time coordinate\n", " 'ens_dn' : 'ensemble'}) # dimension name for the forecast realization coordinate\n", "\n", "# Dictionary defining the relevant variables/dimensions for obs_netcdf\n", "obs_dict = ({'event_vn' : 'fud', # variable name for the ice-free date or freeze-up date variable\n", " 'time_vn' : 'time'}, # variable name for the time coordinate\n", " {'time_dn' : 'time'}) # dimension name for the time coordinate\n", "\n", "im = 10 # initialization month\n", "si_time = sitdates.sitdates(event='fud')\n", "a = si_time.get_min(im)\n", "b = si_time.get_max(im)\n", "\n", "# calibrate \n", "ncgr.ncgr_fullfield(fcst_netcdf, hc_netcdf, obs_netcdf, out_netcdf,\n", " a, b, model_dict, obs_dict, \n", " clim_netcdf=clim_netcdf) \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Detailed explanation\n", "\n", "Import necessary modules and create a variable for the event of interest (can be either `event='fud'` or `event='ifd'`):" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from NCGR import ncgr, sitdates\n", "from netCDF4 import Dataset\n", "import netCDF4 as nc4\n", "\n", "event = 'fud' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now define a string-type variable for each of the NetCDF files (path+filename) to be used as inputs to ``ncgr.ncgr_fullfield()``. Details on these files are given below. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fcst_netcdf = './Data/fud_fcst_2020_im10.nc' # forecast to calibrate\n", "hc_netcdf = './Data/fud_hc_1981_2019_im10.nc' # training forecasts/hindcasts\n", "obs_netcdf = './Data/fud_obs_1981_2019_im10.nc' # training observations\n", "clim_netcdf = './Data/fud_clim_2011_2019_im10.nc' # observations for definining climatology" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Details of input NetCDF files\n", "\n", "* All of the files must have the same spatial coordinates. \n", "* The time-like variable in each file must be CF compliant.\n", "* The files contain ice-free dates or freeze-up dates, not daily ice concentration values. Thus, it is up to the user to compute those dates and create these files before using `NCGR`.\n", "\n", "#### fcst_netcdf \n", "* Must contain a variable with the name 'ifd' or 'fud', containing the ice-free date (if 'ifd') or freeze-up date (if 'fud') forecast to be calibrated. The values should be in day-of-year format (e.g. 1=Jan. 1, 273=Sep. 31, 365=Dec. 31).\n", "* Dimensions of the {'ifd','fud'} variable must be (in plain language): `(time, ensemble, latitude, longitude)` \n", " * The actual dimension/variable names can be anything, and will be specified shortly using the `model_dict` variable. \n", " * The shape of the time dimension should be 1, and the corresponding variable should contain the start date of the forecast.\n", "* Masked values are ignored in the input files and the output file created will contain masked value s in the same locations.\n", "\n", "#### hc_netcdf \n", "* Same as `fcst_netcdf`, except that the shape of the time variable should be :math:`N`, corresponding to the number of years in the hindcast record. The corresponding variable should contain the start dates of all the hindcasts used for training.\n", "\n", "#### obs_netcdf\n", "* Same as `hc_netcdf`, except that the dimensions of the {'ifd','fud'} variable must be (in plain language): `(time, latitude, longitude)` (i.e. the observed dates do not have an ensemble-like coordinate).\n", "\n", "#### clim_netcdf\n", "* Same as `obs_netcdf`, except that this file contains the observed ice-free or freeze-up dates to be used to define the climatology relative to which forecast probabilities are computed. For this example, `clim_netcdf` was created directly from `obs_netcdf` by selecting the last 10 years of observations to be used for the climatology." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll define a string-type variable for the output NetCDF file (path+filename) to be created:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# output filename (this usually doesn't exist yet; if it does, it will be overwritten)\n", "out_netcdf = './Data/fud_fcst_ncgr_2020_im10.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two dictionaries need to be created that define the relevant variable names and dimension names in the input NetCDF files. First, create a dictionary representing the relevant terms in the `fcst_netcdf` and `hc_netcdf` files. In practice, for your own NetCDF files, you would want to replace the entries to the right of each \":\" with those in your NetCDF files. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model_dict = ({'event_vn' : event, # variable name of the ice-free date or freeze-up date variable\n", " 'time_vn' : 'time'}, # variable name of the time coordinate\n", " {'time_dn' : 'time', # dimension name of the time coordinate\n", " 'ens_dn' : 'ensemble'}) # dimension name of the forecast realization/ensemble coordinate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, create a dictionary representing the relevant terms in `obs_netcdf` and `clim_netcdf`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "obs_dict = ({'event_vn' : event, # variable name for the ice-free date or freeze-up date variable\n", " 'time_vn' : 'time'}, # variable name for the time coordinate\n", " {'time_dn' : 'time'}) # dimension name for the time coordinate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll define the minimum and maximum dates possible" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "minimum date (DOY and YYYY/MM/DD): (273, '2014/10/01')\n", "maximum date (DOY and YYYY/MM/DD): (455, '2015/04/01')\n" ] } ], "source": [ "# Relevant variables for time\n", "im = 10 # initialization month\n", "si_time = sitdates.sitdates(event=event) # instantiate sitdates class\n", "a = si_time.get_min(im) # minimum date for the event\n", "b = si_time.get_max(im) # maximum date for the event\n", "print(\"minimum date (DOY and YYYY/MM/DD):\", (a, si_time.doy_to_date(a, format='%Y/%m/%d')))\n", "print(\"maximum date (DOY and YYYY/MM/DD):\", (b, si_time.doy_to_date(b, format='%Y/%m/%d')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above section of code, we've made use of the `sitdates` module by instantiating it with the event variable. By doing so, and by calling on `si_time.get_min()` and `si_time.get_max()` with the initialization month as an input argument, the minimum and maximum dates possible are returned. I've printed these out in day-of-year and YYYY/MM/DD format using the function `si_time.doy_to_date()`. Reading the documentation for `sitdates` you'll see that default dates are set to those given in [1]. \n", "\n", "### *Useful side-note:*\n", "If rather different conventions are used for your data, you can change the dates associated with `si_time.get_min()` and `si_time.get_max()` using `si_time.set_min()` and `si_time.set_max()`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have everything we need to perform the calibration, which can be done with the following function call. I've commented this out so that it isn't actually executed when running this full notebook (it takes about an hour)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#ncgr.ncgr_fullfield(fcst_netcdf, hc_netcdf, obs_netcdf, out_netcdf,\n", "# a, b, model_dict, obs_dict, \n", "# clim_netcdf=clim_netcdf) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "\n", "An output file `./Data/fud_fcst_ncgr_2020_im10.nc` already exists that we'll now load to plot the probabilistic forecast as a three category forecast." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: invalid value encountered in less\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: invalid value encountered in greater\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in less\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in greater\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: invalid value encountered in less\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: invalid value encountered in greater\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:73: RuntimeWarning: All-NaN slice encountered\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:74: RuntimeWarning: All-NaN slice encountered\n", "/home/arlan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:75: RuntimeWarning: All-NaN slice encountered\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Probability for Early, Near-normal, or Late FUD \\n From 10/2020 (cf 2011-2019)')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "from matplotlib import cm\n", "import matplotlib as mpl\n", "\n", "\n", "###############################################################################\n", "################## User-defined variables #####################################\n", "###############################################################################\n", "\n", "event = 'fud'\n", "tc = 0.5 # threshold SIC for the event (needed to apply observation mask)\n", "\n", "# initialization month\n", "im = 10\n", "im_id = \"%02d\" % im\n", "\n", "\n", "# Climatology years (for computing forecast anomalies and event probabilities; \n", "# also just for creating filenames)\n", "clim_yr_s = 2011\n", "clim_yr_f = 2019\n", "\n", "# Forecast year (also just for creating filenames)\n", "fcst_yr = 2020\n", "\n", "# the directories where the data are located and where the produced figure will be saved\n", "dir_out = './Data/'\n", "\n", "# path+filename of NCGR-calibrated forecast \n", "ncgr_netcdf = dir_out+event+'_fcst_ncgr_'+str(fcst_yr)+'_im'+im_id+'.nc'\n", "\n", "# load observed SIC for day prior to the initialization date \n", "# this was obtained from https://nsidc.org/data/g10016 and interpolated onto the model grid.\n", "# this is used to define the pre-occurrence of the event in the case that the forecast initialization\n", "# does not accurately capture the event (e.g. due to discrepancies from assimilation)\n", "sic_obs = Dataset(dir_out+'seaice_conc_daily_icdr_nh_f18_20200930_v01r00_rg.nc').variables['seaice_conc_cdr'][:][0]\n", "\n", "###########################\n", "ncgr_fcst_file = Dataset(ncgr_netcdf)\n", "# forecast probabilities for early, normal, late event\n", "ncgr_p_en = ncgr_fcst_file['prob_EN'][:][0]\n", "ncgr_p_nn = ncgr_fcst_file['prob_NN'][:][0]\n", "ncgr_p_ln = ncgr_fcst_file['prob_LN'][:][0]\n", "\n", "# climatological terciles\n", "clim_terc_low = ncgr_fcst_file['clim_1_3'][:][0]\n", "clim_terc_up = ncgr_fcst_file['clim_2_3'][:][0]\n", "\n", "# forecst probabilities for pre/non occurrence of the event\n", "fcst_pre = ncgr_fcst_file['prob_pre'][:][0]\n", "fcst_non = ncgr_fcst_file['prob_non'][:][0]\n", "\n", "# set masked values to nan\n", "fill_value = ncgr_fcst_file['prob_EN']._FillValue\n", "ncgr_p_en[ncgr_p_en==fill_value] = np.nan\n", "ncgr_p_nn[ncgr_p_nn==fill_value] = np.nan\n", "ncgr_p_ln[ncgr_p_ln==fill_value] = np.nan\n", "\n", "# load spatial variables and make 2d grid\n", "lat = ncgr_fcst_file['latitude'][:]\n", "lon = ncgr_fcst_file['longitude'][:]\n", "LON, LAT = np.meshgrid(lon,lat)\n", "\n", "# prep arrays to be filled with most likely category probability\n", "ncgr_p_en_new = np.zeros(ncgr_p_en.shape)\n", "ncgr_p_nn_new = np.zeros(ncgr_p_nn.shape)\n", "ncgr_p_ln_new = np.zeros(ncgr_p_ln.shape)\n", "\n", "# if category is most likely, set to the probability for that category (else it will be zero)\n", "ncgr_p_en_new[ncgr_p_en==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)] = ncgr_p_en[ncgr_p_en==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)]\n", "ncgr_p_nn_new[ncgr_p_nn==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)] = ncgr_p_nn[ncgr_p_nn==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)]\n", "ncgr_p_ln_new[ncgr_p_ln==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)] = ncgr_p_ln[ncgr_p_ln==np.nanmax(np.array([ncgr_p_en,ncgr_p_nn,ncgr_p_ln]),axis=0)]\n", "\n", "# mask for when the climatological 2/3 tercile is equal to the last day of the forecast or season.\n", "clim_ut_mask = np.zeros(ncgr_p_nn_new.shape)\n", "clim_ut_mask[(clim_terc_up==b)&(ncgr_p_nn_new>0.0)] = 1.0\n", "\n", "# mask for white color in areas where the IFD (FUD) does not (has already) occur(red) at the end (start) \n", "# of the forecast\n", "ice_mask = np.zeros(ncgr_p_nn_new.shape)\n", "if event=='ifd':\n", " ice_mask[(fcst_non==1.0)|(sic_obstc)] = 1.0\n", "\n", "########### Plotting #####################\n", "# useful function for the colorbar\n", "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n", " # cuts off the ends of cmap colors at minval and maxval\n", " new_cmap = mpl.colors.LinearSegmentedColormap.from_list(\n", " 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n", " cmap(np.linspace(minval, maxval, n)))\n", " return new_cmap\n", "\n", "# function for setting up Cartopy map\n", "def set_up_subplot(fig,subplot=111):\n", " crs_np = ccrs.NorthPolarStereo(central_longitude=-45)\n", " ax = fig.add_subplot(subplot,projection=crs_np)\n", " \n", " xll, yll = crs_np.transform_point(279.26,33.92, ccrs.Geodetic())\n", " xur, yur = crs_np.transform_point(102.34,31.37, ccrs.Geodetic())\n", " \n", " ax.set_extent([xll,xur,yll,yur],crs=crs_np)\n", "\n", " ax.add_feature(cfeature.OCEAN,facecolor='c', zorder=1)\n", " ax.add_feature(cfeature.LAND,facecolor='0.3', zorder=3)\n", " ax.add_feature(cfeature.LAKES,facecolor='c',linestyle='-', edgecolor='k',zorder=3)\n", " ax.coastlines(resolution='110m',linewidth=1,color='k',zorder=3) \n", " return ax\n", "\n", "# levels for each colorbar\n", "clevs = [0.4, 0.6, 0.8, 1.0]\n", "clevs_lab = ['40','60','80','100']\n", "clevs_lab = [n+'%' for n in clevs_lab]\n", "clevs_ticks = np.array(clevs)\n", "\n", "# colormaps for each category (note the color for early and late categories depends on the event)\n", "if event=='fud':\n", " cmap_ln = cm.YlOrRd\n", " cmap_nn = cm.Greens\n", " cmap_en = cm.Blues\n", "if event=='ifd':\n", " cmap_en = cm.YlOrRd\n", " cmap_nn = cm.Greens\n", " cmap_ln = cm.Blues\n", "\n", "cmap_en = truncate_colormap(cmap_en,0.3,1.0)\n", "cmap_nn = truncate_colormap(cmap_nn,0.3,1.0)\n", "cmap_ln = truncate_colormap(cmap_ln,0.3,1.0)\n", "\n", "cmap_en.set_under('0.75')\n", "cmap_nn.set_under('0.75')\n", "cmap_ln.set_under('0.75')\n", "\n", "cmap_en.set_over(cm.YlOrRd(256))\n", "cmap_nn.set_over(cm.Greens(256))\n", "cmap_ln.set_over(cm.Blues(256))\n", "\n", "norm_en = mpl.colors.BoundaryNorm(clevs, cmap_en.N)\n", "norm_nn = mpl.colors.BoundaryNorm(clevs, cmap_nn.N)\n", "norm_ln = mpl.colors.BoundaryNorm(clevs, cmap_ln.N)\n", "\n", "#############################################################\n", "\n", "fig = plt.figure(num=1,figsize=(8.5,9))\n", "plt.clf()\n", "\n", "ax = set_up_subplot(fig)\n", "\n", "datain_masked = np.ma.array(ice_mask, mask=ice_mask==0.0)\n", "\n", "masked_array1 = np.ma.array(ncgr_p_en_new, mask=ncgr_p_en_new==0.0)\n", "masked_array2 = np.ma.array(ncgr_p_nn_new, mask=ncgr_p_nn_new==0.0)\n", "masked_array3 = np.ma.array(ncgr_p_ln_new, mask=ncgr_p_ln_new==0.0)\n", "\n", "\n", "ax.pcolormesh(LON,LAT,datain_masked, cmap=cm.Greys,\n", " rasterized=True,transform=ccrs.PlateCarree(), zorder=2) \n", "\n", "im1 = ax.pcolormesh(LON,LAT,masked_array1,vmin=0.4,vmax=1.0,\n", " cmap=cmap_en,norm=norm_en,rasterized=True,transform=ccrs.PlateCarree(), zorder=2) \n", "\n", "im2 = ax.pcolormesh(LON,LAT,masked_array2,vmin=0.4,vmax=1.0,\n", " cmap=cmap_nn,norm=norm_nn,rasterized=True,transform=ccrs.PlateCarree(), zorder=2)\n", "\n", "im3 = ax.pcolormesh(LON,LAT,masked_array3,vmin=0.4,vmax=1.0,\n", " cmap=cmap_ln,norm=norm_ln,rasterized=True,transform=ccrs.PlateCarree(), zorder=2)\n", "\n", " \n", "########### hatching for when the upper tercile for climatology includes the last day ############\n", "label = 'Probability includes \\n no '+event.upper()\n", "masked_array4 = np.ma.array(clim_ut_mask, mask=clim_ut_mask==0.0)\n", "\n", "plt.rcParams['hatch.color'] = 'white'\n", "plt.rcParams['hatch.linewidth'] = 0.5\n", "cs1 = ax.pcolor(LON,LAT,masked_array4, hatch='xxxxx', alpha=0.,\n", " rasterized=True,transform=ccrs.PlateCarree(), zorder=2, label=label)\n", "\n", "cs2 = mpl.patches.Patch(alpha=0.0, hatch=cs1._hatch, label=label)\n", "l = ax.legend(handles=[cs2], loc='upper right', frameon=False)\n", "for text in l.get_texts():\n", " text.set_color('w')\n", "\n", "######### colorbars\n", "cbar_ax1 = fig.add_axes([0.035, 0.04, 0.025, 0.25])\n", "cb1 = fig.colorbar(im1,cax=cbar_ax1,orientation='vertical',format='%d', ticks=clevs_ticks,\n", " spacing='uniform', drawedges=True, extend='min', boundaries=[0]+clevs, extendfrac='auto')\n", "cbar_ax1.set_yticklabels(clevs_lab,fontsize=10)\n", "\n", "cbar_ax2 = fig.add_axes([0.035, 0.34, 0.025, 0.25])\n", "cb2 = fig.colorbar(im2,cax=cbar_ax2,orientation='vertical',format='%d', ticks=clevs_ticks,\n", " spacing='uniform',drawedges=True, extend='min', boundaries=[0]+clevs, extendfrac='auto')\n", "cbar_ax2.set_yticklabels(clevs_lab,fontsize=10)\n", "\n", "cbar_ax3 = fig.add_axes([0.035, 0.63, 0.025, 0.25])\n", "cb3 = fig.colorbar(im3,cax=cbar_ax3,orientation='vertical',format='%d', ticks=clevs_ticks,\n", " spacing='uniform',drawedges=True, extend='min', boundaries=[0]+clevs, extendfrac='auto')\n", "\n", "cbar_ax1.set_yticklabels(clevs_lab,fontsize=14)\n", "cbar_ax2.set_yticklabels(clevs_lab,fontsize=14)\n", "cbar_ax3.set_yticklabels(clevs_lab,fontsize=14)\n", "\n", "cbar_ax1.set_ylabel('Early', fontsize=16,fontweight='semibold')\n", "cbar_ax2.set_ylabel('Near-normal', fontsize=16,fontweight='semibold')\n", "cbar_ax3.set_ylabel('Late', fontsize=16,fontweight='semibold')\n", "\n", "cbar_ax1.yaxis.set_label_position('left')\n", "cbar_ax2.yaxis.set_label_position('left')\n", "cbar_ax3.yaxis.set_label_position('left')\n", "\n", "\n", "cb1.outline.set_linewidth(2)\n", "cb1.outline.set_edgecolor('k')\n", "cb1.dividers.set_color('w')\n", "cb1.dividers.set_linewidth(1.5)\n", "\n", "cb2.outline.set_linewidth(2)\n", "cb2.outline.set_edgecolor('k')\n", "cb2.dividers.set_color('w')\n", "cb2.dividers.set_linewidth(1.5)\n", "\n", "cb3.outline.set_linewidth(2)\n", "cb3.outline.set_edgecolor('k')\n", "cb3.dividers.set_color('w')\n", "cb3.dividers.set_linewidth(1.5)\n", "\n", "fig.text(0.065,0.06,'EC',fontsize=14)\n", "fig.text(0.065,0.36,'EC',fontsize=14)\n", "fig.text(0.065,0.65,'EC',fontsize=14)\n", "\n", "ax.outline_patch.set_linewidth(1.5)\n", "\n", "\n", "fig.subplots_adjust(left=0.05, right=0.98, top=0.91, bottom=0.01)\n", "\n", "ax.set_title('Probability for Early, Near-normal, or Late '+event.upper()+' \\n From '+im_id+'/'+str(fcst_yr)+' (cf '+str(clim_yr_s)+'-'+str(clim_yr_f)+')',\n", " fontsize=20,pad=10.)\n", "\n", "#plt.savefig(dir_out+'/'+event.upper()+'_im'+str(im)+'_3category.png',dpi=700)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A couple things about the plot:\n", "* The white area in the central Arctic is where the forecast is for a 100% chance of having already occurred at the time of initialization. If the plotting code above were applied to an ice-free date forecast, a white region would also exist in the central Arctic corresponding to where ice retreat is forecasted to not occur with 100% probability.\n", "\n", "* The cyan area represents open ocean where no freeze-up has occured over the climatological record (i.e. the lower and upper terciles for the climatology are both equal to the date `a`.)\n", "\n", "* When the climatological upper tercile is equal to the last day of the season, by definition the \"near-normal\" category includes this date in its limits. To highlight these locations on the map, I've used hatching to indicate where the upper tercile includes the last day of the season." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }