diff --git a/CHANGELOG.md b/CHANGELOG.md
index afda4119c4423d29f6b70a6c6b8168beda26dd78..0f34100e56a1fdb73d55ff3382624c0d48f018e4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,8 +20,10 @@
             - `curl` & `wget`, see [wget_curl.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/wget_curl.ipynb)
             - `intake`, see [intake.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/intake.ipynb)
         - `IRIDL` including overview, see [IRIDL.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/IRIDL.ipynb)
+        - `intake` catalogs for `IRIDL` [`SubX`](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/SubX_catalog.yml) and [`S2S`](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/S2S_catalog.yml) see [IRIDL.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/IRIDL.ipynb) (#9 !10, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring)
     - SubX-Project models: `IRIDL` including overview, see [IRIDL.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/IRIDL.ipynb)
     - How to access password-protected S2S-Project output from IRIDL with xarray? see [IRIDL.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/IRIDL.ipynb)
+    - Access NOAA CPC observations via opendap from `IRIDL`, see [IRIDL.ipynb](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/data_access/IRIDL.ipynb) (#9 !10, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring)
 - fix `netcdf4` version to `1.5.4` for `opendap` to work lazily with `xarray` (!2, !7, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring))
 
 
diff --git a/environment.yml b/environment.yml
index 6e3ac7d425de3f4946d3dbe6536032b132210336..dee69b4205f8d526b70b69138e65b0a4438e49eb 100644
--- a/environment.yml
+++ b/environment.yml
@@ -24,8 +24,8 @@ dependencies:
   - netcdf4
   - pip
   - pip:
-    - climetlab >= 0.7.0
-    - climetlab_s2s_ai_challenge >= 0.6.3
+    - climetlab >= 0.8.0
+    - climetlab_s2s_ai_challenge >= 0.7.1
     - configargparse # for weatherbench
     - netcdf4==1.5.4
 prefix: "/opt/conda"
diff --git a/notebooks/data_access/IRIDL.ipynb b/notebooks/data_access/IRIDL.ipynb
index f1e11dfd484a232188b6ea8696652d5d4f04d8b7..08a19b1587110d6ee5b487fdd7362a7cdea84d92 100644
--- a/notebooks/data_access/IRIDL.ipynb
+++ b/notebooks/data_access/IRIDL.ipynb
@@ -4,14 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Access from `iridl.ldeo.columbia.edu`"
+    "# `iridl.ldeo.columbia.edu`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "IRI Data Library (IRIDL) hosts various subseasonal initialized forecast and hindcast simulations:\n",
+    "IRI Data Library (IRIDL) hosts various subseasonal initialized forecast, hindcast simulations and observations:\n",
     "- `S2S project`:\n",
     "    - http://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/\n",
     "        - hindcast/reforecast: one variable, one model:\n",
@@ -20,15 +20,39 @@
     "    - http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/\n",
     "        - hindcast/reforecast: one variable, one model:\n",
     "        - login not required\n",
+    "- `NOAA CPC` observations:\n",
+    "    - ground truth for `s2s-ai-challenge`\n",
+    "    - login not required\n",
+    "    - `pr`: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.extREALTIME/.rain/\n",
+    "    - `t2m`: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/\n",
+    "---\n",
     "- Notes:\n",
     "    - Output on IRIDL is not always on the 1.5 degree grid requested for the competition. Also dimension names and coordinates differ.\n",
     "    - Beware that most models are not only initialized on thursdays. It is not forbidden to use simulations which are started on other weekdays, buy please pay attention that you may only use information available on `forecast_time`, i.e. if the model is initialized on Mondays, you have to use the day 14+3=17 to day 27+3=30 forecast for week 3-4.\n",
+    "    - Beware that conventions (variable name, standard_name, coordinates, output format) from `SubX` and `S2S` might differ.\n",
+    "    - First check whether you can find 2020 `test` and previous `training` data for a given feature.\n",
     "---\n",
-    "This notebook also provides opendap magic, i.e. commands added to the opendap URL which preprocess data server-side. (not implemented)\n",
+    "This notebook also provides opendap magic, i.e. commands added to the opendap URL which preprocess data server-side: select and aggregate `lead_time` `L` and `forecast_time` `S`. Regridding `X` and `Y`.\n",
     "\n",
     "---"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Todo:\n",
+    "- [ ] how to handle `hdate` best?\n",
+    "- [ ] how to not download empty `S`? specify weekly stride in `S` via ingrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `IRIDL` cookie"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -52,7 +76,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing /work/s2s-ai-challenge-template/.daprc\n"
+      "Overwriting /work/s2s-ai-challenge-template/.daprc\n"
      ]
     }
    ],
@@ -89,7 +113,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing /work/s2s-ai-challenge-template/.cookie_iridl\n"
+      "Overwriting /work/s2s-ai-challenge-template/.cookie_iridl\n"
      ]
     }
    ],
@@ -100,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -110,21 +134,19 @@
       "/opt/conda/lib/python3.8/site-packages/xarray/backends/cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.core.options.set_options at 0x7efe3cb51fd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "import xarray as xr\n",
-    "xr.set_options(display_style='text')"
+    "xr.set_options(display_style='text')\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# S2S"
    ]
   },
   {
@@ -139,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,45 +192,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre>&lt;xarray.DataArray &#x27;t2m&#x27; (hdate: 26, forecast_time: 637, lead_time: 46, latitude: 121, longitude: 240)&gt;\n",
-       "dask.array&lt;open_dataset-f89df07098f6ce22c120a08e3f3f29a52t, shape=(26, 637, 46, 121, 240), dtype=float32, chunksize=(8, 91, 15, 46, 60), chunktype=numpy.ndarray&gt;\n",
+       "<pre>&lt;xarray.DataArray &#x27;t2m&#x27; (hdate: 26, forecast_time: 641, lead_time: 46, latitude: 121, longitude: 240)&gt;\n",
+       "dask.array&lt;open_dataset-f89df07098f6ce22c120a08e3f3f29a52t, shape=(26, 641, 46, 121, 240), dtype=float32, chunksize=(7, 197, 12, 33, 60), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
        "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
-       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
        "  * hdate          (hdate) object 1995-07-01 00:00:00 ... 2020-07-01 00:00:00\n",
-       "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-06-17\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-07-01\n",
        "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
        "Attributes:\n",
        "    pointwidth:      0\n",
        "    gribPDSpattern:  04XXXX003D0000\n",
+       "    standard_name:   air_temperature\n",
        "    long_name:       2-meter Temperature\n",
-       "    units:           K\n",
-       "    standard_name:   air_temperature</pre>"
+       "    units:           K</pre>"
       ],
       "text/plain": [
-       "<xarray.DataArray 't2m' (hdate: 26, forecast_time: 637, lead_time: 46, latitude: 121, longitude: 240)>\n",
-       "dask.array<open_dataset-f89df07098f6ce22c120a08e3f3f29a52t, shape=(26, 637, 46, 121, 240), dtype=float32, chunksize=(8, 91, 15, 46, 60), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray 't2m' (hdate: 26, forecast_time: 641, lead_time: 46, latitude: 121, longitude: 240)>\n",
+       "dask.array<open_dataset-f89df07098f6ce22c120a08e3f3f29a52t, shape=(26, 641, 46, 121, 240), dtype=float32, chunksize=(7, 197, 12, 33, 60), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
        "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
-       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
        "  * hdate          (hdate) object 1995-07-01 00:00:00 ... 2020-07-01 00:00:00\n",
-       "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-06-17\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-07-01\n",
        "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
        "Attributes:\n",
        "    pointwidth:      0\n",
        "    gribPDSpattern:  04XXXX003D0000\n",
+       "    standard_name:   air_temperature\n",
        "    long_name:       2-meter Temperature\n",
-       "    units:           K\n",
-       "    standard_name:   air_temperature"
+       "    units:           K"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,16 +242,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(88.496735436, 'GB')"
+       "(89.052444908, 'GB')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -296,53 +318,53 @@
       "ECCC on-the-fly forecast_time freq:W-THU \n",
       " Coordinates:\n",
       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
-      "  * hdate          (hdate) object 1995-07-01 00:00:00 ... 2017-07-01 00:00:00\n",
-      "  * forecast_time  (forecast_time) datetime64[ns] 2016-01-07 ... 2021-06-03\n",
+      "  * forecast_time  (forecast_time) datetime64[ns] 2016-01-07 ... 2021-06-10\n",
       "  * realization    (realization) float32 1.0 2.0 3.0\n",
       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+      "  * hdate          (hdate) object 1995-07-01 00:00:00 ... 2017-07-01 00:00:00\n",
       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 31 days 12... \n",
-      " Frozen(SortedKeysDict({'latitude': 121, 'hdate': 23, 'forecast_time': 283, 'realization': 3, 'longitude': 240, 'lead_time': 32})) 72.5842064 GB \n",
+      " Frozen(SortedKeysDict({'latitude': 121, 'forecast_time': 284, 'realization': 3, 'longitude': 240, 'hdate': 23, 'lead_time': 32})) 72.840687688 GB \n",
       "\n",
       "ECMF on-the-fly forecast_time freq not found \n",
       " Coordinates:\n",
       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
-      "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
       "  * hdate          (hdate) object 1995-07-01 00:00:00 ... 2020-07-01 00:00:00\n",
-      "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-06-17\n",
+      "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 45 days 12...\n",
+      "  * forecast_time  (forecast_time) datetime64[ns] 2015-05-14 ... 2021-07-01\n",
       "  * realization    (realization) float32 1.0 2.0 3.0 4.0 ... 7.0 8.0 9.0 10.0\n",
       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5 \n",
-      " Frozen(SortedKeysDict({'latitude': 121, 'lead_time': 46, 'hdate': 26, 'forecast_time': 637, 'realization': 10, 'longitude': 240})) 884.967290356 GB \n",
+      " Frozen(SortedKeysDict({'latitude': 121, 'hdate': 26, 'lead_time': 46, 'forecast_time': 641, 'realization': 10, 'longitude': 240})) 890.524384788 GB \n",
       "\n",
       "HMCR on-the-fly forecast_time freq not found \n",
       " Coordinates:\n",
-      "  * latitude       (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 60 days 12...\n",
-      "  * hdate          (hdate) object 1985-07-01 00:00:00 ... 2010-07-01 00:00:00\n",
-      "  * forecast_time  (forecast_time) datetime64[ns] 2015-01-07 ... 2021-06-03\n",
+      "  * latitude       (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
+      "  * forecast_time  (forecast_time) datetime64[ns] 2015-01-07 ... 2021-06-10\n",
       "  * realization    (realization) float32 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0\n",
-      "  * longitude      (longitude) float32 0.0 2.507 5.014 ... 353.5 356.0 358.5 \n",
-      " Frozen(SortedKeysDict({'latitude': 73, 'lead_time': 61, 'hdate': 26, 'forecast_time': 335, 'realization': 9, 'longitude': 144})) 201.0647102 GB \n",
+      "  * longitude      (longitude) float32 0.0 2.507 5.014 ... 353.5 356.0 358.5\n",
+      "  * hdate          (hdate) object 1985-07-01 00:00:00 ... 2010-07-01 00:00:00 \n",
+      " Frozen(SortedKeysDict({'lead_time': 61, 'latitude': 73, 'forecast_time': 336, 'realization': 9, 'longitude': 144, 'hdate': 26})) 201.66490336 GB \n",
       "\n",
       "model=ISAC failed due to OSError: [Errno -90] NetCDF: file not found: b'https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ISAC/.reforecast/.perturbed/.2m_above_ground/.2t/dods' \n",
       "\n",
       "JMA not on-the-fly forecast_time freq:D \n",
       " Coordinates:\n",
+      "  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 32 days 33 days\n",
       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
       "  * forecast_time  (forecast_time) datetime64[ns] 1981-01-10T12:00:00 ... 201...\n",
       "  * realization    (realization) float32 1.0 2.0 3.0 4.0\n",
-      "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
-      "  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 32 days 33 days \n",
-      " Frozen(SortedKeysDict({'latitude': 121, 'forecast_time': 10948, 'realization': 4, 'longitude': 240, 'lead_time': 33})) 167.867087068 GB \n",
+      "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5 \n",
+      " Frozen(SortedKeysDict({'lead_time': 33, 'latitude': 121, 'forecast_time': 10948, 'realization': 4, 'longitude': 240})) 167.867087068 GB \n",
       "\n",
       "KMA on-the-fly forecast_time freq:D \n",
       " Coordinates:\n",
+      "  * hdate          (hdate) object 1991-07-01 00:00:00 ... 2010-07-01 00:00:00\n",
       "  * latitude       (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
       "  * lead_time      (lead_time) timedelta64[ns] 0 days 12:00:00 ... 59 days 12...\n",
-      "  * hdate          (hdate) object 1991-07-01 00:00:00 ... 2010-07-01 00:00:00\n",
-      "  * forecast_time  (forecast_time) datetime64[ns] 2016-11-01 ... 2021-06-01\n",
+      "  * forecast_time  (forecast_time) datetime64[ns] 2016-11-01 ... 2021-06-17\n",
       "  * realization    (realization) float32 1.0 2.0\n",
       "  * longitude      (longitude) float32 0.0 2.507 5.014 ... 353.5 356.0 358.5 \n",
-      " Frozen(SortedKeysDict({'latitude': 73, 'lead_time': 60, 'hdate': 20, 'forecast_time': 1674, 'realization': 2, 'longitude': 144})) 168.932059708 GB \n",
+      " Frozen(SortedKeysDict({'hdate': 20, 'latitude': 73, 'lead_time': 60, 'forecast_time': 1690, 'realization': 2, 'longitude': 144})) 170.546703036 GB \n",
       "\n",
       "NCEP not on-the-fly forecast_time freq:D \n",
       " Coordinates:\n",
@@ -410,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -447,11 +469,11 @@
        "  * longitude      (longitude) float32 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
        "Attributes:\n",
        "    pointwidth:     0.0\n",
+       "    cell_methods:   time: mean\n",
+       "    units:          Kelvin_scale\n",
        "    standard_name:  air_temperature\n",
        "    long_name:      2-meter Air Temperature\n",
-       "    level_type:     2 meters above ground\n",
-       "    cell_methods:   time: mean\n",
-       "    units:          Kelvin_scale</pre>"
+       "    level_type:     2 meters above ground</pre>"
       ],
       "text/plain": [
        "<xarray.DataArray 't2m' (forecast_time: 887, realization: 10, lead_time: 45, latitude: 181, longitude: 360)>\n",
@@ -464,14 +486,14 @@
        "  * longitude      (longitude) float32 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
        "Attributes:\n",
        "    pointwidth:     0.0\n",
+       "    cell_methods:   time: mean\n",
+       "    units:          Kelvin_scale\n",
        "    standard_name:  air_temperature\n",
        "    long_name:      2-meter Air Temperature\n",
-       "    level_type:     2 meters above ground\n",
-       "    cell_methods:   time: mean\n",
-       "    units:          Kelvin_scale"
+       "    level_type:     2 meters above ground"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -492,7 +514,7 @@
        "(104.03446566, 'GB')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -525,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -678,9 +700,18 @@
     "- https://iridl.ldeo.columbia.edu/dochelp/Documentation/funcindex.html?Set-Language=en"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `curl` or `wget`\n",
+    "\n",
+    "You can always work file-based and download from IRIDL via `curl` or `wget`. However, nicer is direct access via `opendap` and `xarray`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -689,7 +720,7 @@
        "0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -735,99 +766,1305 @@
        "    tp             (realization, forecast_time, latitude, longitude) float64 ..."
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
     "ds = xr.open_dataset(fname).rename({'X':'longitude', 'Y':'latitude', 'S':'forecast_time', 'M':'realization', 'aprod':'tp'}).assign_coords(lead_time=pd.Timedelta('14 d'))\n",
     "ds"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## opendap"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.080404184 GB\n"
+      "1.132103944 GB\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<pre>&lt;xarray.DataArray &#x27;tp&#x27; (forecast_time: 886, realization: 11, latitude: 181, longitude: 360)&gt;\n",
-       "dask.array&lt;open_dataset-187ec1ad5a15edadd11711d7cbe1f114pr, shape=(886, 11, 181, 360), dtype=float64, chunksize=(423, 4, 82, 120), chunktype=numpy.ndarray&gt;\n",
+       "<pre>&lt;xarray.DataArray &#x27;tp&#x27; (forecast_time: 886, realization: 11, latitude: 121, longitude: 240)&gt;\n",
+       "dask.array&lt;mul, shape=(886, 11, 121, 240), dtype=float32, chunksize=(443, 6, 102, 120), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
-       "  * latitude       (latitude) float32 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0\n",
        "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2016-12-28\n",
-       "  * longitude      (longitude) float32 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
        "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0 10.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
        "    lead_time      timedelta64[ns] 14 days\n",
-       "Attributes: (12/13)\n",
-       "    pointwidth:                                     0.0\n",
-       "    standard_name:                                  precipitation_flux\n",
-       "    parameter_template_discipline_category_number:  1\\n0\\n1\\n8\n",
-       "    parameter_discipline_and_category:              Meteorological products, ...\n",
-       "    long_name:                                      Total Precipitation\n",
-       "    grid_type:                                      Latitude/longitude\n",
-       "    ...                                             ...\n",
-       "    level:                                          0.0\n",
-       "    center:                                         US National Weather Servi...\n",
-       "    production_status:                              Operational products\n",
-       "    level_type:                                     surface\n",
-       "    file_missing_value:                             1e+20\n",
-       "    history:                                        Averaged over L[14.5 days...</pre>"
+       "Attributes:\n",
+       "    units:      kg m-2\n",
+       "    long_name:  total precipitation</pre>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'tp' (forecast_time: 886, realization: 11, latitude: 181, longitude: 360)>\n",
-       "dask.array<open_dataset-187ec1ad5a15edadd11711d7cbe1f114pr, shape=(886, 11, 181, 360), dtype=float64, chunksize=(423, 4, 82, 120), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray 'tp' (forecast_time: 886, realization: 11, latitude: 121, longitude: 240)>\n",
+       "dask.array<mul, shape=(886, 11, 121, 240), dtype=float32, chunksize=(443, 6, 102, 120), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * latitude       (latitude) float32 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0\n",
        "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2016-12-28\n",
-       "  * longitude      (longitude) float32 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
        "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0 10.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
        "    lead_time      timedelta64[ns] 14 days\n",
-       "Attributes: (12/13)\n",
-       "    pointwidth:                                     0.0\n",
-       "    standard_name:                                  precipitation_flux\n",
-       "    parameter_template_discipline_category_number:  1\\n0\\n1\\n8\n",
-       "    parameter_discipline_and_category:              Meteorological products, ...\n",
-       "    long_name:                                      Total Precipitation\n",
-       "    grid_type:                                      Latitude/longitude\n",
-       "    ...                                             ...\n",
-       "    level:                                          0.0\n",
-       "    center:                                         US National Weather Servi...\n",
-       "    production_status:                              Operational products\n",
-       "    level_type:                                     surface\n",
-       "    file_missing_value:                             1e+20\n",
-       "    history:                                        Averaged over L[14.5 days..."
+       "Attributes:\n",
+       "    units:      kg m-2\n",
+       "    long_name:  total precipitation"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# aggregate w34 precip to tp\n",
-    "ds = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%202000)/(0000%2031%20Dec%202019)/RANGEEDGES/L/(14)/(28)/RANGEEDGES/[L]sum/dods',\n",
+    "# server-side aggregate w34 precip to tp and regrid to 1.5 degree\n",
+    "forecast = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%202000)/(0000%2031%20Dec%202019)/RANGEEDGES/L/(14)/(28)/RANGEEDGES/[L]average/X/0/1.5/358.5/GRID/Y/90/1.5/-90/GRID/dods',\n",
     "                     chunks='auto').rename({'X':'longitude', 'Y':'latitude', 'S':'forecast_time', 'M':'realization', 'pr':'tp'}).assign_coords(lead_time=pd.Timedelta('14 d'))\n",
-    "print(ds.nbytes/1e9,'GB')\n",
-    "ds.tp"
+    "print(forecast.nbytes/1e9, 'GB')\n",
+    "\n",
+    "forecast = forecast * 3600 * 24 * 14 # convert from kg m-2 s-1 to to kg m-2 14day-1\n",
+    "forecast['tp'].attrs.update({'units':'kg m-2', 'long_name': 'total precipitation'})  # omitting biweekly is units\n",
+    "forecast.tp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### create `forecast_like_observations` from observations with `time` dimension\n",
+    "\n",
+    "[climetlab_s2s_ai_challenge.extra.forecast_like_observations](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge/blob/47539214d78251e30b79bd4b0dbb4a6a085cc9bf/climetlab_s2s_ai_challenge/extra.py#L43)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import climetlab as cml\n",
+    "from climetlab_s2s_ai_challenge.extra import forecast_like_observations, create_valid_time_from_forecast_time_and_lead_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data.  This dataset has been dowloaded from IRIDL. By downloading this data you also agree to the terms and conditions defined at https://iridl.ldeo.columbia.edu.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:00<00:00, 147.08it/s]\n",
+      "/opt/conda/lib/python3.8/site-packages/climetlab/readers/__init__.py:86: UserWarning: Error loading wrapper grib: /opt/conda/lib/python3.8/site-packages/gribapi/_bindings.cpython-38-x86_64-linux-gnu.so: undefined symbol: codes_bufr_key_is_header\n",
+      "  warnings.warn(f\"Error loading wrapper {name}: {e}\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8154)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-04-28\n",
+       "  * latitude   (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "Data variables:\n",
+       "    pr         (time, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...\n",
+       "    created_by_software:  climetlab-s2s-ai-challenge\n",
+       "    created_by_script:    tools/observations/makefile</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8154)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-04-28\n",
+       "  * latitude   (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "Data variables:\n",
+       "    pr         (time, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...\n",
+       "    created_by_software:  climetlab-s2s-ai-challenge\n",
+       "    created_by_script:    tools/observations/makefile"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# download precipitation_flux pr\n",
+    "obs_ds = cml.load_dataset('s2s-ai-challenge-observations', parameter='pr').to_xarray()\n",
+    "\n",
+    "obs_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# less data heavy example: reduce forecast\n",
+    "forecast = forecast.sel(forecast_time='2000', realization=[0, 1]).expand_dims('lead_time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign valid_time to forecasts\n",
+    "forecast = forecast.assign_coords(valid_time=create_valid_time_from_forecast_time_and_lead_time(forecast.forecast_time, forecast.lead_time))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (forecast_time: 51, latitude: 121, lead_time: 30, longitude: 240, realization: 2)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2000-12-27\n",
+       "  * realization    (realization) float32 0.0 1.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 13 days 14 days ... 42 days\n",
+       "    valid_time     (lead_time, forecast_time) datetime64[ns] 2000-01-26 ... 2...\n",
+       "Data variables:\n",
+       "    *empty*\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (forecast_time: 51, latitude: 121, lead_time: 30, longitude: 240, realization: 2)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2000-12-27\n",
+       "  * realization    (realization) float32 0.0 1.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 13 days 14 days ... 42 days\n",
+       "    valid_time     (lead_time, forecast_time) datetime64[ns] 2000-01-26 ... 2...\n",
+       "Data variables:\n",
+       "    *empty*\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create an empty daily lead_time forecast dataset to input into forecast_like_observations, which requires continuous daily leads, first lead_time stays empty\n",
+    "dummy_forecast_daily = forecast.coords.to_dataset().isel(lead_time=[0]*30).assign_coords(lead_time=[pd.Timedelta(f'{i} d') for i in range(13,13+30)])\n",
+    "dummy_forecast_daily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (forecast_time: 51, latitude: 121, lead_time: 30, longitude: 240)\n",
+       "Coordinates:\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 13 days 14 days ... 42 days\n",
+       "    valid_time     (lead_time, forecast_time) datetime64[ns] 2000-01-26 ... 2...\n",
+       "  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2000-12-27\n",
+       "Data variables:\n",
+       "    tp             (lead_time, forecast_time, latitude, longitude) float32 na...\n",
+       "Attributes:\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...\n",
+       "    created_by_software:  climetlab-s2s-ai-challenge\n",
+       "    created_by_script:    tools/observations/makefile\n",
+       "    function:             climetlab_s2s_ai_challenge.extra.forecast_like_obse...</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (forecast_time: 51, latitude: 121, lead_time: 30, longitude: 240)\n",
+       "Coordinates:\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 13 days 14 days ... 42 days\n",
+       "    valid_time     (lead_time, forecast_time) datetime64[ns] 2000-01-26 ... 2...\n",
+       "  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2000-01-12 ... 2000-12-27\n",
+       "Data variables:\n",
+       "    tp             (lead_time, forecast_time, latitude, longitude) float32 na...\n",
+       "Attributes:\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...\n",
+       "    created_by_software:  climetlab-s2s-ai-challenge\n",
+       "    created_by_script:    tools/observations/makefile\n",
+       "    function:             climetlab_s2s_ai_challenge.extra.forecast_like_obse..."
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create tp from pr and creates forecast_time and lead_time from valid_time\n",
+    "obs_lead_time_forecast_time = forecast_like_observations(dummy_forecast_daily, obs_ds)\n",
+    "obs_lead_time_forecast_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f1b8aae3c10>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "obs_lead_time_forecast_time.isel(forecast_time=0, lead_time=15).tp.plot(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.2 s, sys: 2.17 s, total: 7.37 s\n",
+      "Wall time: 18.2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time forecast = forecast.isel(forecast_time=0, lead_time=0, realization=0).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f1b8895b190>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forecast.tp.plot(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## speedtest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.132103944 GB\n",
+      "CPU times: user 21.6 s, sys: 12.8 s, total: 34.4 s\n",
+      "Wall time: 9min 47s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# speedtest: for one variable to download: server-side lead aggregate and regrid: 1.1 GB to download\n",
+    "forecast = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.EMC/.GEFS/.hindcast/.pr/S/(0000%206%20Jan%202000)/(0000%2031%20Dec%202019)/RANGEEDGES/L/(14)/(28)/RANGEEDGES/[L]average/X/0/1.5/358.5/GRID/Y/90/1.5/-90/GRID/dods',\n",
+    "                     chunks='auto').rename({'X':'longitude', 'Y':'latitude', 'S':'forecast_time', 'M':'realization', 'pr':'tp'}).assign_coords(lead_time=pd.Timedelta('14 d'))\n",
+    "print(forecast.nbytes/1e9, 'GB')\n",
+    "forecast = forecast.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IRIDL `intake` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build [`intake` catalogs](https://intake.readthedocs.io/en/latest/catalog.html#yaml-format) to access `S2S` and `SubX` model output easier by reducing download size with server-side preprocessing. Preprocessed data is opened via `opendap` in `xarray`.\n",
+    "\n",
+    "Note that `lead_time` aggregation here is not fine-tuned for parameters to directly match biweekly aggregates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SubX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting SubX_catalog.yml\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile SubX_catalog.yml\n",
+    "plugins:\n",
+    "  source:\n",
+    "    - module: intake_xarray\n",
+    "\n",
+    "sources:\n",
+    "  training-input:\n",
+    "    description: SubX hindcasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html\n",
+    "    driver: opendap\n",
+    "    parameters:\n",
+    "      center:\n",
+    "        description: name of the center issuing the hindcast\n",
+    "        type: str\n",
+    "        default: EMC\n",
+    "        allowed: [CESM, ECCC, EMC, ESRL, GMAO, NCEP, NRL, RSMAS]\n",
+    "      model:\n",
+    "        description: name of the model\n",
+    "        type: str\n",
+    "        default: GEFS\n",
+    "        allowed: [30LCESM1, 46LCESM1, GEM, GEPS6, GEPS5, GEFS, GEFSv12, FIMr1p1, GEOS_V2p1, CFSv2, NESM, CCSM4]\n",
+    "      grid:\n",
+    "        description: regrid to this global resolution\n",
+    "        type: float\n",
+    "        default: 1.5\n",
+    "      lead_start:\n",
+    "        description: aggregation start lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 14\n",
+    "      lead_end:\n",
+    "        description: aggregation end lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 27        \n",
+    "      param:\n",
+    "        description: variable name\n",
+    "        type: str\n",
+    "        default: pr\n",
+    "        #allowed: [pr]\n",
+    "    xarray_kwargs:\n",
+    "        engine: netcdf4\n",
+    "    args:\n",
+    "      urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.{{center}}/.{{model}}/.hindcast/.{{param}}/L/({{lead_start}})/({{lead_end}})/RANGEEDGES/[L]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods\n",
+    "\n",
+    "  test-input:\n",
+    "    description: SubX forecasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html\n",
+    "    driver: opendap\n",
+    "    parameters:\n",
+    "      center:\n",
+    "        description: name of the center issuing the forecast\n",
+    "        type: str\n",
+    "        default: EMC\n",
+    "        allowed: [CESM, ECCC, EMC, ESRL, GMAO, NCEP, NRL, RSMAS]\n",
+    "      model:\n",
+    "        description: name of the model\n",
+    "        type: str\n",
+    "        default: GEFS\n",
+    "        allowed: [30LCESM1, 46LCESM1, GEM, GEPS6, GEPS5, GEFS, GEFSv12, FIMr1p1, GEOS_V2p1, CFSv2, NESM, CCSM4]\n",
+    "      grid:\n",
+    "        description: regrid to this global resolution\n",
+    "        type: float\n",
+    "        default: 1.5\n",
+    "      lead_start:\n",
+    "        description: aggregation start lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 14\n",
+    "      lead_end:\n",
+    "        description: aggregation end lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 27        \n",
+    "      param:\n",
+    "        description: variable name, see http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html\n",
+    "        type: str\n",
+    "        default: pr\n",
+    "        #allowed: [pr] \n",
+    "    xarray_kwargs:\n",
+    "        engine: netcdf4\n",
+    "    args:\n",
+    "        urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.{{center}}/.{{model}}/.forecast/.{{param}}/S/(0000%201%20Jan%202020)/(0000%2031%20Dec%202020)/RANGEEDGES/L/({{lead_start}})/({{lead_end}})/RANGEEDGES/[L]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import intake\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import intake_xarray\n",
+    "cat = intake.open_catalog('SubX_catalog.yml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_SubX_one_lead(dataset='training-input', **cat_kwargs):\n",
+    "    url = cat[dataset](**cat_kwargs).urlpath\n",
+    "    # cat[dataset].to_dask() is somehow slow\n",
+    "    ds = intake_xarray.OpenDapSource(url, engine='netcdf4').to_dask()\n",
+    "    ds = ds.rename({'X':'longitude', 'Y':'latitude', 'S':'forecast_time', 'M':'realization'})\n",
+    "    return ds\n",
+    "\n",
+    "def load_SubX(dataset='training-input', **cat_kwargs):\n",
+    "    lead_1 = load_SubX_one_lead(dataset=dataset, lead_start=14, lead_end=27, **cat_kwargs).load()\n",
+    "    lead_2 = load_SubX_one_lead(dataset=dataset, lead_start=28, lead_end=41, **cat_kwargs).load()\n",
+    "    ds = xr.concat([lead_1, lead_2], 'lead_time')\n",
+    "    ds = ds.assign_coords(lead_time=[pd.Timedelta(f'{i} d') for i in [14, 28]])\n",
+    "    return ds\n",
+    "\n",
+    "#load_SubX_one_lead(lead_start=28, lead_end=41)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 41.8 s, sys: 29.3 s, total: 1min 11s\n",
+      "Wall time: 6min 29s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (forecast_time: 939, latitude: 121, lead_time: 2, longitude: 240, realization: 11)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 1999-01-06 ... 2016-12-28\n",
+       "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0 10.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    pr             (lead_time, forecast_time, realization, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (forecast_time: 939, latitude: 121, lead_time: 2, longitude: 240, realization: 11)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 1999-01-06 ... 2016-12-28\n",
+       "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0 10.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    pr             (lead_time, forecast_time, realization, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# one variable two biweekly averages: hindcast\n",
+    "%time hindcast = load_SubX(dataset='training-input')\n",
+    "\n",
+    "hindcast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.399642296, 'GB')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hindcast.nbytes/1e9,'GB'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 30.5 s, sys: 17.8 s, total: 48.3 s\n",
+      "Wall time: 3min 13s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (forecast_time: 366, latitude: 121, lead_time: 2, longitude: 240, realization: 21)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2020-01-01 ... 2020-12-31\n",
+       "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0 20.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    pr             (lead_time, forecast_time, realization, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (forecast_time: 366, latitude: 121, lead_time: 2, longitude: 240, realization: 21)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 2020-01-01 ... 2020-12-31\n",
+       "  * realization    (realization) float32 0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0 20.0\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    pr             (lead_time, forecast_time, realization, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# one variable two biweekly averages: forecast\n",
+    "%time forecast = load_SubX(dataset='test-input')\n",
+    "\n",
+    "forecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.785615992, 'GB')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forecast.nbytes/1e9,'GB'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## S2S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting S2S_catalog.yml\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile S2S_catalog.yml\n",
+    "plugins:\n",
+    "  source:\n",
+    "    - module: intake_xarray\n",
+    "\n",
+    "sources:\n",
+    "  training-input:\n",
+    "    description: S2S hindcasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/overview.html\n",
+    "    driver: opendap\n",
+    "    parameters:\n",
+    "      center:\n",
+    "        description: name of the center issuing the hindcast\n",
+    "        type: str\n",
+    "        default: ECMF\n",
+    "        allowed: [BOM, CNRM, ECCC, ECMF, HMCR, ISAC, JMA, KMA, NCEP, UKMO]\n",
+    "      grid:\n",
+    "        description: regrid to this global resolution\n",
+    "        type: float\n",
+    "        default: 1.5\n",
+    "      lead_name:\n",
+    "        description: name of the lead_time dimension\n",
+    "        type: str\n",
+    "        default: LA\n",
+    "        allowed: [LA, L]\n",
+    "      lead_start:\n",
+    "        description: aggregation start lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 14\n",
+    "      lead_end:\n",
+    "        description: aggregation end lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 27\n",
+    "      experiment_type:\n",
+    "        description: type of experiment\n",
+    "        type: str\n",
+    "        default: perturbed\n",
+    "        allowed: [control, perturbed, RMMS]\n",
+    "      group:\n",
+    "        description: group of variables\n",
+    "        type: str\n",
+    "        default: 2m_above_ground\n",
+    "        #allowed: [2m_above_ground, ...] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/\n",
+    "      param:\n",
+    "        description: variable name\n",
+    "        type: str\n",
+    "        default: 2t\n",
+    "        #allowed: [2t] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/\n",
+    "    xarray_kwargs:\n",
+    "        engine: netcdf4\n",
+    "    args:\n",
+    "      urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.{{center}}/.reforecast/.{{experiment_type}}/.{{group}}/{{param}}/{{lead_name}}/({{lead_start}})/({{lead_end}})/RANGEEDGES/[{{lead_name}}]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods\n",
+    "\n",
+    "\n",
+    "  test-input:\n",
+    "    description: S2S forecasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/overview.html\n",
+    "    driver: opendap\n",
+    "    parameters:\n",
+    "      center:\n",
+    "        description: name of the center issuing the hindcast\n",
+    "        type: str\n",
+    "        default: ECMF\n",
+    "        allowed: ['BOM','CNRM','ECCC','ECMF','HMCR','ISAC','JMA','KMA','NCEP','UKMO']\n",
+    "      grid:\n",
+    "        description: regrid to this global resolution\n",
+    "        type: float\n",
+    "        default: 1.5\n",
+    "      lead_name:\n",
+    "        description: name of the lead_time dimension\n",
+    "        type: str\n",
+    "        default: LA\n",
+    "        allowed: [LA, L, L1]\n",
+    "      lead_start:\n",
+    "        description: aggregation start lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 14\n",
+    "      lead_end:\n",
+    "        description: aggregation end lead passed to RANGEEDGES\n",
+    "        type: int\n",
+    "        default: 27\n",
+    "      experiment_type:\n",
+    "        description: type of experiment\n",
+    "        type: str\n",
+    "        default: perturbed\n",
+    "        allowed: [control, perturbed, RMMS]\n",
+    "      group:\n",
+    "        description: group of variables\n",
+    "        type: str\n",
+    "        default: 2m_above_ground\n",
+    "        #allowed: see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/\n",
+    "      param:\n",
+    "        description: variable name\n",
+    "        type: str\n",
+    "        default: 2t\n",
+    "        #allowed: [2t] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/\n",
+    "    xarray_kwargs:\n",
+    "        engine: netcdf4\n",
+    "    args:\n",
+    "        urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.{{center}}/.forecast/.{{experiment_type}}/.{{group}}/{{param}}/S/(0000%201%20Jan%202020)/(0000%2031%20Dec%202020)/RANGEEDGES/{{lead_name}}/({{lead_start}})/({{lead_end}})/RANGEEDGES/[{{lead_name}}]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import intake\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import intake_xarray\n",
+    "cat = intake.open_catalog('S2S_catalog.yml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_S2S_one_lead(dataset='training-input', **cat_kwargs):\n",
+    "    cat = intake.open_catalog('S2S_catalog.yml')\n",
+    "    url = cat[dataset](**cat_kwargs).urlpath\n",
+    "    # cat[dataset].to_dask() is somehow slow\n",
+    "    ds = intake_xarray.OpenDapSource(url, engine='netcdf4', decode_times=False).to_dask()\n",
+    "    # calendar '360' not recognized, but '360_day'\n",
+    "    if 'hdate' in ds.coords:\n",
+    "        if ds.hdate.attrs['calendar'] == '360':\n",
+    "            ds.hdate.attrs['calendar'] = '360_day'\n",
+    "\n",
+    "    ds = xr.decode_cf(ds).rename({'X':'longitude', 'Y':'latitude', 'S':'forecast_time'})\n",
+    "    \n",
+    "    if 'M' in ds.coords:\n",
+    "        ds = ds.rename({'M': 'realization'})\n",
+    "    \n",
+    "    #aggregated before\n",
+    "    #for c in ['LA', 'L', 'L1']:\n",
+    "    #    if c in ds.coords:\n",
+    "    #        ds = ds.rename({c:'lead_time'})\n",
+    "    \n",
+    "    #rename_data_var_dict = {'2t':'t2m'}\n",
+    "    #for v in rename_data_var_dict.keys():\n",
+    "    #    if v in ds.data_vars:\n",
+    "    #        ds = ds.rename({v: rename_data_var_dict[v]})\n",
+    "    return ds\n",
+    "\n",
+    "def load_S2S(dataset='training-input', **cat_kwargs):\n",
+    "    lead_1 = load_S2S_one_lead(dataset=dataset, lead_start=14, lead_end=27, **cat_kwargs).load()\n",
+    "    lead_2 = load_S2S_one_lead(dataset=dataset, lead_start=28, lead_end=41, **cat_kwargs).load()\n",
+    "    ds = xr.concat([lead_1, lead_2], 'lead_time')\n",
+    "    ds = ds.assign_coords(lead_time=[pd.Timedelta(f'{i} d') for i in [14, 28]])\n",
+    "    return ds\n",
+    "\n",
+    "#load_S2S_one_lead(dataset='training-input', lead_start=28, lead_end=41, center='BOM', experiment_type='control').nbytes/1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.31 s, sys: 3.97 s, total: 13.3 s\n",
+      "Wall time: 33.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (forecast_time: 2376, latitude: 121, lead_time: 2, longitude: 240)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 1981-01-01 ... 2013-12-26\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    2t             (lead_time, forecast_time, latitude, longitude) float32 23...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (forecast_time: 2376, latitude: 121, lead_time: 2, longitude: 240)\n",
+       "Coordinates:\n",
+       "  * forecast_time  (forecast_time) datetime64[ns] 1981-01-01 ... 2013-12-26\n",
+       "  * longitude      (longitude) float32 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+       "  * latitude       (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days\n",
+       "Data variables:\n",
+       "    2t             (lead_time, forecast_time, latitude, longitude) float32 23...\n",
+       "Attributes:\n",
+       "    Conventions:  IRIDL"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# one variable two biweekly averages: hindcast, reduce download size by taking control (without realizations)\n",
+    "%time hindcast = load_S2S(dataset='training-input', center='BOM', experiment_type='control')\n",
+    "hindcast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.552012788"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hindcast.nbytes/1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one variable two biweekly averages: forecast\n",
+    "# * 14 converts mean to sum\n",
+    "%time forecast = load_S2S(dataset='test-input', param='tp', group='sfc_precip', lead_name='L1', center='BOM') * 14\n",
+    "\n",
+    "forecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forecast.nbytes/1e9,'GB'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# observations\n",
+    "\n",
+    "Shows how observations with `time` dimension are downloaded from `IRIDL`. Use [`climetlab_s2s_ai_challenge.extra.forecast_like_observations`](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge/blob/47539214d78251e30b79bd4b0dbb4a6a085cc9bf/climetlab_s2s_ai_challenge/extra.py#L43) to convert to observations like forecasts `forecast-like-observations` with `lead_time` and `forecast_time` dimensions and `valid_time` coordinate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/xarray/backends/cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "xr.set_options(display_style='text')\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `t2m`\n",
+    "\n",
+    "http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/xarray/backends/plugins.py:61: RuntimeWarning: Engine 'cfgrib' loading failed:\n",
+      "/opt/conda/lib/python3.8/site-packages/gribapi/_bindings.cpython-38-x86_64-linux-gnu.so: undefined symbol: codes_bufr_key_is_header\n",
+      "  warnings.warn(f\"Engine {name!r} loading failed:\\n{ex}\", RuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "chunk_dim='T'\n",
+    "grid=1.5\n",
+    "tmin = xr.open_dataset(f'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmin/T/(0000%201%20Jan%201999)/(0000%2001%20Apr%202021)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods', chunks={chunk_dim:100})\n",
+    "tmax = xr.open_dataset(f'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/.tmax/T/(0000%201%20Jan%201999)/(0000%2001%20Apr%202021)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods', chunks={chunk_dim:100})\n",
+    "\n",
+    "t = (tmin.rename({'tmin':'t'}) + tmax.rename({'tmax':'t'})) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.DataArray &#x27;T&#x27; (T: 8126)&gt;\n",
+       "array([2451180., 2451181., 2451182., ..., 2459303., 2459304., 2459305.],\n",
+       "      dtype=float32)\n",
+       "Coordinates:\n",
+       "  * T        (T) float32 2.451e+06 2.451e+06 2.451e+06 ... 2.459e+06 2.459e+06\n",
+       "Attributes:\n",
+       "    standard_name:  time\n",
+       "    pointwidth:     1\n",
+       "    calendar:       standard\n",
+       "    gridtype:       0\n",
+       "    units:          julian_day</pre>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'T' (T: 8126)>\n",
+       "array([2451180., 2451181., 2451182., ..., 2459303., 2459304., 2459305.],\n",
+       "      dtype=float32)\n",
+       "Coordinates:\n",
+       "  * T        (T) float32 2.451e+06 2.451e+06 2.451e+06 ... 2.459e+06 2.459e+06\n",
+       "Attributes:\n",
+       "    standard_name:  time\n",
+       "    pointwidth:     1\n",
+       "    calendar:       standard\n",
+       "    gridtype:       0\n",
+       "    units:          julian_day"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8126)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-03-31\n",
+       "  * longitude  (longitude) float32 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "  * latitude   (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 dask.array&lt;chunksize=(100, 121, 240), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    source_dataset_name:  temperature daily from NOAA NCEP CPC: Climate Predi...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8126)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-03-31\n",
+       "  * longitude  (longitude) float32 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "  * latitude   (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "Data variables:\n",
+       "    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(100, 121, 240), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    source_dataset_name:  temperature daily from NOAA NCEP CPC: Climate Predi...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t[\"T\"] = pd.date_range(start=\"1999-01-01\", freq=\"1D\", periods=t.T.size)\n",
+    "t = t.rename({\"X\": \"longitude\", \"Y\": \"latitude\", \"T\": \"time\"})\n",
+    "t[\"t\"].attrs = tmin[\"tmin\"].attrs\n",
+    "\n",
+    "# metadata\n",
+    "t = t.rename({\"t\": \"t2m\"})\n",
+    "t = t + 273.15\n",
+    "t[\"t2m\"].attrs[\"units\"] = \"K\"\n",
+    "t[\"t2m\"].attrs[\"long_name\"] = \"2m Temperature\"\n",
+    "t[\"t2m\"].attrs[\"standard_name\"] = \"air_temperature\"\n",
+    "t.attrs.update(\n",
+    "    {\n",
+    "        \"source_dataset_name\": \"temperature daily from NOAA NCEP CPC: Climate Prediction Center\",\n",
+    "        \"source_hosting\": \"IRIDL\",\n",
+    "        \"source_url\": \"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/\",\n",
+    "    }\n",
+    ")\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time t = t.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `pr` to calc `tp`\n",
+    "\n",
+    "http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.extREALTIME/.rain/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rain = xr.open_dataset(f'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.extREALTIME/.rain/T/(0000%201%20Jan%201999)/(0000%2001%20Apr%202021)/RANGEEDGES/X/0/{grid}/358.5/GRID/Y/90/{grid}/-90/GRID/dods', chunks={chunk_dim:100})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.DataArray &#x27;T&#x27; (T: 8127)&gt;\n",
+       "array([&#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1999-01-03T00:00:00.000000000&#x27;, ..., &#x27;2021-03-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-03-31T00:00:00.000000000&#x27;, &#x27;2021-04-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
+       "Coordinates:\n",
+       "  * T        (T) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-03-31 2021-04-01\n",
+       "Attributes:\n",
+       "    pointwidth:  0\n",
+       "    gridtype:    0</pre>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'T' (T: 8127)>\n",
+       "array(['1999-01-01T00:00:00.000000000', '1999-01-02T00:00:00.000000000',\n",
+       "       '1999-01-03T00:00:00.000000000', ..., '2021-03-30T00:00:00.000000000',\n",
+       "       '2021-03-31T00:00:00.000000000', '2021-04-01T00:00:00.000000000'],\n",
+       "      dtype='datetime64[ns]')\n",
+       "Coordinates:\n",
+       "  * T        (T) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-03-31 2021-04-01\n",
+       "Attributes:\n",
+       "    pointwidth:  0\n",
+       "    gridtype:    0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rain.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8127)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-04-01\n",
+       "  * longitude  (longitude) float32 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "  * latitude   (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "Data variables:\n",
+       "    pr         (time, latitude, longitude) float32 dask.array&lt;chunksize=(100, 121, 240), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:          IRIDL\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (latitude: 121, longitude: 240, time: 8127)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-04-01\n",
+       "  * longitude  (longitude) float32 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
+       "  * latitude   (latitude) float32 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+       "Data variables:\n",
+       "    pr         (time, latitude, longitude) float32 dask.array<chunksize=(100, 121, 240), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:          IRIDL\n",
+       "    source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+       "    source_hosting:       IRIDL\n",
+       "    source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rain = rain.rename({\"rain\": \"pr\"}).rename({\"X\": \"longitude\", \"Y\": \"latitude\", \"T\": \"time\"})\n",
+    "\n",
+    "# metadata pr\n",
+    "rain[\"pr\"].attrs[\"units\"] = \"kg m-2 day-1\"\n",
+    "rain[\"pr\"].attrs[\"long_name\"] = \"precipitation flux\"\n",
+    "rain[\"pr\"].attrs[\"standard_name\"] = \"precipitation_flux\"\n",
+    "rain.attrs.update(\n",
+    "    {\n",
+    "        \"source_dataset_name\": \"NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 extREALTIME rain: Precipitation data\",\n",
+    "        \"source_hosting\": \"IRIDL\",\n",
+    "        \"source_url\": \"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.extREALTIME/.rain\",\n",
+    "    }\n",
+    ")\n",
+    "rain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time rain = rain.load()"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/data_access/S2S_catalog.yml b/notebooks/data_access/S2S_catalog.yml
new file mode 100644
index 0000000000000000000000000000000000000000..67eece36385f08acc443b79376ce39d42316a9c8
--- /dev/null
+++ b/notebooks/data_access/S2S_catalog.yml
@@ -0,0 +1,97 @@
+plugins:
+  source:
+    - module: intake_xarray
+
+sources:
+  training-input:
+    description: S2S hindcasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/overview.html
+    driver: opendap
+    parameters:
+      center:
+        description: name of the center issuing the hindcast
+        type: str
+        default: ECMF
+        allowed: [BOM, CNRM, ECCC, ECMF, HMCR, ISAC, JMA, KMA, NCEP, UKMO]
+      grid:
+        description: regrid to this global resolution
+        type: float
+        default: 1.5
+      lead_name:
+        description: name of the lead_time dimension
+        type: str
+        default: LA
+        allowed: [LA, L]
+      lead_start:
+        description: aggregation start lead passed to RANGEEDGES
+        type: int
+        default: 14
+      lead_end:
+        description: aggregation end lead passed to RANGEEDGES
+        type: int
+        default: 27
+      experiment_type:
+        description: type of experiment
+        type: str
+        default: perturbed
+        allowed: [control, perturbed, RMMS]
+      group:
+        description: group of variables
+        type: str
+        default: 2m_above_ground
+        #allowed: [2m_above_ground, ...] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/
+      param:
+        description: variable name
+        type: str
+        default: 2t
+        #allowed: [2t] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/
+    xarray_kwargs:
+        engine: netcdf4
+    args:
+      urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.{{center}}/.reforecast/.{{experiment_type}}/.{{group}}/{{param}}/{{lead_name}}/({{lead_start}})/({{lead_end}})/RANGEEDGES/[{{lead_name}}]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods
+
+
+  test-input:
+    description: S2S forecasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/overview.html
+    driver: opendap
+    parameters:
+      center:
+        description: name of the center issuing the hindcast
+        type: str
+        default: ECMF
+        allowed: ['BOM','CNRM','ECCC','ECMF','HMCR','ISAC','JMA','KMA','NCEP','UKMO']
+      grid:
+        description: regrid to this global resolution
+        type: float
+        default: 1.5
+      lead_name:
+        description: name of the lead_time dimension
+        type: str
+        default: LA
+        allowed: [LA, L, L1]
+      lead_start:
+        description: aggregation start lead passed to RANGEEDGES
+        type: int
+        default: 14
+      lead_end:
+        description: aggregation end lead passed to RANGEEDGES
+        type: int
+        default: 27
+      experiment_type:
+        description: type of experiment
+        type: str
+        default: perturbed
+        allowed: [control, perturbed, RMMS]
+      group:
+        description: group of variables
+        type: str
+        default: 2m_above_ground
+        #allowed: see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/
+      param:
+        description: variable name
+        type: str
+        default: 2t
+        #allowed: [2t] see https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.ECMF/.reforecast/.perturbed/
+    xarray_kwargs:
+        engine: netcdf4
+    args:
+        urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/.{{center}}/.forecast/.{{experiment_type}}/.{{group}}/{{param}}/S/(0000%201%20Jan%202020)/(0000%2031%20Dec%202020)/RANGEEDGES/{{lead_name}}/({{lead_start}})/({{lead_end}})/RANGEEDGES/[{{lead_name}}]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods 
diff --git a/notebooks/data_access/SubX_catalog.yml b/notebooks/data_access/SubX_catalog.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9c41682d7d6a705b6ae881c0f1f22c650a871c26
--- /dev/null
+++ b/notebooks/data_access/SubX_catalog.yml
@@ -0,0 +1,76 @@
+plugins:
+  source:
+    - module: intake_xarray
+
+sources:
+  training-input:
+    description: SubX hindcasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html
+    driver: opendap
+    parameters:
+      center:
+        description: name of the center issuing the hindcast
+        type: str
+        default: EMC
+        allowed: [CESM, ECCC, EMC, ESRL, GMAO, NCEP, NRL, RSMAS]
+      model:
+        description: name of the model
+        type: str
+        default: GEFS
+        allowed: [30LCESM1, 46LCESM1, GEM, GEPS6, GEPS5, GEFS, GEFSv12, FIMr1p1, GEOS_V2p1, CFSv2, NESM, CCSM4]
+      grid:
+        description: regrid to this global resolution
+        type: float
+        default: 1.5
+      lead_start:
+        description: aggregation start lead passed to RANGEEDGES
+        type: int
+        default: 14
+      lead_end:
+        description: aggregation end lead passed to RANGEEDGES
+        type: int
+        default: 27        
+      param:
+        description: variable name
+        type: str
+        default: pr
+        #allowed: [pr]
+    xarray_kwargs:
+        engine: netcdf4
+    args:
+      urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.{{center}}/.{{model}}/.hindcast/.{{param}}/L/({{lead_start}})/({{lead_end}})/RANGEEDGES/[L]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods
+
+  test-input:
+    description: SubX forecasts from IRIDL regridded to 1.5 deg grid and aggregated by mean over lead, http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html
+    driver: opendap
+    parameters:
+      center:
+        description: name of the center issuing the forecast
+        type: str
+        default: EMC
+        allowed: [CESM, ECCC, EMC, ESRL, GMAO, NCEP, NRL, RSMAS]
+      model:
+        description: name of the model
+        type: str
+        default: GEFS
+        allowed: [30LCESM1, 46LCESM1, GEM, GEPS6, GEPS5, GEFS, GEFSv12, FIMr1p1, GEOS_V2p1, CFSv2, NESM, CCSM4]
+      grid:
+        description: regrid to this global resolution
+        type: float
+        default: 1.5
+      lead_start:
+        description: aggregation start lead passed to RANGEEDGES
+        type: int
+        default: 14
+      lead_end:
+        description: aggregation end lead passed to RANGEEDGES
+        type: int
+        default: 27        
+      param:
+        description: variable name, see http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/outline.html
+        type: str
+        default: pr
+        #allowed: [pr] 
+    xarray_kwargs:
+        engine: netcdf4
+    args:
+        urlpath: http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.{{center}}/.{{model}}/.forecast/.{{param}}/S/(0000%201%20Jan%202020)/(0000%2031%20Dec%202020)/RANGEEDGES/L/({{lead_start}})/({{lead_end}})/RANGEEDGES/[L]average/X/0/{{grid}}/358.5/GRID/Y/90/{{grid}}/-90/GRID/dods