From b28bfbb59536061735ba518c94bf32d00cbbed04 Mon Sep 17 00:00:00 2001 From: Jacqueline Nugent Date: Fri, 14 Jun 2024 18:08:08 +0000 Subject: [PATCH] Completed regrid/plot example following the cookbook template --- notebooks/ex-regrid-plot.ipynb | 4025 ++++------------------- notebooks/images/logos/cartopy_logo.png | Bin 0 -> 341563 bytes notebooks/images/logos/rooki_logo.png | Bin 0 -> 6774 bytes 3 files changed, 579 insertions(+), 3446 deletions(-) create mode 100644 notebooks/images/logos/cartopy_logo.png create mode 100644 notebooks/images/logos/rooki_logo.png diff --git a/notebooks/ex-regrid-plot.ipynb b/notebooks/ex-regrid-plot.ipynb index 5bedac2..1a08e71 100644 --- a/notebooks/ex-regrid-plot.ipynb +++ b/notebooks/ex-regrid-plot.ipynb @@ -2,3673 +2,765 @@ "cells": [ { "cell_type": "markdown", - "id": "50b5d7e7-df4e-4992-a29b-8804b081a320", - "metadata": {}, + "id": "7ec06613-53cd-494c-ade6-8a3a156f77a0", + "metadata": { + "tags": [] + }, "source": [ - "# **DRAFT**\n", - "This is a work in progress :)" + "\"ESGF\n", + "\"Rooki\n", + "\"Cartopy" ] }, { - "cell_type": "code", - "execution_count": 1, - "id": "e07f3f93-5dc9-429a-b336-4a14d01ac78a", + "cell_type": "markdown", + "id": "50b5d7e7-df4e-4992-a29b-8804b081a320", "metadata": { "tags": [] }, - "outputs": [], "source": [ - "from intake_esgf import ESGFCatalog\n", - "import matplotlib.pyplot as plt" + "# Demo: Regridding and Plotting with Rooki and Cartopy \n" ] }, { "cell_type": "markdown", - "id": "da700ab5-47b3-4283-92a5-bf60213be07d", - "metadata": {}, + "id": "abd4b497-cdbf-4c29-857c-3017abf9e927", + "metadata": { + "tags": [] + }, "source": [ - "##### Todo - make the search more elegant" + "---" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "379997ac-5da1-408a-9f57-9036a4e188a5", + "cell_type": "markdown", + "id": "0f79862d-7181-4f04-966c-19b5e03a22a5", "metadata": {}, - "outputs": [], "source": [ - "cat = ESGFCatalog()\n" + "## Overview\n", + "In this notebook, we demonstrate how to use Rooki to regrid CMIP model data and plot it in Cartopy for two examples:\n", + "\n", + "1. Regrid two CMIP models onto the same grid \n", + "1. Coarsen the output for one model" ] }, { "cell_type": "markdown", - "id": "7cdb9bc7-1d01-4a02-a171-86b226b5c5f4", + "id": "4f1db315-fb2d-466d-bd6e-8a4ef18b6cf1", "metadata": { "tags": [] }, "source": [ - "#### Get one model" + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to intake-esgf](https://projectpythia.org/esgf-cookbook/notebooks/intro-search.html) | Necessary | |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n", + "| [Using Rooki to access CMIP6 data](https://projectpythia.org/esgf-cookbook/notebooks/rooki.html) | Helpful | Familiarity with rooki |\n", + "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", + "\n", + "- **Time to learn**: 15 minutes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7cbc5d91-db3f-4afd-9093-c3abc7dec82b", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2582d535-9b99-4115-b0ee-7459acd76ec0", + "metadata": {}, + "source": [ + "## Imports\n" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "a580fe34-dcb6-4606-8683-0d00ab34c43d", + "execution_count": null, + "id": "ad4562c9-f5eb-496e-9e17-6453f426e910", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "55c7294b6da445d68b5fae22d9ac90d2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " Searching indices: 0%| |0/2 [ ?index/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary information for 1 results:\n", - "grid_label [gn]\n", - "source_id [CESM2]\n", - "mip_era [CMIP6]\n", - "member_id [r1i1p1f1]\n", - "variable_id [pr]\n", - "datetime_stop [2014-12-15T12:00:00Z]\n", - "table_id [Amon]\n", - "activity_drs [CMIP]\n", - "experiment_id [historical]\n", - "datetime_start [1850-01-15T12:00:00Z]\n", - "institution_id [NCAR]\n", - "project [CMIP6]\n", - "dtype: object\n" - ] - } - ], + "outputs": [], "source": [ - "cat.search(\n", - " experiment_id=\"historical\",\n", - " source_id=\"CESM2\",\n", - " frequency=\"mon\",\n", - " variable_id=\"pr\",\n", - " member_id=\"r1i1p1f1\",\n", - ")\n", - "print(cat)\n" + "import os\n", + "\n", + "import rooki.operators as ops\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "from intake_esgf import ESGFCatalog\n", + "from rooki import rooki\n", + "from matplotlib.gridspec import GridSpec\n", + "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "d22971ce-0594-4836-a364-b0abcdc47934", + "cell_type": "markdown", + "id": "c2b47b1d-db2d-4074-8c92-bb71fa0459a7", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4f8fa3183e784290bd6007f25af0ebe9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Get file information: 0%| |0/2 [ ?index/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "pr_Amon_CESM2_historical_r1i1p1f1_gn_...: 0%| |0.00/343M [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3b3b4391d01f48e2ac74bd1af63e9693", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Adding cell measures: 0%| |0/1 [ ?dataset/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "areacella_fx_CESM2_historical_r1i1p1f...: 0%| |0.00/43.8k [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dsd = cat.to_dataset_dict()" + "source": [ + "## Example 1: Regrid two CMIP6 models onto the same grid" + ] + }, + { + "cell_type": "markdown", + "id": "cc1d512a-68d3-43cf-aac7-6ca233d9ef73", + "metadata": {}, + "source": [ + "In this example, we want to compare the historical precipitation output between two CMIP models, CESM2 and CanESM5. Here will will look at the annual mean precipitation for 2010. " + ] + }, + { + "cell_type": "markdown", + "id": "46f5fba3-7410-465c-abdf-4e338855284c", + "metadata": {}, + "source": [ + "### Access the desired datasets using intake-esgf and rooki" ] }, { "cell_type": "markdown", - "id": "5489ad11-3c34-43ac-8e22-6c428dbff1bd", + "id": "c5f4dc65-0dff-4023-880c-f511cbc58666", "metadata": { "tags": [] }, "source": [ - "#### Get another" + "The function and workflow to read in CMPI6 data using `intake-esgf` and `rooki` in the next few cells are adapted from [intake-esgf-with-rooki.ipynb](https://github.com/ProjectPythia/esgf-cookbook/blob/cf69015a464b68ee28cfdd4a27cee4e9d6ca2ca9/notebooks/use-intake-esgf-with-rooki.ipynb). Essentially, we use `intake-esgf` to find the dataset IDs we want and then subset and average them using `rooki`." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "92402d46-25e5-40c8-a6a1-9cb36a79207c", + "execution_count": null, + "id": "d053a676-2a27-4be0-93c0-eafb9671c0bc", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cbf4cf6500aa46c980bb9b7e0db8d9fb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " Searching indices: 0%| |0/2 [ ?index/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary information for 1 results:\n", - "grid_label [gr]\n", - "source_id [E3SM-1-0]\n", - "mip_era [CMIP6]\n", - "member_id [r6i2p2f1]\n", - "variable_id [pr]\n", - "table_id [Amon]\n", - "activity_drs [CMIP]\n", - "experiment_id [historical]\n", - "datetime_start [1850-01-16T00:00:00Z]\n", - "institution_id [UCSB]\n", - "project [CMIP6]\n", - "dtype: object\n" - ] - } - ], + "outputs": [], "source": [ - "cat.search(\n", - " experiment_id=\"historical\",\n", - " source_id=\"E3SM-1-0\",\n", - " frequency=\"mon\",\n", - " variable_id=\"pr\",\n", - " member_id=\"r6i2p2f1\",\n", - ")\n", - "print(cat)\n" + "def separate_dataset_id(full_dataset):\n", + " return full_dataset[0].split(\"|\")[0]\n" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "398fd04a-d1e4-45ea-bd32-1da89c499a90", + "execution_count": null, + "id": "095db615-275a-4dbc-8467-833fd7992aed", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "09167f14d6684e48bad54815094e888c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Get file information: 0%| |0/2 [ ?index/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "pr_Amon_E3SM-1-0_historical_r6i2p2f1_...: 0%| |0.00/139M [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "pr_Amon_E3SM-1-0_historical_r6i2p2f1_...: 0%| |0.00/139M [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "pr_Amon_E3SM-1-0_historical_r6i2p2f1_...: 0%| |0.00/41.8M [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "pr_Amon_E3SM-1-0_historical_r6i2p2f1_...: 0%| |0.00/139M [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "872fab3115cd419a82c83efcab7692af", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Adding cell measures: 0%| |0/1 [ ?dataset/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "areacella_fx_E3SM-1-0_historical_r6i2...: 0%| |0.00/50.1k [?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dsd2 = cat.to_dataset_dict()" + "outputs": [], + "source": [ + "cat = ESGFCatalog()\n", + "cat.search(\n", + " activity_id='CMIP',\n", + " experiment_id=[\"historical\",],\n", + " variable_id=[\"pr\"],\n", + " member_id='r1i1p1f1',\n", + " grid_label='gn',\n", + " table_id=\"Amon\",\n", + " source_id = [ \"CESM2\", \"CanESM5\"]\n", + " )\n", + "\n", + "dsets = [separate_dataset_id(dataset) for dataset in list(cat.df.id.values)]\n", + "dsets\n" ] }, { "cell_type": "markdown", - "id": "eaf817c4-8962-49ca-8825-b7293b5ec00f", + "id": "777f6bc4-f3a8-4110-bc2a-82cbf227ec4e", "metadata": {}, "source": [ - "### Two models are on different grids" + "Subset the data to get the precipitation variable for 2010 and then average by time:" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "9ed09ae4-868e-475c-9036-a2ceb9ab20ef", + "execution_count": null, + "id": "bf653879-96b5-48e0-be9b-0f0cc08152e2", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# first gets the dataset, second gets the variable/data array\n", - "# will be different if you have multiple variables... TODO!\n", - "pr1 = dsd[\"pr\"][\"pr\"]\n", - "pr2 = dsd2[\"pr\"][\"pr\"]" + "dset_list = [[]]*len(dsets)\n", + "\n", + "for i, dset_id in enumerate(dsets):\n", + " wf = ops.AverageByTime(\n", + " ops.Subset(\n", + " ops.Input('pr', [dset_id]),\n", + " time='2010/2010'\n", + " )\n", + " )\n", + "\n", + " resp = wf.orchestrate()\n", + "\n", + " # if it worked, add the dataset to our list\n", + " if resp.ok:\n", + " dset_list[i] = resp.datasets()[0]\n", + " \n", + " # if it failed, tell us why\n", + " else:\n", + " print(resp.status)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e040d078-3981-4246-a10b-c50cf104d8ed", + "metadata": {}, + "source": [ + "Print the dataset list to get an overview of the metadata structure:" ] }, { "cell_type": "code", - "execution_count": 62, - "id": "65dee0b2-18a9-4670-bddf-29fb5f2980dd", + "execution_count": null, + "id": "b2ed096a-2cfc-4e51-9b2a-43b9ee4f103e", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Size: 2kB\n", - "array([-90. , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n", - " -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n", - " -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n", - " -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n", - " -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n", - " -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n", - " -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n", - " -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n", - " -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n", - " -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n", - " -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n", - " -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n", - " -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n", - " -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n", - " -10.837696, -9.895288, -8.95288 , -8.010471, -7.068063, -6.125654,\n", - " -5.183246, -4.240838, -3.298429, -2.356021, -1.413613, -0.471204,\n", - " 0.471204, 1.413613, 2.356021, 3.298429, 4.240838, 5.183246,\n", - " 6.125654, 7.068063, 8.010471, 8.95288 , 9.895288, 10.837696,\n", - " 11.780105, 12.722513, 13.664921, 14.60733 , 15.549738, 16.492147,\n", - " 17.434555, 18.376963, 19.319372, 20.26178 , 21.204188, 22.146597,\n", - " 23.089005, 24.031414, 24.973822, 25.91623 , 26.858639, 27.801047,\n", - " 28.743455, 29.685864, 30.628272, 31.570681, 32.513089, 33.455497,\n", - " 34.397906, 35.340314, 36.282723, 37.225131, 38.167539, 39.109948,\n", - " 40.052356, 40.994764, 41.937173, 42.879581, 43.82199 , 44.764398,\n", - " 45.706806, 46.649215, 47.591623, 48.534031, 49.47644 , 50.418848,\n", - " 51.361257, 52.303665, 53.246073, 54.188482, 55.13089 , 56.073298,\n", - " 57.015707, 57.958115, 58.900524, 59.842932, 60.78534 , 61.727749,\n", - " 62.670157, 63.612565, 64.554974, 65.497382, 66.439791, 67.382199,\n", - " 68.324607, 69.267016, 70.209424, 71.151832, 72.094241, 73.036649,\n", - " 73.979058, 74.921466, 75.863874, 76.806283, 77.748691, 78.691099,\n", - " 79.633508, 80.575916, 81.518325, 82.460733, 83.403141, 84.34555 ,\n", - " 85.287958, 86.230366, 87.172775, 88.115183, 89.057592, 90. ])\n", - "Coordinates:\n", - " * lat (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", - "Attributes:\n", - " axis: Y\n", - " bounds: lat_bnds\n", - " standard_name: latitude\n", - " title: Latitude\n", - " type: double\n", - " units: degrees_north\n", - " valid_max: 90.0\n", - " valid_min: -90.0" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pr1.lat" + "outputs": [], + "source": [ + "print(dset_list)\n" + ] + }, + { + "cell_type": "markdown", + "id": "776f84fd-e329-42e8-bab4-54253636aefc", + "metadata": {}, + "source": [ + "### Compare the precipitation data between models" + ] + }, + { + "cell_type": "markdown", + "id": "ee469ea1-e402-4e55-b709-0de01e7875b3", + "metadata": {}, + "source": [ + "First, let's quickly plot the 2010 annual mean precipitation for each model to see what we're working with. Since precipitation values vary greatly in magnitude, using a log-normalized colormap makes the data easier to visualize. " ] }, { "cell_type": "code", - "execution_count": 63, - "id": "23bf623f-2f8f-4fba-80e3-d56a8c2621dd", + "execution_count": null, + "id": "e49b55e3-1970-4410-8557-9328f31853fb", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray 'lat' (lat: 180)> Size: 1kB\n",
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-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
-       "Attributes:\n",
-       "    bounds:         lat_bnds\n",
-       "    units:          degrees_north\n",
-       "    axis:           Y\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude
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If we try to take the difference outright, it fails! " ] }, { "cell_type": "code", - "execution_count": 23, - "id": "f8fac83b-50e2-4036-839b-bb0c3556d2e8", + "execution_count": null, + "id": "4276c97e-d798-42b7-846b-98f6460ce897", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# import xarray as xr\n", - "# import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as mpatches\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "from matplotlib.gridspec import GridSpec\n", - "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n" + "# pr_diff = dset_list[0].pr - dset_list[1].pr" ] }, { - "cell_type": "code", - "execution_count": 106, - "id": "97055388-90d1-4e51-ba10-ea46b3fd297d", + "cell_type": "markdown", + "id": "5c7745fe-2ddd-4232-ad1c-c76601909db7", "metadata": { "tags": [] }, - "outputs": [], "source": [ - "# also harcoded...\n", - "figsize = (6, 2)\n" + "The models have different grids so we can't directly subtract the data. We can use the `grid` attribute to get information on which grid each uses." ] }, { "cell_type": "code", - "execution_count": 107, - "id": "70e1bbdd-63a7-44c7-bbd4-8e8812e0abe9", + "execution_count": null, + "id": "11beadd3-beef-4337-a6cf-1fdd1d657ddf", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Harcoded colorbar limits - make better!\n", - "vmin=1e-8\n", - "vmax=5e-4\n" + "print(dset_list[0].grid)\n", + "print(dset_list[1].grid)" + ] + }, + { + "cell_type": "markdown", + "id": "4947c0a2-e796-4a57-aaaf-d4ba07fb26a6", + "metadata": {}, + "source": [ + "### Regrid the models onto the same grid with Rooki" + ] + }, + { + "cell_type": "markdown", + "id": "4823272f-614b-437c-8331-95c24c267b47", + "metadata": { + "tags": [] + }, + "source": [ + "Look at the documentation on the `regrid` operator to see the available grid types and regrid methods:" ] }, { "cell_type": "code", - "execution_count": 108, - "id": "7e111bc5-42cd-4d3a-b7f4-8df3479e0e9c", + "execution_count": null, + "id": "423e4302-6aa6-42e7-9686-d99c1b8cd3af", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# pick some time step just so you have 2D data\n", - "# TODO - average!\n", - "pr1t = pr1.isel(time=3)\n", - "pr2t = pr2.isel(time=3)" + "rooki.regrid?" ] }, { "cell_type": "markdown", - "id": "1e9e4fd2-7804-4f28-83cc-3178463f0127", + "id": "8f6d5022-7973-4133-b321-5b4804f5eb9b", "metadata": { "tags": [] }, "source": [ - "### TODO - Mollweide or Robinson?\n", - "need to troubleshoot that" + "Here we'll do the same process as before to read in and subset the datasets with rooki, but now we **regrid using `ops.Regrid` before averaging over time**. In this example, we use `method=nearest_s2d` to regrid each model onto the target grid using a nearest neighbors method. The target grid is a 1.25° grid, specified by `grid='1pt25deg'`.\n" ] }, { "cell_type": "code", - "execution_count": 140, - "id": "86ab0c4a-ce76-48ab-843d-e79333bdd58c", + "execution_count": null, + "id": "4993b311-f18b-4d79-8902-a6040aff3271", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# set up figure\n", - "fig = plt.figure(figsize=figsize)\n", - "gs = GridSpec(2, 2, height_ratios=[1, 0.1], hspace=0.2)#, wspace=0.5)\n", + "rg_list = [[]]*len(dsets)\n", "\n", - "# specify the projection\n", - "proj = ccrs.PlateCarree()\n", + "for i, dset_id in enumerate(dsets):\n", + " wf = ops.AverageByTime(\n", + " ops.Regrid(\n", + " ops.Subset(\n", + " ops.Input('pr', [dset_id]),\n", + " time='2010/2010'\n", + " ),\n", + " method='nearest_s2d',\n", + " grid='1pt25deg'\n", + " )\n", + " )\n", "\n", - "# set up plots for each model\n", - "ax1 = plt.subplot(gs[0, 0], projection=proj)\n", - "ax2 = plt.subplot(gs[0, 1], projection=proj)\n", - "ax1.set_global()\n", - "ax2.set_global()\n", "\n", - "# axis where the colorbar will go\n", - "ax_cb = plt.subplot(gs[1, :]) \n", - "ax_cb.axis(\"off\")\n", - "\n", - "# plot on the axes\n", - "pcm1 = ax1.pcolormesh(pr1t.lon, pr1t.lat, pr1t, norm=mcolors.LogNorm(vmin=vmin, vmax=vmax),\n", - " transform=proj)\n", - "pcm2 = ax2.pcolormesh(pr2t.lon, pr2t.lat, pr2t, norm=mcolors.LogNorm(vmin=vmin, vmax=vmax), \n", - " transform=proj)\n", - "\n", - "# add coastlines \n", - "ax1.add_feature(cfeature.COASTLINE)\n", - "ax2.add_feature(cfeature.COASTLINE)\n", - "\n", - "\n", - "\n", - "# since the colormap/scale is the same, we can use either pcolormesh to \n", - "# set the colorbar\n", - "ax_cb_ins = inset_axes(ax_cb, width=\"50%\", height=\"100%\", loc=\"center\")\n", - "cbar = plt.colorbar(pcm1, cax=ax_cb_ins, orientation=\"horizontal\")\n", + " resp = wf.orchestrate()\n", " \n", - "\n", - "plt.show()\n" + " # if it worked, add the regridded dataset to our list\n", + " if resp.ok:\n", + " rg_list[i] = resp.datasets()[0]\n", + " \n", + " # if it failed, tell us why\n", + " else:\n", + " print(resp.status)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "dfb8efaf-a4ab-4709-b1eb-a38c6f4bade2", + "metadata": {}, + "source": [ + "Print the list of regridded datasets to get an overview of the metadata structure. Note how `lat` and `lon` are now the same and each dataset has additional attributes, including `grid_original` and `regrid_operation`, which all keep track of the regridding operations we just completed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e38e4a39-0465-4ed8-8a5c-6bdcd61d77c1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(rg_list)" ] }, { "cell_type": "markdown", - "id": "f63dfd8d-5e22-448b-874c-3994ecdf1e98", + "id": "d048801f-fb36-46b2-84af-f1b61768727c", "metadata": {}, "source": [ - "### Try to find the difference in precip between the models" + "Now they are on the same grid!" ] }, { "cell_type": "code", - "execution_count": 142, - "id": "a3f7d020-a705-4729-abaa-05103e005567", + "execution_count": null, + "id": "59b6fefa-90e7-49c7-b816-27351a0f51f4", "metadata": { "tags": [] }, "outputs": [], "source": [ - "pr_diff = pr1t - pr2t" + "print(rg_list[0].grid)\n", + "print(rg_list[1].grid)" ] }, { "cell_type": "markdown", - "id": "a5866f39-481b-4287-9acb-c818913f5eba", + "id": "168c41e8-7ae2-4ee6-9020-94f9de00500d", "metadata": {}, "source": [ - "No y dimension because the grids have different latitudes!" + "### Quick plot the before and after for each model\n", + "The plots largely look the same, as they should - with the nearest neighbors method, we are just shifting the precipitation data onto a different grid without averaging between grid cells." ] }, { "cell_type": "code", - "execution_count": 143, - "id": "1ddadb78-772b-449e-9345-cf9b62e37cf3", + "execution_count": null, + "id": "a9d5213f-65a1-415f-b3fe-6860d70fd14d", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray 'pr' (lat: 0, lon: 72)> Size: 0B\n",
-       "dask.array<subtract, shape=(0, 72), dtype=float32, chunksize=(0, 72), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 0B \n",
-       "  * lon      (lon) float64 576B 2.5 7.5 12.5 17.5 ... 342.5 347.5 352.5 357.5
" - ], - "text/plain": [ - " Size: 0B\n", - "dask.array\n", - "Coordinates:\n", - " * lat (lat) float64 0B \n", - " * lon (lon) float64 576B 2.5 7.5 12.5 17.5 ... 342.5 347.5 352.5 357.5" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pr_diff" + "outputs": [], + "source": [ + "print(dset_list[0].source_id)\n", + "for ds in [dset_list[0], rg_list[0]]:\n", + " ds.pr.plot(norm=mcolors.LogNorm())\n", + " plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "dfc3c800-984d-40a5-a492-a3b2f1003491", - "metadata": {}, + "id": "0fabcf8b-c9e2-40c4-b7e1-ce7de3809d29", + "metadata": { + "tags": [] + }, "outputs": [], - "source": [] + "source": [ + "print(dset_list[1].source_id)\n", + "for ds in [dset_list[1], rg_list[1]]:\n", + " ds.pr.plot(norm=mcolors.LogNorm())\n", + " plt.show()\n" + ] }, { "cell_type": "markdown", - "id": "38ae8179-67f8-4146-a047-300ba9965d91", + "id": "36031d82-c46f-4da5-8cbb-82067ade465b", "metadata": {}, "source": [ - "## Try regridding\n", - "See docs here: https://github.com/roocs/rooki/blob/master/notebooks/demo/demo-rooki-regrid-cmip6.ipynb" + "#### Take the difference between precipitation datasets and plot it\n", + "Now that both models are on the same grid, we can subtract the precipitation datasets and plot the difference!" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "eafdef10-a4c2-4265-8df8-6a208aa43000", + "execution_count": null, + "id": "4f781e26-0c43-45e9-be89-31d3575f4c99", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import os\n", - "os.environ['ROOK_URL'] = 'http://rook.dkrz.de/wps'\n", + "pr_diff = rg_list[0] - rg_list[1]\n", "\n", - "from rooki import rooki\n", - "from rooki import operators as ops\n" + "pr_diff.pr.plot(cmap=\"bwr\")\n", + "plt.show()\n" ] }, { "cell_type": "markdown", - "id": "d4cc8767-09b6-4d13-b614-e36bc8202752", + "id": "e1fc1957-c334-4431-9e0a-15715106b5d6", + "metadata": {}, + "source": [ + "### Plot everything together\n", + "Plot the regridded precipitation data as well as the difference between models on the same figure. We can use `Cartopy` to make it pretty. With `GridSpec`, we can also split up the figure and organize it to use the same colorbar for more than one panel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ab0c4a-ce76-48ab-843d-e79333bdd58c", "metadata": { "tags": [] }, + "outputs": [], "source": [ - "View available grid types/regrid methods:" + "# set up figure\n", + "fig = plt.figure(figsize=(6, 8))\n", + "gs = GridSpec(3, 2, width_ratios=[1, 0.1], hspace=0.2)\n", + "\n", + "# specify the projection\n", + "proj = ccrs.Mollweide()\n", + "\n", + "# set up plots for each model\n", + "axpr_1 = plt.subplot(gs[0, 0], projection=proj)\n", + "axpr_2 = plt.subplot(gs[1, 0], projection=proj)\n", + "axdiff = plt.subplot(gs[2, 0], projection=proj)\n", + "\n", + "# axes where the colorbar will go \n", + "axcb_pr = plt.subplot(gs[:2, 1]) \n", + "axcb_diff = plt.subplot(gs[2, 1])\n", + "axcb_pr.axis(\"off\")\n", + "axcb_diff.axis(\"off\")\n", + "\n", + "# plot the precipitation for both models\n", + "for i, ax in enumerate([axpr_1, axpr_2]):\n", + " ds_rg = rg_list[i]\n", + " pcm = ax.pcolormesh(ds_rg.lon, ds_rg.lat, ds_rg.pr.isel(time=0), norm=mcolors.LogNorm(vmin=1e-7, vmax=3e-4),\n", + " transform=ccrs.PlateCarree()\n", + " )\n", + " ax.set_title(ds_rg.parent_source_id)\n", + " ax.add_feature(cfeature.COASTLINE)\n", + " \n", + "# now plot the difference\n", + "pcmd = axdiff.pcolormesh(pr_diff.lon, pr_diff.lat, pr_diff.pr.isel(time=0), cmap=\"bwr\", vmin=-3e-4, vmax=3e-4,\n", + " transform=ccrs.PlateCarree()\n", + " )\n", + "axdiff.set_title(\"{a} - {b}\".format(a=rg_list[0].parent_source_id, b=rg_list[1].parent_source_id))\n", + "axdiff.add_feature(cfeature.COASTLINE)\n", + "\n", + "# set the precipitation colorbar\n", + "axcb_pr_ins = inset_axes(axcb_pr, width=\"50%\", height=\"75%\", loc=\"center\")\n", + "cbar_pr = plt.colorbar(pcm, cax=axcb_pr_ins, orientation=\"vertical\", extend=\"both\")\n", + "cbar_pr.set_label(\"{n} ({u})\".format(n=rg_list[0].pr.long_name, u=rg_list[0].pr.units))\n", + "\n", + "# set the difference colorbar\n", + "axcb_diff_ins = inset_axes(axcb_diff, width=\"50%\", height=\"100%\", loc=\"center\")\n", + "cbar_diff = plt.colorbar(pcmd, cax=axcb_diff_ins, orientation=\"vertical\", extend=\"both\")\n", + "cbar_diff.set_label(\"Difference ({u})\".format(u=pr_diff.pr.units))\n", + "\n", + "plt.show()\n" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "03b9e64d-dcb2-49d6-b622-9f5ce2164d8a", + "cell_type": "markdown", + "id": "a77d6b7b-5729-4ba3-9e72-6f960dbe3253", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mrooki\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest_s2d'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Run regridding operator on climate model data using daops (xarray).\n", - "\n", - "Parameters\n", - "----------\n", - "collection : string\n", - " A dataset identifier or list of comma separated identifiers. Example: c3s-cmip5.output1.ICHEC.EC-EARTH.historical.day.atmos.day.r1i1p1.tas.latest\n", - "method : {'nearest_s2d', 'bilinear', 'conservative', 'patch'}string\n", - " Please specify regridding method like consevative or bilinear. Default: nearest_s2d\n", - "grid : {'auto', '0pt25deg', '0pt25deg_era5', '0pt5deg_lsm', '0pt625x0pt5deg', '0pt75deg', '1deg', '1pt25deg', '2pt5deg'}string\n", - " Please specify output grid resolution for regridding. Default: auto\n", - "\n", - "Returns\n", - "-------\n", - "output : ComplexData:mimetype:`application/metalink+xml; version=4.0`\n", - " Metalink v4 document with references to NetCDF files.\n", - "prov : ComplexData:mimetype:`application/json`\n", - " Provenance document using W3C standard.\n", - "prov_plot : ComplexData:mimetype:`image/png`\n", - " Provenance document as diagram.\n", - "\u001b[0;31mFile:\u001b[0m ~/esgf-cookbook/notebooks/\n", - "\u001b[0;31mType:\u001b[0m method" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "rooki.regrid?" + "## Example 2: Coarsen the output for one model" + ] + }, + { + "cell_type": "markdown", + "id": "919dd269-6a7a-48d9-a477-f1e76ed48d11", + "metadata": {}, + "source": [ + "We can also use `Rooki` to regrid the data from one model onto a coarser grid. In this case, it may make more sense to use a conservative regridding method, which will conserve the physical fluxes between grid cells, rather than the nearest neighbors method we used in Example 1. " + ] + }, + { + "cell_type": "markdown", + "id": "d32f8ab0-05ef-41a6-a599-f4a289103c2e", + "metadata": {}, + "source": [ + "### Get the data using intake-esgf and Rooki again" ] }, { "cell_type": "markdown", - "id": "f666385e-a021-45e2-8b8a-6092b6c2c541", + "id": "81cdf527-cec0-4353-98d7-29cc3effa014", "metadata": {}, "source": [ - "### Compare 1 degree to 2.5 degrees\n", - "#### TODO - extract/search for what the input name should be?\n", - "##### Use conservative method here (not nearest neighbors) bc changing the resolution" + "In this example, we'll look at the annual mean near-surface air temperature for CESM2 in 2010. " ] }, { "cell_type": "code", - "execution_count": 3, - "id": "5ac22bed-bef0-4f47-8699-afd50d3b0f6e", + "execution_count": null, + "id": "d64f5c8d-6355-4225-b5c8-8f2c344fa241", "metadata": { "tags": [] }, "outputs": [], "source": [ - "tas = ops.Input(\n", - " 'tas', ['c3s-cmip6.ScenarioMIP.INM.INM-CM5-0.ssp245.r1i1p1f1.day.tas.gr1.v20190619']\n", - ")\n" + "cat = ESGFCatalog()\n", + "cat.search(\n", + " activity_id='CMIP',\n", + " experiment_id=[\"historical\",],\n", + " variable_id=[\"tas\"],\n", + " member_id='r1i1p1f1',\n", + " grid_label='gn',\n", + " table_id=\"Amon\",\n", + " source_id = [ \"CESM2\"]\n", + " )\n", + "\n", + "dsets = [separate_dataset_id(dataset) for dataset in list(cat.df.id.values)]\n", + "dsets\n" + ] + }, + { + "cell_type": "markdown", + "id": "a647dc7d-e107-49ef-87de-08dbf9024cd2", + "metadata": {}, + "source": [ + "First, get the dataset with the original grid:" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "b9054b55-07be-482f-8287-289a399d751f", + "execution_count": null, + "id": "9c70d292-6e0e-43dd-b64e-5b143e38590e", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading to /tmp/metalink_uajgyg7n/tas_day_INM-CM5-0_ssp245_r1i1p1f1_gr1_20160101-20161231.nc.\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 32MB\n",
-       "Dimensions:    (time: 365, bnds: 2, lat: 120, lon: 180)\n",
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-       "  * time       (time) object 3kB 2016-01-01 12:00:00 ... 2016-12-31 12:00:00\n",
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-       "    time_bnds  (time, bnds) object 6kB ...\n",
-       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
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-       "Attributes: (12/47)\n",
-       "    Conventions:            CF-1.7 CMIP-6.2\n",
-       "    activity_id:            ScenarioMIP\n",
-       "    branch_method:          standard\n",
-       "    branch_time_in_child:   60225.0\n",
-       "    branch_time_in_parent:  60225.0\n",
-       "    contact:                Evgeny Volodin (volodinev@gmail.com)\n",
-       "    ...                     ...\n",
-       "    title:                  INM-CM5-0 output prepared for CMIP6\n",
-       "    tracking_id:            hdl:21.14100/89cab4a2-c867-4bb5-a215-622f969cd844\n",
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<xarray.Dataset> Size: 15MB\n",
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-       "    branch_method:                standard\n",
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-       "    branch_time_in_parent:        60225.0\n",
-       "    contact:                      Evgeny Volodin (volodinev@gmail.com)\n",
-       "    ...                           ...\n",
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-       "    nominal_resolution_original:  100 km\n",
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" - ], - "text/plain": [ - " Size: 15MB\n", - "Dimensions: (lat: 72, lon: 144, bnds: 2, time: 365)\n", - "Coordinates:\n", - " * lat (lat) float64 576B -88.75 -86.25 -83.75 ... 83.75 86.25 88.75\n", - " * lon (lon) float64 1kB 1.25 3.75 6.25 8.75 ... 351.2 353.8 356.2 358.8\n", - " lat_bnds (lat, bnds) float64 1kB ...\n", - " lon_bnds (lon, bnds) float64 2kB ...\n", - " * time (time) object 3kB 2016-01-01 12:00:00 ... 2016-12-31 12:00:00\n", - " height float64 8B ...\n", - " time_bnds (time, bnds) object 6kB ...\n", - "Dimensions without coordinates: bnds\n", - "Data variables:\n", - " tas (time, lat, lon) float32 15MB ...\n", - "Attributes: (12/52)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: ScenarioMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 60225.0\n", - " branch_time_in_parent: 60225.0\n", - " contact: Evgeny Volodin (volodinev@gmail.com)\n", - " ... ...\n", - " grid_original: gs2x1.5\n", - " grid_label_original: gr1\n", - " nominal_resolution_original: 100 km\n", - " regrid_operation: conservative_120x180_72x144\n", - " regrid_tool: xESMF_v0.8.2\n", - " regrid_weights_uid: 046d352045d899caf55bd49408cff1e6_d4ebf55323..." - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 2.5 degrees\n", - "wf2 = ops.Subset(tas, time=\"2016/2016\")\n", - "wf2 = ops.Regrid(wf2, method=\"conservative\", grid=\"2pt5deg\")\n", + "outputs": [], + "source": [ + "ds_og.grid" + ] + }, + { + "cell_type": "markdown", + "id": "a52f11e3-a17f-4a74-849e-a4219de6a2c9", + "metadata": {}, + "source": [ + "The native grid is 0.9°x1.25°, so let's try coarsening to a 1.25°x1.25° grid using the conservative method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "710606ac-1c41-4b27-b2c4-2ae4957357ea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "wf = ops.AverageByTime(\n", + " ops.Regrid(\n", + " ops.Subset(\n", + " ops.Input('tas', [dsets[0]]),\n", + " time='2010/2010'\n", + " ),\n", + " method='conservative',\n", + " grid='1pt25deg'\n", + " )\n", + ")\n", "\n", - "resp2 = wf2.orchestrate()\n", - "resp2.ok\n", + "resp = wf.orchestrate()\n", "\n", - "ds_25 = resp2.datasets()[0]\n", - "ds_25\n" + "if resp.ok:\n", + " ds_125 = resp.datasets()[0]\n", + "else:\n", + " print(resp.status)\n", + " " ] }, { "cell_type": "code", - "execution_count": 6, - "id": "b5d0276a-0d91-413a-be76-753ebf04ca4d", + "execution_count": null, + "id": "f51dd55a-a419-4a24-bc2f-4bdaeb435318", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading to /tmp/metalink_x89nq9a4/tas_day_INM-CM5-0_ssp245_r1i1p1f1_gr_20160101-20161231_regrid-conservative-180x360_cells_grid.nc.\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 95MB\n",
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-       "Data variables:\n",
-       "    tas        (time, lat, lon) float32 95MB ...\n",
-       "Attributes: (12/52)\n",
-       "    Conventions:                  CF-1.7 CMIP-6.2\n",
-       "    activity_id:                  ScenarioMIP\n",
-       "    branch_method:                standard\n",
-       "    branch_time_in_child:         60225.0\n",
-       "    branch_time_in_parent:        60225.0\n",
-       "    contact:                      Evgeny Volodin (volodinev@gmail.com)\n",
-       "    ...                           ...\n",
-       "    grid_original:                gs2x1.5\n",
-       "    grid_label_original:          gr1\n",
-       "    nominal_resolution_original:  100 km\n",
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-       "    regrid_tool:                  xESMF_v0.8.2\n",
-       "    regrid_weights_uid:           046d352045d899caf55bd49408cff1e6_d24f4f689f...
" - ], - "text/plain": [ - " Size: 95MB\n", - "Dimensions: (lat: 180, lon: 360, bnds: 2, time: 365)\n", - "Coordinates:\n", - " * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", - " * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n", - " lat_bnds (lat, bnds) float64 3kB ...\n", - " lon_bnds (lon, bnds) float64 6kB ...\n", - " * time (time) object 3kB 2016-01-01 12:00:00 ... 2016-12-31 12:00:00\n", - " height float64 8B ...\n", - " time_bnds (time, bnds) object 6kB ...\n", - "Dimensions without coordinates: bnds\n", - "Data variables:\n", - " tas (time, lat, lon) float32 95MB ...\n", - "Attributes: (12/52)\n", - " Conventions: CF-1.7 CMIP-6.2\n", - " activity_id: ScenarioMIP\n", - " branch_method: standard\n", - " branch_time_in_child: 60225.0\n", - " branch_time_in_parent: 60225.0\n", - " contact: Evgeny Volodin (volodinev@gmail.com)\n", - " ... ...\n", - " grid_original: gs2x1.5\n", - " grid_label_original: gr1\n", - " nominal_resolution_original: 100 km\n", - " regrid_operation: conservative_120x180_180x360\n", - " regrid_tool: xESMF_v0.8.2\n", - " regrid_weights_uid: 046d352045d899caf55bd49408cff1e6_d24f4f689f..." - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1 degree\n", - "wf1 = ops.Subset(tas, time=\"2016/2016\")\n", - "wf1 = ops.Regrid(wf1, method=\"conservative\", grid=\"1deg\")\n", + "outputs": [], + "source": [ + "ds_125.grid" + ] + }, + { + "cell_type": "markdown", + "id": "a8556572-54a6-42b0-94d8-cc562d40ccd3", + "metadata": {}, + "source": [ + "We can also make it even coarser by regridding to a 2.5°x2.5° grid:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71a84338-90cf-49ec-b120-8f071213cf3e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "wf = ops.AverageByTime(\n", + " ops.Regrid(\n", + " ops.Subset(\n", + " ops.Input('tas', [dsets[0]]),\n", + " time='2010/2010'\n", + " ),\n", + " method='conservative',\n", + " grid='2pt5deg'\n", + " )\n", + ")\n", "\n", - "resp1 = wf1.orchestrate()\n", - "resp1.ok\n", + "resp = wf.orchestrate()\n", "\n", - "ds_1 = resp1.datasets()[0]\n", - "ds_1\n" + "if resp.ok:\n", + " ds_25 = resp.datasets()[0]\n", + "else:\n", + " print(resp.status)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "412c50c9-0a30-4f28-8c97-c8a3d957de51", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds_25.grid" ] }, { @@ -3676,51 +768,62 @@ "id": "087503d7-e4ac-42f8-82a6-770a92795780", "metadata": {}, "source": [ - "### Plot 2.5deg vs. default vs. 1deg" + "### Plot each dataset to look at the coarsened grids" + ] + }, + { + "cell_type": "markdown", + "id": "020b10a4-95ff-4ecf-a203-32db9f3beaf9", + "metadata": {}, + "source": [ + "Make a quick plot first:" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8c433546-4b3d-44e4-814e-527b316ca3e3", + "execution_count": null, + "id": "96f1af51-6836-436f-a09f-1d6375614f98", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "from matplotlib.gridspec import GridSpec\n", - "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n" + "for ds in [ds_og, ds_125, ds_25]:\n", + " ds[\"tas\"].plot()\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "919dc7a1-ad2a-4291-aa90-c2d622f1d433", + "metadata": {}, + "source": [ + "### Plot the coarsened datsets together using Cartopy" + ] + }, + { + "cell_type": "markdown", + "id": "e4a00e56-4b5c-47da-ae9c-ae4b1ce0686d", + "metadata": { + "tags": [] + }, + "source": [ + "Now let's zoom in on a smaller region, the continental US, to get a clear view of the difference in grid resolution. Here we can also decrease the colorbar limits to better see how the variable `tas` varies within the smaller region." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "0679c3e5-f2bc-4e50-8b4d-f0cefa54d65a", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot together\n", - "\n", + "outputs": [], + "source": [ + "# set up the figure\n", "fig = plt.figure(figsize=(6, 8))\n", - "gs = GridSpec(3, 2, width_ratios=[1, 0.1], height_ratios=[1, 1, 1], hspace=0.2, wspace=0.2)\n", + "gs = GridSpec(3, 2, width_ratios=[1, 0.1], height_ratios=[1, 1, 1], hspace=0.3, wspace=0.2)\n", "\n", "# specify the projection\n", "proj = ccrs.PlateCarree()\n", @@ -3735,25 +838,32 @@ "axcb = plt.subplot(gs[:, 1])\n", "\n", "# loop through each dataset and its corresponding axis\n", - "for i, ds in enumerate([ds_25, ds_g, ds_1]):\n", - " plot_ds = ds[\"tas\"].isel(time=180)\n", + "for i, dset in enumerate([ds_og, ds_125, ds_25]):\n", + " plot_ds = dset.tas.isel(time=0)\n", " ax = axes_list[i]\n", - " pcm = ax.pcolormesh(plot_ds.lon, plot_ds.lat, plot_ds, vmin=285, vmax=315, transform=proj)\n", + " pcm = ax.pcolormesh(plot_ds.lon, plot_ds.lat, plot_ds, vmin=270, vmax=302.5, transform=proj)\n", + " \n", + " # add borders and coastlines\n", " ax.add_feature(cfeature.BORDERS)\n", " ax.coastlines()\n", " \n", - " # focus on CONUS for this example\n", + " # limit to CONUS for this example\n", " ax.set_xlim(-130, -60)\n", " ax.set_ylim(22, 52)\n", " \n", + " # add grid labels on bottom & left only\n", + " gl = ax.gridlines(color=\"None\", draw_labels=True)\n", + " gl.top_labels = False\n", + " gl.right_labels = False\n", + " \n", " # label with the regrid type; if it fails, that means it hasn't been regridded\n", - " # (so label with \"original grid\")\n", + " # (so label with the grid attribute instead)\n", " try:\n", - " ax.set_title(ds.regrid_operation)\n", + " ax.set_title(dset.regrid_operation)\n", " except:\n", - " ax.set_title(\"original grid ({g})\".format(g=ds.grid))\n", + " ax.set_title(dset.grid)\n", " \n", - "# set the colorbar\n", + "# use the same colorbar for all plots\n", "axcb.axis(\"off\")\n", "axcb_ins = inset_axes(axcb, width=\"50%\", height=\"75%\", loc=\"center\")\n", "cbar = plt.colorbar(pcm, cax=axcb_ins, orientation=\"vertical\", extend=\"both\")\n", @@ -3763,12 +873,35 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "ec3e63a6-fd9e-4d3d-b1ac-66f1bf943a7e", + "cell_type": "markdown", + "id": "e57aee30-caff-4916-bb95-efa00ff15ba4", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9f883994-e2f8-4ce9-8ca2-56fedb2e1a58", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "`Rooki` offers a quick and easy way to regrid CMIP model data that can be located using `intake-esgf`. `Cartopy` lets us easily customize the plot to neatly display the geospatial data. " + ] + }, + { + "cell_type": "markdown", + "id": "6c9caac9-3de5-4842-90c5-e4de995c06ef", + "metadata": {}, + "source": [ + "## Resources and references\n", + "* [Regridding overview from NCAR](https://climatedataguide.ucar.edu/climate-tools/regridding-overview), including descriptions of various regridding methods\n", + "* [Rooki regridding example notebook](https://github.com/roocs/rooki/blob/master/notebooks/demo/demo-rooki-regrid-cmip6.ipynb)\n", + "* [Rooki documentation](https://rooki.readthedocs.io/en/latest/)\n", + "* [Cartopy logo image source](https://scitools.org.uk/cartopy/docs/v0.16/gallery/logo.html)\n", + "* [Rooki logo image source](https://rooki.readthedocs.io/en/latest/#)" + ] } ], "metadata": { diff --git a/notebooks/images/logos/cartopy_logo.png b/notebooks/images/logos/cartopy_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..66bf2d521dea7854cb92220d5c001adcb6f85386 GIT binary patch literal 341563 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