Extração_de_dados_de_Precipitação_no_RS.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/andrebelem/pythonverse/blob/main/Extra%C3%A7%C3%A3o_de_dados_de_Precipita%C3%A7%C3%A3o_no_RS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"display: flex; align-items: center;\">\n",
        "    <img src=\"http://observatoriooceanografico.org/wp-content/uploads/elementor/thumbs/logo-o2-2-p31vla9ib17i85olifd4l1xk0e5lktrkal6u0s4c6o.png\" style=\"width: 100px;\"/>\n",
        "    <h1 style=\"margin-left: 20px;\">Extração de dados de precipitação na nuvem do CESM-LENS (via AWS) no Rio Grande do Sul, Brasil.</h1>\n",
        "</div>\n",
        "\n",
        "\n",
        "Criado por [A.L. Belém](mailto:andrebelem@id.uff.br) @ [Observatório Oceanográfico](http://www.observatoiriooceanografico.uff.br) 2024 (versão 1) <br>\n",
        "Para maiores informações, acesse o [`pythonverse`](https://github.com/andrebelem/pythonverse) no meu github, e use a referência [![DOI](https://zenodo.org/badge/342919527.svg)](https://zenodo.org/badge/latestdoi/342919527)\n",
        "\n",
        "###### Última atualização: 09 de Maio de 2024.\n",
        "\n",
        "O objetivo deste notebook é \"extrair\" dados do CESM-LENS na nuvem do NCAR na Amazon, sobre o Rio Grande do Sul, especificamente dados de precipitação diária máxima e precipitação diária, para os períodos \"histórico\", \"20th Century\" e o cenário do IPCC \"RPC8.5\", para depois fazer um processamento mais profundo posteriormente. Mais detalhes sobre essa versão, procure os documentos no https://github.com/NCAR/cesm-lens-aws e fique atento para outros notebooks no `pythonverse`.<br>\n",
        "\n",
        "**Note** que ao final eu gravo os resultados em uma área do meu google drive, e portanto, estou montando o gdrive no meu google colab antes de iniciar. Adapte para suas necessidades.\n",
        "\n",
        "O primeiro passo é a instalação de praticamente tudo que vou precisar aqui (abaixo)."
      ],
      "metadata": {
        "id": "5jpl3ySotjU2"
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    },
    {
      "cell_type": "code",
      "source": [
        "# instalando o módulo para trabalhar com o bucket do AWS e mais algumas atualizações necessárias\n",
        "# note que dependendo das versões do COLAB, isso pode ser alterado. Vale a pena verificar a necessidade de\n",
        "# reinstalação do xarray completo (neste caso, para permitir flexibilidade com o cftime)\n",
        "%%capture\n",
        "!pip install s3fs\n",
        "!pip install Intake-esm\n",
        "!pip install -U xarray[complete] # reinstala todo o xarray (útil nessa versão do colab por conta do cftime)\n",
        "\n",
        "!pip install salem  # e também com o salem e o rasterio\n",
        "!pip install rasterio\n",
        "\n",
        "!pip install cartopy # porque queremos fazer mapas legais.\n",
        "# provavelmente o seu runtime irá reiniciar. Não tem problema ! É normal isso acontecer."
      ],
      "metadata": {
        "id": "OUwAIcr4t7WS"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# esta linha é necessária por conta de algumas atualizações. Talvez em um futuro seja desnecessário.\n",
        "!pip install pandas>=2.1.0 # uma pequena atualização também por conta do bucket da AWS"
      ],
      "metadata": {
        "id": "wdear4I4uYVI"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, o segundo passo é baixar os shapes das UGRHs que estão no meu github, mas podem ser obtidas no site da [ANA](https://metadados.snirh.gov.br/geonetwork/srv/eng/catalog.search#/metadata/985f8821-2da3-4108-85a9-95985b37f3fe)."
      ],
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      "cell_type": "code",
      "source": [
        "!wget https://github.com/andrebelem/pythonverse/raw/main/datasets/SNIRH_UGRHs_2020.zip"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "MSm-n1oIu4qK",
        "outputId": "273d308f-50e7-4e3c-dd65-7f2a431f4984"
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      "execution_count": 3,
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            "--2024-05-10 14:48:27--  https://github.com/andrebelem/pythonverse/raw/main/datasets/SNIRH_UGRHs_2020.zip\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://media.githubusercontent.com/media/andrebelem/pythonverse/main/datasets/SNIRH_UGRHs_2020.zip [following]\n",
            "--2024-05-10 14:48:27--  https://media.githubusercontent.com/media/andrebelem/pythonverse/main/datasets/SNIRH_UGRHs_2020.zip\n",
            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13113509 (13M) [application/zip]\n",
            "Saving to: ‘SNIRH_UGRHs_2020.zip’\n",
            "\n",
            "SNIRH_UGRHs_2020.zi 100%[===================>]  12.51M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-05-10 14:48:28 (96.0 MB/s) - ‘SNIRH_UGRHs_2020.zip’ saved [13113509/13113509]\n",
            "\n"
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      "cell_type": "code",
      "source": [
        "# vamos explorar esse arquivo e separar as UGRHs que compõem o RS, ou seja,\n",
        "# apenas aquelas que estão abaixo de 26°S de latitude\n",
        "\n",
        "import geopandas as gpd\n",
        "ugrh = gpd.read_file('SNIRH_UGRHs_2020.zip')\n",
        "\n",
        "rs = ugrh[ugrh.bounds['maxy'] < -26]\n",
        "# para facilitar a extração:\n",
        "sul_ugrh = rs.dissolve()\n",
        "print(f'Bounds:{sul_ugrh.bounds}')"
      ],
      "metadata": {
        "id": "-JS8stvjvDGi",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db5505bc-e428-4fa0-82d8-a3c040522ffa"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bounds:        minx       miny       maxx       maxy\n",
            "0 -57.643885 -33.752081 -49.280653 -26.241184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora que temos um shapefile para recortar nossos datasets, vamos obter os dados do CESM-LENS.\n",
        "\n",
        "## CESM-LENS na nuvem (AWS S3)\n",
        "\n",
        "Aqui vou obter os campos do CESM-Large Ensemble para os experimentos \"20C\" (ou 20 century: 1920 à 2005) e \"HIST\" que é período histórico de 1850-1920, além do cenário RCP8.5 (até 2100). Veja abaixo que estou interessado <u>**apenas**</u> na precipitação máxima (ou na precipitação diária, dependendo do dataset). Note ainda que o procedimento aqui é para separar os datasets e grava-los em netcdf, já que são campos muito grandes e os \"chunks\" de tempo demoram muito para serem executados se fizessemos uma computação na nuvem. Obviamente eu já poderia fazer toda a análise aqui, mas preferi primeiro extrair, gravar e depois processar em outro notebook. Você pode fazer o mesmo para qualquer variável. Use sua criatividade.\n",
        "\n",
        "**Detalhe importante**: Como reinstalamos o xarray, ele não reconhece de primeira o arquivo METADATA. Note então que tenho que rodar a célula abaixo 2x (!)"
      ],
      "metadata": {
        "id": "TFIQc0XJycma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# de alguma forma, o python não reconhece em uma primeira rodada o arquivo METADATA.\n",
        "# Rode 2x essa célula !\n",
        "import intake\n",
        "col = intake.open_esm_datastore(\"https://raw.githubusercontent.com/NCAR/cesm-lens-aws/main/intake-catalogs/aws-cesm1-le.json\")"
      ],
      "metadata": {
        "id": "KjZ5YfBeyaw9"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# estou interessado na variável que contém a \"taxa de precipitação\". vou listar todas as opções aqui\n",
        "col.df[(col.df['long_name'].str.contains('precipitation rate', na=False))&(col.df['variable']=='PRECTMX')]"
      ],
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              "   variable                                          long_name component  \\\n",
              "12  PRECTMX  maximum (convective and large-scale) precipita...       atm   \n",
              "54  PRECTMX  maximum (convective and large-scale) precipita...       atm   \n",
              "\n",
              "   experiment frequency  vertical_levels spatial_domain units  \\\n",
              "12        20C     daily              1.0         global   m/s   \n",
              "54       HIST     daily              1.0         global   m/s   \n",
              "\n",
              "             start_time             end_time  \\\n",
              "12  1920-01-01 12:00:00  2005-12-31 12:00:00   \n",
              "54  1850-01-01 12:00:00  1919-12-31 12:00:00   \n",
              "\n",
              "                                                 path  \n",
              "12  s3://ncar-cesm-lens/atm/daily/cesmLE-20C-PRECT...  \n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# e o mesmo para o experimento RCP8.5\n",
        "col.df[(col.df['long_name'].str.contains('precipitation rate', na=False))&(col.df['experiment'].str.contains('RCP'))]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "1KKjbF-2mRKK",
        "outputId": "65a7af89-063e-4c03-977c-94beab8aed3b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    variable                                          long_name component  \\\n",
              "74     PRECL  large-scale (stable) precipitation rate (liq +...       atm   \n",
              "77     PRECT  total (convective and large-scale) precipitati...       atm   \n",
              "100    PRECT  total (convective and large-scale) precipitati...       atm   \n",
              "113    PRECT  total (convective and large-scale) precipitati...       atm   \n",
              "205    PRECC          convective precipitation rate (liq + ice)       atm   \n",
              "206    PRECL  large-scale (stable) precipitation rate (liq +...       atm   \n",
              "\n",
              "    experiment          frequency  vertical_levels spatial_domain units  \\\n",
              "74       RCP85              daily              1.0         global   m/s   \n",
              "77       RCP85              daily              1.0         global   m/s   \n",
              "100      RCP85  hourly6-2026-2035              1.0         global   m/s   \n",
              "113      RCP85  hourly6-2071-2080              1.0         global   m/s   \n",
              "205      RCP85            monthly              1.0         global   m/s   \n",
              "206      RCP85            monthly              1.0         global   m/s   \n",
              "\n",
              "              start_time             end_time  \\\n",
              "74   2006-01-01 12:00:00  2100-12-31 12:00:00   \n",
              "77   2006-01-01 12:00:00  2100-12-31 12:00:00   \n",
              "100  2026-01-01 00:00:00  2036-01-01 00:00:00   \n",
              "113  2071-01-01 00:00:00  2081-01-01 00:00:00   \n",
              "205  2006-01-16 12:00:00  2100-12-16 12:00:00   \n",
              "206  2006-01-16 12:00:00  2100-12-16 12:00:00   \n",
              "\n",
              "                                                  path  \n",
              "74   s3://ncar-cesm-lens/atm/daily/cesmLE-RCP85-PRE...  \n",
              "77   s3://ncar-cesm-lens/atm/daily/cesmLE-RCP85-PRE...  \n",
              "100  s3://ncar-cesm-lens/atm/hourly6-2026-2035/cesm...  \n",
              "113  s3://ncar-cesm-lens/atm/hourly6-2071-2080/cesm...  \n",
              "205  s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-P...  \n",
              "206  s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-P...  "
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              "      <td>1.0</td>\n",
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              "      <td>2026-01-01 00:00:00</td>\n",
              "      <td>2036-01-01 00:00:00</td>\n",
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              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>PRECT</td>\n",
              "      <td>total (convective and large-scale) precipitati...</td>\n",
              "      <td>atm</td>\n",
              "      <td>RCP85</td>\n",
              "      <td>hourly6-2071-2080</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>m/s</td>\n",
              "      <td>2071-01-01 00:00:00</td>\n",
              "      <td>2081-01-01 00:00:00</td>\n",
              "      <td>s3://ncar-cesm-lens/atm/hourly6-2071-2080/cesm...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>PRECC</td>\n",
              "      <td>convective precipitation rate (liq + ice)</td>\n",
              "      <td>atm</td>\n",
              "      <td>RCP85</td>\n",
              "      <td>monthly</td>\n",
              "      <td>1.0</td>\n",
              "      <td>global</td>\n",
              "      <td>m/s</td>\n",
              "      <td>2006-01-16 12:00:00</td>\n",
              "      <td>2100-12-16 12:00:00</td>\n",
              "      <td>s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-P...</td>\n",
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              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>PRECL</td>\n",
              "      <td>large-scale (stable) precipitation rate (liq +...</td>\n",
              "      <td>atm</td>\n",
              "      <td>RCP85</td>\n",
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            ],
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note que há uma variável chamada PRECTMX que é o \"maximum (convective and large-scale) precipitation rate\". Já para os experimentos de RCP, vou usar a variável PRECT. Você pode ter uma ideia melhor das variáveis olhando aqui https://www.cesm.ucar.edu/community-projects/lens2/output-variables."
      ],
      "metadata": {
        "id": "y2_ouuL90Ttt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# vou fazer um subset onde separo apenas o que eu quero, frequência diária\n",
        "col_subset = col.search(experiment=[\"HIST\",\"20C\"], frequency=\"daily\", variable=[\"PRECTMX\"])\n",
        "col_subset2 = col.search(experiment=[\"RCP85\"], frequency=\"daily\", variable=[\"PRECT\"])\n",
        "# agora vou criar datasets do meu subset\n",
        "dsets = col_subset.to_dataset_dict(zarr_kwargs={\"consolidated\": True}, storage_options={\"anon\": True})\n",
        "dsets2 = col_subset2.to_dataset_dict(zarr_kwargs={\"consolidated\": True}, storage_options={\"anon\": True})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "ZvQsfLlS0cqU",
        "outputId": "4248a477-5778-4db0-ab94-68055376925c"
      },
      "execution_count": 9,
      "outputs": [
        {
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          "text": [
            "\n",
            "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
            "\t'component.experiment.frequency'\n",
            "\n",
            "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
            "\t'component.experiment.frequency'\n"
          ]
        },
        {
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      "cell_type": "code",
      "source": [
        "# Apenas para verificar, os dados estão aqui:\n",
        "print(dsets.keys(),dsets2.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsHyCE-q0rIu",
        "outputId": "86a24e61-2386-4465-f831-16a1c52b3cd9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['atm.20C.daily', 'atm.HIST.daily']) dict_keys(['atm.RCP85.daily'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compatibiliza as longitudes entre o shape e o dataset com uma margem de 1 grau\n",
        "lons = slice(sul_ugrh.bounds['minx'].values[0]+360-1, sul_ugrh.bounds['maxx'].values[0]+360+1)\n",
        "lats = slice(sul_ugrh.bounds['miny'].values[0]-1, sul_ugrh.bounds['maxy'].values[0]+1)\n",
        "\n",
        "# a minha estratégia aqui foi obter um campo um pouco maior do que os bounds do meu shapefile. No processamento isso será importante."
      ],
      "metadata": {
        "id": "LXoAX00b229S"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extraindo o dataset de cada experimento dentro do que precisamos\n",
        "# note que estou compatibilizando as longitudes entre o shape e o dataset\n",
        "ds_hist = dsets['atm.HIST.daily'].sel(lon=lons,lat=lats)\n",
        "ds_20C = dsets['atm.20C.daily'].sel(lon=lons,lat=lats)\n",
        "ds_rcp85 = dsets2['atm.RCP85.daily'].sel(lon=lons,lat=lats)"
      ],
      "metadata": {
        "id": "Q0PQYxdX0xVf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Converter as longitudes de 0-360 para -180 a +180\n",
        "ds_20C['lon'] = ((ds_20C['lon'] + 180) % 360) - 180\n",
        "ds_hist['lon'] = ((ds_hist['lon'] + 180) % 360) - 180\n",
        "ds_rcp85['lon'] = ((ds_rcp85['lon'] + 180) % 360) - 180"
      ],
      "metadata": {
        "id": "PoCyvQ_OEsRx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# agora vamos passar tudo que já fizemos do dask para a memória. Isso pode demorar um pouco !\n",
        "# ##### CAUTELA #### cada execução pode demorar aprox. 1h2-1h30\n",
        "from dask.diagnostics import ProgressBar\n",
        "ProgressBar().register()"
      ],
      "metadata": {
        "id": "f51neRnfnSqO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "O `Dask` é uma biblioteca Python projetada para processamento paralelo e distribuído de grandes conjuntos de dados. Quando você executa operações em um objeto `Xarray` com o backend `Dask`, como por exemplo, filtragem, agregação ou cálculos complexos, essas operações não são executadas imediatamente. Em vez disso, o `Dask` constrói um gráfico de tarefas que representa as operações a serem realizadas nos dados. É o que chamamos de \"lazy evaluation\", o que significa que as operações não são realizadas até que seja necessário obter o resultado final. Por um lado, isso é bom para trabalhar com grandes volumes de dados, mas se você está fazendo uma \"exploração\" de dados, essa demora pode prejudicar bastante o seu trabalho.\n",
        "\n",
        "Quando você chama o método `.load()` em um objeto `Xarray` com o backend `Dask`, isso atua como um gatilho para iniciar a execução real das operações pendentes e carregar os resultados na memória. Ao chamar `.load()`, você instrui o `Dask` a realizar as operações necessárias para calcular o resultado e carregar os dados calculados na memória, convertendo-os em um objeto `Xarray` regular. Então, **você tem que ter certeza do que está fazendo**."
      ],
      "metadata": {
        "id": "oO5tV-vpsLAa"
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    },
    {
      "cell_type": "code",
      "source": [
        "ds_20C.load() # executa para 20C"
      ],
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          "height": 330
        },
        "id": "UwzqQW5poLcU",
        "outputId": "64557c02-548a-42db-80a5-0b19f6da4f14"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[########################################] | 100% Completed | 53m 0s\n"
          ]
        },
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              "  * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105\n",
              "  * time       (time) object 251kB 1920-01-01 12:00:00 ... 2005-12-31 12:00:00\n",
              "    time_bnds  (time, nbnd) object 502kB 1920-01-01 00:00:00 ... 2006-01-01 0...\n",
              "Dimensions without coordinates: nbnd\n",
              "Data variables:\n",
              "    PRECTMX    (member_id, time, lat, lon) float32 402MB 4.925e-08 ... 8.146e-08\n",
              "Attributes: (12/25)\n",
              "    Conventions:                       CF-1.0\n",
              "    NCO:                               4.4.2\n",
              "    Version:                           $Name$\n",
              "    important_note:                    This data is part of the project 'Blin...\n",
              "    initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....\n",
              "    logname:                           mudryk\n",
              "    ...                                ...\n",
              "    intake_esm_attrs:units:            m/s\n",
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              "    intake_esm_attrs:end_time:         2005-12-31 12:00:00\n",
              "    intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-2...\n",
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              "    intake_esm_dataset_key:            atm.20C.daily"
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              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 403MB\n",
              "Dimensions:    (member_id: 40, time: 31390, lat: 10, lon: 8, nbnd: 2)\n",
              "Coordinates:\n",
              "  * lat        (lat) float64 80B -34.4 -33.46 -32.51 ... -27.8 -26.86 -25.92\n",
              "  * lon        (lon) float64 64B -57.5 -56.25 -55.0 ... -51.25 -50.0 -48.75\n",
              "  * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105\n",
              "  * time       (time) object 251kB 1920-01-01 12:00:00 ... 2005-12-31 12:00:00\n",
              "    time_bnds  (time, nbnd) object 502kB 1920-01-01 00:00:00 ... 2006-01-01 0...\n",
              "Dimensions without coordinates: nbnd\n",
              "Data variables:\n",
              "    PRECTMX    (member_id, time, lat, lon) float32 402MB 4.925e-08 ... 8.146e-08\n",
              "Attributes: (12/25)\n",
              "    Conventions:                       CF-1.0\n",
              "    NCO:                               4.4.2\n",
              "    Version:                           $Name$\n",
              "    important_note:                    This data is part of the project &#x27;Blin...\n",
              "    initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....\n",
              "    logname:                           mudryk\n",
              "    ...                                ...\n",
              "    intake_esm_attrs:units:            m/s\n",
              "    intake_esm_attrs:start_time:       1920-01-01 12:00:00\n",
              "    intake_esm_attrs:end_time:         2005-12-31 12:00:00\n",
              "    intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-2...\n",
              "    intake_esm_attrs:_data_format_:    zarr\n",
              "    intake_esm_dataset_key:            atm.20C.daily</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8af9c6e9-b9d8-441a-9ea2-cc087742f0b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8af9c6e9-b9d8-441a-9ea2-cc087742f0b4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 40</li><li><span class='xr-has-index'>time</span>: 31390</li><li><span class='xr-has-index'>lat</span>: 10</li><li><span class='xr-has-index'>lon</span>: 8</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e68ba74d-161b-47a8-b8c6-a74572f0f390' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e68ba74d-161b-47a8-b8c6-a74572f0f390' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.4 -33.46 ... -26.86 -25.92</div><input id='attrs-0970faaf-943e-4f13-a2fe-9171f688720c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0970faaf-943e-4f13-a2fe-9171f688720c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16ef7922-1d4a-4869-84a8-974eedf59780' class='xr-var-data-in' type='checkbox'><label for='data-16ef7922-1d4a-4869-84a8-974eedf59780' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-34.397907, -33.455498, -32.513088, -31.570681, -30.628273, -29.685863,\n",
              "       -28.743456, -27.801046, -26.858639, -25.916231])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-57.5 -56.25 -55.0 ... -50.0 -48.75</div><input id='attrs-9ea918df-783f-4d13-b699-30c7c93632bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ea918df-783f-4d13-b699-30c7c93632bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da49ed79-92cf-4c85-8822-06e4676c35ad' class='xr-var-data-in' type='checkbox'><label for='data-da49ed79-92cf-4c85-8822-06e4676c35ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-57.5 , -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 101 102 103 104 105</div><input id='attrs-6b1da3b9-28aa-4f4c-8baf-1f3867cb3d23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b1da3b9-28aa-4f4c-8baf-1f3867cb3d23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83475e7c-2128-4527-abad-3ddf50d069c2' class='xr-var-data-in' type='checkbox'><label for='data-83475e7c-2128-4527-abad-3ddf50d069c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
              "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
              "        29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-01 12:00:00 ... 2005-12-...</div><input id='attrs-6dba0314-491a-4ad4-b93f-703b53287f00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dba0314-491a-4ad4-b93f-703b53287f00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19b2ba55-c976-47fb-a962-21f7312950c3' class='xr-var-data-in' type='checkbox'><label for='data-19b2ba55-c976-47fb-a962-21f7312950c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1920, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1920, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
              "       cftime.DatetimeNoLeap(2005, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2005, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2005, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
              "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-01 00:00:00 ... 2006-01-...</div><input id='attrs-404d0a79-5fe2-4ef5-95b6-ab040d4f8768' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-404d0a79-5fe2-4ef5-95b6-ab040d4f8768' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29442080-d8c0-447c-a35c-3a2bae9abf4e' class='xr-var-data-in' type='checkbox'><label for='data-29442080-d8c0-447c-a35c-3a2bae9abf4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time interval endpoints</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(1920, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1920, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1920, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1920, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1920, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1920, 1, 4, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       ...,\n",
              "       [cftime.DatetimeNoLeap(2005, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2005, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2005, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2005, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2005, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2006, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
              "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eaf34163-4d17-487d-89b6-46f6d6081f0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eaf34163-4d17-487d-89b6-46f6d6081f0a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PRECTMX</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.925e-08 4.729e-08 ... 8.146e-08</div><input id='attrs-8efd3547-2514-4ad2-afd7-bf85d8546d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8efd3547-2514-4ad2-afd7-bf85d8546d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3b88ca-1d2a-44ac-92ca-366a973b5488' class='xr-var-data-in' type='checkbox'><label for='data-2b3b88ca-1d2a-44ac-92ca-366a973b5488' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Maximum (convective and large-scale) precipitation rate (liq+ice)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[4.92462178e-08, 4.72946304e-08, 1.21934633e-07, ...,\n",
              "          8.10217049e-09, 2.27597905e-08, 2.08021032e-08],\n",
              "         [9.00407926e-08, 1.05079195e-07, 1.15289374e-07, ...,\n",
              "          1.31270257e-08, 4.99450259e-09, 1.93846699e-08],\n",
              "         [8.38549852e-08, 1.72892499e-07, 1.87919412e-07, ...,\n",
              "          1.35700668e-08, 3.00274188e-08, 6.15555962e-08],\n",
              "         ...,\n",
              "         [7.76973650e-08, 6.50937011e-08, 7.02845213e-08, ...,\n",
              "          9.09857008e-08, 1.02502376e-07, 6.16130933e-08],\n",
              "         [4.94677330e-08, 4.67842511e-08, 5.53019639e-08, ...,\n",
              "          7.10271308e-08, 8.61169269e-08, 4.61760727e-08],\n",
              "         [2.23296333e-08, 3.25973417e-08, 3.91221739e-08, ...,\n",
              "          5.69457086e-08, 7.98254334e-08, 5.17099146e-08]],\n",
              "\n",
              "        [[6.06594386e-10, 1.13074228e-09, 3.49237728e-09, ...,\n",
              "          2.14423483e-08, 2.40069049e-08, 1.59574007e-08],\n",
              "         [1.11538778e-09, 1.60252578e-09, 8.10664691e-09, ...,\n",
              "          1.66116241e-08, 4.12711421e-09, 4.02095290e-09],\n",
              "         [7.74778286e-09, 2.03956425e-08, 4.92147052e-08, ...,\n",
              "          1.50853747e-08, 3.30915952e-08, 2.97663636e-08],\n",
              "...\n",
              "          1.22135106e-07, 1.23387125e-07, 9.16246350e-08],\n",
              "         [1.13613421e-08, 1.24291457e-08, 2.66145754e-08, ...,\n",
              "          1.04652912e-07, 1.30996042e-07, 8.73113422e-08],\n",
              "         [1.66176211e-08, 1.37186360e-08, 1.46495038e-08, ...,\n",
              "          8.50553263e-08, 9.36105451e-08, 1.08453207e-07]],\n",
              "\n",
              "        [[1.61776086e-07, 1.72563503e-07, 1.84418710e-07, ...,\n",
              "          1.53333968e-08, 4.11015755e-08, 1.90583123e-08],\n",
              "         [1.70469235e-07, 1.76386649e-07, 2.20389268e-07, ...,\n",
              "          3.59143257e-08, 5.84527591e-08, 8.43552499e-08],\n",
              "         [2.23575483e-07, 2.37665773e-07, 2.14267160e-07, ...,\n",
              "          8.61591190e-08, 7.53328493e-08, 1.01686759e-07],\n",
              "         ...,\n",
              "         [3.53196015e-08, 1.69128107e-08, 1.51043444e-08, ...,\n",
              "          6.88886388e-08, 1.00951752e-07, 6.00327255e-08],\n",
              "         [1.07717746e-08, 5.54211255e-09, 1.43270453e-08, ...,\n",
              "          6.19616571e-08, 8.97939998e-08, 5.15951548e-08],\n",
              "         [5.47051959e-09, 4.06782474e-09, 1.54800581e-08, ...,\n",
              "          4.38587797e-08, 5.42347642e-08, 8.14596177e-08]]]],\n",
              "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2d0eb41-8a80-4c43-ae0e-c2ed309a503a' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2d0eb41-8a80-4c43-ae0e-c2ed309a503a' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83256063-3999-412c-b47b-97bc71a14760' class='xr-index-data-in' type='checkbox'/><label for='index-83256063-3999-412c-b47b-97bc71a14760' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-34.3979072570801, -33.4554977416992, -32.5130882263184,\n",
              "       -31.5706806182861, -30.6282730102539,  -29.685863494873,\n",
              "       -28.7434558868408,   -27.80104637146, -26.8586387634277,\n",
              "       -25.9162311553955],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7b538a25-a8f5-4a41-9c81-a8160b40a24e' class='xr-index-data-in' type='checkbox'/><label for='index-7b538a25-a8f5-4a41-9c81-a8160b40a24e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-57.5, -56.25, -55.0, -53.75, -52.5, -51.25, -50.0, -48.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72be7f16-abf6-4666-bf39-d3dea07eef79' class='xr-index-data-in' type='checkbox'/><label for='index-72be7f16-abf6-4666-bf39-d3dea07eef79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
              "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
              "        29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105],\n",
              "      dtype=&#x27;int64&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9796b2f8-3c72-4f23-81a1-dbdaf5bca1fd' class='xr-index-data-in' type='checkbox'/><label for='index-9796b2f8-3c72-4f23-81a1-dbdaf5bca1fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1920-01-01 12:00:00, 1920-01-02 12:00:00, 1920-01-03 12:00:00,\n",
              "             1920-01-04 12:00:00, 1920-01-05 12:00:00, 1920-01-06 12:00:00,\n",
              "             1920-01-07 12:00:00, 1920-01-08 12:00:00, 1920-01-09 12:00:00,\n",
              "             1920-01-10 12:00:00,\n",
              "             ...\n",
              "             2005-12-22 12:00:00, 2005-12-23 12:00:00, 2005-12-24 12:00:00,\n",
              "             2005-12-25 12:00:00, 2005-12-26 12:00:00, 2005-12-27 12:00:00,\n",
              "             2005-12-28 12:00:00, 2005-12-29 12:00:00, 2005-12-30 12:00:00,\n",
              "             2005-12-31 12:00:00],\n",
              "            dtype=&#x27;object&#x27;, length=31390, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19264aeb-2161-4ac6-8cb7-6e40e6381cd0' class='xr-section-summary-in' type='checkbox'  ><label for='section-19264aeb-2161-4ac6-8cb7-6e40e6381cd0' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>NCO :</span></dt><dd>4.4.2</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>important_note :</span></dt><dd>This data is part of the project &#x27;Blind Evaluation of Lossy Data-Compression in LENS&#x27;.  Please exercise caution before using this data for other purposes.</dd><dt><span>initial_file :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc</dd><dt><span>logname :</span></dt><dd>mudryk</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>topography_file :</span></dt><dd>/scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;PRECTMX&#x27;]</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>PRECTMX</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>maximum (convective and large-scale) precipitation rate (liq+ice)</dd><dt><span>intake_esm_attrs:component :</span></dt><dd>atm</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>20C</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>1.0</dd><dt><span>intake_esm_attrs:spatial_domain :</span></dt><dd>global</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>m/s</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>1920-01-01 12:00:00</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>2005-12-31 12:00:00</dd><dt><span>intake_esm_attrs:path :</span></dt><dd>s3://ncar-cesm-lens/atm/daily/cesmLE-20C-PRECTMX.zarr</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>atm.20C.daily</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
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        "ds_hist.load() # executa para o hist (que é bem mais rápido pois é apenas 1 membro)"
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        "outputId": "2043df2a-ec6c-44e7-c4cb-e967e07600dc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[########################################] | 100% Completed | 76.33 s\n"
          ]
        },
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              "    intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-H...\n",
              "    intake_esm_attrs:_data_format_:    zarr\n",
              "    intake_esm_dataset_key:            atm.HIST.daily</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b62395d1-de32-4e6d-bef2-1cb650bbc594' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b62395d1-de32-4e6d-bef2-1cb650bbc594' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 25550</li><li><span class='xr-has-index'>lat</span>: 10</li><li><span class='xr-has-index'>lon</span>: 8</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-136ee781-40d7-4eab-b785-8c65b84452b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-136ee781-40d7-4eab-b785-8c65b84452b0' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.4 -33.46 ... -26.86 -25.92</div><input id='attrs-f116ec1f-8d40-41b5-8cde-adf88efdb933' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f116ec1f-8d40-41b5-8cde-adf88efdb933' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fbe8ea2-0072-43f2-b5a8-8eb2085b6cd0' class='xr-var-data-in' type='checkbox'><label for='data-8fbe8ea2-0072-43f2-b5a8-8eb2085b6cd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-34.397907, -33.455498, -32.513088, -31.570681, -30.628273, -29.685863,\n",
              "       -28.743456, -27.801046, -26.858639, -25.916231])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-57.5 -56.25 -55.0 ... -50.0 -48.75</div><input id='attrs-1df4cabe-93fb-4f1b-ad45-7f4945c832da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1df4cabe-93fb-4f1b-ad45-7f4945c832da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e74760-b9af-4d59-8f25-b818665451a4' class='xr-var-data-in' type='checkbox'><label for='data-20e74760-b9af-4d59-8f25-b818665451a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-57.5 , -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-137eaa53-a835-4688-8f8b-88e2f4203a1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-137eaa53-a835-4688-8f8b-88e2f4203a1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54848d4d-f0d4-4b0f-8ed4-c1a8b008c561' class='xr-var-data-in' type='checkbox'><label for='data-54848d4d-f0d4-4b0f-8ed4-c1a8b008c561' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 12:00:00 ... 1919-12-...</div><input id='attrs-a1e10143-2970-45ed-a0ed-9571a368ce81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1e10143-2970-45ed-a0ed-9571a368ce81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d78df27b-f8d7-4655-8255-e20bfcb2dc25' class='xr-var-data-in' type='checkbox'><label for='data-d78df27b-f8d7-4655-8255-e20bfcb2dc25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1850, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1850, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
              "       cftime.DatetimeNoLeap(1919, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1919, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(1919, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
              "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 1920-01-...</div><input id='attrs-694f11a0-c4f3-499e-81bd-64be53362f25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-694f11a0-c4f3-499e-81bd-64be53362f25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d52c1b1b-ec77-424a-bba8-29c7f505f469' class='xr-var-data-in' type='checkbox'><label for='data-d52c1b1b-ec77-424a-bba8-29c7f505f469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time interval endpoints</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1850, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1850, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1850, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1850, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1850, 1, 4, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       ...,\n",
              "       [cftime.DatetimeNoLeap(1919, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1919, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1919, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1919, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(1919, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(1920, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
              "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07081908-f469-4efc-8ffa-3d2c3c778691' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07081908-f469-4efc-8ffa-3d2c3c778691' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PRECTMX</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.213e-13 1.322e-09 ... 1.831e-08</div><input id='attrs-26b0960a-5469-413e-adfd-1ffa7897a45f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26b0960a-5469-413e-adfd-1ffa7897a45f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79afb7ee-c597-423e-bb2a-707fbace59e9' class='xr-var-data-in' type='checkbox'><label for='data-79afb7ee-c597-423e-bb2a-707fbace59e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Maximum (convective and large-scale) precipitation rate (liq+ice)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[2.21259683e-13, 1.32210765e-09, 5.58075008e-08, ...,\n",
              "         1.67793992e-08, 5.60438984e-09, 4.69837547e-09],\n",
              "        [4.35558741e-15, 1.12652465e-09, 2.81059981e-10, ...,\n",
              "         3.82379628e-10, 1.18480836e-09, 2.03759787e-09],\n",
              "        [4.45694646e-08, 1.64846674e-17, 2.13662386e-08, ...,\n",
              "         3.88889321e-09, 3.92527255e-09, 3.37110651e-09],\n",
              "        ...,\n",
              "        [2.18212804e-07, 1.89421826e-07, 1.63500772e-07, ...,\n",
              "         2.72430662e-21, 3.26551741e-09, 8.83906229e-12],\n",
              "        [2.91639736e-07, 1.94228164e-07, 8.94870595e-08, ...,\n",
              "         1.61633547e-20, 4.30423128e-08, 3.22692628e-08],\n",
              "        [2.48847414e-07, 1.70683293e-07, 4.11782217e-08, ...,\n",
              "         7.69329322e-10, 7.37482608e-08, 4.08254266e-08]],\n",
              "\n",
              "       [[1.00945634e-08, 4.03836919e-09, 1.50078083e-08, ...,\n",
              "         9.36961939e-08, 1.17776047e-07, 1.18541323e-07],\n",
              "        [6.62416788e-09, 1.32681013e-08, 2.39392541e-08, ...,\n",
              "         1.01087288e-07, 1.08435970e-07, 1.20865138e-07],\n",
              "        [1.50701247e-08, 3.87563936e-08, 1.01362211e-07, ...,\n",
              "         1.66232638e-07, 1.27170637e-07, 1.13636325e-07],\n",
              "...\n",
              "        [5.59545832e-08, 5.48107835e-08, 5.62765443e-08, ...,\n",
              "         1.06288091e-07, 6.69859759e-08, 1.26614319e-08],\n",
              "        [6.82505714e-08, 5.72812304e-08, 5.05516446e-08, ...,\n",
              "         9.70179741e-08, 1.10971513e-07, 6.70129978e-08],\n",
              "        [6.75359999e-08, 4.39151684e-08, 4.30413145e-08, ...,\n",
              "         7.87309915e-08, 7.30858716e-08, 1.35575988e-07]],\n",
              "\n",
              "       [[1.32171166e-07, 1.30941359e-07, 1.35808648e-07, ...,\n",
              "         6.97084446e-10, 2.22881535e-09, 1.26275501e-09],\n",
              "        [1.33703480e-07, 1.56836592e-07, 1.29632241e-07, ...,\n",
              "         9.28177646e-10, 7.03482439e-10, 8.12438339e-10],\n",
              "        [1.31615792e-07, 1.27230066e-07, 1.06138806e-07, ...,\n",
              "         4.12946594e-10, 2.28938493e-10, 9.46313472e-10],\n",
              "        ...,\n",
              "        [1.22692549e-08, 1.95827567e-08, 2.90524760e-08, ...,\n",
              "         3.64573189e-08, 3.65957611e-08, 1.85104443e-09],\n",
              "        [5.55611290e-09, 1.39449243e-08, 2.94511633e-08, ...,\n",
              "         4.61985294e-08, 4.82551705e-08, 4.74364326e-09],\n",
              "        [6.30263886e-09, 1.40072363e-08, 2.97609954e-08, ...,\n",
              "         4.20001385e-08, 5.39048770e-08, 1.83108853e-08]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d207f106-e193-40ff-81f3-6a54dfa8aa25' class='xr-section-summary-in' type='checkbox'  ><label for='section-d207f106-e193-40ff-81f3-6a54dfa8aa25' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1bf8cc6-4839-473c-a52e-dc4ec53452a8' class='xr-index-data-in' type='checkbox'/><label for='index-b1bf8cc6-4839-473c-a52e-dc4ec53452a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-34.3979072570801, -33.4554977416992, -32.5130882263184,\n",
              "       -31.5706806182861, -30.6282730102539,  -29.685863494873,\n",
              "       -28.7434558868408,   -27.80104637146, -26.8586387634277,\n",
              "       -25.9162311553955],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad751b68-3020-459f-bcae-d275f8e4865d' class='xr-index-data-in' type='checkbox'/><label for='index-ad751b68-3020-459f-bcae-d275f8e4865d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-57.5, -56.25, -55.0, -53.75, -52.5, -51.25, -50.0, -48.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6de8f0d7-0674-41d9-b031-5289bd570df4' class='xr-index-data-in' type='checkbox'/><label for='index-6de8f0d7-0674-41d9-b031-5289bd570df4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 12:00:00, 1850-01-02 12:00:00, 1850-01-03 12:00:00,\n",
              "             1850-01-04 12:00:00, 1850-01-05 12:00:00, 1850-01-06 12:00:00,\n",
              "             1850-01-07 12:00:00, 1850-01-08 12:00:00, 1850-01-09 12:00:00,\n",
              "             1850-01-10 12:00:00,\n",
              "             ...\n",
              "             1919-12-22 12:00:00, 1919-12-23 12:00:00, 1919-12-24 12:00:00,\n",
              "             1919-12-25 12:00:00, 1919-12-26 12:00:00, 1919-12-27 12:00:00,\n",
              "             1919-12-28 12:00:00, 1919-12-29 12:00:00, 1919-12-30 12:00:00,\n",
              "             1919-12-31 12:00:00],\n",
              "            dtype=&#x27;object&#x27;, length=25550, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8da3bf79-3622-43d2-8626-af5d1be9d864' class='xr-section-summary-in' type='checkbox'  ><label for='section-8da3bf79-3622-43d2-8626-af5d1be9d864' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>NCO :</span></dt><dd>4.4.2</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>important_note :</span></dt><dd>This data is part of the project &#x27;Blind Evaluation of Lossy Data-Compression in LENS&#x27;.  Please exercise caution before using this data for other purposes.</dd><dt><span>initial_file :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc</dd><dt><span>logname :</span></dt><dd>mudryk</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>topography_file :</span></dt><dd>/scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;PRECTMX&#x27;]</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>PRECTMX</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>maximum (convective and large-scale) precipitation rate (liq+ice)</dd><dt><span>intake_esm_attrs:component :</span></dt><dd>atm</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>HIST</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>1.0</dd><dt><span>intake_esm_attrs:spatial_domain :</span></dt><dd>global</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>m/s</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>1850-01-01 12:00:00</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>1919-12-31 12:00:00</dd><dt><span>intake_esm_attrs:path :</span></dt><dd>s3://ncar-cesm-lens/atm/daily/cesmLE-HIST-PRECTMX.zarr</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>atm.HIST.daily</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_rcp85.load() # executa para o rcp8.5 (que também é longo, com 105 membros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "F9-q-yLc1q9M",
        "outputId": "660a3161-e8e7-463d-8427-ec07bb340208"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[########################################] | 100% Completed | 61m 40s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.Dataset> Size: 445MB\n",
              "Dimensions:    (member_id: 40, time: 34675, lat: 10, lon: 8, nbnd: 2)\n",
              "Coordinates:\n",
              "  * lat        (lat) float64 80B -34.4 -33.46 -32.51 ... -27.8 -26.86 -25.92\n",
              "  * lon        (lon) float64 64B -57.5 -56.25 -55.0 ... -51.25 -50.0 -48.75\n",
              "  * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105\n",
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 445MB\n",
              "Dimensions:    (member_id: 40, time: 34675, lat: 10, lon: 8, nbnd: 2)\n",
              "Coordinates:\n",
              "  * lat        (lat) float64 80B -34.4 -33.46 -32.51 ... -27.8 -26.86 -25.92\n",
              "  * lon        (lon) float64 64B -57.5 -56.25 -55.0 ... -51.25 -50.0 -48.75\n",
              "  * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105\n",
              "  * time       (time) object 277kB 2006-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
              "    time_bnds  (time, nbnd) object 555kB 2006-01-01 00:00:00 ... 2101-01-01 0...\n",
              "Dimensions without coordinates: nbnd\n",
              "Data variables:\n",
              "    PRECT      (member_id, time, lat, lon) float32 444MB 2.779e-12 ... 3.297e-07\n",
              "Attributes: (12/26)\n",
              "    Conventions:                       CF-1.0\n",
              "    NCO:                               4.4.2\n",
              "    Version:                           $Name$\n",
              "    host:                              tcs-f02n07\n",
              "    important_note:                    This data is part of the project &#x27;Blin...\n",
              "    initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....\n",
              "    ...                                ...\n",
              "    intake_esm_attrs:units:            m/s\n",
              "    intake_esm_attrs:start_time:       2006-01-01 12:00:00\n",
              "    intake_esm_attrs:end_time:         2100-12-31 12:00:00\n",
              "    intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-R...\n",
              "    intake_esm_attrs:_data_format_:    zarr\n",
              "    intake_esm_dataset_key:            atm.RCP85.daily</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7818d3de-1282-4356-9bda-67bccb47c669' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7818d3de-1282-4356-9bda-67bccb47c669' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 40</li><li><span class='xr-has-index'>time</span>: 34675</li><li><span class='xr-has-index'>lat</span>: 10</li><li><span class='xr-has-index'>lon</span>: 8</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bda576ed-9757-475d-83cc-55cb020d2960' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bda576ed-9757-475d-83cc-55cb020d2960' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.4 -33.46 ... -26.86 -25.92</div><input id='attrs-82aa7340-3e5f-46de-88b9-cae715db464f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82aa7340-3e5f-46de-88b9-cae715db464f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1589de86-ce0f-4710-9545-840f1fd6ffbb' class='xr-var-data-in' type='checkbox'><label for='data-1589de86-ce0f-4710-9545-840f1fd6ffbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-34.397907, -33.455498, -32.513088, -31.570681, -30.628273, -29.685863,\n",
              "       -28.743456, -27.801046, -26.858639, -25.916231])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-57.5 -56.25 -55.0 ... -50.0 -48.75</div><input id='attrs-ac853e83-a81f-4105-963d-829ba2205cd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac853e83-a81f-4105-963d-829ba2205cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b895ec-e7a2-411e-bf4c-ad79a84719cf' class='xr-var-data-in' type='checkbox'><label for='data-65b895ec-e7a2-411e-bf4c-ad79a84719cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-57.5 , -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 101 102 103 104 105</div><input id='attrs-5b271120-5c1a-46ce-b7bb-399ec0ae5feb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b271120-5c1a-46ce-b7bb-399ec0ae5feb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ef160ac-cada-4a18-8670-574c873c26bf' class='xr-var-data-in' type='checkbox'><label for='data-0ef160ac-cada-4a18-8670-574c873c26bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
              "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
              "        29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2006-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-d2a31702-f5ef-423f-94f7-f69b77872c3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2a31702-f5ef-423f-94f7-f69b77872c3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-476863e9-6f74-4616-8e9f-fbadebfe11a1' class='xr-var-data-in' type='checkbox'><label for='data-476863e9-6f74-4616-8e9f-fbadebfe11a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2006, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2006, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2006, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
              "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
              "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
              "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2006-01-01 00:00:00 ... 2101-01-...</div><input id='attrs-814c57a7-a1ca-44bc-8a2c-dfa6e73a0962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-814c57a7-a1ca-44bc-8a2c-dfa6e73a0962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-201ca97a-626d-4542-b5df-2f7726ff6c4c' class='xr-var-data-in' type='checkbox'><label for='data-201ca97a-626d-4542-b5df-2f7726ff6c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time interval endpoints</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(2006, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2006, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2006, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2006, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2006, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2006, 1, 4, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       ...,\n",
              "       [cftime.DatetimeNoLeap(2100, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2100, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2100, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2100, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
              "       [cftime.DatetimeNoLeap(2100, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
              "        cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
              "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173ee80d-6396-4e6b-a567-a380535c24b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-173ee80d-6396-4e6b-a567-a380535c24b5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PRECT</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.779e-12 8.322e-11 ... 3.297e-07</div><input id='attrs-6ecc5a05-2f2d-445a-af9a-01ee8d68b188' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ecc5a05-2f2d-445a-af9a-01ee8d68b188' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a31ebf0c-480f-422a-9013-87a2b0e0f362' class='xr-var-data-in' type='checkbox'><label for='data-a31ebf0c-480f-422a-9013-87a2b0e0f362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Total (convective and large-scale) precipitation rate (liq + ice)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 2.77887544e-12,  8.32218947e-11,  9.38779957e-11, ...,\n",
              "           2.79030510e-09,  1.31513822e-09,  1.78837226e-07],\n",
              "         [ 1.34817338e-14,  7.87985163e-11,  3.92731527e-15, ...,\n",
              "           4.43783499e-09,  7.69225217e-09,  1.54537574e-07],\n",
              "         [ 1.12974219e-17,  5.62741347e-12,  1.32422226e-10, ...,\n",
              "           1.49360402e-09,  1.79658439e-08,  1.15074265e-07],\n",
              "         ...,\n",
              "         [ 2.42115119e-20,  1.31714237e-10,  7.28892365e-17, ...,\n",
              "           4.87094916e-08,  6.48484866e-09,  1.14903487e-09],\n",
              "         [ 9.45942991e-10,  2.19627275e-10,  1.03837632e-08, ...,\n",
              "           4.41454020e-08,  8.16581025e-09,  4.58555993e-09],\n",
              "         [ 1.55250260e-16,  2.67217271e-09,  1.94275760e-08, ...,\n",
              "           4.06620549e-16,  9.82419146e-10,  4.13259542e-08]],\n",
              "\n",
              "        [[ 3.58554608e-09,  1.58294462e-08,  2.31655921e-08, ...,\n",
              "           9.17407217e-09,  5.90461866e-08,  9.61714122e-08],\n",
              "         [ 1.60627600e-09,  1.17290293e-08,  1.67650338e-08, ...,\n",
              "           9.61933999e-09,  3.99841547e-08,  7.45484030e-08],\n",
              "         [ 8.06115086e-09,  6.92807900e-09,  8.63371330e-09, ...,\n",
              "           7.03597092e-09,  2.20062173e-08,  4.36470415e-08],\n",
              "...\n",
              "           8.48917949e-08,  7.16103159e-08,  6.66157263e-08],\n",
              "         [ 1.28917401e-08,  1.39540477e-08,  2.78520442e-08, ...,\n",
              "           6.23890912e-08,  9.22939734e-08,  1.23380829e-07],\n",
              "         [ 1.39213503e-08,  2.12074305e-08,  3.26392566e-08, ...,\n",
              "           6.50998189e-08,  1.62074272e-07,  2.35087796e-07]],\n",
              "\n",
              "        [[ 9.68231273e-10,  3.52266949e-09,  5.13519760e-09, ...,\n",
              "           2.32075958e-08,  1.53497162e-08,  8.87637075e-09],\n",
              "         [ 4.07178868e-09,  1.02664508e-08,  1.56915032e-08, ...,\n",
              "           4.36713385e-08,  5.52101156e-08,  4.04835561e-08],\n",
              "         [ 2.37576736e-09,  5.37557199e-09,  9.06870490e-09, ...,\n",
              "           4.89773484e-08,  8.96898484e-08,  1.15929026e-07],\n",
              "         ...,\n",
              "         [ 2.31479920e-08,  3.93010247e-08,  5.51724604e-08, ...,\n",
              "           1.10282237e-07,  1.40717916e-07,  2.78107137e-07],\n",
              "         [ 3.36825607e-08,  4.98297545e-08,  8.65623235e-08, ...,\n",
              "           1.41842079e-07,  1.70013934e-07,  3.22827731e-07],\n",
              "         [ 4.14139052e-08,  7.10655144e-08,  1.08177403e-07, ...,\n",
              "           1.31785555e-07,  1.92278222e-07,  3.29661333e-07]]]],\n",
              "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19ec38f8-2541-4971-b14e-943c7e8c6044' class='xr-section-summary-in' type='checkbox'  ><label for='section-19ec38f8-2541-4971-b14e-943c7e8c6044' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e595855-691d-4216-be45-e7697c63a14c' class='xr-index-data-in' type='checkbox'/><label for='index-9e595855-691d-4216-be45-e7697c63a14c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-34.3979072570801, -33.4554977416992, -32.5130882263184,\n",
              "       -31.5706806182861, -30.6282730102539,  -29.685863494873,\n",
              "       -28.7434558868408,   -27.80104637146, -26.8586387634277,\n",
              "       -25.9162311553955],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-24c14e2e-a107-4625-abc4-30dfc99366ae' class='xr-index-data-in' type='checkbox'/><label for='index-24c14e2e-a107-4625-abc4-30dfc99366ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-57.5, -56.25, -55.0, -53.75, -52.5, -51.25, -50.0, -48.75], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10fa34b6-f5db-4ef2-9a0a-8e599be13265' class='xr-index-data-in' type='checkbox'/><label for='index-10fa34b6-f5db-4ef2-9a0a-8e599be13265' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
              "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
              "        29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105],\n",
              "      dtype=&#x27;int64&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95935c19-86da-4c28-8763-776dd81b3520' class='xr-index-data-in' type='checkbox'/><label for='index-95935c19-86da-4c28-8763-776dd81b3520' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2006-01-01 12:00:00, 2006-01-02 12:00:00, 2006-01-03 12:00:00,\n",
              "             2006-01-04 12:00:00, 2006-01-05 12:00:00, 2006-01-06 12:00:00,\n",
              "             2006-01-07 12:00:00, 2006-01-08 12:00:00, 2006-01-09 12:00:00,\n",
              "             2006-01-10 12:00:00,\n",
              "             ...\n",
              "             2100-12-22 12:00:00, 2100-12-23 12:00:00, 2100-12-24 12:00:00,\n",
              "             2100-12-25 12:00:00, 2100-12-26 12:00:00, 2100-12-27 12:00:00,\n",
              "             2100-12-28 12:00:00, 2100-12-29 12:00:00, 2100-12-30 12:00:00,\n",
              "             2100-12-31 12:00:00],\n",
              "            dtype=&#x27;object&#x27;, length=34675, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9895dbe1-7247-4136-91aa-e676e9ff2b3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-9895dbe1-7247-4136-91aa-e676e9ff2b3a' class='xr-section-summary' >Attributes: <span>(26)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>NCO :</span></dt><dd>4.4.2</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>host :</span></dt><dd>tcs-f02n07</dd><dt><span>important_note :</span></dt><dd>This data is part of the project &#x27;Blind Evaluation of Lossy Data-Compression in LENS&#x27;.  Please exercise caution before using this data for other purposes.</dd><dt><span>initial_file :</span></dt><dd>b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc</dd><dt><span>logname :</span></dt><dd>mudryk</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>topography_file :</span></dt><dd>/scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;PRECT&#x27;]</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>PRECT</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>total (convective and large-scale) precipitation rate (liq + ice)</dd><dt><span>intake_esm_attrs:component :</span></dt><dd>atm</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>RCP85</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>1.0</dd><dt><span>intake_esm_attrs:spatial_domain :</span></dt><dd>global</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>m/s</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>2006-01-01 12:00:00</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>2100-12-31 12:00:00</dd><dt><span>intake_esm_attrs:path :</span></dt><dd>s3://ncar-cesm-lens/atm/daily/cesmLE-RCP85-PRECT.zarr</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>atm.RCP85.daily</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# agora eu gravo todos os meus resultados em netcdf\n",
        "ds_20C.to_netcdf('ds_20_RS.nc')\n",
        "ds_hist.to_netcdf('ds_hist_RS.nc')\n",
        "ds_rcp85.to_netcdf('ds_rcp85_RS.nc')\n",
        "\n",
        "# e transfiro para minha área no google drive. Adapte aqui para sua área se desejar.\n",
        "!mv *.nc /content/drive/MyDrive/01_Pesquisa/00_PrecipRS/."
      ],
      "metadata": {
        "id": "xUCxf-8pOE5V"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A partir do momento que você tem isso gravado, as possibilidades de pesquisa são ilimitadas. Se você não gravar o resultado, terá que perder esse tempo (~2h) cada vez que tentar fazer algo aqui no google colab.\n",
        "\n",
        "# Algumas dicas adicionais\n",
        "\n",
        "Para deixar o processo um pouco mais interessante, vou produzir um mapa. Neste caso, vou fazer a média do máximo de precipitação entre todos os membros e todos os anos, <u>apenas para o mes de maio</u>, do dataset `20C`. Vou aproveitar também para converter as unidades para mm/dia."
      ],
      "metadata": {
        "id": "IEF3Ks9xtmkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# note que a unidade de medida desses campos são em m/s (fluxo de água)\n",
        "# portanto, para converter em mm/dia eu vou usar a função abaixo\n",
        "def mm_per_day(precip):\n",
        "    ''' Esta função converte a precipitação dada em m/s para mm/dia (mais condizente com o que conhecemos de meteorologia)\n",
        "        Basicamente, 1 metro = 1000 milimetros, e dia = 86400 segundos.\n",
        "    '''\n",
        "    return precip * 1000 * 86400"
      ],
      "metadata": {
        "id": "NG-mTv8dEc7G"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# construindo um campo apenas para fazer um plot\n",
        "may_median = ds_20C.sel(time=ds_20C['time.month'] == 5).PRECTMX.max(dim=['time','member_id']).pipe(mm_per_day)\n",
        "may_median.plot() # caso você queira ver como ficou"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "MtCbp9t7DgkW",
        "outputId": "67321869-925d-4387-d358-3d8a01871f29"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7a43ebd1cd30>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CAUTELA**: Este é um plot simples, mostrando apenas <u>o máximo</u> em cada ponto de grade. Aqui vou plotar um mapa decente, com escala de cores, que corresponde a precipitação máxima, entre todos os membros e todos os anos, para o mes de maio, entre 1920 e 2005 (dataset 20C) do CESM-LENS. Note que os valores não tem uma distribuição contínua, justamente porque este é um pseudo-*heatmap* e não uma medida essencialmente estatística."
      ],
      "metadata": {
        "id": "GQlbZPgrFKRi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazendo um plot \"decente\"\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cartopy.crs as ccrs\n",
        "from cartopy.feature import NaturalEarthFeature, LAND, COASTLINE\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "ax = plt.axes(projection=ccrs.PlateCarree())\n",
        "\n",
        "plot = may_median.plot(ax=ax,\n",
        "                transform=ccrs.PlateCarree(), x='lon', y='lat',\n",
        "                cmap='PuRd',add_colorbar=False)\n",
        "# título do plot\n",
        "plt.title('Precipitação máxima (heatmap) para qualquer dia do mes de Maio 1920-2005 \\n Dados do CESM-LENS 20th Century')\n",
        "cbar = plt.colorbar(plot, ax=ax, orientation='vertical')\n",
        "cbar.set_label('Mediana de Precipitação (mm/dia)')\n",
        "\n",
        "states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
        "                             name='admin_1_states_provinces_lines')\n",
        "contries = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
        "                             name='admin_0_countries')\n",
        "\n",
        "ax.add_feature(states, facecolor='none', edgecolor='black')\n",
        "ax.add_feature(contries, facecolor='none', edgecolor='black')\n",
        "ax.gridlines(draw_labels=True,crs=ccrs.PlateCarree(),x_inline=False,y_inline=False,color = \"None\")\n",
        "\n",
        "# aqui plota o shapefile das UGRHs do sul.\n",
        "sul_ugrh.geometry.plot(ax=ax,facecolor='none',edgecolor='blue',linewidth=2,label='Limite das UGRHs')\n",
        "\n",
        "ax.coastlines()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "X8auXMFODmu0",
        "outputId": "b0def350-fbae-424d-a606-77faf9f7727d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_1_states_provinces_lines.zip\n",
            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_countries.zip\n",
            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip\n",
            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este é um excelente exemplo de como trabalhar com dados grandes na nuvem. O tempo total para rodar este notebook é basicamente condicionado pelo tempo dos `.load()`s dos datasets (aqui: >2h).\n",
        "\n",
        "Boa sorte !\n",
        "\n",
        "**PS**: Se você gostou, você pode [comprar um café para mim !](https://www.buymeacoffee.com/andrebelem)😉\n"
      ],
      "metadata": {
        "id": "txgKbjM_wPmg"
      }
    }
  ]
}