diff --git a/science/.gitignore b/science/.gitignore
index 51a4444..a92d827 100644
--- a/science/.gitignore
+++ b/science/.gitignore
@@ -149,3 +149,6 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+session_store.db
+.viz
diff --git a/science/README.md b/science/README.md
index 3c4ab0d..9c7f277 100644
--- a/science/README.md
+++ b/science/README.md
@@ -10,15 +10,20 @@ Take a look at the [Kedro documentation](https://docs.kedro.org) to get started.
 
 The project contains one pipeline for now: `globe`
 
-### `lowvshigh`
+### `global`
 
-Pipeline to generate the comparisson between low and high resolution simulations. Currently it has:
+Generates global videos of wind and so
 
-- splits nextgems global datasets into a set of tiffs (one per timestep) to use in blender to render a rotating globe.
-- video generation pipeline for a regions defined in `conf/parameters.yml`
 
+### `scenarios`
 
-## How to install dependencies
+Scenario comparison, image and videos
+
+### `zooms`
+
+Zoomed in videos for the globe visualization
+
+##  Dependencies
 
 Declare any dependencies in `requirements.txt` for `pip` installation.
 
@@ -35,19 +40,14 @@ You can run your Kedro project with:
 ```
 kedro run
 ```
-I recomend use the `ParallelRunner` to run the nodes in parallel 
-
-```
-kedro run --runner=ParallelRunner
-```
 
-### Run a subset of the pipeline
+### Run a single pipeline
 
 Kedro allows run subsets by selecting only nodes, pipelines or tags. Check the tags in the pipeline code or in kedro viz.
 For example to run only the detailed videos pipelines use
 
 ```
-kedro run --runner=ParallelRunner --tags zoomin
+kedro run --pipeline zoomms --tags precipitation
 ```
 
 
@@ -67,4 +67,4 @@ In order to get the best out of the template:
 * Don't remove any lines from the `.gitignore` file we provide
 * Make sure your results can be reproduced by following a [data engineering convention](https://docs.kedro.org/en/stable/faq/faq.html#what-is-data-engineering-convention)
 * Don't commit data to your repository
-* Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/`
\ No newline at end of file
+* Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/`
diff --git a/science/conf/base/catalog.yml b/science/conf/base/catalog.yml
index 6b3f8fe..72fc7ec 100644
--- a/science/conf/base/catalog.yml
+++ b/science/conf/base/catalog.yml
@@ -1,3 +1,7 @@
+test_video:
+  type: video.VideoDataset
+  filepath: data/03_primary/test.mp4
+
 # ------------- WINDSPEED --------------
 
 wind_speed_global_100km.raw:
@@ -8,33 +12,15 @@ wind_speed_global_10km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
   filepath: data/01_raw/nextgems/ws_global_10km.nc
 
-wind_speed_global_100km.parts:
-  type: partitions.PartitionedDataset
-  path: data/03_primary/ws-100-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
-wind_speed_global_10km.parts:
-  type: partitions.PartitionedDataset
-  path: data/03_primary/ws-10-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
 wind_speed_global_10km.video:
   type: video.VideoDataset
   filepath: data/03_primary/wind_speed_global_10km.mp4
-  fourcc: "h264"
+  fourcc: avc1
 
 wind_speed_global_100km.video:
   type: video.VideoDataset
   filepath: data/03_primary/wind_speed_global_100km.mp4
-  fourcc: "h264"
+  fourcc: avc1
 
 
 # -------------- CLOUD COVER --------------
@@ -48,36 +34,26 @@ cloud_cover_100km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
   filepath: data/01_raw/nextgems/lcc_global_100km.nc
 
-
-cloud_cover_10km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/amazonia-10-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
-cloud_cover_100km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/amazonia-100-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
 cloud_cover_10km.video:
   type: video.VideoDataset
   filepath: data/03_primary/cloud_cover_10km.mp4
+  fourcc: avc1
 
 cloud_cover_100km.video:
   type: video.VideoDataset
-  filepath: data/03_primary/cloud_cover_100km.mp4
+  filepath: data/03_primary/cloud_cover_global_100km.mp4
+  fourcc: avc1
 
 
 # -------------- PRECIPITATION --------------
 
+total_precipitation_10km.mapbox_credentials:
+  type: science.datasets.credentials.CredentialsDataset
+  credentials: mapbox
+
+total_precipitation_10km.basemap:
+  type: kedro_datasets.pillow.ImageDataset
+  filepath: data/01_raw/amazonia_basemap.png
 
 total_precipitation_10km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
@@ -87,55 +63,36 @@ total_precipitation_100km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
   filepath: data/01_raw/nextgems/tp_global_100km.nc
 
-total_precipitation_10km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/hurricane-10-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
-total_precipitation_100km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/hurricane-100-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
-
 total_precipitation_10km.video:
   type: video.VideoDataset
-  filepath: data/03_primary/tp_global_10km.mp4
+  filepath: data/03_primary/tp_amazonia_10km.mp4
+  fourcc: avc1
 
 total_precipitation_100km.video:
   type: video.VideoDataset
   filepath: data/03_primary/tp_global_100km.mp4
-
+  fourcc: avc1
 
 # -------------- TEMP --------------
 
 
-temp_10km.raw:
+temperature_10km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
   filepath: data/01_raw/nextgems/tas_global_10km.nc
 
-temp_10km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/tas-10-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
-
+temperature_100km.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/nextgems/tas_global_100km.nc
 
-temp_10km.video:
+temperature_10km.video:
   type: video.VideoDataset
   filepath: data/03_primary/tas_10km.mp4
+  fourcc: avc1
 
+temperature_100km.video:
+  type: video.VideoDataset
+  filepath: data/03_primary/tas_global_100km.mp4
+  fourcc: avc1
 
 # -------------- SEA SURFACE TEMP --------------
 
@@ -144,60 +101,84 @@ sst_10km.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
   filepath: data/01_raw/nextgems/sst_global_10km.nc
 
-sst_10km.parts:
-  type: partitions.PartitionedDataset
-  path: data/02_intermediate/sst-10-parts
-  dataset: 
-    type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-    save_args:
-      compress: zstd
-  filename_suffix: ".tif"
+
+sst_100km.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/nextgems/sst_global_100km.nc
 
 
 sst_10km.video:
   type: video.VideoDataset
   filepath: data/03_primary/sst_10km.mp4
+  fourcc: avc1
+
 
+sst_100km.video:
+  type: video.VideoDataset
+  filepath: data/03_primary/sst_global_100km.mp4
+  fourcc: avc1
 
 # ============= SCENARIOS ==============
 
+europe_plus2k.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/destine/2K/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_europe.nc
+
+europe_plus2k.image:
+  type: kedro_datasets.pillow.ImageDataset
+  filepath: data/03_primary/europe_plus_2k_scenario.png
 
-plus2k.raw:
+europe_hist.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
-  filepath: data/01_raw/destine/IFS_FESOM_storyline/latlon_new/2K/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_europe.nc
+  filepath: data/01_raw/destine/hist/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_europe.nc
 
-# plus2k.parts:
-#   type: partitions.PartitionedDataset
-#   path: data/02_intermediate/plus2k
-#   dataset: 
-#     type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-#     save_args:
-#       compress: zstd
-#   filename_suffix: ".tif"
 
-plus2k.video:
-  type: video.VideoDataset
-  filepath: data/03_primary/plus_2k_scenario.mp4
+europe_hist.image:
+  type: pillow.ImageDataset
+  filepath: data/03_primary/europe_hist_scenario.png
 
 
-hist.raw:
+iberia_plus2k.raw:
   type: kedro_datasets_experimental.netcdf.NetCDFDataset
-  filepath: data/01_raw/destine/IFS_FESOM_storyline/latlon_new/hist/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_europe.nc
+  filepath: data/01_raw/destine/2K/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_iberia.nc
 
-# hist.parts:
-#   type: partitions.PartitionedDataset
-#   path: data/02_intermediate/hist
-#   dataset: 
-#     type: kedro_datasets_experimental.rioxarray.GeoTIFFDataset
-#     save_args:
-#       compress: zstd
-#   filename_suffix: ".tif"
+iberia_plus2k.image:
+  type: pillow.ImageDataset
+  filepath: data/03_primary/iberia_plus_2k_scenario.png
 
+iberia_hist.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/destine/hist/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_iberia.nc
+
+iberia_hist.image:
+  type: pillow.ImageDataset
+  filepath: data/03_primary/iberia_hist_scenario.png
+
+observations.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/destine/2K/2018_07_29_T00_00_to_2018_08_09_T23_00_2t_europe.nc
 
-hist.video:
+observations.video:
   type: video.VideoDataset
-  filepath: data/03_primary/hist_scenario.mp4
+  filepath: data/03_primary/observations.mp4
+  fourcc: avc1
+
+# ============= CAPACITY FACTOR ==============
+
+capacity_factor_100.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/nextgems/cf_global_100km.nc
+
+capacity_factor_100.video:
+  type: video.VideoDataset
+  filepath: data/03_primary/capacity_factor_100km.mp4
+  fourcc: avc1
+
+capacity_factor_10.raw:
+  type: kedro_datasets_experimental.netcdf.NetCDFDataset
+  filepath: data/01_raw/nextgems/cf_global_10km.nc
 
-diff.video:
+capacity_factor_10.video:
   type: video.VideoDataset
-  filepath: data/03_primary/diff_video.mp4
+  filepath: data/03_primary/capacity_factor_10km.mp4
+  fourcc: avc1
diff --git a/science/conf/base/parameters.yml b/science/conf/base/parameters.yml
index 9bc43f4..c943418 100644
--- a/science/conf/base/parameters.yml
+++ b/science/conf/base/parameters.yml
@@ -1,59 +1,85 @@
-global_video_10:
-    cmap: "viridis"
+# ------- GLOBAL ----------
+default:
+    cmap: "YlGn"
+    scale:  1
+    vmin: 0
+    vmax: 1
+
+global_wind_video_10:
+    cmap: "tempo"
     scale: 1
+    vmin: 0
+    vmax: 20
 
-global_video_100:
-    cmap: "viridis"
-    scale: 10
+global_wind_video_100:
+    cmap: "tempo"
+    scale: 1
+    vmin: 0
+    vmax: 20
+
+capacity_factor:
+    cmap: "GWA_r"
+    scale:  1
+    vmin: 0
+    vmax: 1
+
+# ------- ZOOM IN ----------
 
 hurricane_10km_render_params:
     cmap: "Blues_r"
     scale: 4
+    vmin: 0
+    vmax: 0.99
     bbox:
         minx: -88.23
         miny: 21.37
         maxx: -51.77
         maxy: 42.37
 
-hurricane_100km_render_params:
-    cmap: "viridis"
-    scale: 20
-    bbox:
-        minx: -84.088
-        miny: 15.89
-        maxx: -55.88
-        maxy: 41.15
+cloud_cover_100km_global:
+    cmap: "Blues_r"
+    scale: 1
+    vmin: 0
+    vmax: 0.99
 
-amazonia_10km_render_params:
-    cmap: "viridis"
+total_precipitation_10km:
+    cmap: "rain_custom"
     scale: 4
+    # vmin: 0
+    # vmax: 0.08
+    basemap: true
+    basemap_style: "cm2xjiks300pc01qwbgo8a9f4"
     bbox:
-        minx: -79.72
-        miny: -16.60
-        maxx: -45.27
-        maxy: 7.672
+        minx: -75
+        miny: -12
+        maxx: -52
+        maxy: 6
+
+total_precipitation_100km_global:
+    cmap: "rain_custom"
+    scale: 1
+    vmin: 0
+    vmax: 0.1
 
 temperature_10km_render_params:
-    cmap: "winter"
+    cmap: "Spectral_r"
     scale: 4
+    vmin: 277
+    vmax: 319
     bbox:
-        minx: -12.1350
-        miny: 32.5146
-        maxx: 4.7930
-        maxy: 44.1825
-
-amazonia_100km_render_params:
-    cmap: "Greens_r"
-    scale: 20
-    bbox:
-        minx: -67.53
-        miny: -8.55
-        maxx: -47.76
-        maxy: 8.64
+        minx: -12
+        miny: 33
+        maxx: 6
+        maxy: 46
 
+temperature_100km_global:
+    cmap: "inferno"
+    scale: 1
+    vmin: 277
+    vmax: 319
 
 sst_10km_render_params:
-    cmap: "plasma"
+    cmap: "thermal"
     scale: 4
     bbox:
         minx: -129
@@ -61,14 +87,46 @@ sst_10km_render_params:
         maxx: -93
         maxy: 8.5
 
+sst_100km_global:
+    cmap: "thermal"
+    scale: 1
+    vmin: 292.329
+    vmax: 304.38
+
+# ------ SCENARIOS ---------
+
 scenarios:
-    cmap: "winter"
-    scale: 2
-    fps: 20
+    cmap: "Spectral_r"
+    scale: 10
+    date: "2018-08-07"
+    # we need to fix the temp range so the normal and +2 scenario can be compared side by side
     vmin: 269.56
     vmax: 324.11
 
+europe_scenario:
+    cmap: "Spectral_r"
+    scale: 10
+    date: "2018-08-07"
+    vmin: 285.15  # 12 C
+    vmax: 318.15  # 45 C
+    bbox:
+        minx: -20
+        miny: 35
+        maxx: 20
+        maxy: 55
+
 diff_video:
     cmap: "RdBu_r"
     scale: 2
-    fps: 20
\ No newline at end of file
+    vmin: -5
+    vmax: 5
+
+observations:
+    cmap: "Spectral_r"
+    scale: 10
+    dataset_name: "tasmax"
+    bbox:
+      minx: -10
+      miny: 35
+      maxx: 15
+      maxy: 45
diff --git a/science/data/03_primary/ws-100-parts/.gitkeep b/science/data/03_primary/ws-100-parts/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/science/notebooks/destinee_explore.ipynb b/science/notebooks/destinee_explore.ipynb
index f5c54a7..3cecff6 100644
--- a/science/notebooks/destinee_explore.ipynb
+++ b/science/notebooks/destinee_explore.ipynb
@@ -222,7 +222,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/science/notebooks/nextgems_explore.ipynb b/science/notebooks/nextgems_explore.ipynb
index 17c0c86..4108b58 100644
--- a/science/notebooks/nextgems_explore.ipynb
+++ b/science/notebooks/nextgems_explore.ipynb
@@ -2,28 +2,37 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-30T15:13:39.867306Z",
+     "start_time": "2024-10-30T15:13:39.368996Z"
+    },
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# import cartopy.crs as ccrs\n",
-    "# import cartopy.feature as cfeature\n",
-    "import matplotlib.animation as animation\n",
+    "import cartopy.crs as ccrs\n",
     "import matplotlib.pyplot as plt\n",
+    "import tqdm\n",
     "import xarray as xr\n",
-    "from IPython.display import HTML\n"
+    "from IPython.display import HTML\n",
+    "from matplotlib import animation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-30T15:13:41.040465Z",
+     "start_time": "2024-10-30T15:13:40.904740Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "da_low = xr.open_dataarray(\"../data/01_raw/nextgems/sst_global_100km.nc\")\n",
-    "# da_high = xr.open_dataarray(\"../data/01_raw/nextgems/ws_global_10km.nc\")\n",
+    "da_low = xr.open_dataarray(\"../data/01_raw/nextgems/cf_global_10km.nc\")\n",
+    "da_high = xr.open_dataarray(\"../data/01_raw/nextgems/ws_global_10km.nc\")\n",
     "da_low"
    ]
   },
@@ -43,12 +52,14 @@
    "outputs": [],
    "source": [
     "def animate(da: xr.DataArray) -> animation:\n",
-    "    fig, ax = plt.subplots(1, 1, figsize=[12,6])\n",
+    "    fig, ax = plt.subplots(1, 1, figsize=[12, 6])\n",
     "    vmin = float(da.min())\n",
     "    vmax = float(da.max())\n",
+    "\n",
     "    def plot_step(time):\n",
     "        \"\"\"Plot a time step of the animation.\"\"\"\n",
-    "        da.isel(time=time).plot(ax=ax, add_colorbar=False,  vmin=vmin, vmax=vmax)\n",
+    "        da.isel(time=time).plot(ax=ax, add_colorbar=False, vmin=vmin, vmax=vmax)\n",
+    "\n",
     "    return animation.FuncAnimation(fig, plot_step, 168, interval=50, blit=False)"
    ]
   },
@@ -79,7 +90,7 @@
    "source": [
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-10, 45))\n",
-    "da_low.sel(time='2040-09-01 00:00:00').plot.imshow(ax=ax, transform=ccrs.PlateCarree())"
+    "da_low.sel(time=\"2040-09-01 00:00:00\").plot.imshow(ax=ax, transform=ccrs.PlateCarree())"
    ]
   },
   {
@@ -90,7 +101,7 @@
    "source": [
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-10, 45))\n",
-    "da_high.sel(time='2040-09-01 00:00:00').plot.imshow(ax=ax, transform=ccrs.PlateCarree())"
+    "da_high.sel(time=\"2040-09-01 00:00:00\").plot.imshow(ax=ax, transform=ccrs.PlateCarree())"
    ]
   },
   {
@@ -104,11 +115,16 @@
     "\n",
     "def plot_step(frame, fig):\n",
     "    \"\"\"Plot a time step of the animation.\"\"\"\n",
-    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(0+frame, 30))\n",
-    "    da_low.isel(time=frame%120).plot.imshow(add_colorbar=False, ax=ax, cmap=\"viridis\", transform=ccrs.PlateCarree())\n",
+    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(0 + frame, 30))\n",
+    "    da_low.isel(time=frame % 120).plot.imshow(\n",
+    "        add_colorbar=False, ax=ax, cmap=\"viridis\", transform=ccrs.PlateCarree()\n",
+    "    )\n",
     "    ax.set_title(\"\")\n",
     "\n",
-    "ani = animation.FuncAnimation(fig, plot_step, 360, interval=50, blit=False, fargs=(fig,))"
+    "\n",
+    "ani = animation.FuncAnimation(\n",
+    "    fig, plot_step, 360, interval=50, blit=False, fargs=(fig,)\n",
+    ")"
    ]
   },
   {
@@ -126,7 +142,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ani.save('globe_ws_low_res.mp4')"
+    "ani.save(\"globe_ws_low_res.mp4\")"
    ]
   },
   {
@@ -157,7 +173,9 @@
     "        ax.set_title(\"\")\n",
     "        ims.append([im])\n",
     "\n",
-    "    return animation.ArtistAnimation(fig, ims, interval=50, blit=True, repeat_delay=1000)"
+    "    return animation.ArtistAnimation(\n",
+    "        fig, ims, interval=50, blit=True, repeat_delay=1000\n",
+    "    )"
    ]
   },
   {
@@ -195,8 +213,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(0, 30))\n",
     "\n",
@@ -213,7 +229,7 @@
     "\n",
     "\n",
     "def update_mesh(t, fig):\n",
-    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(t, 30))\n",
+    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(t, 30))  # noqa: F841\n",
     "    mesh.set_array(da_low.isel(time=t % 168))\n",
     "\n",
     "\n",
@@ -223,7 +239,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -237,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/science/notebooks/observations.ipynb b/science/notebooks/observations.ipynb
new file mode 100644
index 0000000..9c4d459
--- /dev/null
+++ b/science/notebooks/observations.ipynb
@@ -0,0 +1,732 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T12:44:30.188423Z",
+     "start_time": "2024-11-08T12:44:30.185710Z"
+    }
+   },
+   "source": "import xarray as xr",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T12:41:53.496201Z",
+     "start_time": "2024-11-08T12:41:52.573562Z"
+    }
+   },
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+    "ds = xr.load_dataset(\"../data/01_raw/obs/tasmax_201808.nc\")[\"tasmax\"]\n",
+    "ds"
+   ],
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+       "Coordinates:\n",
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+       "Attributes:\n",
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+       "    code:           167\n",
+       "    table:          128\n",
+       "    cell_methods:   time: maximum\n",
+       "    long_name:      Daily Maximum Near-Surface Air Temperature\n",
+       "    standard_name:  air_temperature"
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+       "        [219.73717, 219.73912, 219.73912, ..., 219.73521, 219.73717,\n",
+       "         219.73717],\n",
+       "        [218.95366, 218.95366, 218.95366, ..., 218.95366, 218.95366,\n",
+       "         218.95366]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        ...,\n",
+       "        [219.48994, 219.48994, 219.4919 , ..., 219.48604, 219.48604,\n",
+       "         219.48799],\n",
+       "        [219.49385, 219.4958 , 219.4958 , ..., 219.4919 , 219.4919 ,\n",
+       "         219.49385],\n",
+       "        [218.56221, 218.56221, 218.56221, ..., 218.56221, 218.56221,\n",
+       "         218.56221]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 248B 2018-08-01T11:30:00 ... 2018-08-31T11...\n",
+       "  * lon      (lon) float64 29kB 0.0 0.1 0.2 0.3 0.4 ... 359.6 359.7 359.8 359.9\n",
+       "  * lat      (lat) float64 14kB 90.0 89.9 89.8 89.7 ... -89.7 -89.8 -89.9 -90.0\n",
+       "Attributes:\n",
+       "    units:          K\n",
+       "    code:           167\n",
+       "    table:          128\n",
+       "    cell_methods:   time: maximum\n",
+       "    long_name:      Daily Maximum Near-Surface Air Temperature\n",
+       "    standard_name:  air_temperature</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tasmax'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>lat</span>: 1801</li><li><span class='xr-has-index'>lon</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-496a28a7-7fbd-4fb8-a66c-de8d87f3f2a5' class='xr-array-in' type='checkbox' checked><label for='section-496a28a7-7fbd-4fb8-a66c-de8d87f3f2a5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 218.6 218.6 218.6 218.6 218.6 218.6</span></div><div class='xr-array-data'><pre>array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        ...,\n",
+       "        [224.2037 , 224.2037 , 224.20566, ..., 224.19785, 224.1998 ,\n",
+       "         224.20175],\n",
+       "        [224.20761, 224.20761, 224.20956, ..., 224.20175, 224.2037 ,\n",
+       "         224.20566],\n",
+       "        [222.33397, 222.33397, 222.33397, ..., 222.33397, 222.33397,\n",
+       "         222.33397]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "...\n",
+       "        [219.73131, 219.73131, 219.73326, ..., 219.72935, 219.72935,\n",
+       "         219.73131],\n",
+       "        [219.73717, 219.73912, 219.73912, ..., 219.73521, 219.73717,\n",
+       "         219.73717],\n",
+       "        [218.95366, 218.95366, 218.95366, ..., 218.95366, 218.95366,\n",
+       "         218.95366]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        ...,\n",
+       "        [219.48994, 219.48994, 219.4919 , ..., 219.48604, 219.48604,\n",
+       "         219.48799],\n",
+       "        [219.49385, 219.4958 , 219.4958 , ..., 219.4919 , 219.4919 ,\n",
+       "         219.49385],\n",
+       "        [218.56221, 218.56221, 218.56221, ..., 218.56221, 218.56221,\n",
+       "         218.56221]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-13ff6c5c-0882-4829-98cf-3c3cecf6fe89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13ff6c5c-0882-4829-98cf-3c3cecf6fe89' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-08-01T11:30:00 ... 2018-08-...</div><input id='attrs-0a04013d-041e-459d-8a6c-9810445721a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a04013d-041e-459d-8a6c-9810445721a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28501d21-85c0-4283-89c0-f6b617b15b4c' class='xr-var-data-in' type='checkbox'><label for='data-28501d21-85c0-4283-89c0-f6b617b15b4c' 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>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-08-01T11:30:00.000000000&#x27;, &#x27;2018-08-02T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-03T11:30:00.000000000&#x27;, &#x27;2018-08-04T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-05T11:30:00.000000000&#x27;, &#x27;2018-08-06T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-07T11:30:00.000000000&#x27;, &#x27;2018-08-08T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-09T11:30:00.000000000&#x27;, &#x27;2018-08-10T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-11T11:30:00.000000000&#x27;, &#x27;2018-08-12T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-13T11:30:00.000000000&#x27;, &#x27;2018-08-14T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-15T11:30:00.000000000&#x27;, &#x27;2018-08-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-17T11:30:00.000000000&#x27;, &#x27;2018-08-18T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-19T11:30:00.000000000&#x27;, &#x27;2018-08-20T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-21T11:30:00.000000000&#x27;, &#x27;2018-08-22T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-23T11:30:00.000000000&#x27;, &#x27;2018-08-24T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-25T11:30:00.000000000&#x27;, &#x27;2018-08-26T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-27T11:30:00.000000000&#x27;, &#x27;2018-08-28T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-29T11:30:00.000000000&#x27;, &#x27;2018-08-30T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-31T11:30:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>0.0 0.1 0.2 ... 359.7 359.8 359.9</div><input id='attrs-f49cd467-b371-44eb-8250-d1a6965845dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f49cd467-b371-44eb-8250-d1a6965845dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b12d08-db5f-414d-ac37-68ef13f9cc6e' class='xr-var-data-in' type='checkbox'><label for='data-60b12d08-db5f-414d-ac37-68ef13f9cc6e' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 1.000e-01, 2.000e-01, ..., 3.597e+02, 3.598e+02, 3.599e+02])</pre></div></li><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'>90.0 89.9 89.8 ... -89.9 -90.0</div><input id='attrs-8248839f-a7da-49c6-af35-3db6ca344771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8248839f-a7da-49c6-af35-3db6ca344771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-689e79fa-af9b-4871-9d26-5be5fe79238f' class='xr-var-data-in' type='checkbox'><label for='data-689e79fa-af9b-4871-9d26-5be5fe79238f' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.9,  89.8, ..., -89.8, -89.9, -90. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42296dd0-9298-4f2b-b88a-8d2f49d18513' class='xr-section-summary-in' type='checkbox'  ><label for='section-42296dd0-9298-4f2b-b88a-8d2f49d18513' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f6225fa-adda-4236-9b4c-bae5fff1e509' class='xr-index-data-in' type='checkbox'/><label for='index-6f6225fa-adda-4236-9b4c-bae5fff1e509' 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(DatetimeIndex([&#x27;2018-08-01 11:30:00&#x27;, &#x27;2018-08-02 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-03 11:30:00&#x27;, &#x27;2018-08-04 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-05 11:30:00&#x27;, &#x27;2018-08-06 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-07 11:30:00&#x27;, &#x27;2018-08-08 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-09 11:30:00&#x27;, &#x27;2018-08-10 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-11 11:30:00&#x27;, &#x27;2018-08-12 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-13 11:30:00&#x27;, &#x27;2018-08-14 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-15 11:30:00&#x27;, &#x27;2018-08-16 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-17 11:30:00&#x27;, &#x27;2018-08-18 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-19 11:30:00&#x27;, &#x27;2018-08-20 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-21 11:30:00&#x27;, &#x27;2018-08-22 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-23 11:30:00&#x27;, &#x27;2018-08-24 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-25 11:30:00&#x27;, &#x27;2018-08-26 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-27 11:30:00&#x27;, &#x27;2018-08-28 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-29 11:30:00&#x27;, &#x27;2018-08-30 11:30:00&#x27;,\n",
+       "               &#x27;2018-08-31 11:30:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</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-b2ff4ea3-5106-40a3-9c51-64642bb5f7d7' class='xr-index-data-in' type='checkbox'/><label for='index-b2ff4ea3-5106-40a3-9c51-64642bb5f7d7' 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([                0.0,                 0.1,                 0.2,\n",
+       "       0.30000000000000004,                 0.4,                 0.5,\n",
+       "        0.6000000000000001,  0.7000000000000001,                 0.8,\n",
+       "                       0.9,\n",
+       "       ...\n",
+       "                     359.0,               359.1,  359.20000000000005,\n",
+       "                     359.3,  359.40000000000003,               359.5,\n",
+       "                     359.6,  359.70000000000005,               359.8,\n",
+       "        359.90000000000003],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=3600))</pre></div></li><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-4dbe5c1a-9ad7-4bb9-a0f0-16a0aaa05400' class='xr-index-data-in' type='checkbox'/><label for='index-4dbe5c1a-9ad7-4bb9-a0f0-16a0aaa05400' 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([              90.0,               89.9,               89.8,\n",
+       "                     89.7,               89.6,               89.5,\n",
+       "                     89.4,               89.3,               89.2,\n",
+       "                     89.1,\n",
+       "       ...\n",
+       "       -89.10000000000002, -89.20000000000002, -89.30000000000001,\n",
+       "                    -89.4,              -89.5, -89.60000000000002,\n",
+       "       -89.70000000000002, -89.80000000000001,              -89.9,\n",
+       "                    -90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1801))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b23b5800-6859-4522-b3e1-79bd01582c05' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b23b5800-6859-4522-b3e1-79bd01582c05' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>code :</span></dt><dd>167</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>cell_methods :</span></dt><dd>time: maximum</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T12:50:59.338469Z",
+     "start_time": "2024-11-08T12:50:59.205844Z"
+    }
+   },
+   "source": "r = ds.rio.write_crs(4326).rename(lon=\"x\", lat=\"y\").isel(time=1)",
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T14:05:38.553873Z",
+     "start_time": "2024-11-08T14:05:34.572143Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "r.plot()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x766328f98d50>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T14:05:11.431621Z",
+     "start_time": "2024-11-08T14:05:11.250375Z"
+    }
+   },
+   "source": "r.rio.clip_box(minx=-20, miny=35, maxx=20, maxy=55).plot(cmap=\"Spectral_r\")",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x766329018fd0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-08T13:01:05.203550Z",
+     "start_time": "2024-11-08T13:01:05.188928Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "r.rio.coordinates",
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'RasterArray' object has no attribute 'coordinates'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[18], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrio\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcoordinates\u001B[49m\n",
+      "\u001B[0;31mAttributeError\u001B[0m: 'RasterArray' object has no attribute 'coordinates'"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "digital-twins",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/science/post-proces.sh b/science/post-proces.sh
new file mode 100755
index 0000000..1cba053
--- /dev/null
+++ b/science/post-proces.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+set -eux
+
+# Set the video bitrate and quality
+BITRATE="1M"
+CRF="30"
+
+# Loop through all .mp4 files in the current directory
+for input_file in data/03_primary/*.mp4; do
+  # Get the base name without extension
+  base_name=$(basename "$input_file" .mp4)
+
+  # First pass (no actual output, just analysis)
+  ffmpeg -i "$input_file" -c:v libvpx-vp9 -b:v $BITRATE -crf $CRF -pass 1 -an -f null /dev/null
+
+  # Second pass (produce output .webm file)
+  ffmpeg -i "$input_file" -c:v libvpx-vp9 -b:v $BITRATE -crf $CRF -pass 2 -an "data/03_primary/${base_name}.webm"
+
+  # Clean up the log files created during the two-pass process
+  rm -f ffmpeg2pass-0.log
+done
+
+echo "Conversion completed for all .mp4 files!"
diff --git a/science/pyproject.toml b/science/pyproject.toml
index 39a3a3a..43acf6e 100644
--- a/science/pyproject.toml
+++ b/science/pyproject.toml
@@ -16,18 +16,18 @@ science = "science.__main__:main"
 docs = [
     "docutils<0.21",
     "sphinx>=5.3,<7.3",
-     "sphinx_rtd_theme==2.0.0",
+    "sphinx_rtd_theme==2.0.0",
     "nbsphinx==0.8.1",
     "sphinx-autodoc-typehints==1.20.2",
     "sphinx_copybutton==0.5.2",
     "ipykernel>=5.3, <7.0",
     "Jinja2<3.2.0",
-    "myst-parser>=1.0,<2.1"
+    "myst-parser>=1.0,<2.1",
 ]
 
 [tool.setuptools.dynamic]
-dependencies = {file = "requirements.txt"}
-version = {attr = "science.__version__"}
+dependencies = { file = "requirements.txt" }
+version = { attr = "science.__version__" }
 
 [tool.setuptools.packages.find]
 where = ["src"]
@@ -37,7 +37,14 @@ namespaces = false
 package_name = "science"
 project_name = "science"
 kedro_init_version = "0.19.6"
-tools = ['Linting', 'Testing', 'Custom Logging', 'Documentation', 'Data Structure', 'Kedro Viz']
+tools = [
+    'Linting',
+    'Testing',
+    'Custom Logging',
+    'Documentation',
+    'Data Structure',
+    'Kedro Viz',
+]
 example_pipeline = "False"
 source_dir = "src"
 
@@ -51,19 +58,22 @@ fail_under = 0
 show_missing = true
 exclude_lines = ["pragma: no cover", "raise NotImplementedError"]
 
-[tool.ruff.format]
-docstring-code-format = true
-
 [tool.ruff]
 line-length = 88
 show-fixes = true
+
+[tool.ruff.lint]
+
 select = [
-    "F",   # Pyflakes
-    "W",   # pycodestyle
-    "E",   # pycodestyle
-    "I",   # isort
-    "UP",  # pyupgrade
-    "PL",  # Pylint
-    "T201", # Print Statement
+    "F",  # Pyflakes
+    "W",  # pycodestyle
+    "E",  # pycodestyle
+    "I",  # isort
+    "UP", # pyupgrade
+    "PL", # Pylint
 ]
-ignore = ["E501"]  # Ruff format takes care of line-too-long
+ignore = ["E501"] # Ruff format takes care of line-too-long
+
+[tool.pyright]
+venvPath = "."
+venv = ".venv"
diff --git a/science/src/science/datasets/credentials.py b/science/src/science/datasets/credentials.py
new file mode 100644
index 0000000..4d25906
--- /dev/null
+++ b/science/src/science/datasets/credentials.py
@@ -0,0 +1,17 @@
+from typing import Any, Dict
+
+from kedro.io import AbstractDataset
+
+
+class CredentialsDataset(AbstractDataset):
+    def __init__(self, credentials: Dict[str, Any] | None = None):
+        self._credentials = credentials
+
+    def _load(self) -> dict | None:
+        return self._credentials
+
+    def _save(self) -> None:
+        print("save")
+
+    def _describe(self) -> Dict[str, Any]:
+        return dict(credentials=self._credentials)
diff --git a/science/src/science/pipelines/animations/nodes.py b/science/src/science/pipelines/animations/nodes.py
deleted file mode 100644
index ee038c5..0000000
--- a/science/src/science/pipelines/animations/nodes.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""
-This is a boilerplate pipeline 'globe_compact'
-generated using Kedro 0.19.6
-"""
-import logging
-from math import isnan
-from typing import Any, Callable
-
-import cartopy  # noqa: F401
-import cartopy.crs as ccrs
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import xarray as xr
-from kedro_datasets.video.video_dataset import SequenceVideo
-from PIL import Image
-from skimage.transform import rescale
-
-log = logging.getLogger(__name__)
-
-
-def georef_nextgems_dataset(ds: xr.Dataset) -> xr.Dataset:
-    return ds.rio.write_crs(4326).rename(lon="x", lat="y")
-
-
-def clip_to_boundary(raster: xr.Dataset, bbox: dict[str, float]) -> xr.Dataset:
-    """Clip the raster to the boundary area"""
-    log.info(f"Clipping to {bbox=}")
-    return raster.rio.clip_box(**bbox)
-
-
-def split_by_timestep(ds: xr.Dataset) -> dict[str, xr.DataArray]:
-    return {
-        f"{ts.item()}": ds.sel(time=ts).to_dataarray().squeeze(drop=True)
-        for ts in ds.coords["time"].data
-    }
-
-
-def parts_to_video(
-    parts: dict[str, Callable[[], xr.DataArray]], params: dict[Any:Any]
-) -> SequenceVideo:
-    cmap_lut = matplotlib.colormaps.get_cmap(params.get("cmap"))(range(256))
-    cmap_lut = (cmap_lut[..., 0:3] * 255).astype(np.uint8)
-    imgs = []
-    for _, dataset in parts.items():
-        if callable(dataset):
-            # This comes from a partition dataset which is a callable only if reading files.
-            # When it is a memoryfile it is not a callable
-            dataset = dataset()[0]  # noqa: PLW2901
-        # flip array to make the 0,0 origin of the image at the upper corner for PIL
-        grey = np.flipud(dataset.values)
-        grey = rescale(grey, params.get("scale"))
-
-        # scale the values to 0-255
-        if params.get("cmap") == "RdBu_r":
-            grey = matplotlib.colors.CenteredNorm(halfrange=10)(grey) * 255
-            grey = grey.astype(np.uint8)
-        else:
-            _min = params.get("vmin") or np.nanmin(grey)
-            _max = params.get("vmax") or np.nanmax(grey)
-            grey = (grey.astype(float) - _min) * 255 / (_max - _min)
-
-        grey = np.nan_to_num(grey)
-        grey = grey.astype(np.uint8)
-        result = np.zeros((*grey.shape, 3), dtype=np.uint8)
-        np.take(cmap_lut, grey, axis=0, out=result)
-        imgs.append(Image.fromarray(result))
-
-    return SequenceVideo(imgs, fps=params.get("fps") or 20, fourcc="h264")
-
-
-# super slow
-def parts_to_video_matplotlib(
-    parts: dict[str, Callable[[], xr.DataArray]], params: dict[Any:Any]
-) -> SequenceVideo:
-    imgs = []
-    for dataset_id, dataset in parts.items():
-        fig = plt.figure(figsize=params.get("figsize"))
-        ax = fig.add_subplot(1, 1, 1, projection=ccrs.EqualEarth())
-        ax.coastlines()
-        ax.imshow(
-            dataset,
-            transform=ccrs.PlateCarree(),
-            # extent=extent,
-            origin="upper",
-            cmap=params.get("cmap"),
-        )
-        fig.canvas.draw()  # to initialize the renderer and get the img rgba buffer directly from figure
-        imgs.append(
-            Image.frombytes(
-                "RGBa", fig.canvas.get_width_height(), fig.canvas.buffer_rgba()
-            )
-        )
-        plt.close()  # close the figure, I hate matplotlib
-    return SequenceVideo(imgs, fps=20)
-
-
-def diff(a: xr.Dataset, b: xr.Dataset) -> xr.Dataset:
-    return a - b
diff --git a/science/src/science/pipelines/animations/pipeline.py b/science/src/science/pipelines/animations/pipeline.py
deleted file mode 100644
index d7b58a1..0000000
--- a/science/src/science/pipelines/animations/pipeline.py
+++ /dev/null
@@ -1,180 +0,0 @@
-"""
-This is a boilerplate pipeline 'globe_compact'
-generated using Kedro 0.19.6
-"""
-from kedro.pipeline import Pipeline, node
-from kedro.pipeline.modular_pipeline import pipeline
-
-from science.pipelines.animations.nodes import (
-    clip_to_boundary,
-    diff,
-    georef_nextgems_dataset,
-    parts_to_video,
-    split_by_timestep,
-)
-
-
-def create_pipeline(**kwargs) -> Pipeline:
-    global_base_pipeline = pipeline(
-        [
-            node(
-                func=georef_nextgems_dataset,
-                inputs="raw",
-                outputs="georef",
-                name="georeferenciate-dataset",
-            ),
-            node(
-                func=split_by_timestep,
-                inputs="georef",
-                outputs="parts",
-                name="split-by-timestep",
-            ),
-            node(
-                func=parts_to_video,
-                inputs=["parts", "params:video"],
-                outputs="video",
-            ),
-        ]
-    )
-    zoom_in_base_pipeline = pipeline(
-        [
-            node(
-                func=georef_nextgems_dataset,
-                inputs="raw",
-                outputs="georef",
-                name="georeferenciate-dataset",
-            ),
-            node(
-                func=clip_to_boundary,
-                inputs=["georef", "params:bbox"],
-                outputs="clipped",
-                name="clip-dataset",
-            ),
-            node(
-                func=split_by_timestep,
-                inputs="clipped",
-                outputs="parts",
-                name="split-by-timestep",
-            ),
-            node(
-                func=parts_to_video,
-                inputs=["parts", "params:video"],
-                outputs="video",
-            ),
-        ]
-    )
-
-    scenarios_base_pipeline = pipeline(
-        [
-            node(
-                func=georef_nextgems_dataset,
-                inputs="raw",
-                outputs="georef",
-                name="georeferenciate-dataset",
-            ),
-            node(
-                func=split_by_timestep,
-                inputs="georef",
-                outputs="parts",
-                name="split-by-timestep",
-            ),
-            node(
-                func=parts_to_video,
-                inputs=["parts", "params:video"],
-                outputs="video",
-            ),
-        ]
-    )
-
-    # -------- Globe animations ------------
-
-    global_wind_10km_pipe = pipeline(
-        pipe=global_base_pipeline,
-        namespace="wind_speed_global_10km",
-        parameters={"video": "params:global_video_10"},
-        tags=["windspeed", "global", "high_resolution"],
-    )
-
-    global_wind_100km_pipe = pipeline(
-        pipe=global_base_pipeline,
-        namespace="wind_speed_global_100km",
-        parameters={"video": "params:global_video_100"},
-        tags=["windspeed", "global", "low_resolution"],
-    )
-
-    # -------- Zoom ins -------------
-    hurricane_cloud_pipe = pipeline(
-        pipe=zoom_in_base_pipeline,
-        parameters={
-            "bbox": "params:hurricane_10km_render_params.bbox",
-            "video": "params:hurricane_10km_render_params",
-        },
-        namespace="cloud_cover_10km",
-        tags=["hurricane", "zoomin", "high_resoltuion"],
-    )
-
-    amazonia_precipitation_pipe = pipeline(
-        pipe=zoom_in_base_pipeline,
-        parameters={
-            "bbox": "params:amazonia_10km_render_params.bbox",
-            "video": "params:amazonia_10km_render_params",
-        },
-        namespace="total_precipitation_10km",
-        tags=["amazonia", "zoomin", "high_resoltuion"],
-    )
-
-    temp_pipe = pipeline(
-        pipe=zoom_in_base_pipeline,
-        parameters={
-            "bbox": "params:temperature_10km_render_params.bbox",
-            "video": "params:temperature_10km_render_params",
-        },
-        namespace="temp_10km",
-        tags=["pyrines", "zoomin", "high_resolution"],
-    )
-
-    sst_pipe = pipeline(
-        pipe=zoom_in_base_pipeline,
-        parameters={
-            "bbox": "params:sst_10km_render_params.bbox",
-            "video": "params:sst_10km_render_params",
-        },
-        namespace="sst_10km",
-        tags=["sst", "zoomin", "high_resolution"],
-    )
-
-    # ------ Scenarios --------------
-
-    plus2k_pipe = pipeline(
-        pipe=scenarios_base_pipeline,
-        parameters={"video": "params:scenarios"},
-        namespace="plus2k",
-        tags=["scenarios"],
-    )
-    hist_pipe = pipeline(
-        pipe=scenarios_base_pipeline,
-        parameters={"video": "params:scenarios"},
-        namespace="hist",
-        tags=["scenarios"],
-    )
-
-    diff_scenarios = pipeline(
-        [
-            node(diff, ["plus2k.raw", "hist.raw"], "scenarios_diff"),
-            node(split_by_timestep, "scenarios_diff", "diff_parts"),
-            node(parts_to_video, ["diff_parts", "params:diff_video"], "diff.video"),
-        ],
-        tags=["scenarios", "diff"],
-    )
-
-    return (
-        global_wind_10km_pipe
-        + global_wind_100km_pipe
-        + hurricane_cloud_pipe
-        + amazonia_precipitation_pipe
-        + temp_pipe
-        + sst_pipe
-        + plus2k_pipe
-        + hist_pipe
-        + diff_scenarios
-    )
diff --git a/science/src/science/pipelines/common_nodes.py b/science/src/science/pipelines/common_nodes.py
new file mode 100644
index 0000000..7589ca7
--- /dev/null
+++ b/science/src/science/pipelines/common_nodes.py
@@ -0,0 +1,148 @@
+"""
+This is a boilerplate pipeline 'globe_compact'
+generated using Kedro 0.19.6
+"""
+
+import logging
+from typing import Any, Callable, Sequence, TypeAlias
+
+import cartopy  # noqa: F401
+import cmocean
+import matplotlib
+import matplotlib.colors
+import numpy as np
+import xarray as xr
+from kedro_datasets.video.video_dataset import SequenceVideo
+from matplotlib.colors import LinearSegmentedColormap
+from PIL import Image
+from skimage.transform import rescale
+
+log = logging.getLogger(__name__)
+
+Parts: TypeAlias = dict[str, Callable[[], xr.DataArray]]
+
+
+def register_cmaps():
+    GLOBAL_WIND_ATLAS_CMAP = [
+        (153, 51, 102),
+        (165, 47, 90),
+        (176, 43, 77),
+        (188, 39, 65),
+        (199, 35, 52),
+        (211, 31, 40),
+        (226, 63, 40),
+        (232, 78, 41),
+        (238, 92, 41),
+        (245, 106, 41),
+        (246, 137, 53),
+        (247, 160, 63),
+        (248, 184, 73),
+        (249, 208, 82),
+        (250, 232, 92),
+        (212, 221, 87),
+        (178, 211, 83),
+        (145, 202, 79),
+        (111, 192, 75),
+        (73, 181, 70),
+        (73, 173, 99),
+        (73, 165, 124),
+        (72, 158, 148),
+        (72, 150, 173),
+        (72, 142, 202),
+        (90, 158, 212),
+        (106, 173, 220),
+        (123, 187, 229),
+        (141, 204, 238),
+        (178, 226, 249),
+        (197, 233, 250),
+    ]
+    normalized_colors = np.array(GLOBAL_WIND_ATLAS_CMAP) / 255.0
+    cmap = LinearSegmentedColormap.from_list("GWA", normalized_colors)
+    cmap = cmap.reversed()
+    matplotlib.colormaps.register(cmap, force=True)
+    log.info(f"Registered colormap '{cmap.name}'")
+
+
+def georef_nextgems_dataset(ds: xr.Dataset) -> xr.Dataset:
+    return ds.rio.write_crs(4326).rename(lon="x", lat="y")
+
+
+def get_min_max(
+    ds: xr.Dataset | xr.DataArray, params: dict[str, Any]
+) -> tuple[float, float]:
+    _min = params.get("vmin") or float(ds.min().to_dataarray().values[0])
+    _max = params.get("vmax") or float(ds.max().to_dataarray().values[0])
+    log.info(f"Min max is: {_min=}, {_max=}")
+    return _min, _max
+
+
+def split_by_timestep(ds: xr.DataArray) -> dict[str, xr.DataArray]:
+    return {
+        f"{ts.item()}": ds.sel(time=ts).to_dataarray().squeeze(drop=True)
+        for ts in ds.coords["time"].data
+    }
+
+
+def parts_to_frames(
+    parts: Parts,
+    scale_factor: int,
+    min_max: tuple | None,
+    cmap_lut: np.ndarray,
+    cmap_name: str,
+) -> Sequence[Image.Image]:
+    frames = []
+    log.info("Computing frames...")
+    for _, dataset in parts.items():
+        if callable(dataset):
+            # This comes from a partition dataset which is a callable only if reading files.
+            # When it is a memoryfile it is not a callable
+            dataset = dataset()[0]  # noqa: PLW2901
+        # flip array to make the 0,0 origin of the image at the upper corner for PIL
+        grey = np.flipud(dataset.values)
+        if scale_factor and scale_factor > 1:
+            grey = rescale(grey, scale_factor, order=3)
+        _min = min_max[0] if min_max is not None else np.nanmin(grey)
+        _max = min_max[1] if min_max is not None else np.nanmax(grey)
+        # scale the values to 0-255
+        if cmap_name == "RdBu_r":  # special case for this specific diverging colormap
+            grey = (
+                matplotlib.colors.TwoSlopeNorm(vmin=_min, vcenter=0, vmax=_max)(grey)
+                * 255
+            )
+            grey = np.clip(grey, a_min=0, a_max=255)
+            grey = grey.astype(np.uint8)
+        else:
+            grey = (grey.astype(float) - _min) * 255 / (_max - _min)
+            grey = np.clip(grey, a_min=0, a_max=255)
+        grey = np.nan_to_num(grey)
+        grey = grey.astype(np.uint8)
+        img_array = np.zeros((*grey.shape, 3), dtype=np.uint8)
+        np.take(cmap_lut, grey, axis=0, out=img_array)
+        frames.append(Image.fromarray(img_array))
+    return frames
+
+
+def parts_to_video(
+    parts: Parts,
+    params: dict[str, Any],
+    min_max: tuple[float, float] | None = None,
+) -> SequenceVideo:
+    register_cmaps()
+    cmap_name = params["cmap"]
+    cmap_lut = get_cmap_lut(cmap_name)
+    frames = parts_to_frames(parts, params["scale"], min_max, cmap_lut, cmap_name)
+    log.info(f"Video has {len(frames)} frames")
+    return SequenceVideo(frames, fps=24)
+
+
+def get_cmap_lut(cmap_name: str) -> np.ndarray:
+    cmap = matplotlib.colormaps.get(cmap_name) or cmocean.cm.cmap_d.get(cmap_name)
+    if cmap is None:
+        raise ValueError(f"cmap '{cmap_name}' not found")
+    cmap_lut = cmap(range(256))
+    cmap_lut = (cmap_lut[..., 0:3] * 255).astype(np.uint8)
+    return cmap_lut
+
+
+def diff(a: xr.Dataset, b: xr.Dataset) -> xr.Dataset:
+    return a - b
diff --git a/science/src/science/pipelines/animations/__init__.py b/science/src/science/pipelines/global/__init__.py
similarity index 100%
rename from science/src/science/pipelines/animations/__init__.py
rename to science/src/science/pipelines/global/__init__.py
diff --git a/science/src/science/pipelines/global/pipeline.py b/science/src/science/pipelines/global/pipeline.py
new file mode 100644
index 0000000..f87b8be
--- /dev/null
+++ b/science/src/science/pipelines/global/pipeline.py
@@ -0,0 +1,103 @@
+"""
+Global videos pipeline
+"""
+
+from kedro.pipeline import Pipeline, node
+from kedro.pipeline.modular_pipeline import pipeline
+
+from science.pipelines.common_nodes import (
+    georef_nextgems_dataset,
+    get_min_max,
+    parts_to_video,
+    split_by_timestep,
+)
+
+
+def create_pipeline(**kwargs) -> Pipeline:
+    global_base_pipeline = pipeline(
+        [
+            node(
+                func=georef_nextgems_dataset,
+                inputs="raw",
+                outputs="georef",
+            ),
+            node(
+                func=split_by_timestep,
+                inputs="georef",
+                outputs="parts",
+            ),
+            node(
+                func=get_min_max,
+                inputs=["georef", "params:video"],
+                outputs="minmax",
+            ),
+            node(
+                func=parts_to_video,
+                inputs=["parts", "params:video", "minmax"],
+                outputs="video",
+            ),
+        ]
+    )
+
+    global_wind_10km_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="wind_speed_global_10km",
+        parameters={"video": "params:global_wind_video_10"},
+        tags=["windspeed", "global", "high_resolution"],
+    )
+
+    global_wind_100km_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="wind_speed_global_100km",
+        parameters={"video": "params:global_wind_video_100"},
+        tags=["windspeed", "global", "low_resolution"],
+    )
+    global_cloud_cover_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="cloud_cover_100km",
+        parameters={"video": "params:cloud_cover_100km_global"},
+        tags=["cloudcover", "global", "low_resolution"],
+    )
+    global_total_precipitation_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="total_precipitation_100km",
+        parameters={"video": "params:total_precipitation_100km_global"},
+        tags=["precipitation", "global", "low_resolution"],
+    )
+    global_temperature_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="temperature_100km",
+        parameters={"video": "params:temperature_100km_global"},
+        tags=["temperature", "global", "low_resolution"],
+    )
+    global_sst_pipe = pipeline(
+        pipe=global_base_pipeline,
+        namespace="sst_100km",
+        parameters={"video": "params:sst_100km_global"},
+        tags=["sst", "global", "low_resolution"],
+    )
+
+    capacity_factor_100 = pipeline(
+        pipe=global_base_pipeline,
+        namespace="capacity_factor_100",
+        parameters={"video": "params:capacity_factor"},
+        tags=["capacity_factor"],
+    )
+
+    capacity_factor_10 = pipeline(
+        pipe=global_base_pipeline,
+        namespace="capacity_factor_10",
+        parameters={"video": "params:capacity_factor"},
+        tags=["capacity_factor"],
+    )
+
+    return (
+        global_wind_10km_pipe
+        + global_wind_100km_pipe
+        + global_cloud_cover_pipe
+        + global_temperature_pipe
+        + global_total_precipitation_pipe
+        + global_sst_pipe
+        + capacity_factor_100
+        + capacity_factor_10
+    )
diff --git a/science/src/science/pipelines/scenarios/__init__.py b/science/src/science/pipelines/scenarios/__init__.py
new file mode 100644
index 0000000..a115836
--- /dev/null
+++ b/science/src/science/pipelines/scenarios/__init__.py
@@ -0,0 +1,3 @@
+from .pipeline import create_pipeline
+
+__all__ = ["create_pipeline"]
diff --git a/science/src/science/pipelines/scenarios/nodes.py b/science/src/science/pipelines/scenarios/nodes.py
new file mode 100644
index 0000000..d4de959
--- /dev/null
+++ b/science/src/science/pipelines/scenarios/nodes.py
@@ -0,0 +1,50 @@
+import io
+from typing import Any
+
+import matplotlib
+import numpy as np
+import xarray as xr
+from matplotlib import pyplot as plt
+from PIL import Image
+from skimage.transform import rescale
+
+from science.pipelines.common_nodes import get_cmap_lut
+
+
+def select_by_date(da: xr.Dataset, params: dict) -> xr.DataArray:
+    return da.sel(time=params["date"]).max(dim="time").to_dataarray().squeeze()
+
+
+def array_to_image(da: xr.DataArray, min_max: tuple[int, int], params: dict[str, Any]):
+    cmap_name = params["cmap"]
+    cmap_lut = get_cmap_lut(cmap_name)
+    grey = np.flipud(da.values)
+    if params["scale"] and params["scale"] > 1:
+        grey = rescale(grey, params["scale"], order=3)
+    vmin = min_max[0] if min_max is not None else np.nanmin(grey)
+    vmax = min_max[1] if min_max is not None else np.nanmax(grey)
+    norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)
+    grey = (norm(grey) * 255).astype(np.uint8)
+    img_array = np.zeros((*grey.shape, 3), dtype=np.uint8)
+    np.take(cmap_lut, grey, axis=0, out=img_array)
+    return Image.fromarray(img_array)
+
+
+def plot_array(da: xr.DataArray, min_max: tuple[int, int], params: dict[str, Any]):
+    cmap_name = params["cmap"]
+    values = np.flipud(da.values)
+    if params["scale"] and params["scale"] > 1:
+        values = rescale(values, params["scale"], order=3)
+    fig, ax = plt.subplots()
+    ax.imshow(values, cmap=cmap_name, vmin=min_max[0], vmax=min_max[1])
+    ax.contour(
+        values, levels=[308.15], linestyles="-", linewidths=0.3, colors="k"
+    )  # contour at 35°C
+    ax.contour(
+        values, levels=[315.15], linestyles="-.", linewidths=0.3, colors="k"
+    )  # contour at 42°C
+    ax.set_axis_off()
+    buf = io.BytesIO()
+    fig.savefig(buf, dpi=300, pad_inches=0, bbox_inches="tight")
+    buf.seek(0)
+    return Image.open(buf)
diff --git a/science/src/science/pipelines/scenarios/pipeline.py b/science/src/science/pipelines/scenarios/pipeline.py
new file mode 100644
index 0000000..4509a9c
--- /dev/null
+++ b/science/src/science/pipelines/scenarios/pipeline.py
@@ -0,0 +1,120 @@
+from kedro.pipeline import Pipeline, node
+from kedro.pipeline.modular_pipeline import pipeline
+
+from science.pipelines.common_nodes import (
+    georef_nextgems_dataset,
+    get_min_max,
+    parts_to_video,
+    split_by_timestep,
+)
+from science.pipelines.scenarios.nodes import plot_array, select_by_date
+from science.pipelines.zooms.nodes import clip_to_boundary
+
+
+def create_pipeline(**kwargs) -> Pipeline:
+    scenarios_base_video_pipeline = pipeline(
+        [
+            node(
+                func=georef_nextgems_dataset,
+                inputs="raw",
+                outputs="georef",
+            ),
+            node(
+                func=clip_to_boundary,
+                inputs=["georef", "params:bbox"],
+                outputs=["crop", "_"],
+            ),
+            node(
+                func=split_by_timestep,
+                inputs="crop",
+                outputs="parts",
+            ),
+            node(
+                func=get_min_max,
+                inputs=["crop", "params:video"],
+                outputs="minmax",
+            ),
+            node(
+                func=parts_to_video,
+                inputs=["parts", "params:video", "minmax"],
+                outputs="video",
+            ),
+        ]
+    )
+
+    static_image_base_pipeline = pipeline(
+        [
+            node(
+                func=georef_nextgems_dataset,
+                inputs="raw",
+                outputs="georef",
+            ),
+            node(
+                func=select_by_date,
+                inputs=["georef", "params:image"],
+                outputs="selection",
+            ),
+            node(
+                func=clip_to_boundary,
+                inputs=["selection", "params:bbox"],
+                outputs=["crop", "_"],
+            ),
+            node(
+                func=get_min_max,
+                inputs=["crop", "params:image"],
+                outputs="minmax",
+            ),
+            node(
+                func=plot_array,
+                inputs=["crop", "minmax", "params:image"],
+                outputs="image",
+            ),
+        ]
+    )
+
+    europe_plus2k_pipe = pipeline(
+        pipe=static_image_base_pipeline,
+        namespace="europe_plus2k",
+        parameters={
+            "image": "params:europe_scenario",
+            "bbox": "params:europe_scenario.bbox",
+        },
+        tags=["europe"],
+    )
+    europe_hist_pipe = pipeline(
+        pipe=static_image_base_pipeline,
+        namespace="europe_hist",
+        parameters={
+            "image": "params:europe_scenario",
+            "bbox": "params:europe_scenario.bbox",
+        },
+        tags=["europe"],
+    )
+
+    iberia_plus2k_pipe = pipeline(
+        pipe=static_image_base_pipeline,
+        namespace="iberia_plus2k",
+        parameters={"image": "params:scenarios"},
+        tags=["iberia"],
+    )
+
+    iberia_hist_pipe = pipeline(
+        pipe=static_image_base_pipeline,
+        namespace="iberia_hist",
+        parameters={"image": "params:scenarios"},
+        tags=["iberia"],
+    )
+
+    observations_pipe = pipeline(
+        pipe=scenarios_base_video_pipeline,
+        namespace="observations",
+        parameters={"video": "params:observations", "bbox": "params:observations.bbox"},
+        tags=["obs"],
+    )
+    return (
+        europe_plus2k_pipe
+        + europe_hist_pipe
+        + iberia_plus2k_pipe
+        + iberia_hist_pipe
+        + observations_pipe
+    )
diff --git a/science/src/science/pipelines/zooms/__init__.py b/science/src/science/pipelines/zooms/__init__.py
new file mode 100644
index 0000000..a115836
--- /dev/null
+++ b/science/src/science/pipelines/zooms/__init__.py
@@ -0,0 +1,3 @@
+from .pipeline import create_pipeline
+
+__all__ = ["create_pipeline"]
diff --git a/science/src/science/pipelines/zooms/nodes.py b/science/src/science/pipelines/zooms/nodes.py
new file mode 100644
index 0000000..23ff0f9
--- /dev/null
+++ b/science/src/science/pipelines/zooms/nodes.py
@@ -0,0 +1,108 @@
+import logging
+from io import BytesIO
+from typing import Any, Callable
+
+import httpx
+import matplotlib
+import matplotlib.colors
+import numpy as np
+import xarray as xr
+from kedro_datasets.video.video_dataset import SequenceVideo
+from PIL import Image
+from skimage.transform import rescale
+
+log = logging.getLogger(__name__)
+
+RAIN_CMAP_RAW = [
+    [0, [40, 16, 158, 0]],
+    [3, [40, 16, 158, 20]],
+    [8, [40, 16, 158, 100]],
+    [14, [0, 101, 154, 180]],
+    [20, [0, 144, 147, 220]],
+    [26, [0, 179, 125, 240]],
+    [32, [117, 208, 89, 255]],
+    [36, [220, 220, 30, 255]],
+    [40, [244, 202, 8, 255]],
+    [44, [245, 168, 24, 255]],
+    [48, [236, 130, 63, 255]],
+    [52, [205, 75, 75, 255]],
+    [56, [182, 45, 100, 255]],
+    [60, [156, 16, 109, 255]],
+    [64, [125, 0, 108, 255]],
+    [68, [92, 0, 100, 255]],
+    [100, [0, 0, 0, 255]],
+    [101, [0, 0, 0, 0]],
+    [255, [0, 0, 0, 0]],
+]
+
+
+RAIN_CMAP = np.asarray([x[1] for x in RAIN_CMAP_RAW])
+RAIN_CMAP_IDX = np.asarray([x[0] for x in RAIN_CMAP_RAW])
+
+
+def clip_to_boundary(
+    raster: xr.Dataset, bbox: dict[str, float] | None = None
+) -> tuple[xr.Dataset, tuple[int, int]]:
+    """Clip the raster to the boundary area"""
+    log.info(f"Clipping to {bbox=}")
+    if not bbox:
+        return raster, (raster.sizes["x"], raster.sizes["y"])
+    clipped_raster = raster.rio.clip_box(**bbox)
+    return clipped_raster, (clipped_raster.sizes["x"], clipped_raster.sizes["y"])
+
+
+def get_basemap(
+    credentials: dict, shape: tuple[int, int], params: dict[str, Any]
+) -> Image.Image:
+    bbox_flat = [v for _, v in params["bbox"].items()]
+    w = shape[0] * params["scale"]
+    h = shape[1] * params["scale"]
+    url = f"https://api.mapbox.com/styles/v1/bielstela/{params['basemap_style']}/static/{bbox_flat}/{w}x{h}"
+    log.info(f"Downloading basemap from: {url}")
+    res = httpx.get(url, params={"access_token": credentials["token"]})
+    assert res.status_code == httpx.codes(
+        200
+    ), f"Failed request with {res.status_code}, {res.content}"
+    return Image.open(BytesIO(res.content)).convert("RGB")
+
+
+def parts_to_video_with_basemap(
+    parts: dict[str, Callable[[], xr.DataArray]],
+    params: dict[str, Any],
+    basemap: Image.Image,
+    min_max: tuple[float, float] | None = None,
+) -> SequenceVideo:
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
+        "rain", RAIN_CMAP / 255, N=256
+    )
+    cmap_lut = cmap(range(256))
+    cmap_lut = (cmap_lut * 255).astype(np.uint8)
+    imgs = []
+    for _, dataset in parts.items():
+        if callable(dataset):
+            # This comes from a partition dataset which is a callable only if reading files.
+            # When it is a memoryfile it is not a callable
+            dataset = dataset()[0]  # noqa: PLW2901
+        # flip array to make the 0,0 origin of the image at the upper corner for PIL
+        grey = np.flipud(dataset.values)
+        scale_factor = params.get("scale")
+        if scale_factor and scale_factor > 1:
+            grey = rescale(grey, scale_factor)
+
+        _min = min_max[0] if min_max is not None else np.nanmin(grey)
+        _max = min_max[1] if min_max is not None else np.nanmax(grey)
+
+        # # scale the values to 0-255
+        # grey = (grey.astype(float) - _min) * 255 / (_max - _min)
+        # grey = np.clip(grey, a_min=0, a_max=255)
+        norm = matplotlib.colors.PowerNorm(0.7, _min, _max, clip=True)
+        grey = (norm(grey) * 255).astype(np.uint8)
+        result = np.zeros((*grey.shape, 4), dtype=np.uint8)
+        np.take(cmap_lut, grey, axis=0, out=result)
+        foreground_image = Image.fromarray(result, mode="RGBA")
+        background_image = basemap.copy().convert("RGBA")
+        imgs.append(
+            Image.alpha_composite(background_image, foreground_image).convert("RGB")
+        )
+    log.info(f"Videos has {len(imgs)} frames")
+    return SequenceVideo(imgs, fps=24)
diff --git a/science/src/science/pipelines/zooms/pipeline.py b/science/src/science/pipelines/zooms/pipeline.py
new file mode 100644
index 0000000..676aeb7
--- /dev/null
+++ b/science/src/science/pipelines/zooms/pipeline.py
@@ -0,0 +1,134 @@
+from kedro.pipeline import Pipeline, node
+from kedro.pipeline.modular_pipeline import pipeline
+
+from science.pipelines.common_nodes import (
+    georef_nextgems_dataset,
+    get_min_max,
+    parts_to_video,
+    split_by_timestep,
+)
+from science.pipelines.zooms.nodes import (
+    clip_to_boundary,
+    get_basemap,
+    parts_to_video_with_basemap,
+)
+
+
+def create_pipeline(**kwargs) -> Pipeline:
+    zoom_in_base_pipeline = pipeline(
+        [
+            node(
+                func=georef_nextgems_dataset,
+                inputs="raw",
+                outputs="georef",
+            ),
+            node(
+                func=clip_to_boundary,
+                inputs=["georef", "params:bbox"],
+                outputs=["clipped", "shape"],
+            ),
+            node(
+                func=get_min_max,
+                inputs=["clipped", "params:video"],
+                outputs="minmax",
+            ),
+            node(
+                func=split_by_timestep,
+                inputs="clipped",
+                outputs="local_parts",
+            ),
+            node(
+                func=parts_to_video,
+                inputs=["local_parts", "params:video", "minmax"],
+                outputs="video",
+            ),
+        ]
+    )
+
+    amazonia_precipitation_pipe = pipeline(
+        [
+            node(
+                func=georef_nextgems_dataset,
+                inputs="total_precipitation_10km.raw",
+                outputs="total_precipitation_10km.georef",
+            ),
+            node(
+                func=clip_to_boundary,
+                inputs=[
+                    "total_precipitation_10km.georef",
+                    "params:total_precipitation_10km.bbox",
+                ],
+                outputs=[
+                    "total_precipitation_10km.clipped",
+                    "total_precipitation_10km.shape",
+                ],
+            ),
+            node(
+                func=get_basemap,
+                inputs=[
+                    "total_precipitation_10km.mapbox_credentials",
+                    "total_precipitation_10km.shape",
+                    "params:total_precipitation_10km",
+                ],
+                outputs="total_precipitation_10km.basemap",
+            ),
+            node(
+                func=get_min_max,
+                inputs=[
+                    "total_precipitation_10km.clipped",
+                    "params:total_precipitation_10km",
+                ],
+                outputs="total_precipitation_10km.minmax",
+            ),
+            node(
+                func=split_by_timestep,
+                inputs="total_precipitation_10km.clipped",
+                outputs="total_precipitation_10km.local_parts",
+            ),
+            node(
+                func=parts_to_video_with_basemap,
+                inputs=[
+                    "total_precipitation_10km.local_parts",
+                    "params:total_precipitation_10km",
+                    "total_precipitation_10km.basemap",
+                    "total_precipitation_10km.minmax",
+                ],
+                outputs="total_precipitation_10km.video",
+            ),
+        ],
+        tags=["precipitation", "zoomin", "high_resoltuion"],
+    )
+
+    hurricane_cloud_pipe = pipeline(
+        pipe=zoom_in_base_pipeline,
+        namespace="cloud_cover_10km",
+        parameters={
+            "bbox": "params:hurricane_10km_render_params.bbox",
+            "video": "params:hurricane_10km_render_params",
+        },
+        tags=["cloudcover", "zoomin", "high_resoltuion"],
+    )
+
+    temperature_pipe = pipeline(
+        pipe=zoom_in_base_pipeline,
+        namespace="temperature_10km",
+        parameters={
+            "bbox": "params:temperature_10km_render_params.bbox",
+            "video": "params:temperature_10km_render_params",
+        },
+        tags=["temperature", "zoomin", "high_resolution"],
+    )
+
+    sst_pipe = pipeline(
+        pipe=zoom_in_base_pipeline,
+        namespace="sst_10km",
+        parameters={
+            "bbox": "params:sst_10km_render_params.bbox",
+            "video": "params:sst_10km_render_params",
+        },
+        tags=["sst", "zoomin", "high_resolution"],
+    )
+
+    return (
+        hurricane_cloud_pipe + amazonia_precipitation_pipe + temperature_pipe + sst_pipe
+    )
diff --git a/science/data/03_primary/ws-10-parts/.gitkeep b/science/tests/pipelines/test/__init__.py
similarity index 100%
rename from science/data/03_primary/ws-10-parts/.gitkeep
rename to science/tests/pipelines/test/__init__.py