![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
"
]
@@ -35,9 +37,9 @@
"execution": {}
},
"source": [
- "Today marks the transition point from our study of the physical aspect of the climate system and climate change to the socio-economic effects of climate change as well as mitigation and adaptation strategies. You will start the day learning about individual and collective action.\n",
+ "Today marks the last day of an intense week, where we covered the study of the physical aspect of the climate system and climate change and its socio-economic effects as well as mitigation and adaptation strategies. You will start the day learning about individual and collective action.\n",
"\n",
- "What is it that we can expect from climate change in the future? How do do energy and food systems specifically contribute to climate change and what can we do to minimize this contribution? In his lecture, Dr Paul Behrens is talks about what choices we can make as individuals and as societies to improve the chances for a better future.\n",
+ "What is it that we can expect from climate change in the future? How do energy and food systems specifically contribute to climate change and what can we do to minimize this contribution? In his lecture, Dr. Paul Behrens talks about what choices we can make as individuals and as societies to improve the chances for a better future. \n",
"You will then focus the rest of your day on advancing your research projects. There will be no tutorials."
]
},
@@ -59,7 +61,22 @@
"cellView": "form",
"execution": {}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fc4d62174a794907b031880e6fb2795c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# @markdown\n",
"\n",
@@ -127,38 +144,52 @@
"cellView": "form",
"execution": {}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you want to download the slides: https://osf.io/download/3tc2h/\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "045624d0",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Arctic Sea Ice Change\n",
+ "\n",
+ "**Content creators:** Alistair Duffey, Will Gregory, Michel Tsamados\n",
+ "\n",
+ "**Content reviewers:** Paul Heubel, Laura Paccini, Jenna Pearson\n",
+ "\n",
+ "**Content editors:** Paul Heubel\n",
+ "\n",
+ "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
+ "\n",
+ "**Our 2024 Sponsors:** CMIP, NFDI4Earth\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64da422f",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "428dba8079dd4726934d8fabe46e6179",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Project Background\n",
+ "\n",
+ "from ipywidgets import widgets\n",
+ "from IPython.display import YouTubeVideo\n",
+ "from IPython.display import IFrame\n",
+ "from IPython.display import display\n",
+ "\n",
+ "\n",
+ "class PlayVideo(IFrame):\n",
+ " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+ " self.id = id\n",
+ " if source == 'Bilibili':\n",
+ " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+ " elif source == 'Osf':\n",
+ " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+ " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+ "\n",
+ "\n",
+ "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+ " tab_contents = []\n",
+ " for i, video_id in enumerate(video_ids):\n",
+ " out = widgets.Output()\n",
+ " with out:\n",
+ " if video_ids[i][0] == 'Youtube':\n",
+ " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+ " height=H, fs=fs, rel=0)\n",
+ " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+ " else:\n",
+ " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+ " height=H, fs=fs, autoplay=False)\n",
+ " if video_ids[i][0] == 'Bilibili':\n",
+ " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+ " elif video_ids[i][0] == 'Osf':\n",
+ " print(f'Video available at https://osf.io/{video.id}')\n",
+ " display(video)\n",
+ " tab_contents.append(out)\n",
+ " return tab_contents\n",
+ "\n",
+ "\n",
+ "video_ids = [('Youtube', 'gt6o3lqu5zA'), ('Bilibili', '
"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a4cc9acb-3363-4a13-879d-73378ddb6a74",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Data Exploration Notebook\n",
+ "\n",
+ "## Project Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b68292ec",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# google colab installs\n",
+ "\n",
+ "# !pip install --quiet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e541a40",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/esmpy/interface/loadESMF.py:92: VersionWarning: ESMF installation version 8.7.0 beta snapshot, ESMPy version 8.7.0b7\n",
+ " warnings.warn(\"ESMF installation version {}, ESMPy version {}\".format(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Imports\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "import dask\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "from xmip.preprocessing import combined_preprocessing\n",
+ "from xmip.utils import google_cmip_col\n",
+ "from xmip.postprocessing import match_metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07545499",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# @title Figure settings\n",
+ "\n",
+ "#import ipywidgets as widgets # interactive display\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c6569502-58dc-43a7-a95f-5fa5f8828179",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# Helper functions\n",
+ "import os\n",
+ "import pooch\n",
+ "import tempfile\n",
+ "\n",
+ "def pooch_load(filelocation=None, filename=None, processor=None):\n",
+ " shared_location = \"/home/jovyan/shared/Data/projects/SeaIce\" # this is different for each day\n",
+ " user_temp_cache = tempfile.gettempdir()\n",
+ "\n",
+ " if os.path.exists(os.path.join(shared_location, filename)):\n",
+ " file = os.path.join(shared_location, filename)\n",
+ " else:\n",
+ " file = pooch.retrieve(\n",
+ " filelocation,\n",
+ " known_hash=None,\n",
+ " fname=os.path.join(user_temp_cache, filename),\n",
+ " processor=processor,\n",
+ " )\n",
+ "\n",
+ " return file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee7c9ef5",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## CESM2-WACCM: Climate model simulations of sea ice concentration\n",
+ "\n",
+ "Here we use the output from [CESM2-WACCM, the Community Earth System Model (version 2, CESM2)](https://doi.org/10.1029/2019ms001916), with the [Whole Atmosphere Community Climate Model (WACCM)](https://doi.org/10.1029/2019JD030943) as its atmosphere component. \n",
+ "\n",
+ "We use the historical scenario, which runs with historical forcings from 1850 to 2014. Note that this scenario is just one instance of internal variability in a world forced by historical GHGs. \n",
+ "\n",
+ "A note on the sea ice output for CMIP6 models: \n",
+ "\n",
+ "The Sea Ice component of models is generally output on the ocean grid, which is normally not a simple lat/lon grid, unlike many atmosphere model components. Here we use the variable ***siconca*** which stands for *sea ice concentration atmosphere* - this is the sea ice concentration re-gridded onto the model's atmosphere grid and is somewhat easier to work with.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "867ff5a5-6b39-459d-b89f-d224126147b6",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "Let's search the CMIP6 catalog for some sea ice concentration (`siconca`) data from the CESM Earth System Model. We pick a certain ensemble member (`r1i1p1f1`) to reduce the amount of data to download. At first, our scenarios of interest are the `historical` and the middle-of-the-road `ssp245` ones. As we get the data on the native grid (`gn`), we must also download the grid cell area in the next step.\n",
+ "\n",
+ "*Hint: for a detailed explanation of the following `xmip` functionalities and other CMIP6 related terms, please refer to:*\n",
+ "* *W1D5 [Tutorial 7](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D5_ClimateModeling/W1D5_Tutorial7.ipynb),*\n",
+ "* *W2D1 [Tutorial 1](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W2D1_FutureClimate-IPCCIPhysicalBasis/W2D1_Tutorial1.ipynb),*\n",
+ "* *our [CMIP Resource bank](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md),* \n",
+ "* *xmip's [tutorials](https://cmip6-preprocessing.readthedocs.io/en/latest/tutorial.html)*\n",
+ "* *[this lecture](https://earth-env-data-science.github.io/lectures/models/cmip.html) by Ryan Abernathy, Professor of Earth and Environmental Sciences at Columbia University*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62ca691c-6efe-4598-932f-32f5e9aac31b",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
+ "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 3MB\n", + "Dimensions: (member_id: 1, dcpp_init_year: 1, y: 192, x: 288, nbnd: 2)\n", + "Coordinates:\n", + " * y (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n", + " * x (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " lon_bounds (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n", + " lat_bounds (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n", + " * nbnd (nbnd) int64 16B 0 1\n", + " lon (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n", + " lat (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n", + " * member_id (member_id) object 8B 'r1i1p1f1'\n", + " * dcpp_init_year (dcpp_init_year) float64 8B nan\n", + "Data variables:\n", + " areacella (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n", + "Attributes: (12/60)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: ScenarioMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 735110.0\n", + " branch_time_in_parent: 735110.0\n", + " case_id: 966\n", + " ... ...\n", + " intake_esm_attrs:variable_id: areacella\n", + " intake_esm_attrs:grid_label: gn\n", + " intake_esm_attrs:zstore: gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n", + " intake_esm_attrs:version: 20190815\n", + " intake_esm_attrs:_data_format_: zarr\n", + " intake_esm_dataset_key: ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i...
<xarray.Dataset> Size: 441MB\n", + "Dimensions: (member_id: 1, dcpp_init_year: 1, time: 1980, y: 192,\n", + " x: 288, nbnd: 2)\n", + "Coordinates:\n", + " * y (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n", + " * x (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * time (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:...\n", + " lon_bounds (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n", + " time_bounds (time, nbnd) object 32kB dask.array<chunksize=(1980, 2), meta=np.ndarray>\n", + " lat_bounds (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n", + " * nbnd (nbnd) int64 16B 0 1\n", + " lon (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n", + " lat (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n", + " * member_id (member_id) object 8B 'r1i1p1f1'\n", + " * dcpp_init_year (dcpp_init_year) float64 8B nan\n", + " areacella (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n", + "Data variables:\n", + " siconca (member_id, dcpp_init_year, time, y, x) float32 438MB dask.array<chunksize=(1, 1, 600, 192, 288), meta=np.ndarray>\n", + "Attributes: (12/61)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: CMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 674885.0\n", + " branch_time_in_parent: 20075.0\n", + " case_id: 4\n", + " ... ...\n", + " intake_esm_attrs:variable_id: siconca\n", + " intake_esm_attrs:grid_label: gn\n", + " intake_esm_attrs:zstore: gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/...\n", + " intake_esm_attrs:version: 20190507\n", + " intake_esm_attrs:_data_format_: zarr\n", + " intake_esm_dataset_key: CMIP.NCAR.CESM2-WACCM.historical.r1i1p1...
<xarray.Dataset> Size: 231MB\n", + "Dimensions: (member_id: 1, dcpp_init_year: 1, time: 1032, y: 192,\n", + " x: 288, nbnd: 2)\n", + "Coordinates:\n", + " * y (y) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n", + " * x (x) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * time (time) object 8kB 2015-01-15 12:00:00 ... 2100-12-15 12:0...\n", + " lon_bounds (x, nbnd, y) float64 885kB dask.array<chunksize=(288, 2, 192), meta=np.ndarray>\n", + " time_bounds (time, nbnd) object 17kB dask.array<chunksize=(1032, 2), meta=np.ndarray>\n", + " lat_bounds (y, nbnd, x) float64 885kB dask.array<chunksize=(192, 2, 288), meta=np.ndarray>\n", + " * nbnd (nbnd) int64 16B 0 1\n", + " lon (x, y) float64 442kB 360.0 360.0 360.0 ... 358.8 358.8 358.8\n", + " lat (x, y) float64 442kB -90.0 -89.06 -88.12 ... 89.06 90.0\n", + " * member_id (member_id) object 8B 'r1i1p1f1'\n", + " * dcpp_init_year (dcpp_init_year) float64 8B nan\n", + " areacella (member_id, dcpp_init_year, y, x) float32 221kB dask.array<chunksize=(1, 1, 192, 288), meta=np.ndarray>\n", + "Data variables:\n", + " siconca (member_id, dcpp_init_year, time, y, x) float32 228MB dask.array<chunksize=(1, 1, 516, 192, 288), meta=np.ndarray>\n", + "Attributes: (12/61)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: ScenarioMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 735110.0\n", + " branch_time_in_parent: 735110.0\n", + " case_id: 966\n", + " ... ...\n", + " intake_esm_attrs:variable_id: siconca\n", + " intake_esm_attrs:grid_label: gn\n", + " intake_esm_attrs:zstore: gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2...\n", + " intake_esm_attrs:version: 20190815\n", + " intake_esm_attrs:_data_format_: zarr\n", + " intake_esm_dataset_key: ScenarioMIP.NCAR.CESM2-WACCM.ssp245.r1i...
<xarray.Dataset> Size: 1GB\n", + "Dimensions: (latitude: 240, longitude: 1440, time: 2016)\n", + "Coordinates:\n", + " * latitude (latitude) float32 960B 89.88 89.62 89.38 ... 30.38 30.12\n", + " * longitude (longitude) float32 6kB 0.125 0.375 0.625 ... 359.6 359.9\n", + " * time (time) object 16kB 1850-01-15 00:00:00 ... 2017-12-15 00...\n", + "Data variables:\n", + " seaice_conc (time, latitude, longitude) uint8 697MB ...\n", + " seaice_source (time, latitude, longitude) uint8 697MB ...\n", + " LandRegion_mask (latitude, longitude) uint8 346kB ...\n", + " Gridcell_Area (latitude) float32 960B ...\n", + "Attributes:\n", + " version: 2.0\n", + " release_date: February 28, 2019\n", + " Conventions: CF-1.4\n", + " citation: https://doi.org/10.1111/j.1931-0846.2016.12195.x\n", + " dataset_doi: https://nsidc.org/data/g10010
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Heatwaves\n",
+ "\n",
+ "**Content creators:** Wil Laura\n",
+ "\n",
+ "**Content reviewers:** Will Gregory, Paul Heubel, Laura Paccini, Jenna Pearson\n",
+ "\n",
+ "**Content editors:** Paul Heubel\n",
+ "\n",
+ "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
+ "\n",
+ "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "bd6d4f947c0e4138b8069f6bd309e3b8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Tab(children=(Output(), Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili', 'Osf'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Project Background\n",
+ "\n",
+ "from ipywidgets import widgets\n",
+ "from IPython.display import YouTubeVideo\n",
+ "from IPython.display import IFrame\n",
+ "from IPython.display import display\n",
+ "\n",
+ "\n",
+ "class PlayVideo(IFrame):\n",
+ " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+ " self.id = id\n",
+ " if source == 'Bilibili':\n",
+ " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+ " elif source == 'Osf':\n",
+ " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+ " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+ "\n",
+ "\n",
+ "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+ " tab_contents = []\n",
+ " for i, video_id in enumerate(video_ids):\n",
+ " out = widgets.Output()\n",
+ " with out:\n",
+ " if video_ids[i][0] == 'Youtube':\n",
+ " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+ " height=H, fs=fs, rel=0)\n",
+ " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+ " else:\n",
+ " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+ " height=H, fs=fs, autoplay=False)\n",
+ " if video_ids[i][0] == 'Bilibili':\n",
+ " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+ " elif video_ids[i][0] == 'Osf':\n",
+ " print(f'Video available at https://osf.io/{video.id}')\n",
+ " display(video)\n",
+ " tab_contents.append(out)\n",
+ " return tab_contents\n",
+ "\n",
+ "video_ids = [('Youtube', 'aXBq-A8JsPE'), ('Bilibili', '
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Data Exploration Notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## Project Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# google colab installs\n",
+ "#!pip install pandas==1.5.3\n",
+ "\n",
+ "# !pip install cartopy --quiet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# import packages\n",
+ "#import xclim\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import xarray as xr\n",
+ "import matplotlib.pyplot as plt\n",
+ "#import matplotlib.dates as mdates\n",
+ "import cartopy.feature as cfeature\n",
+ "import cartopy.crs as ccrs\n",
+ "\n",
+ "#from xclim.core.calendar import percentile_doy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# @title Helper functions\n",
+ "import os\n",
+ "import pooch\n",
+ "import tempfile\n",
+ "\n",
+ "def pooch_load(filelocation=None, filename=None, processor=None):\n",
+ " shared_location = \"/home/jovyan/shared/Data/projects/Heatwaves\" # this is different for each day\n",
+ " user_temp_cache = tempfile.gettempdir()\n",
+ "\n",
+ " if os.path.exists(os.path.join(shared_location, filename)):\n",
+ " file = os.path.join(shared_location, filename)\n",
+ " else:\n",
+ " file = pooch.retrieve(\n",
+ " filelocation,\n",
+ " known_hash=None,\n",
+ " fname=os.path.join(user_temp_cache, filename),\n",
+ " processor=processor,\n",
+ " )\n",
+ "\n",
+ " return file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# @title Figure settings\n",
+ "\n",
+ "import ipywidgets as widgets # interactive display\n",
+ "\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## ECMWF Reanalysis v5 (ERA5): Air Temperature at 2m\n",
+ "\n",
+ "You will utilize the ERA5 dataset to examine temperature trends and heatwaves, applying the loading methods introduced in W1D1. Please, see the W1D2 course material for more information on reanalysis data. Besides, you can read more about ERA5 here: [Climate reanalysis](https://climate.copernicus.eu/climate-reanalysis).\n",
+ "\n",
+ "Specifically, in this project, you will focus on near-surface temperature, which refers to the temperature of air at $2 \\text{m}$ above the surface of land, sea, or inland waters, temperature with units of Kelvin $\\left(\\text{K}\\right)$.\n",
+ "\n",
+ "You will access the following subsampled data through the OSF cloud storage to simplify downloading. For the project, it is necessary to download data yourself when you are interested in exploring other regional subsets or variables. Please have a look at the [`get_ERA5_reanalysis_data.ipynb`](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D2_StateoftheClimateOceanandAtmosphereReanalysis/get_ERA5_reanalysis_data.ipynb) notebook, where we show how to use the Climate Data Store (CDS) API to get a subset of the huge ECMWF ERA5 Reanalysis data set."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "In the following, along with a small subsample file that was downloaded beforehand, we show how to load, explore, and visualize ERA5 data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://osf.io/download/z9xfv/' to file '/tmp/file_sample.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 2236e254419e163ed9ae6ad44b61bfe39d5fb2bd943dbd912093e703c5fa1485\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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| \n", + " | STRUCTURE_NAME | \n", + "ACTION | \n", + "REF_AREA | \n", + "Reference area | \n", + "FREQ | \n", + "Frequency of observation | \n", + "MEASURE | \n", + "Measure | \n", + "AGE | \n", + "Age | \n", + "SEX | \n", + "Sex | \n", + "UNIT_MEASURE | \n", + "Unit of measure | \n", + "TIME_PERIOD | \n", + "OBS_VALUE | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Mortality by week | \n", + "I | \n", + "SWE | \n", + "Sweden | \n", + "W | \n", + "Weekly | \n", + "M | \n", + "Mortality | \n", + "_T | \n", + "Total | \n", + "_T | \n", + "Total | \n", + "DT | \n", + "Deaths | \n", + "2015-W01 | \n", + "1927 | \n", + "
| 1 | \n", + "Mortality by week | \n", + "I | \n", + "SWE | \n", + "Sweden | \n", + "W | \n", + "Weekly | \n", + "M | \n", + "Mortality | \n", + "_T | \n", + "Total | \n", + "_T | \n", + "Total | \n", + "DT | \n", + "Deaths | \n", + "2015-W02 | \n", + "1966 | \n", + "
| 2 | \n", + "Mortality by week | \n", + "I | \n", + "SWE | \n", + "Sweden | \n", + "W | \n", + "Weekly | \n", + "M | \n", + "Mortality | \n", + "_T | \n", + "Total | \n", + "_T | \n", + "Total | \n", + "DT | \n", + "Deaths | \n", + "2015-W03 | \n", + "1935 | \n", + "
| 3 | \n", + "Mortality by week | \n", + "I | \n", + "SWE | \n", + "Sweden | \n", + "W | \n", + "Weekly | \n", + "M | \n", + "Mortality | \n", + "_T | \n", + "Total | \n", + "_T | \n", + "Total | \n", + "DT | \n", + "Deaths | \n", + "2015-W04 | \n", + "1946 | \n", + "
| 4 | \n", + "Mortality by week | \n", + "I | \n", + "SWE | \n", + "Sweden | \n", + "W | \n", + "Weekly | \n", + "M | \n", + "Mortality | \n", + "_T | \n", + "Total | \n", + "_T | \n", + "Total | \n", + "DT | \n", + "Deaths | \n", + "2015-W05 | \n", + "1975 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Ocean Acidification\n",
+ "\n",
+ "\n",
+ "**Content creators:** C. Gabriela Mayorga Adame, Lidia Krinova\n",
+ "\n",
+ "**Content reviewers:** Will Gregory, Paul Heubel, Laura Paccini, Jenna Pearson, Ohad Zivan\n",
+ "\n",
+ "**Content editors:** Paul Heubel\n",
+ "\n",
+ "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
+ "\n",
+ "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "execution": {},
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8a5e84792d5843b8b86763755987168c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Project Background\n",
+ "\n",
+ "from ipywidgets import widgets\n",
+ "from IPython.display import YouTubeVideo\n",
+ "from IPython.display import IFrame\n",
+ "from IPython.display import display\n",
+ "\n",
+ "\n",
+ "class PlayVideo(IFrame):\n",
+ " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+ " self.id = id\n",
+ " if source == \"Bilibili\":\n",
+ " src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
+ " elif source == \"Osf\":\n",
+ " src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
+ " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+ "\n",
+ "\n",
+ "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+ " tab_contents = []\n",
+ " for i, video_id in enumerate(video_ids):\n",
+ " out = widgets.Output()\n",
+ " with out:\n",
+ " if video_ids[i][0] == \"Youtube\":\n",
+ " video = YouTubeVideo(\n",
+ " id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
+ " )\n",
+ " print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
+ " else:\n",
+ " video = PlayVideo(\n",
+ " id=video_ids[i][1],\n",
+ " source=video_ids[i][0],\n",
+ " width=W,\n",
+ " height=H,\n",
+ " fs=fs,\n",
+ " autoplay=False,\n",
+ " )\n",
+ " if video_ids[i][0] == \"Bilibili\":\n",
+ " print(\n",
+ " f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
+ " )\n",
+ " elif video_ids[i][0] == \"Osf\":\n",
+ " print(f\"Video available at https://osf.io/{video.id}\")\n",
+ " display(video)\n",
+ " tab_contents.append(out)\n",
+ " return tab_contents\n",
+ "\n",
+ "\n",
+ "video_ids = [('Youtube', 'NAgrB8HxMMk'), ('Bilibili', 'BV1fM4y1x7g8')]\n",
+ "tab_contents = display_videos(video_ids, W=730, H=410)\n",
+ "tabs = widgets.Tab()\n",
+ "tabs.children = tab_contents\n",
+ "for i in range(len(tab_contents)):\n",
+ " tabs.set_title(i, video_ids[i][0])\n",
+ "display(tabs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you want to download the slides: https://osf.io/download/n7wdy/\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " 
\n",
+ "\n",
+ "*Note: The dashed boxes are socio-economic questions.*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Data Exploration Notebook\n",
+ "## Project Setup\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {},
+ "tags": [
+ "colab"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# google colab installs\n",
+ "\n",
+ "# !mamaba install netCDF4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {},
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# imports\n",
+ "\n",
+ "import random\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "import pooch\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "import tempfile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# helper functions\n",
+ "\n",
+ "def pooch_load(filelocation=None,filename=None,processor=None):\n",
+ " shared_location='/home/jovyan/shared/Data/Projects/Ocean_Acidification' # this is different for each day\n",
+ " user_temp_cache=tempfile.gettempdir()\n",
+ "\n",
+ " if os.path.exists(os.path.join(shared_location,filename)):\n",
+ " file = os.path.join(shared_location,filename)\n",
+ " else:\n",
+ " file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
+ "\n",
+ " return file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## NOAA Ocean pH and Acidity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "### Global surface ocean acidification indicators from 1750 to 2100 (NCEI Accession 0259391)\n",
+ "\n",
+ "This data package contains a hybrid surface ocean acidification (OA) data product that is produced based on three recent observational data products:\n",
+ "- Surface Ocean CO2 Atlas (SOCAT, version 2022)\n",
+ "- Global Ocean Data Analysis Product version 2 (GLODAPv2, version 2022)\n",
+ "- Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021), and 14 Earth System Models from the sixth phase of the Coupled Model Intercomparison Project ([CMIP6](https://github.com/ClimateMatchAcademy/course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md)).\n",
+ "\n",
+ "The trajectories of ten OA indicators are included in this data product:\n",
+ "- Fugacity of carbon dioxide\n",
+ "- pH on Total Scale\n",
+ "- Total hydrogen ion content\n",
+ "- Free hydrogen ion content\n",
+ "- Carbonate ion content\n",
+ "- Aragonite saturation state\n",
+ "- Calcite saturation state\n",
+ "- Revelle Factor\n",
+ "- Total dissolved inorganic carbon content\n",
+ "- Total alkalinity content\n",
+ "\n",
+ "These OA trajectories are provided under preindustrial conditions, historical conditions, and future Shared Socioeconomic Pathways: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 from 1750 to 2100 on a global surface ocean grid. These OA trajectories are improved relative to previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs over the 21st century.\n",
+ "\n",
+ "**Citation:**\n",
+ "Jiang, L.-Q., Dunne, J., Carter, B. R., Tjiputra, J. F., Terhaar, J., Sharp, J. D., et al. (2023). Global surface ocean acidification indicators from 1750 to 2100. Journal of Advances in Modeling Earth Systems, 15, e2022MS003563. https://doi.org/10.1029/2022MS003563\n",
+ "\n",
+ "**Dataset**: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "We can load and visualize the **surface pH** as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {},
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0259391/nc/median/pHT_median_historical.nc' to file '/tmp/pHT_median_historical.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 5a88450b240954e1db5771e06c54278d0dd3e1edbdfcd7b05f25bec3b2c47f1f\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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| \n", + " | 1980 | \n", + "5 | \n", + "1980.375 | \n", + "340.44 | \n", + "0.10 | \n", + "338.88 | \n", + "0.07 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "1980 | \n", + "6 | \n", + "1980.458 | \n", + "339.99 | \n", + "0.09 | \n", + "339.22 | \n", + "0.07 | \n", + "
| 1 | \n", + "1980 | \n", + "7 | \n", + "1980.542 | \n", + "338.44 | \n", + "0.16 | \n", + "339.28 | \n", + "0.07 | \n", + "
| 2 | \n", + "1980 | \n", + "8 | \n", + "1980.625 | \n", + "337.21 | \n", + "0.19 | \n", + "339.50 | \n", + "0.06 | \n", + "
| 3 | \n", + "1980 | \n", + "9 | \n", + "1980.708 | \n", + "337.05 | \n", + "0.12 | \n", + "339.44 | \n", + "0.06 | \n", + "
| 4 | \n", + "1980 | \n", + "10 | \n", + "1980.792 | \n", + "337.83 | \n", + "0.07 | \n", + "339.17 | \n", + "0.07 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 521 | \n", + "2023 | \n", + "11 | \n", + "2023.875 | \n", + "420.34 | \n", + "0.10 | \n", + "420.50 | \n", + "0.06 | \n", + "
| 522 | \n", + "2023 | \n", + "12 | \n", + "2023.958 | \n", + "421.54 | \n", + "0.10 | \n", + "420.90 | \n", + "0.06 | \n", + "
| 523 | \n", + "2024 | \n", + "1 | \n", + "2024.042 | \n", + "422.22 | \n", + "0.10 | \n", + "421.12 | \n", + "0.06 | \n", + "
| 524 | \n", + "2024 | \n", + "2 | \n", + "2024.125 | \n", + "422.69 | \n", + "0.10 | \n", + "421.28 | \n", + "0.06 | \n", + "
| 525 | \n", + "2024 | \n", + "3 | \n", + "2024.208 | \n", + "423.16 | \n", + "0.10 | \n", + "421.52 | \n", + "0.06 | \n", + "
526 rows × 7 columns
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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "045624d0",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Precipitation Variability and Extreme Events\n",
+ "\n",
+ "**Content creators:** Will Gregory, Laura Paccini, Raphael Rocha\n",
+ "\n",
+ "**Content reviewers:** Paul Heubel, Jenna Pearson\n",
+ "\n",
+ "**Content editors:** Paul Heubel\n",
+ "\n",
+ "**Production editors:** Paul Heubel, Konstantine Tsafatinos\n",
+ "\n",
+ "**Our 2024 Sponsors:** CMIP, NFDI4Earth\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64da422f",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3a10a6ba095a404584a17e51d0b3733a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Tab(children=(Output(), Output()), selected_index=0, titles=('Youtube', 'Bilibili'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Project Background\n",
+ "\n",
+ "from ipywidgets import widgets\n",
+ "from IPython.display import YouTubeVideo\n",
+ "from IPython.display import IFrame\n",
+ "from IPython.display import display\n",
+ "\n",
+ "class PlayVideo(IFrame):\n",
+ " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+ " self.id = id\n",
+ " if source == \"Bilibili\":\n",
+ " src = f\"https://player.bilibili.com/player.html?bvid={id}&page={page}\"\n",
+ " elif source == \"Osf\":\n",
+ " src = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render\"\n",
+ " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+ "\n",
+ "\n",
+ "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+ " tab_contents = []\n",
+ " for i, video_id in enumerate(video_ids):\n",
+ " out = widgets.Output()\n",
+ " with out:\n",
+ " if video_ids[i][0] == \"Youtube\":\n",
+ " video = YouTubeVideo(\n",
+ " id=video_ids[i][1], width=W, height=H, fs=fs, rel=0\n",
+ " )\n",
+ " print(f\"Video available at https://youtube.com/watch?v={video.id}\")\n",
+ " else:\n",
+ " video = PlayVideo(\n",
+ " id=video_ids[i][1],\n",
+ " source=video_ids[i][0],\n",
+ " width=W,\n",
+ " height=H,\n",
+ " fs=fs,\n",
+ " autoplay=False,\n",
+ " )\n",
+ " if video_ids[i][0] == \"Bilibili\":\n",
+ " print(\n",
+ " f\"Video available at https://www.bilibili.com/video/{video.id}\"\n",
+ " )\n",
+ " elif video_ids[i][0] == \"Osf\":\n",
+ " print(f\"Video available at https://osf.io/{video.id}\")\n",
+ " display(video)\n",
+ " tab_contents.append(out)\n",
+ " return tab_contents\n",
+ "\n",
+ "\n",
+ "video_ids = [('Youtube', '49XHRe61LI8'), ('Bilibili', 'BV1Au411L7fo')]\n",
+ "tab_contents = display_videos(video_ids, W=730, H=410)\n",
+ "tabs = widgets.Tab()\n",
+ "tabs.children = tab_contents\n",
+ "for i in range(len(tab_contents)):\n",
+ " tabs.set_title(i, video_ids[i][0])\n",
+ "display(tabs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b52fef8b",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "79181962c56a48ee96b0c3a4980d5cf6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Slides\n",
+ "# @markdown These are the slides for the video introduction to the project\n",
+ "\n",
+ "from ipywidgets import widgets\n",
+ "from IPython.display import IFrame\n",
+ "\n",
+ "link_id = \"djpgh\"\n",
+ "\n",
+ "download_link = f\"https://osf.io/download/{link_id}/\"\n",
+ "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
+ "# @markdown\n",
+ "out = widgets.Output()\n",
+ "with out:\n",
+ " print(f\"If you want to download the slides: {download_link}\")\n",
+ " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
+ "display(out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63b697d5",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "**In this project**, you will explore rain gauge and satellite data from the CHIRPS data set to extract rain estimates and land surface reflectance, respectively. These data will enable identification of extreme events in your region of interest. Besides investigating the relationships between these variables, you are encouraged to study the impact of extreme events on changes in vegetation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff7ae1c9",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "# Project Template\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e114dfda-1ff2-4ec9-a338-8c5f5e8e17a0",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## Data Exploration Notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8af2bd88-1cfe-46cd-82b3-5c58958ae6ad",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## Project Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b68292ec",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# google colab installs\n",
+ "\n",
+ "# !pip install s3fs boto3 botocore --quiet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e541a40",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# Imports\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "import pooch\n",
+ "import os\n",
+ "import tempfile\n",
+ "import pandas as pd\n",
+ "import s3fs\n",
+ "import boto3\n",
+ "import botocore\n",
+ "import datetime"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3692a9ca",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# @title Helper functions\n",
+ "\n",
+ "def pooch_load(filelocation=None,filename=None,processor=None):\n",
+ " shared_location='/home/jovyan/shared/Data/Projects/Precipitation' # this is different for each day\n",
+ " user_temp_cache=tempfile.gettempdir()\n",
+ "\n",
+ " if os.path.exists(os.path.join(shared_location,filename)):\n",
+ " file = os.path.join(shared_location,filename)\n",
+ " else:\n",
+ " file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)\n",
+ "\n",
+ " return file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07545499",
+ "metadata": {
+ "cellView": "form",
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "# @title Figure settings\n",
+ "\n",
+ "import ipywidgets as widgets # interactive display\n",
+ "\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "plt.style.use(\"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02a3777f",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "## CHIRPS Version 2.0 Global Daily 0.25°\n",
+ "\n",
+ "The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a high-resolution precipitation dataset developed by the Climate Hazards Group at the University of California, Santa Barbara. It combines satellite-derived precipitation estimates with ground-based station data to provide gridded precipitation data at a quasi-global scale between 50°S-50°N. \n",
+ "\n",
+ "Read more about CHIRPS here:\n",
+ "\n",
+ "* [The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes](https://www.nature.com/articles/sdata201566)\n",
+ "\n",
+ "* [Climate Hazard Group CHG Wiki](https://wiki.chc.ucsb.edu/CHIRPS_FAQ)\n",
+ "\n",
+ "* [Data storage location](https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f36c1fa",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "### Indices for Extreme Events\n",
+ "The Expert Team on Climate Change Detection and Indices ([ETCCDI](http://etccdi.pacificclimate.org/list_27_indices.shtml)) has defined various indices that focus on different aspects such as duration or intensity of extreme events. The following functions provide examples of how to compute indices for each category. You can modify these functions to suit your specific needs or create your own custom functions. Here are some tips you can use:\n",
+ "\n",
+ "- Most of the indices require daily data, so in order to select a specific season you can just use `xarray` to subset the data. Example:\n",
+ "\n",
+ "```python\n",
+ "daily_precip_DJF = data_chirps.sel(time=data_chirps['time.season']=='DJF')\n",
+ "```\n",
+ "\n",
+ "- A common threshold for a wet event is precipitation greater than or equal to $1 \\text{ mm}/\\text{day}$, while a dry (or non-precipitating) event is defined as precipitation less than $1 \\text{ mm}/\\text{day}$.\n",
+ "- Some of the indices are based on percentiles. You can define a base period climatology to calculate percentile thresholds, such as the 5th, 10th, 90th, and 95th percentiles, to determine extreme events (cf. in particular W2D3)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9697620",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [],
+ "source": [
+ "def calculate_cdd_index(data):\n",
+ " \"\"\"\n",
+ " This function takes a daily precipitation dataset as input and calculates\n",
+ " the Consecutive Dry Days (CDD) index, which represents the longest sequence\n",
+ " of consecutive days with precipitation less than 1mm. The input data should\n",
+ " be a DataArray with time coordinates, and the function returns a DataArray\n",
+ " with the CDD values for each unique year in the input data.\n",
+ " Parameters:\n",
+ " ----------\n",
+ " - data (xarray.DataArray): The input daily precipitation data should be\n",
+ " a dataset (eg. for chirps_data the DataArray would be chirps_data.precip)\n",
+ " Returns:\n",
+ " -------\n",
+ " - cdd (xarray.DataArray): The calculated CDD index\n",
+ "\n",
+ " \"\"\"\n",
+ " # create a boolean array for dry days (PR < 1mm)\n",
+ " dry_days = data < 1\n",
+ " # initialize CDD array\n",
+ " cdd = np.zeros(len(data.groupby(\"time.year\")))\n",
+ " # get unique years as a list\n",
+ " unique_years = list(data.groupby(\"time.year\").groups.keys())\n",
+ " # iterate for each day\n",
+ " for i, year in enumerate(unique_years):\n",
+ " consecutive_trues = []\n",
+ " current_count = 0\n",
+ " for day in dry_days.sel(time=data[\"time.year\"] == year).values:\n",
+ " if day:\n",
+ " current_count += 1\n",
+ " else:\n",
+ " if current_count > 0:\n",
+ " consecutive_trues.append(current_count)\n",
+ " current_count = 0\n",
+ " if current_count > 0:\n",
+ " consecutive_trues.append(current_count)\n",
+ " # print(consecutive_trues)\n",
+ " # CDD is the largest number of consecutive days\n",
+ " cdd[i] = np.max(consecutive_trues)\n",
+ " # transform to dataset\n",
+ " cdd_da = xr.DataArray(cdd, coords={\"year\": unique_years}, dims=\"year\")\n",
+ " return cdd_da"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ad439e00",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1981.days_p25.nc' to file '/tmp/chirps-v2.0.1981.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: abf5aa47db1a47b1bb0358591d02adb7cdc765af1de83834aa0a36157828f0cb\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1982.days_p25.nc' to file '/tmp/chirps-v2.0.1982.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 5264a039135cbe00fff7c2b8296123ddc652db67a3086ac1d84bc7cfd91d1b3c\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1983.days_p25.nc' to file '/tmp/chirps-v2.0.1983.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 8083ed549a4027fddad74e4a3be75c8587ad0830de3be0200c56b295d81cbe26\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1984.days_p25.nc' to file '/tmp/chirps-v2.0.1984.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: e1c3edab18b4bd69a659a42e95ca9a97051b7055738fdf4b63796ff199bbf2a4\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1985.days_p25.nc' to file '/tmp/chirps-v2.0.1985.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 795f6ef239d5a88b0afc682875ad5567f61d694980346c656db5445af428b3ab\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1986.days_p25.nc' to file '/tmp/chirps-v2.0.1986.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 3ad6497e71ee565d2ecdb895993f2ca9b4db6ed0efb702eb000b934b7ea4438f\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1987.days_p25.nc' to file '/tmp/chirps-v2.0.1987.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: a47dd1314d10589213b7d807fb8f1486972f2a0fcf081d899b414304c09aad8d\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1988.days_p25.nc' to file '/tmp/chirps-v2.0.1988.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 07b7a89ac7e7f1e16cad1db1100217a60a124964b77693e6323b0c1316f813e3\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1989.days_p25.nc' to file '/tmp/chirps-v2.0.1989.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: a81c7a0916bf81e4047cda8d6320cc5c94720f758f232c50b80502a840702ab8\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1990.days_p25.nc' to file '/tmp/chirps-v2.0.1990.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 10a95daa84df0e1934c462c3687cff0accff907557f92c6c1e7ee0d2077ed0d4\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1991.days_p25.nc' to file '/tmp/chirps-v2.0.1991.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: f549808c69601cf288b4f0f42b8ac5337c348f06d1fb55aa86b67e9bbd3837f9\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1992.days_p25.nc' to file '/tmp/chirps-v2.0.1992.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: ae26310b18369c1015e6b70db6dfb7ad1c138f846bd7c322e0b6794a26423f56\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1993.days_p25.nc' to file '/tmp/chirps-v2.0.1993.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 292ff168ef7f4420d36606f7ed84aa5380dd979a90351762926ef4c569e90f56\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1994.days_p25.nc' to file '/tmp/chirps-v2.0.1994.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: f856963801db2a62fa5c35537810f1649f19d32c84f1c4f2bb3fd3dcc82ba7ec\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1995.days_p25.nc' to file '/tmp/chirps-v2.0.1995.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 54c7a408d49112465479a06a3d1e5b37cb10b526c2cf20215f2b75579c8c2a0e\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1996.days_p25.nc' to file '/tmp/chirps-v2.0.1996.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: b0a4604e5a213671e6d591ad959c2156685ee026b54fbc8ad87860e35f3db4df\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1997.days_p25.nc' to file '/tmp/chirps-v2.0.1997.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: a04019a26e20988e35d8d14e88f1083aabb8104434c556c21e3d29e1c30ae731\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1998.days_p25.nc' to file '/tmp/chirps-v2.0.1998.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 15f3e33707c14b3ef26b525cb116766b847f2295c60aa41c6ae73df10e748e98\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.1999.days_p25.nc' to file '/tmp/chirps-v2.0.1999.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: dbb0a6a0ee1ff5e3ba5439101bedc24dad0951810a03a6f5cdc96e4b08c06716\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2000.days_p25.nc' to file '/tmp/chirps-v2.0.2000.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 57014c9ff72c8997b5b141793c06ffd1a8d8f5c6d1308789ac8e12c3860e5790\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2001.days_p25.nc' to file '/tmp/chirps-v2.0.2001.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 8550c96c7f8c29cbc6a1be3be56d7f769125d15b9ac7bc6c4f0fc4a76034f0fa\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2002.days_p25.nc' to file '/tmp/chirps-v2.0.2002.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 8027aaf7e005d74073d2c744d9c8a0343ffade272c1d1f8c420972bcbade2231\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2003.days_p25.nc' to file '/tmp/chirps-v2.0.2003.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 5b5a538e187ca651bc1fcc25eb445d31a05067b5a109bd8bb4651db11346cf6f\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2004.days_p25.nc' to file '/tmp/chirps-v2.0.2004.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 5e671526f27567e9218d275f7a47b6c0d7e0acf718edfee64c5843f5ee497e93\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2005.days_p25.nc' to file '/tmp/chirps-v2.0.2005.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 2185afd5c3d110da52a9a81eb97f25b37f30db5c0478e536a84c957351e44b53\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2006.days_p25.nc' to file '/tmp/chirps-v2.0.2006.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 3f78d5e733ea38bef25e47af9b3a907a96e27b8334cc04e4898837239e6539e6\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2007.days_p25.nc' to file '/tmp/chirps-v2.0.2007.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: c4594aeaafd7b9e7065577563799bca461582723e6b32df332206b43cb853138\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2008.days_p25.nc' to file '/tmp/chirps-v2.0.2008.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 6fe5dc8facd7750b6c08ecd0cadaaf19570acccd6a15f20efcbdd960230b5551\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2009.days_p25.nc' to file '/tmp/chirps-v2.0.2009.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: e6ccdc38aa146793f2c92858412122ef14a7a34332940bec03bd520cc77dc235\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2010.days_p25.nc' to file '/tmp/chirps-v2.0.2010.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: f6b6bd962626c038959a743b5f6e6c22b0bd6647b89b4a08b0f2305ac77f7277\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2011.days_p25.nc' to file '/tmp/chirps-v2.0.2011.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 45f437d8f1c7237b7043ebf4fec800461252af02e7cbcc87a844ddf0d1a911d2\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2012.days_p25.nc' to file '/tmp/chirps-v2.0.2012.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 27792b3d028ca5e745a8fe45c4f133a4183469512d2e55ead950d54a5a5a890b\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2013.days_p25.nc' to file '/tmp/chirps-v2.0.2013.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: d531a704ed1a40be2d509b1d984913194015f84721edc555f2de8c000bcab638\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2014.days_p25.nc' to file '/tmp/chirps-v2.0.2014.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: c3225de85da99422bc5fde9dbd1bdabff206fc2902d811944169348c8352d521\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2015.days_p25.nc' to file '/tmp/chirps-v2.0.2015.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 0c770adf07884a25610ea00d66d5e221529159746d3d76d9affea69c77666233\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2016.days_p25.nc' to file '/tmp/chirps-v2.0.2016.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 8ff4f6284c1d37b50e8b1e47dbd9f6e108a9ee3b17a6752bb18cc0b7384526d3\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2017.days_p25.nc' to file '/tmp/chirps-v2.0.2017.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 9692ca1fee495b8091084b1e5d29ea618483e67886a281f82b4a03555861d6b8\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2018.days_p25.nc' to file '/tmp/chirps-v2.0.2018.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 05b0850b8b8c16a2d5d33ddc6ba89d503084dc7374845214289be1b411a30d3f\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2019.days_p25.nc' to file '/tmp/chirps-v2.0.2019.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 311723f2f8d2af3307da198ebafece0c081200f336444a4261516539348fed38\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2020.days_p25.nc' to file '/tmp/chirps-v2.0.2020.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 2ab3b142141c5cfc708bc666dc89900005b7452b66324515fca1baa8dff00402\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2021.days_p25.nc' to file '/tmp/chirps-v2.0.2021.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 1b2db33508896ed1932b9db7e6dad6136e90687d3fe630f08066dbd279e0dabb\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2022.days_p25.nc' to file '/tmp/chirps-v2.0.2022.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 5f9b88f0623083759ba351a263bf23afd7fb26b13e23d051d5bb22a55aa0bcb9\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.2023.days_p25.nc' to file '/tmp/chirps-v2.0.2023.days_p25.nc'.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SHA256 hash of downloaded file: 8430cc8afd1a69205ecf757a2d8d95f3e6fbc44544326b27b4d534a82ee9c14c\n",
+ "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# code to retrieve and load the data\n",
+ "\n",
+ "years = range(1981,2024) # the years you want. we want 1981 till 2023\n",
+ "file_paths = ['https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/chirps-v2.0.' + str(year) + '.days_p25.nc' for year in years] # the format of the files\n",
+ "filenames = ['chirps-v2.0.'+str(year)+'.days_p25.nc' for year in years] # the format of the files\n",
+ "\n",
+ "downloaded_files=[ pooch_load(fpath,fname) for (fpath,fname) in zip(file_paths,filenames)] # download all of the files\n",
+ "\n",
+ "#### open data via xarray\n",
+ "chirps_data = xr.open_mfdataset(\n",
+ " downloaded_files, combine=\"by_coords\"\n",
+ ") # open the files as one dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "660f8525",
+ "metadata": {
+ "execution": {}
+ },
+ "source": [
+ "We can now visualize the content of the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "114e4a72",
+ "metadata": {
+ "execution": {}
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<xarray.Dataset> Size: 36GB\n", + "Dimensions: (latitude: 400, longitude: 1440, time: 15705)\n", + "Coordinates:\n", + " * latitude (latitude) float32 2kB -49.88 -49.62 -49.38 ... 49.38 49.62 49.88\n", + " * longitude (longitude) float32 6kB -179.9 -179.6 -179.4 ... 179.6 179.9\n", + " * time (time) datetime64[ns] 126kB 1981-01-01 1981-01-02 ... 2023-12-31\n", + "Data variables:\n", + " precip (time, latitude, longitude) float32 36GB dask.array<chunksize=(61, 67, 240), meta=np.ndarray>\n", + "Attributes: (12/15)\n", + " Conventions: CF-1.6\n", + " title: CHIRPS Version 2.0\n", + " history: created by Climate Hazards Group\n", + " version: Version 2.0\n", + " date_created: 2015-10-07\n", + " creator_name: Pete Peterson\n", + " ... ...\n", + " reference: Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros,...\n", + " comments: time variable denotes the first day of the given day.\n", + " acknowledgements: The Climate Hazards Group InfraRed Precipitation with ...\n", + " ftp_url: ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CH...\n", + " website: http://chg.geog.ucsb.edu/data/chirps/index.html\n", + " faq: http://chg-wiki.geog.ucsb.edu/wiki/CHIRPS_FAQ
<xarray.Dataset> Size: 11GB\n", + "Dimensions: (latitude: 3600, longitude: 7200, time: 31, ncrs: 1, nv: 2)\n", + "Coordinates:\n", + " * latitude (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n", + " * longitude (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n", + " * time (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n", + "Dimensions without coordinates: ncrs, nv\n", + "Data variables:\n", + " crs (time, ncrs) int16 62B dask.array<chunksize=(1, 1), meta=np.ndarray>\n", + " lat_bnds (time, latitude, nv) float32 893kB dask.array<chunksize=(1, 3600, 2), meta=np.ndarray>\n", + " lon_bnds (time, longitude, nv) float32 2MB dask.array<chunksize=(1, 7200, 2), meta=np.ndarray>\n", + " NDVI (time, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>\n", + " TIMEOFDAY (time, latitude, longitude) float64 6GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>\n", + " QA (time, latitude, longitude) int16 2GB dask.array<chunksize=(1, 3600, 7200), meta=np.ndarray>\n", + "Attributes: (12/48)\n", + " title: Normalized Difference Vegetation ...\n", + " institution: NASA/GSFC/SED/ESD/HBSL/TIS/MODIS-...\n", + " Conventions: CF-1.6, ACDD-1.3\n", + " standard_name_vocabulary: CF Standard Name Table (v25, 05 J...\n", + " naming_authority: gov.noaa.ncei\n", + " license: See the Use Agreement for this CD...\n", + " ... ...\n", + " PercentValidDaytimeData: 33.79\n", + " PercentValidDaytimeLand: 33.79\n", + " PercentValidClearDaytimeLand: 4.54\n", + " PercentValidDaytimeLandInCloudShadow: 0.85\n", + " PercentValidClearDaytimeWater: 0.00\n", + " PercentValidDaytimeWaterInCloudShadow: 0.00
<xarray.DataArray 'NDVI' (time: 31, latitude: 3600, longitude: 7200)> Size: 3GB\n", + "dask.array<where, shape=(31, 3600, 7200), dtype=float32, chunksize=(1, 1200, 2400), chunktype=numpy.ndarray>\n", + "Coordinates:\n", + " * latitude (latitude) float32 14kB 89.97 89.93 89.88 ... -89.93 -89.97\n", + " * longitude (longitude) float32 29kB -180.0 -179.9 -179.9 ... 179.9 180.0\n", + " * time (time) float32 124B 7.305e+03 7.306e+03 ... 7.334e+03 7.335e+03\n", + "Attributes:\n", + " long_name: NOAA Climate Data Record of Normalized Difference Vegetat...\n", + " units: 1\n", + " valid_range: [-1000 10000]\n", + " grid_mapping: crs\n", + " standard_name: normalized_difference_vegetation_index
| \n", + " | Country Name | \n", + "Country Code | \n", + "Series Name | \n", + "Series Code | \n", + "1972 [YR1972] | \n", + "1973 [YR1973] | \n", + "1974 [YR1974] | \n", + "1975 [YR1975] | \n", + "1976 [YR1976] | \n", + "1977 [YR1977] | \n", + "... | \n", + "2012 [YR2012] | \n", + "2013 [YR2013] | \n", + "2014 [YR2014] | \n", + "2015 [YR2015] | \n", + "2016 [YR2016] | \n", + "2017 [YR2017] | \n", + "2018 [YR2018] | \n", + "2019 [YR2019] | \n", + "2020 [YR2020] | \n", + "2021 [YR2021] | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Afghanistan | \n", + "AFG | \n", + "Cereal production (metric tons) | \n", + "AG.PRD.CREL.MT | \n", + "3950000 | \n", + "4270000 | \n", + "4351000 | \n", + "4481000 | \n", + "4624000 | \n", + "4147000 | \n", + "... | \n", + "6379000 | \n", + "6520329 | \n", + "6748023.28 | \n", + "5808288 | \n", + "5532695.42 | \n", + "4892953.97 | \n", + "4133051.85 | \n", + "5583461 | \n", + "6025977 | \n", + "4663880.79 | \n", + "
| 1 | \n", + "Afghanistan | \n", + "AFG | \n", + "Land under cereal production (hectares) | \n", + "AG.LND.CREL.HA | \n", + "3923100 | \n", + "3337000 | \n", + "3342000 | \n", + "3404000 | \n", + "3394000 | \n", + "3388000 | \n", + "... | \n", + "3143000 | \n", + "3182922 | \n", + "3344733 | \n", + "2724070 | \n", + "2793694 | \n", + "2419213 | \n", + "1911652 | \n", + "2641911 | \n", + "3043589 | \n", + "2164537 | \n", + "
| 2 | \n", + "Albania | \n", + "ALB | \n", + "Cereal production (metric tons) | \n", + "AG.PRD.CREL.MT | \n", + "585830 | \n", + "625498 | \n", + "646200 | \n", + "666500 | \n", + "857000 | \n", + "910400 | \n", + "... | \n", + "697400 | \n", + "702870 | \n", + "700370 | \n", + "695000 | \n", + "698430 | \n", + "701734 | \n", + "678196 | \n", + "666065 | \n", + "684023 | \n", + "691126.7 | \n", + "
| 3 | \n", + "Albania | \n", + "ALB | \n", + "Land under cereal production (hectares) | \n", + "AG.LND.CREL.HA | \n", + "331220 | \n", + "339400 | \n", + "334040 | \n", + "328500 | \n", + "350500 | \n", + "357000 | \n", + "... | \n", + "142800 | \n", + "142000 | \n", + "143149 | \n", + "142600 | \n", + "148084 | \n", + "145799 | \n", + "140110 | \n", + "132203 | \n", + "131310 | \n", + "134337 | \n", + "
| 4 | \n", + "Algeria | \n", + "DZA | \n", + "Cereal production (metric tons) | \n", + "AG.PRD.CREL.MT | \n", + "2362625 | \n", + "1595994 | \n", + "1480275 | \n", + "2680452 | \n", + "2313186 | \n", + "1142509 | \n", + "... | \n", + "5137455 | \n", + "4912551 | \n", + "3435535 | \n", + "3761229.6 | \n", + "3445227.37 | \n", + "3478175.14 | \n", + "6066252.82 | \n", + "5633596.78 | \n", + "4393336.75 | \n", + "2784017.29 | \n", + "
5 rows × 54 columns
\n", + "| \n", + " | 0 | \n", + "
|---|---|
| 1972 [YR1972] | \n", + "22703928 | \n", + "
| 1973 [YR1973] | \n", + "23721606 | \n", + "
| 1974 [YR1974] | \n", + "26240014 | \n", + "
| 1975 [YR1975] | \n", + "26238419 | \n", + "
| 1976 [YR1976] | \n", + "31143200 | \n", + "