From 67a043a3b691aeda249d8f10e5bbcc63b3b02666 Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Wed, 15 Jul 2026 12:35:26 +0200
Subject: [PATCH 1/6] rename daily notebooks

---
 notebooks/{data_vis.ipynb => data_vis_sst.ipynb}             | 0
 notebooks/{example_daily.ipynb => example_daily_sst.ipynb}   | 0
 notebooks/{example_hourly.ipynb => example_hourly_sst.ipynb} | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename notebooks/{data_vis.ipynb => data_vis_sst.ipynb} (100%)
 rename notebooks/{example_daily.ipynb => example_daily_sst.ipynb} (100%)
 rename notebooks/{example_hourly.ipynb => example_hourly_sst.ipynb} (100%)

diff --git a/notebooks/data_vis.ipynb b/notebooks/data_vis_sst.ipynb
similarity index 100%
rename from notebooks/data_vis.ipynb
rename to notebooks/data_vis_sst.ipynb
diff --git a/notebooks/example_daily.ipynb b/notebooks/example_daily_sst.ipynb
similarity index 100%
rename from notebooks/example_daily.ipynb
rename to notebooks/example_daily_sst.ipynb
diff --git a/notebooks/example_hourly.ipynb b/notebooks/example_hourly_sst.ipynb
similarity index 100%
rename from notebooks/example_hourly.ipynb
rename to notebooks/example_hourly_sst.ipynb

From 5b3d0df970b6ede1613b37a30e164af17ab6d8d1 Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Wed, 15 Jul 2026 13:09:54 +0200
Subject: [PATCH 2/6] add draft example notebook for watervapor

---
 notebooks/example_hourly_watervapor.ipynb | 386 ++++++++++++++++++++++
 1 file changed, 386 insertions(+)
 create mode 100644 notebooks/example_hourly_watervapor.ipynb

diff --git a/notebooks/example_hourly_watervapor.ipynb b/notebooks/example_hourly_watervapor.ipynb
new file mode 100644
index 0000000..1dab2e6
--- /dev/null
+++ b/notebooks/example_hourly_watervapor.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e6644bb-2d85-47ec-af53-f0960f9314e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import torch\n",
+    "import torch.nn.functional\n",
+    "from climanet.st_encoder_decoder import SpatioTemporalModel\n",
+    "from climanet.utils import pred_to_numpy, set_seed, configure_compute_resources, plot_results, plot_histograms\n",
+    "from climanet.train import train_monthly_model\n",
+    "from climanet.predict import predict_monthly_var\n",
+    "from climanet import STDataset\n",
+    "\n",
+    "from torch.utils.data import random_split\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0f5c8e2-693b-4f6e-8e88-e3c002001b24",
+   "metadata": {},
+   "source": [
+    "## Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f383c4c8-0199-4092-be12-07be525fdec1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# without diurnal cycle\n",
+    "data_folder = Path(\"./eso4clima/watervapor\")\n",
+    "run_dir = \"./runs_hourly_watervapor\"\n",
+    "var_name = \"prw\"\n",
+    "\n",
+    "file_names = [data_folder / f\"202001_hr_ERA5_masked_{var_name}.nc\", data_folder / f\"202002_hr_ERA5_masked_{var_name}.nc\"]\n",
+    "hourly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 160, \"longitude\": 160}).sortby(\"lat\")\n",
+    "\n",
+    "file_names = [data_folder / f\"202001_mon_ERA5_full_{var_name}.nc\", data_folder / f\"202002_mon_ERA5_full_{var_name}.nc\"]\n",
+    "monthly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 160, \"longitude\": 160}).sortby(\"lat\")\n",
+    "\n",
+    "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
+    "lsm_mask = xr.open_dataset(file_name)\n",
+    "# lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4be672b-a2c1-4a6f-89a1-2c22cc6cde02",
+   "metadata": {},
+   "source": [
+    "## Subset data (for fast example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c61aee7b-6083-4466-883c-19aa9a03cfca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1440, 40, 40) (2, 40, 40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # coordinates of subset\n",
+    "# lon_subset = slice(-50, 50) \n",
+    "# lat_subset = slice(-30, 10)\n",
+    "\n",
+    "# tiny sample\n",
+    "lon_subset = slice(-10, 10) \n",
+    "lat_subset = slice(-10, 10)\n",
+    "\n",
+    "daily_subset = hourly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "lsm_subset = lsm_mask.sel(lon=lon_subset, lat=lat_subset)  # True=Land\n",
+    "\n",
+    "print(daily_subset[var_name].shape, monthly_subset[var_name].shape)  # (time, lat, lon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "65edf290-5a9e-4c88-9701-a6ca84c058ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate residuals as target\n",
+    "daily_subset_averaged = daily_subset.resample(time=\"MS\").mean(skipna=True)\n",
+    "daily_subset_averaged[\"time\"] = monthly_subset[\"time\"]\n",
+    "\n",
+    "# Residuals\n",
+    "monthly_subset_res = monthly_subset - daily_subset_averaged"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b69667c-18f9-4f2b-b7ff-0f737a6e1543",
+   "metadata": {},
+   "source": [
+    "## Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1ceb7756-f274-4823-bded-87c0f8f16a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the model (small)\n",
+    "set_seed()\n",
+    "\n",
+    "patch_size = (1, 4, 4)\n",
+    "model = SpatioTemporalModel(patch_size=patch_size, overlap=2, embed_dim=64, dropout=0.2, hidden=64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a9d056b8-2cb1-42af-94d6-9fb58586dcbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patch grid (m x i x j): 2 x 1 x 1 = 2 patches\n",
+      "Overlap: 32 pixels (height), 32 pixels (width)\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create dataset\n",
+    "num_patches = (10, 10)\n",
+    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
+    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\n",
+    "\n",
+    "dataset = STDataset(\n",
+    "    input_da=daily_subset[var_name],\n",
+    "    monthly_da=monthly_subset_res[var_name],\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
+    "    stride=stride,\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    is_hourly=True,\n",
+    "    \n",
+    ")\n",
+    "print(len(dataset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "efa3cc2f-392d-4130-bdc8-2887202d2cb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 0 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create train test data\n",
+    "generator = torch.Generator().manual_seed(42)\n",
+    "train_size = int(0.6 * len(dataset))\n",
+    "validation_size = int(0.3 * len(dataset))\n",
+    "test_size = len(dataset) - train_size - validation_size\n",
+    "train_dataset, validation_dataset, test_dataset = random_split(dataset, [train_size, validation_size, test_size], generator=generator)\n",
+    "print(len(train_dataset), len(validation_dataset), len(test_dataset))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "945ec311-53b3-4cac-a1e3-d16f4508e7a7",
+   "metadata": {},
+   "source": [
+    "### Start training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "67b469b6-5d61-4ff1-a54f-f077965b8979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Device and resources\n",
+    "compute_threads = 120\n",
+    "dataloader_num_workers = 4\n",
+    "device = \"cpu\"\n",
+    "model = configure_compute_resources(model, device=device, compute_threads=compute_threads, dataloader_num_workers=dataloader_num_workers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b9742e67-165f-4b5a-bab0-27b8b0d7070d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'float' object has no attribute 'item'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;66;03m# verbose is True\u001b[39;00m\n\u001b[32m      3\u001b[39m batch_size = \u001b[32m200\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m trained_model = \u001b[43mtrain_monthly_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epoch\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulation_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m      8\u001b[39m total_time = (time.time() - start) / \u001b[32m60.0\u001b[39m\n\u001b[32m      9\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtotal_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Developments/ESO4Clima/ClimaNet/climanet/train.py:131\u001b[39m, in \u001b[36mtrain_monthly_model\u001b[39m\u001b[34m(model, dataset, validation_dataset, shuffle, batch_size, num_epoch, patience, accumulation_steps, optimizer_lr, run_dir, store_model, device, verbose, dataloader_num_workers, verbose_epoch_interval)\u001b[39m\n\u001b[32m    128\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m validation_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    129\u001b[39m     \u001b[38;5;66;03m# Store train loss for gap calculation\u001b[39;00m\n\u001b[32m    130\u001b[39m     avg_train_loss = avg_epoch_loss\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m     _, avg_epoch_loss = \u001b[43mpredict_monthly_var\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    132\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    133\u001b[39m \u001b[43m        \u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    134\u001b[39m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    135\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreturn_numpy\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    137\u001b[39m \u001b[43m        \u001b[49m\u001b[43msave_predictions\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    138\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreturn_loss\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    139\u001b[39m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    140\u001b[39m \u001b[43m        \u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    141\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    142\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    143\u001b[39m     writer.add_scalar(\u001b[33m\"\u001b[39m\u001b[33mLoss/validation\u001b[39m\u001b[33m\"\u001b[39m, avg_epoch_loss, epoch)\n\u001b[32m    145\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m verbose \u001b[38;5;129;01mand\u001b[39;00m epoch % verbose_epoch_interval == \u001b[32m0\u001b[39m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Developments/ESO4Clima/ClimaNet/climanet/predict.py:152\u001b[39m, in \u001b[36mpredict_monthly_var\u001b[39m\u001b[34m(model, dataset, batch_size, return_numpy, save_predictions, return_loss, device, run_dir, verbose, dataloader_num_workers)\u001b[39m\n\u001b[32m    146\u001b[39m             \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m    147\u001b[39m                 \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mProcessed batch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;250m \u001b[39m+\u001b[38;5;250m \u001b[39m\u001b[32m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, with loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss.item()\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    148\u001b[39m             )\n\u001b[32m    150\u001b[39m         writer.add_scalar(\u001b[33m\"\u001b[39m\u001b[33mProgress/Batch\u001b[39m\u001b[33m\"\u001b[39m, i + \u001b[32m1\u001b[39m, idx)\n\u001b[32m--> \u001b[39m\u001b[32m152\u001b[39m average_loss = \u001b[43maverage_loss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitem\u001b[49m() / \u001b[38;5;28mlen\u001b[39m(dataloader)\n\u001b[32m    154\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[32m    155\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAverage loss over all batches: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maverage_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[31mAttributeError\u001b[39m: 'float' object has no attribute 'item'"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "# verbose is True\n",
+    "batch_size = 200\n",
+    "trained_model = train_monthly_model(\n",
+    "    model, train_dataset, validation_dataset=validation_dataset, batch_size=batch_size, num_epoch=10, accumulation_steps=20, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
+    ")\n",
+    "\n",
+    "total_time = (time.time() - start) / 60.0\n",
+    "print(f\"{total_time:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e27a96aa-5560-47b6-91eb-dc4530dca090",
+   "metadata": {},
+   "source": [
+    "## Inspect results and compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inference on test data, verbose is True\n",
+    "batch_size = 200\n",
+    "predictions = predict_monthly_var(\n",
+    "    model=f\"{run_dir}/best_model.pth\", dataset=test_dataset, batch_size=batch_size, save_predictions=True, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "200264a1-c3cb-42d2-95eb-887259ed63cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inference on whole dataset\n",
+    "num_patches = (10, 10)\n",
+    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
+    "\n",
+    "dataset = STDataset(\n",
+    "    input_da=daily_subset[var_name],\n",
+    "    monthly_da=monthly_subset_res[var_name],\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    is_hourly=True,    \n",
+    ")\n",
+    "print(len(dataset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74c0cfd5-4ebf-4df6-b788-19563a76155b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 200\n",
+    "predictions_res = predict_monthly_var(\n",
+    "    model=f\"{run_dir}/best_model.pth\", dataset=dataset, batch_size=batch_size, verbose=False, save_predictions=True, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e2964b3-3fce-4df1-bb97-8677ef3bbfbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to monthly average\n",
+    "predictions = daily_subset_averaged[var_name] + predictions_res[\"predictions\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b0e3f24-c9d0-48e8-858a-48deb8d000d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ocean = ~lsm_subset[\"lsm\"].values\n",
+    "masked_target = monthly_subset[var_name].where(ocean)\n",
+    "masked_pred = predictions.where(ocean)\n",
+    "plot_results(masked_target, masked_pred, label=\"SST K\", title=(\"Target\", \"Prediction\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ba5d204-aefc-4103-8475-c802c603ad3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "err_baseline = (monthly_subset[var_name] - daily_subset_averaged[var_name])  # M - mean(D)\n",
+    "err_predictions = (monthly_subset[var_name] - predictions)  # M - M_hat\n",
+    "\n",
+    "ocean = ~lsm_subset[\"lsm\"].values\n",
+    "masked_err_baseline = err_baseline.where(ocean) \n",
+    "masked_err_predictions = err_predictions.where(ocean) \n",
+    "\n",
+    "plot_results(masked_err_baseline, masked_err_predictions, label=\"error K\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7420dc7-fe1b-4f34-a62e-5914091f04b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_histograms(masked_err_baseline, masked_err_predictions, label=\"error K\", legend_labels=(\"err_Baseline\", \"err_Prediction\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e593e9f-60dd-4054-9267-93fe1f582776",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "climanet",
+   "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": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5545f1fbcbe26fee0a945765d5170879de41c543 Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Thu, 16 Jul 2026 13:47:11 +0200
Subject: [PATCH 3/6] check empty dataset in prediction

---
 climanet/predict.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/climanet/predict.py b/climanet/predict.py
index 0ad5c23..c507dc4 100644
--- a/climanet/predict.py
+++ b/climanet/predict.py
@@ -95,6 +95,10 @@ def predict_monthly_var(
     model.to(device)
     model.eval()
 
+    # Length of the dataset should >=1
+    if len(dataset) < 1:
+        raise ValueError("The dataset is empty. Please provide a non-empty dataset.")
+
     use_cuda = device == "cuda"
     dataloader = DataLoader(
         dataset,

From 0deab33e51d876e58a4934816b0d64f174e6d5d3 Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Thu, 16 Jul 2026 15:36:42 +0200
Subject: [PATCH 4/6] update hourly watervapor notebook

---
 notebooks/example_hourly_watervapor.ipynb | 971 ++++++++++++++++++++--
 1 file changed, 907 insertions(+), 64 deletions(-)

diff --git a/notebooks/example_hourly_watervapor.ipynb b/notebooks/example_hourly_watervapor.ipynb
index 1dab2e6..bc6618e 100644
--- a/notebooks/example_hourly_watervapor.ipynb
+++ b/notebooks/example_hourly_watervapor.ipynb
@@ -18,6 +18,8 @@
     "from climanet import STDataset\n",
     "\n",
     "from torch.utils.data import random_split\n",
+    "from tbparse import SummaryReader\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "import time"
    ]
@@ -45,14 +47,765 @@
     "var_name = \"prw\"\n",
     "\n",
     "file_names = [data_folder / f\"202001_hr_ERA5_masked_{var_name}.nc\", data_folder / f\"202002_hr_ERA5_masked_{var_name}.nc\"]\n",
-    "hourly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 160, \"longitude\": 160}).sortby(\"lat\")\n",
+    "hourly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 80, \"longitude\": 80}).sortby(\"lat\")\n",
     "\n",
     "file_names = [data_folder / f\"202001_mon_ERA5_full_{var_name}.nc\", data_folder / f\"202002_mon_ERA5_full_{var_name}.nc\"]\n",
-    "monthly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 160, \"longitude\": 160}).sortby(\"lat\")\n",
+    "monthly_data = xr.open_mfdataset(file_names, chunks={\"time\": 1, \"latitude\": 80, \"longitude\": 80}).sortby(\"lat\")\n",
     "\n",
     "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
     "lsm_mask = xr.open_dataset(file_name)\n",
-    "# lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})"
+    "# lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})\n",
+    "\n",
+    "# Down sample mask to match the resolution of the monthly data\n",
+    "lsm_mask = lsm_mask.coarsen(lat=2, lon=2, boundary='trim').mean()\n",
+    "# Set <0.5 to 0 and >=0.5 to 1\n",
+    "lsm_mask = (lsm_mask >= 0.5).astype(bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "58568e58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  display: contents;\n",
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+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
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+       "\n",
+       ".xr-group-box {\n",
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+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
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+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:    (time: 2, bnds: 2, lat: 320, lon: 720)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 16B 2020-01-16T11:30:00 2020-02-15T11:30:00\n",
+       "  * lat        (lat) float32 1kB -79.75 -79.25 -78.75 ... 78.75 79.25 79.75\n",
+       "  * lon        (lon) float32 3kB -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 32B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "    prw        (time, lat, lon) float32 2MB dask.array&lt;chunksize=(1, 320, 720), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDI:                     Climate Data Interface version 2.2.4 (https://mp...\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 Mon Jul 06 10:02:21 2026: cdo monmean /work/bd08...\n",
+       "    regrid_method:           bilinear\n",
+       "    frequency:               mon\n",
+       "    CDO:                     Climate Data Operators version 2.2.2 (https://mp...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-29e64d1a-1c5a-4e12-b0d0-2eb8b1891af6' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-29e64d1a-1c5a-4e12-b0d0-2eb8b1891af6' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 320</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div></li><li class='xr-section-item'><input id='section-44f48889-5898-4b67-a5be-ed6fea547bef' class='xr-section-summary-in' type='checkbox' checked /><label for='section-44f48889-5898-4b67-a5be-ed6fea547bef' class='xr-section-summary' title='Expand/collapse section'>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'>2020-01-16T11:30:00 2020-02-15T1...</div><input id='attrs-ff86ba65-9ae6-4e84-a302-0a1965857048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff86ba65-9ae6-4e84-a302-0a1965857048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba496aa9-e937-4e6c-ae7f-b9b8b4f2f84b' class='xr-var-data-in' type='checkbox'><label for='data-ba496aa9-e937-4e6c-ae7f-b9b8b4f2f84b' 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>long_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;2020-01-16T11:30:00.000000000&#x27;, &#x27;2020-02-15T11:30:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>float32</div><div class='xr-var-preview xr-preview'>-79.75 -79.25 ... 79.25 79.75</div><input id='attrs-4056878a-fb80-49bd-afa6-be42ad4d2bc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4056878a-fb80-49bd-afa6-be42ad4d2bc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e6e636d-edee-45ae-b306-93081100bd31' class='xr-var-data-in' type='checkbox'><label for='data-0e6e636d-edee-45ae-b306-93081100bd31' 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>degree_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-79.75, -79.25, -78.75, ...,  78.75,  79.25,  79.75],\n",
+       "      shape=(320,), dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-2faa4c3c-f975-4635-81e2-edef40e3f719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2faa4c3c-f975-4635-81e2-edef40e3f719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28560499-1ee7-42b7-a8d0-71290b71a625' class='xr-var-data-in' type='checkbox'><label for='data-28560499-1ee7-42b7-a8d0-71290b71a625' 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>degree_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
+       "      shape=(720,), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8960fa3d-8153-4767-9ff3-4773f568b715' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8960fa3d-8153-4767-9ff3-4773f568b715' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(2)</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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-45ffa115-769a-4485-9c83-b1609661666f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45ffa115-769a-4485-9c83-b1609661666f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95866d08-e868-45f1-8378-fc6923f2e3ae' class='xr-var-data-in' type='checkbox'><label for='data-95866d08-e868-45f1-8378-fc6923f2e3ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"140.0\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,60.0)\">2</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prw</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 320, 720), meta=np.ndarray&gt;</div><input id='attrs-bddaa400-e1c7-4afb-af47-96321465482f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bddaa400-e1c7-4afb-af47-96321465482f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5f67a2e-c5ae-4da2-a359-43ef29a40880' class='xr-var-data-in' type='checkbox'><label for='data-a5f67a2e-c5ae-4da2-a359-43ef29a40880' 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>atmosphere_mass_content_of_water_vapor</dd><dt><span>long_name :</span></dt><dd>Water Vapor Path</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1038240</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1440</dd><dt><span>GRIB_Ny :</span></dt><dd>721</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.75</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_surface :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.76 MiB </td>\n",
+       "                        <td> 900.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2, 320, 720) </td>\n",
+       "                        <td> (1, 320, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"118\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c14752b-cd84-4ffc-b2fe-825f8ee8e198' class='xr-section-summary-in' type='checkbox' checked /><label for='section-1c14752b-cd84-4ffc-b2fe-825f8ee8e198' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Mon Jul 06 10:02:21 2026: cdo monmean /work/bd0854/b380103/eso4clima/output/watervapor/concatenated/202001_day_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/concatenated/202001_mon_ERA5_full_prw.nc\n",
+       "Mon Jul 06 10:02:21 2026: cdo daymean /work/bd0854/b380103/eso4clima/output/watervapor/concatenated/202001_hr_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/concatenated/202001_day_ERA5_full_prw.nc\n",
+       "Mon Jul 06 10:02:16 2026: cdo cat /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200101_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200102_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200103_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200104_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200105_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200106_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200107_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200108_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200109_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200110_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200111_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200112_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200113_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200114_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200115_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200116_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200117_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200118_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200119_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200120_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200121_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200122_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200123_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200124_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200125_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200126_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200127_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200128_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200129_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200130_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/2020/20200131_ERA5_full_prw.nc /work/bd0854/b380103/eso4clima/output/watervapor/concatenated/202001_hr_ERA5_full_prw.nc\n",
+       "2026-05-04T06:44 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes-2.42.0 with {&quot;source&quot;: &quot;tmp7hm0rmx9/data.grib&quot;, &quot;filter_by_keys&quot;: {&quot;stream&quot;: [&quot;oper&quot;], &quot;stepType&quot;: [&quot;instant&quot;]}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:    (time: 2, bnds: 2, lat: 320, lon: 720)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 16B 2020-01-16T11:30:00 2020-02-15T11:30:00\n",
+       "  * lat        (lat) float32 1kB -79.75 -79.25 -78.75 ... 78.75 79.25 79.75\n",
+       "  * lon        (lon) float32 3kB -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 32B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "    prw        (time, lat, lon) float32 2MB dask.array<chunksize=(1, 320, 720), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDI:                     Climate Data Interface version 2.2.4 (https://mp...\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 Mon Jul 06 10:02:21 2026: cdo monmean /work/bd08...\n",
+       "    regrid_method:           bilinear\n",
+       "    frequency:               mon\n",
+       "    CDO:                     Climate Data Operators version 2.2.2 (https://mp..."
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_data"
    ]
   },
   {
@@ -73,24 +826,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1440, 40, 40) (2, 40, 40)\n"
+      "(1440, 80, 200) (2, 80, 200)\n"
      ]
     }
    ],
    "source": [
-    "# # coordinates of subset\n",
-    "# lon_subset = slice(-50, 50) \n",
-    "# lat_subset = slice(-30, 10)\n",
+    "# coordinates of subset\n",
+    "lon_subset = slice(-50, 50) \n",
+    "lat_subset = slice(-30, 10)\n",
     "\n",
-    "# tiny sample\n",
-    "lon_subset = slice(-10, 10) \n",
-    "lat_subset = slice(-10, 10)\n",
-    "\n",
-    "daily_subset = hourly_data.sel(lon=lon_subset, lat=lat_subset)\n",
-    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "hourly_subset = hourly_data.sel(lon=lon_subset, lat=lat_subset).compute()\n",
+    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset).compute()\n",
     "lsm_subset = lsm_mask.sel(lon=lon_subset, lat=lat_subset)  # True=Land\n",
     "\n",
-    "print(daily_subset[var_name].shape, monthly_subset[var_name].shape)  # (time, lat, lon)"
+    "print(hourly_subset[var_name].shape, monthly_subset[var_name].shape)  # (time, lat, lon)"
    ]
   },
   {
@@ -101,11 +850,11 @@
    "outputs": [],
    "source": [
     "# calculate residuals as target\n",
-    "daily_subset_averaged = daily_subset.resample(time=\"MS\").mean(skipna=True)\n",
-    "daily_subset_averaged[\"time\"] = monthly_subset[\"time\"]\n",
+    "hourly_subset_averaged = hourly_subset.resample(time=\"MS\").mean(skipna=True)\n",
+    "hourly_subset_averaged[\"time\"] = monthly_subset[\"time\"]\n",
     "\n",
     "# Residuals\n",
-    "monthly_subset_res = monthly_subset - daily_subset_averaged"
+    "monthly_subset_res = monthly_subset - hourly_subset_averaged"
    ]
   },
   {
@@ -140,9 +889,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Patch grid (m x i x j): 2 x 1 x 1 = 2 patches\n",
+      "Patch grid (m x i x j): 2 x 6 x 21 = 252 patches\n",
       "Overlap: 32 pixels (height), 32 pixels (width)\n",
-      "2\n"
+      "252\n"
      ]
     }
    ],
@@ -153,7 +902,7 @@
     "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\n",
     "\n",
     "dataset = STDataset(\n",
-    "    input_da=daily_subset[var_name],\n",
+    "    input_da=hourly_subset[var_name],\n",
     "    monthly_da=monthly_subset_res[var_name],\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
     "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
@@ -176,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 0 1\n"
+      "151 75 26\n"
      ]
     }
    ],
@@ -206,9 +955,9 @@
    "outputs": [],
    "source": [
     "# Device and resources\n",
-    "compute_threads = 120\n",
-    "dataloader_num_workers = 4\n",
-    "device = \"cpu\"\n",
+    "compute_threads = 4\n",
+    "dataloader_num_workers = 2\n",
+    "device = \"cpu\" # use cuda if GPU is available\n",
     "model = configure_compute_resources(model, device=device, compute_threads=compute_threads, dataloader_num_workers=dataloader_num_workers)"
    ]
   },
@@ -219,25 +968,31 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'float' object has no attribute 'item'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;66;03m# verbose is True\u001b[39;00m\n\u001b[32m      3\u001b[39m batch_size = \u001b[32m200\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m trained_model = \u001b[43mtrain_monthly_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epoch\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulation_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m      8\u001b[39m total_time = (time.time() - start) / \u001b[32m60.0\u001b[39m\n\u001b[32m      9\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtotal_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Developments/ESO4Clima/ClimaNet/climanet/train.py:131\u001b[39m, in \u001b[36mtrain_monthly_model\u001b[39m\u001b[34m(model, dataset, validation_dataset, shuffle, batch_size, num_epoch, patience, accumulation_steps, optimizer_lr, run_dir, store_model, device, verbose, dataloader_num_workers, verbose_epoch_interval)\u001b[39m\n\u001b[32m    128\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m validation_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    129\u001b[39m     \u001b[38;5;66;03m# Store train loss for gap calculation\u001b[39;00m\n\u001b[32m    130\u001b[39m     avg_train_loss = avg_epoch_loss\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m     _, avg_epoch_loss = \u001b[43mpredict_monthly_var\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    132\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    133\u001b[39m \u001b[43m        \u001b[49m\u001b[43mvalidation_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    134\u001b[39m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    135\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreturn_numpy\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    137\u001b[39m \u001b[43m        \u001b[49m\u001b[43msave_predictions\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    138\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreturn_loss\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    139\u001b[39m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    140\u001b[39m \u001b[43m        \u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    141\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_num_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    142\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    143\u001b[39m     writer.add_scalar(\u001b[33m\"\u001b[39m\u001b[33mLoss/validation\u001b[39m\u001b[33m\"\u001b[39m, avg_epoch_loss, epoch)\n\u001b[32m    145\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m verbose \u001b[38;5;129;01mand\u001b[39;00m epoch % verbose_epoch_interval == \u001b[32m0\u001b[39m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Developments/ESO4Clima/ClimaNet/climanet/predict.py:152\u001b[39m, in \u001b[36mpredict_monthly_var\u001b[39m\u001b[34m(model, dataset, batch_size, return_numpy, save_predictions, return_loss, device, run_dir, verbose, dataloader_num_workers)\u001b[39m\n\u001b[32m    146\u001b[39m             \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m    147\u001b[39m                 \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mProcessed batch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;250m \u001b[39m+\u001b[38;5;250m \u001b[39m\u001b[32m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, with loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss.item()\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    148\u001b[39m             )\n\u001b[32m    150\u001b[39m         writer.add_scalar(\u001b[33m\"\u001b[39m\u001b[33mProgress/Batch\u001b[39m\u001b[33m\"\u001b[39m, i + \u001b[32m1\u001b[39m, idx)\n\u001b[32m--> \u001b[39m\u001b[32m152\u001b[39m average_loss = \u001b[43maverage_loss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitem\u001b[49m() / \u001b[38;5;28mlen\u001b[39m(dataloader)\n\u001b[32m    154\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[32m    155\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAverage loss over all batches: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maverage_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mAttributeError\u001b[39m: 'float' object has no attribute 'item'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: gap between train and val loss: -0.291461\n",
+      "Epoch 0: best_loss = 0.234995\n",
+      "Epoch 20: gap between train and val loss: 0.001581\n",
+      "Epoch 20: best_loss = 0.146412\n",
+      "Epoch 40: gap between train and val loss: 0.002941\n",
+      "Epoch 40: best_loss = 0.145509\n",
+      "Epoch 60: gap between train and val loss: 0.001709\n",
+      "Epoch 60: best_loss = 0.145403\n",
+      "Epoch 80: gap between train and val loss: 0.004099\n",
+      "Epoch 80: best_loss = 0.145300\n",
+      "Training complete. Best loss: 0.145300\n",
+      "Model saved to runs_hourly_watervapor/best_model.pth\n",
+      "63.05\n"
      ]
     }
    ],
    "source": [
     "start = time.time()\n",
     "# verbose is True\n",
-    "batch_size = 200\n",
+    "batch_size = 10\n",
     "trained_model = train_monthly_model(\n",
-    "    model, train_dataset, validation_dataset=validation_dataset, batch_size=batch_size, num_epoch=10, accumulation_steps=20, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
+    "    model, train_dataset, validation_dataset=validation_dataset, batch_size=batch_size, num_epoch=100, accumulation_steps=20, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
     ")\n",
     "\n",
     "total_time = (time.time() - start) / 60.0\n",
@@ -254,13 +1009,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "id": "343c5dce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x77486d6ab770>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load saved training status with tbparse.SummaryReader\n",
+    "reader = SummaryReader(run_dir)\n",
+    "\n",
+    "# plot training and validation loss\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "train_loss = reader.scalars[reader.scalars[\"tag\"] == \"Loss/train\"]\n",
+    "validation_loss = reader.scalars[reader.scalars[\"tag\"] == \"Loss/validation\"]\n",
+    "plt.plot(train_loss[\"step\"], train_loss[\"value\"], label=\"Train Loss\")\n",
+    "plt.plot(validation_loss[\"step\"], validation_loss[\"value\"], label=\"Validation Loss\")\n",
+    "plt.xlabel(\"Step\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed batch 1/2, with loss: 0.1680\n",
+      "Processed batch 2/2, with loss: 0.1741\n",
+      "Average loss over all batches: 0.1710\n",
+      "Predictions saved to './runs_hourly_watervapor'\n"
+     ]
+    }
+   ],
    "source": [
     "# inference on test data, verbose is True\n",
-    "batch_size = 200\n",
+    "batch_size = 20\n",
     "predictions = predict_monthly_var(\n",
     "    model=f\"{run_dir}/best_model.pth\", dataset=test_dataset, batch_size=batch_size, save_predictions=True, device=device, run_dir=run_dir, dataloader_num_workers=dataloader_num_workers,\n",
     ")"
@@ -268,17 +1076,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "200264a1-c3cb-42d2-95eb-887259ed63cb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patch grid (m x i x j): 2 x 2 x 5 = 20 patches\n",
+      "Overlap: 0 pixels (height), 0 pixels (width)\n",
+      "20\n"
+     ]
+    }
+   ],
    "source": [
     "# inference on whole dataset\n",
     "num_patches = (10, 10)\n",
     "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
     "\n",
     "dataset = STDataset(\n",
-    "    input_da=daily_subset[var_name],\n",
+    "    input_da=hourly_subset[var_name],\n",
     "    monthly_da=monthly_subset_res[var_name],\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
     "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
@@ -291,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "74c0cfd5-4ebf-4df6-b788-19563a76155b",
    "metadata": {},
    "outputs": [],
@@ -304,67 +1122,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "7e2964b3-3fce-4df1-bb97-8677ef3bbfbf",
    "metadata": {},
    "outputs": [],
    "source": [
     "# add to monthly average\n",
-    "predictions = daily_subset_averaged[var_name] + predictions_res[\"predictions\"]"
+    "predictions = hourly_subset_averaged[var_name] + predictions_res[\"predictions\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "3b0e3f24-c9d0-48e8-858a-48deb8d000d2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ocean = ~lsm_subset[\"lsm\"].values\n",
     "masked_target = monthly_subset[var_name].where(ocean)\n",
     "masked_pred = predictions.where(ocean)\n",
-    "plot_results(masked_target, masked_pred, label=\"SST K\", title=(\"Target\", \"Prediction\"))"
+    "plot_results(masked_target, masked_pred, label=\"Water Vapour kg/m2\", title=(\"Target\", \"Prediction\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "7ba5d204-aefc-4103-8475-c802c603ad3f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "err_baseline = (monthly_subset[var_name] - daily_subset_averaged[var_name])  # M - mean(D)\n",
+    "err_baseline = (monthly_subset[var_name] - hourly_subset_averaged[var_name])  # M - mean(D)\n",
     "err_predictions = (monthly_subset[var_name] - predictions)  # M - M_hat\n",
     "\n",
     "ocean = ~lsm_subset[\"lsm\"].values\n",
     "masked_err_baseline = err_baseline.where(ocean) \n",
     "masked_err_predictions = err_predictions.where(ocean) \n",
     "\n",
-    "plot_results(masked_err_baseline, masked_err_predictions, label=\"error K\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
+    "plot_results(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "a7420dc7-fe1b-4f34-a62e-5914091f04b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plot_histograms(masked_err_baseline, masked_err_predictions, label=\"error K\", legend_labels=(\"err_Baseline\", \"err_Prediction\"))"
+    "plot_histograms(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", legend_labels=(\"err_Baseline\", \"err_Prediction\"))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e593e9f-60dd-4054-9267-93fe1f582776",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "climanet",
+   "display_name": "climanet (3.14.0)",
    "language": "python",
    "name": "python3"
   },

From 1dea21d6287140e864ee05a90fe717b8f40f2d17 Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Thu, 16 Jul 2026 15:42:12 +0200
Subject: [PATCH 5/6] initiate loss as zero tensor

---
 climanet/predict.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/climanet/predict.py b/climanet/predict.py
index c507dc4..4649b8b 100644
--- a/climanet/predict.py
+++ b/climanet/predict.py
@@ -120,7 +120,7 @@ def predict_monthly_var(
 
     with torch.inference_mode():
         idx = 0
-        average_loss = 0.0
+        average_loss = torch.tensor(0.0, device=device)
         for i, batch in enumerate(dataloader):
             # Move batch to the appropriate device
             batch = {k: v.to(device, non_blocking=use_cuda) for k, v in batch.items()}

From 3ac2df40d3c5109c622d4618dc1cdeae8950409e Mon Sep 17 00:00:00 2001
From: Ou Ku <o.ku@esciencecenter.nl>
Date: Thu, 16 Jul 2026 15:51:18 +0200
Subject: [PATCH 6/6] update notebook

---
 notebooks/example_hourly_watervapor.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/notebooks/example_hourly_watervapor.ipynb b/notebooks/example_hourly_watervapor.ipynb
index bc6618e..e2b4ff1 100644
--- a/notebooks/example_hourly_watervapor.ipynb
+++ b/notebooks/example_hourly_watervapor.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "f383c4c8-0199-4092-be12-07be525fdec1",
    "metadata": {
     "scrolled": true
@@ -54,7 +54,7 @@
     "\n",
     "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
     "lsm_mask = xr.open_dataset(file_name)\n",
-    "# lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})\n",
+    "lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})\n",
     "\n",
     "# Down sample mask to match the resolution of the monthly data\n",
     "lsm_mask = lsm_mask.coarsen(lat=2, lon=2, boundary='trim').mean()\n",