{ "cells": [ { "cell_type": "markdown", "id": "md0", "metadata": {}, "source": [ "# AtmosPair \u2014 paired ERA5 / WRF downscaling walkthrough\n", "\n", "A hands-on guide for loading the **AtmosPair** dataset: paired ERA5 (input) and WRF (target) atmospheric fields over the U.S. Mid-Atlantic for **2007\u20132015**, designed for supervised downscaling.\n", "\n", "| | |\n", "| --- | --- |\n", "| **Dataset** | [`era5wrf/atmospair`](https://huggingface.co/datasets/era5wrf/atmospair) on Hugging Face |\n", "| **License** | CC-BY-4.0 |\n", "| **Format** | Zarr (consolidated) |\n", "| **Shape** | 26,296 timesteps \u00b7 68 ERA5 + 49 WRF channels \u00b7 112 \u00d7 112 grid |\n", "\n", "---\n", "\n", "**What this notebook covers**\n", "\n", "1. Open the Zarr store with xarray and inspect its layout\n", "2. Read the ERA5 / WRF **variable manifests** (channel index, variable name, pressure level, center, scale)\n", "3. Build **train** and **validation** `Dataset` / `DataLoader` objects for supervised downscaling\n", "4. Verify the **normalization round-trip** (`normalize_*` \u2194 `denormalize_*`)\n", "5. Visualise a **paired sample** interactively with channel dropdowns and a timestep slider" ] }, { "cell_type": "markdown", "id": "md1", "metadata": {}, "source": [ "## Where to get the data\n\nThree ways to point the notebook at a data source. Pick one and assign to `DATA` further down.\n\n| Option | Size | How to get it |\n| --- | --- | --- |\n| **Stream the sample from HF** (no download) | ~1 GB | `DATA = \"hf://datasets/era5wrf/atmospair/atmospair_sample.zarr\"` |\n| **Download the sample** (July 2015) | ~1 GB | `huggingface-cli download era5wrf/atmospair atmospair_sample.zarr --repo-type dataset --local-dir ./atmospair_data` |\n| **Download the full corpus** (tar archive) | ~95 GB | `huggingface-cli download era5wrf/atmospair atmospair.zarr.tar --repo-type dataset --local-dir ./atmospair_data` |\n\nAfter downloading the full corpus, untar it once locally:\n\n```bash\ntar -xf ./atmospair_data/atmospair.zarr.tar -C ./atmospair_data\n```\n\nThen point `DATA` at the extracted folder:\n\n```python\nDATA = Path(\"./atmospair_data/atmospair.zarr\")\n```\n\nThe reference `Dataset` class lives in the same HF repo as `example_dataset.py`. To grab it programmatically:\n\n```python\nfrom huggingface_hub import hf_hub_download\nhf_hub_download(\"era5wrf/atmospair\", \"example_dataset.py\", repo_type=\"dataset\")\n```\n\n> **Tip.** Start with the sample subset to verify the pipeline end-to-end, then download and untar the full corpus for training." ] }, { "cell_type": "markdown", "id": "md_intro_deps", "metadata": {}, "source": [ "## Dependencies\n", "\n", "`xarray` \u00b7 `zarr<3` \u00b7 `numpy` \u00b7 `pandas` \u00b7 `matplotlib` \u00b7 `torch` \u00b7 `ipywidgets` \u00b7 `huggingface_hub`\n", "\n", "Uncomment the install line below if running in a fresh environment." ] }, { "cell_type": "code", "execution_count": 1, "id": "cd2", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:16.680076Z", "iopub.status.busy": "2026-05-04T22:45:16.679837Z", "iopub.status.idle": "2026-05-04T22:45:16.684217Z", "shell.execute_reply": "2026-05-04T22:45:16.683432Z" } }, "outputs": [], "source": [ "# !pip install --quiet xarray 'zarr<3' numpy pandas matplotlib torch ipywidgets huggingface_hub" ] }, { "cell_type": "code", "execution_count": 2, "id": "cd3", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:16.686034Z", "iopub.status.busy": "2026-05-04T22:45:16.685887Z", "iopub.status.idle": "2026-05-04T22:45:18.238726Z", "shell.execute_reply": "2026-05-04T22:45:18.238374Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "import torch\n", "from torch.utils.data import DataLoader\n", "from ipywidgets import interact, Dropdown, IntSlider" ] }, { "cell_type": "markdown", "id": "md4", "metadata": {}, "source": [ "## Locate the dataset\n", "\n", "Three options. Pick one and assign to `DATA`." ] }, { "cell_type": "code", "execution_count": 3, "id": "cd5", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:18.240322Z", "iopub.status.busy": "2026-05-04T22:45:18.240184Z", "iopub.status.idle": "2026-05-04T22:45:18.243496Z", "shell.execute_reply": "2026-05-04T22:45:18.243147Z" } }, "outputs": [ { "data": { "text/plain": [ "PosixPath('atmospair_sample.zarr')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# (a) local sample subset\n", "DATA = Path(\"atmospair_sample.zarr\")\n", "\n", "# (b) local full store\n", "# DATA = Path(\"atmospair.zarr\")\n", "\n", "# (c) stream directly from Hugging Face\n", "# DATA = \"hf://datasets/era5wrf/atmospair/atmospair_sample.zarr\"\n", "\n", "DATA" ] }, { "cell_type": "markdown", "id": "md6", "metadata": {}, "source": [ "## Open and inspect" ] }, { "cell_type": "code", "execution_count": 4, "id": "cd7", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:18.244620Z", "iopub.status.busy": "2026-05-04T22:45:18.244551Z", "iopub.status.idle": "2026-05-04T22:45:18.769562Z", "shell.execute_reply": "2026-05-04T22:45:18.769032Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 1GB\n",
       "Dimensions:        (time: 248, era5_channel: 68, south_north: 112,\n",
       "                    west_east: 112, wrf_channel: 45, west_east_stag: 112,\n",
       "                    south_north_stag: 112)\n",
       "Coordinates: (12/13)\n",
       "  * time           (time) datetime64[ns] 2kB 2015-07-01 ... 2015-07-31T21:00:00\n",
       "  * era5_channel   (era5_channel) int64 544B 0 1 2 3 4 5 6 ... 62 63 64 65 66 67\n",
       "    era5_pressure  (era5_channel) float64 544B dask.array<chunksize=(68,), meta=np.ndarray>\n",
       "    era5_variable  (era5_channel) <U26 7kB dask.array<chunksize=(68,), meta=np.ndarray>\n",
       "    XLAT           (south_north, west_east) float32 50kB dask.array<chunksize=(112, 112), meta=np.ndarray>\n",
       "    XLAT_V         (south_north_stag, west_east) float32 50kB dask.array<chunksize=(112, 112), meta=np.ndarray>\n",
       "    ...             ...\n",
       "    XLONG_V        (south_north_stag, west_east) float32 50kB dask.array<chunksize=(112, 112), meta=np.ndarray>\n",
       "  * wrf_channel    (wrf_channel) int64 360B 0 1 2 3 4 5 6 ... 39 40 41 42 43 44\n",
       "    wrf_pressure   (wrf_channel) float64 360B dask.array<chunksize=(24,), meta=np.ndarray>\n",
       "    wrf_variable   (wrf_channel) <U26 5kB dask.array<chunksize=(24,), meta=np.ndarray>\n",
       "    XLAT_U         (south_north, west_east_stag) float32 50kB dask.array<chunksize=(112, 112), meta=np.ndarray>\n",
       "    XLONG_U        (south_north, west_east_stag) float32 50kB dask.array<chunksize=(112, 112), meta=np.ndarray>\n",
       "Dimensions without coordinates: south_north, west_east, west_east_stag,\n",
       "                                south_north_stag\n",
       "Data variables:\n",
       "    era5           (time, era5_channel, south_north, west_east) float32 846MB dask.array<chunksize=(1, 68, 112, 112), meta=np.ndarray>\n",
       "    era5_center    (era5_channel) float32 272B dask.array<chunksize=(68,), meta=np.ndarray>\n",
       "    era5_scale     (era5_channel) float32 272B dask.array<chunksize=(68,), meta=np.ndarray>\n",
       "    era5_valid     (time, era5_channel) bool 17kB dask.array<chunksize=(1, 68), meta=np.ndarray>\n",
       "    wrf            (time, wrf_channel, south_north, west_east) float32 560MB dask.array<chunksize=(1, 45, 56, 56), meta=np.ndarray>\n",
       "    wrf_center     (wrf_channel) float32 180B dask.array<chunksize=(24,), meta=np.ndarray>\n",
       "    wrf_scale      (wrf_channel) float32 180B dask.array<chunksize=(24,), meta=np.ndarray>\n",
       "    wrf_valid      (time) bool 248B dask.array<chunksize=(1,), meta=np.ndarray>
" ], "text/plain": [ " Size: 1GB\n", "Dimensions: (time: 248, era5_channel: 68, south_north: 112,\n", " west_east: 112, wrf_channel: 45, west_east_stag: 112,\n", " south_north_stag: 112)\n", "Coordinates: (12/13)\n", " * time (time) datetime64[ns] 2kB 2015-07-01 ... 2015-07-31T21:00:00\n", " * era5_channel (era5_channel) int64 544B 0 1 2 3 4 5 6 ... 62 63 64 65 66 67\n", " era5_pressure (era5_channel) float64 544B dask.array\n", " era5_variable (era5_channel) \n", " XLAT (south_north, west_east) float32 50kB dask.array\n", " XLAT_V (south_north_stag, west_east) float32 50kB dask.array\n", " ... ...\n", " XLONG_V (south_north_stag, west_east) float32 50kB dask.array\n", " * wrf_channel (wrf_channel) int64 360B 0 1 2 3 4 5 6 ... 39 40 41 42 43 44\n", " wrf_pressure (wrf_channel) float64 360B dask.array\n", " wrf_variable (wrf_channel) \n", " XLAT_U (south_north, west_east_stag) float32 50kB dask.array\n", " XLONG_U (south_north, west_east_stag) float32 50kB dask.array\n", "Dimensions without coordinates: south_north, west_east, west_east_stag,\n", " south_north_stag\n", "Data variables:\n", " era5 (time, era5_channel, south_north, west_east) float32 846MB dask.array\n", " era5_center (era5_channel) float32 272B dask.array\n", " era5_scale (era5_channel) float32 272B dask.array\n", " era5_valid (time, era5_channel) bool 17kB dask.array\n", " wrf (time, wrf_channel, south_north, west_east) float32 560MB dask.array\n", " wrf_center (wrf_channel) float32 180B dask.array\n", " wrf_scale (wrf_channel) float32 180B dask.array\n", " wrf_valid (time) bool 248B dask.array" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_zarr(DATA, consolidated=True)\n", "ds" ] }, { "cell_type": "markdown", "id": "md8", "metadata": {}, "source": [ "## Variable manifest\n", "\n", "Each ERA5 / WRF channel carries a name, a vertical-level identifier (NaN means a surface variable), and the bundled normalization parameters (`center` and `scale`) used by the reference dataloader." ] }, { "cell_type": "code", "execution_count": 5, "id": "cd9", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:18.770970Z", "iopub.status.busy": "2026-05-04T22:45:18.770782Z", "iopub.status.idle": "2026-05-04T22:45:18.886633Z", "shell.execute_reply": "2026-05-04T22:45:18.886239Z" } }, "outputs": [ { "data": { "text/html": [ "
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channel_indexvariablepressure_levelcenterscale
00t0.0287.1305249.599604
11t1.0283.5161749.576634
22t2.0280.1069038.702713
33t3.0273.1340337.102574
44t4.0266.6559456.621786
..................
6363z_pl11.0160372.3750002079.970459
6464z_pl12.0202335.7343752018.924561
6565t2mNaN286.9804089.648900
6666u10NaN0.9428922.931440
6767v10NaN0.0205023.360456
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68 rows \u00d7 5 columns

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" ], "text/plain": [ " channel_index variable pressure_level center scale\n", "0 0 t 0.0 287.130524 9.599604\n", "1 1 t 1.0 283.516174 9.576634\n", "2 2 t 2.0 280.106903 8.702713\n", "3 3 t 3.0 273.134033 7.102574\n", "4 4 t 4.0 266.655945 6.621786\n", ".. ... ... ... ... ...\n", "63 63 z_pl 11.0 160372.375000 2079.970459\n", "64 64 z_pl 12.0 202335.734375 2018.924561\n", "65 65 t2m NaN 286.980408 9.648900\n", "66 66 u10 NaN 0.942892 2.931440\n", "67 67 v10 NaN 0.020502 3.360456\n", "\n", "[68 rows x 5 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "era5_manifest = pd.DataFrame({\n", " \"channel_index\": np.arange(ds.sizes[\"era5_channel\"]),\n", " \"variable\": ds.era5_variable.values.astype(str),\n", " \"pressure_level\": ds.era5_pressure.values,\n", " \"center\": ds.era5_center.values,\n", " \"scale\": ds.era5_scale.values,\n", "})\n", "era5_manifest" ] }, { "cell_type": "code", "execution_count": 6, "id": "cd10", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:18.887951Z", "iopub.status.busy": "2026-05-04T22:45:18.887858Z", "iopub.status.idle": "2026-05-04T22:45:18.898448Z", "shell.execute_reply": "2026-05-04T22:45:18.898150Z" } }, "outputs": [ { "data": { "text/html": [ "
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channel_indexvariablepressure_levelcenterscale
00T2NaN284.99585010.891022
11U10NaN0.6777273.221539
22V10NaN-0.0493913.498643
33U0.00.8093923.776051
44U1.01.1771824.810004
55U2.01.4701205.548637
66U3.01.7234516.060884
77U4.01.9602896.417025
88U5.02.2024346.667762
99U6.02.4833636.844941
1010V0.00.0058484.069026
1111V1.00.1636015.336325
1212V2.00.2735646.256277
1313V3.00.3511906.852526
1414V4.00.4148017.201840
1515V5.00.4832267.382185
1616V6.00.5741237.449575
1717TK0.0285.24591110.731544
1818TK1.0285.32925410.574802
1919TK2.0285.14340210.547438
2020TK3.0284.81057710.556077
2121TK4.0284.36148110.563418
2222TK5.0283.83294710.551333
2323TK6.0283.20983910.504847
2424QVAPOR0.00.0077220.005057
2525QVAPOR1.00.0074870.004966
2626QVAPOR2.00.0073140.004876
2727QVAPOR3.00.0071570.004781
2828QVAPOR4.00.0070110.004686
2929QVAPOR5.00.0068670.004592
3030QVAPOR6.00.0067150.004496
3131Z0.01567.9675291836.498413
3232Z1.02104.7299801833.161377
3333Z2.02739.8283691829.536011
3434Z3.03472.8693851825.880005
3535Z4.04303.5019531822.423218
3636Z5.05231.9858401819.470947
3737Z6.06306.7202151817.239746
3838WPD0.0172.018723362.259369
3939WPD1.0324.692261488.129272
4040WPD2.0505.466492657.061035
4141WPD3.0670.813538875.464722
4242WPD4.0800.6616821095.526367
4343WPD5.0895.9377441293.620117
4444WPD6.0964.7072141466.284546
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" ], "text/plain": [ " channel_index variable pressure_level center scale\n", "0 0 T2 NaN 284.995850 10.891022\n", "1 1 U10 NaN 0.677727 3.221539\n", "2 2 V10 NaN -0.049391 3.498643\n", "3 3 U 0.0 0.809392 3.776051\n", "4 4 U 1.0 1.177182 4.810004\n", "5 5 U 2.0 1.470120 5.548637\n", "6 6 U 3.0 1.723451 6.060884\n", "7 7 U 4.0 1.960289 6.417025\n", "8 8 U 5.0 2.202434 6.667762\n", "9 9 U 6.0 2.483363 6.844941\n", "10 10 V 0.0 0.005848 4.069026\n", "11 11 V 1.0 0.163601 5.336325\n", "12 12 V 2.0 0.273564 6.256277\n", "13 13 V 3.0 0.351190 6.852526\n", "14 14 V 4.0 0.414801 7.201840\n", "15 15 V 5.0 0.483226 7.382185\n", "16 16 V 6.0 0.574123 7.449575\n", "17 17 TK 0.0 285.245911 10.731544\n", "18 18 TK 1.0 285.329254 10.574802\n", "19 19 TK 2.0 285.143402 10.547438\n", "20 20 TK 3.0 284.810577 10.556077\n", "21 21 TK 4.0 284.361481 10.563418\n", "22 22 TK 5.0 283.832947 10.551333\n", "23 23 TK 6.0 283.209839 10.504847\n", "24 24 QVAPOR 0.0 0.007722 0.005057\n", "25 25 QVAPOR 1.0 0.007487 0.004966\n", "26 26 QVAPOR 2.0 0.007314 0.004876\n", "27 27 QVAPOR 3.0 0.007157 0.004781\n", "28 28 QVAPOR 4.0 0.007011 0.004686\n", "29 29 QVAPOR 5.0 0.006867 0.004592\n", "30 30 QVAPOR 6.0 0.006715 0.004496\n", "31 31 Z 0.0 1567.967529 1836.498413\n", "32 32 Z 1.0 2104.729980 1833.161377\n", "33 33 Z 2.0 2739.828369 1829.536011\n", "34 34 Z 3.0 3472.869385 1825.880005\n", "35 35 Z 4.0 4303.501953 1822.423218\n", "36 36 Z 5.0 5231.985840 1819.470947\n", "37 37 Z 6.0 6306.720215 1817.239746\n", "38 38 WPD 0.0 172.018723 362.259369\n", "39 39 WPD 1.0 324.692261 488.129272\n", "40 40 WPD 2.0 505.466492 657.061035\n", "41 41 WPD 3.0 670.813538 875.464722\n", "42 42 WPD 4.0 800.661682 1095.526367\n", "43 43 WPD 5.0 895.937744 1293.620117\n", "44 44 WPD 6.0 964.707214 1466.284546" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wrf_manifest = pd.DataFrame({\n", " \"channel_index\": np.arange(ds.sizes[\"wrf_channel\"]),\n", " \"variable\": ds.wrf_variable.values.astype(str),\n", " \"pressure_level\": ds.wrf_pressure.values,\n", " \"center\": ds.wrf_center.values,\n", " \"scale\": ds.wrf_scale.values,\n", "})\n", "wrf_manifest" ] }, { "cell_type": "markdown", "id": "md11", "metadata": {}, "source": [ "## PyTorch `Dataset` and `DataLoader`\n", "\n", "`PairedDownscalingDataset` filters timesteps by year and applies the bundled validity masks. A timestep is included only when **both sides are valid**: `wrf_valid` is True and every ERA5 channel's `era5_valid` is True for that timestep.\n", "\n", "For the **full corpus** (`atmospair.zarr`), the recommended split is 2007\u20132013 for training and 2014\u20132015 held out for evaluation. The **sample subset** (`atmospair_sample.zarr`) only contains July 2015, so both datasets below use the same year \u2014 illustrative of the API only." ] }, { "cell_type": "code", "execution_count": 7, "id": "cd12", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:18.899642Z", "iopub.status.busy": "2026-05-04T22:45:18.899573Z", "iopub.status.idle": "2026-05-04T22:45:19.085623Z", "shell.execute_reply": "2026-05-04T22:45:19.085130Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PairedDownscalingDataset(n_samples=248, era5_channels=68, wrf_channels=45, normalize=True)\n", "PairedDownscalingDataset(n_samples=248, era5_channels=68, wrf_channels=45, normalize=True)\n" ] } ], "source": [ "from example_dataset import PairedDownscalingDataset\n", "\n", "# Full corpus (uncomment when DATA points at atmospair.zarr):\n", "# train_ds = PairedDownscalingDataset(DATA, years=range(2007, 2014))\n", "# val_ds = PairedDownscalingDataset(DATA, years=[2014, 2015])\n", "\n", "# Sample subset (July 2015 only):\n", "train_ds = PairedDownscalingDataset(DATA, years=[2015])\n", "val_ds = PairedDownscalingDataset(DATA, years=[2015])\n", "\n", "print(train_ds)\n", "print(val_ds)" ] }, { "cell_type": "markdown", "id": "md13", "metadata": {}, "source": [ "## Iterating over batches" ] }, { "cell_type": "code", "execution_count": 8, "id": "cd14", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:19.087007Z", "iopub.status.busy": "2026-05-04T22:45:19.086919Z", "iopub.status.idle": "2026-05-04T22:45:19.133112Z", "shell.execute_reply": "2026-05-04T22:45:19.132651Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "era5 batch: (4, 68, 112, 112)\n", "wrf batch: (4, 45, 112, 112)\n", "timestamps: [np.str_('2015-07-18T00'), np.str_('2015-07-06T06'), np.str_('2015-07-17T21'), np.str_('2015-07-30T03')]\n" ] } ], "source": [ "train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=0)\n", "val_loader = DataLoader(val_ds, batch_size=4, shuffle=False, num_workers=0)\n", "\n", "batch = next(iter(train_loader))\n", "print(f\"era5 batch: {tuple(batch['era5'].shape)}\")\n", "print(f\"wrf batch: {tuple(batch['wrf'].shape)}\")\n", "print(f\"timestamps: {list(batch['time'])}\")" ] }, { "cell_type": "markdown", "id": "md15", "metadata": {}, "source": [ "## Normalization round-trip\n", "\n", "Pull a raw (unnormalized) WRF sample, apply `normalize_wrf`, then `denormalize_wrf`, and verify the data is preserved. The per-channel statistics demonstrate what each step does." ] }, { "cell_type": "code", "execution_count": 9, "id": "cd16", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:19.134389Z", "iopub.status.busy": "2026-05-04T22:45:19.134299Z", "iopub.status.idle": "2026-05-04T22:45:19.244787Z", "shell.execute_reply": "2026-05-04T22:45:19.244309Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WRF channel 0 (T2, pressure nan)\n", " raw | mean: 294.565 std: 1.697\n", " normalized | mean: 0.879 std: 0.156\n", " denormalized| mean: 294.565 std: 1.697\n", "\n", "Round-trip max abs error: 9.766e-04\n" ] } ], "source": [ "raw_ds = PairedDownscalingDataset(DATA, years=[2015], normalize=False)\n", "wrf_raw = raw_ds[0][\"wrf\"] # physical units\n", "wrf_norm = train_ds.normalize_wrf(wrf_raw) # apply per-channel center/scale\n", "wrf_back = train_ds.denormalize_wrf(wrf_norm) # invert the transform\n", "\n", "ch = 0 # WRF channel to inspect\n", "print(f\"WRF channel {ch} ({wrf_manifest.loc[ch, 'variable']}, pressure {wrf_manifest.loc[ch, 'pressure_level']})\")\n", "print(f\" raw | mean: {wrf_raw[ch].mean():>10.3f} std: {wrf_raw[ch].std():>8.3f}\")\n", "print(f\" normalized | mean: {wrf_norm[ch].mean():>10.3f} std: {wrf_norm[ch].std():>8.3f}\")\n", "print(f\" denormalized| mean: {wrf_back[ch].mean():>10.3f} std: {wrf_back[ch].std():>8.3f}\")\n", "print()\n", "print(f\"Round-trip max abs error: {(wrf_raw - wrf_back).abs().max():.3e}\")" ] }, { "cell_type": "markdown", "id": "md17", "metadata": {}, "source": [ "## Visualize a paired sample (interactive)\n", "\n", "Pick a timestep with the slider and any ERA5 / WRF channel from the dropdowns. The figure title shows the actual UTC timestamp." ] }, { "cell_type": "code", "execution_count": 10, "id": "cd18", "metadata": { "execution": { "iopub.execute_input": "2026-05-04T22:45:19.246007Z", "iopub.status.busy": "2026-05-04T22:45:19.245907Z", "iopub.status.idle": "2026-05-04T22:45:19.415470Z", "shell.execute_reply": "2026-05-04T22:45:19.414477Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b80caeff1404048a50f61317eeac15f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=191, description='time idx', max=247), Dropdown(description='ERA5', opti\u2026" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_time = ds.sizes[\"time\"]\n", "\n", "def _label(row):\n", " lvl = row['pressure_level']\n", " return f\"{int(row['channel_index']):2d}: {row['variable']} ({'sfc' if np.isnan(lvl) else f'lvl {int(lvl)}'})\"\n", "\n", "era5_options = [(_label(r), int(r['channel_index'])) for _, r in era5_manifest.iterrows()]\n", "wrf_options = [(_label(r), int(r['channel_index'])) for _, r in wrf_manifest.iterrows()]\n", "\n", "@interact(\n", " time_idx=IntSlider(min=0, max=n_time-1, value=int(np.random.randint(n_time)), description=\"time idx\"),\n", " era5_channel=Dropdown(options=era5_options, value=0, description=\"ERA5\"),\n", " wrf_channel=Dropdown(options=wrf_options, value=0, description=\"WRF\"),\n", ")\n", "def plot_pair(time_idx, era5_channel, wrf_channel):\n", " era5_field = ds.era5.isel(time=time_idx, era5_channel=era5_channel).values\n", " wrf_field = ds.wrf.isel( time=time_idx, wrf_channel=wrf_channel).values\n", " timestamp = np.datetime_as_string(ds.time.values[time_idx], unit=\"h\")\n", "\n", " e_var = era5_manifest.loc[era5_channel, \"variable\"]\n", " e_lvl = era5_manifest.loc[era5_channel, \"pressure_level\"]\n", " w_var = wrf_manifest.loc[wrf_channel, \"variable\"]\n", " w_lvl = wrf_manifest.loc[wrf_channel, \"pressure_level\"]\n", "\n", " e_label = f\"ERA5: {e_var} (level {int(e_lvl)})\" if not np.isnan(e_lvl) else f\"ERA5: {e_var} (surface)\"\n", " w_label = f\"WRF: {w_var} (level {int(w_lvl)})\" if not np.isnan(w_lvl) else f\"WRF: {w_var} (surface)\"\n", "\n", " fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))\n", " im0 = axes[0].imshow(era5_field, origin=\"lower\", cmap=\"RdYlBu_r\")\n", " axes[0].set_title(e_label)\n", " plt.colorbar(im0, ax=axes[0])\n", " im1 = axes[1].imshow(wrf_field, origin=\"lower\", cmap=\"RdYlBu_r\")\n", " axes[1].set_title(w_label)\n", " plt.colorbar(im1, ax=axes[1])\n", " fig.suptitle(f\"Paired sample at {timestamp} UTC\")\n", " plt.tight_layout()\n", " plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "corrdiff_zarr", "language": "python", "name": "corrdiff_zarr" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 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