"
],
"text/plain": [
" Tmax\n",
"time \n",
"1980-01-01 7.24\n",
"1980-01-02 7.16\n",
"1980-01-03 6.53\n",
"1980-01-04 4.46\n",
"1980-01-05 1.78"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Extract temperature data (convert Kelvin to Celsius)\n",
"X_temp = training.isel(point=0).to_dataframe()[['T2max']] - 273.15\n",
"y_temp = targets.isel(point=0).to_dataframe()[['Tmax']]\n",
"\n",
"# Extract precipitation data (convert to mm/day)\n",
"X_pcp = training.isel(point=0).to_dataframe()[['PREC_TOT']] * 24\n",
"y_pcp = targets.isel(point=0).to_dataframe()[['Prec']]\n",
"\n",
"print('Training temperature (first 5 days):')\n",
"display(X_temp.head())\n",
"print('\\nTarget temperature (first 5 days):')\n",
"display(y_temp.head())"
]
},
{
"cell_type": "markdown",
"id": "f06dcde4",
"metadata": {},
"source": [
"## Step 3: Understanding Analog Selection Strategies\n",
"\n",
"The `PureAnalog` class supports four different strategies for selecting and using analog days:\n",
"\n",
"### 1. Best Analog\n",
"- Selects the single closest analog day\n",
"- Fastest and simplest approach\n",
"- Can be deterministic (always same result)\n",
"\n",
"### 2. Sample Analogs\n",
"- Randomly samples from the N best analogs\n",
"- Adds stochasticity to the predictions\n",
"- Good for ensemble generation\n",
"\n",
"### 3. Weight Analogs\n",
"- Uses a weighted average of N analogs\n",
"- Weights based on similarity (closer analogs get more weight)\n",
"- Smooths out predictions\n",
"\n",
"### 4. Mean Analogs\n",
"- Simple average of N best analogs\n",
"- Equal weight to all selected analogs\n",
"- Balances simplicity and robustness\n",
"\n",
"Let's compare these strategies on temperature data."
]
},
{
"cell_type": "markdown",
"id": "e7d5edee",
"metadata": {},
"source": [
"## Step 4: Apply Different Analog Strategies\n",
"\n",
"We'll split our data into training and testing periods, then compare the four analog strategies."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "1f4fe3a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training period: 1980-01-01 11:30:00 to 1982-09-26 11:30:00\n",
"Testing period: 1982-09-27 11:30:00 to 2001-12-31 11:31:00\n",
"\n",
"Training samples: 1000\n",
"Testing samples: 7036\n"
]
}
],
"source": [
"# Split data: first 1000 days for training, rest for testing\n",
"train_size = 1000\n",
"X_train, X_test = X_temp[:train_size], X_temp[train_size:]\n",
"y_train, y_test = y_temp[:train_size], y_temp[train_size:]\n",
"\n",
"print(f'Training period: {X_train.index[0]} to {X_train.index[-1]}')\n",
"print(f'Testing period: {X_test.index[0]} to {X_test.index[-1]}')\n",
"print(f'\\nTraining samples: {len(X_train)}')\n",
"print(f'Testing samples: {len(X_test)}')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "25575a13",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Fitting best_analog...\n",
" RMSE: 3.066°C\n",
"\n",
"Fitting sample_analogs...\n",
" RMSE: 3.066°C\n",
"\n",
"Fitting weight_analogs...\n",
" RMSE: 2.481°C\n",
"\n",
"Fitting mean_analogs...\n",
" RMSE: 2.322°C\n",
"\n",
"All strategies fitted successfully!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
"# Dictionary to store results (clear any previous results)\n",
"results = {}\n",
"\n",
"# Test each analog strategy\n",
"strategies = ['best_analog', 'sample_analogs', 'weight_analogs', 'mean_analogs']\n",
"n_analogs = 10 # Number of analogs to consider\n",
"\n",
"for strategy in strategies:\n",
" print(f'\\nFitting {strategy}...')\n",
"\n",
" # Initialize and fit the model\n",
" model = PureAnalog(kind=strategy, n_analogs=n_analogs)\n",
" model.fit(X_train, y_train)\n",
"\n",
" # Generate predictions (extract only the 'pred' column)\n",
" predictions_full = model.predict(X_test)\n",
" predictions = predictions_full[['pred']] # Keep only prediction column\n",
" results[strategy] = predictions\n",
"\n",
" # Calculate simple performance metric (RMSE)\n",
" rmse = np.sqrt(np.mean((predictions.values - y_test.values) ** 2))\n",
" print(f' RMSE: {rmse:.3f}°C')\n",
"\n",
"print('\\nAll strategies fitted successfully!')"
]
},
{
"cell_type": "markdown",
"id": "6700d4f5",
"metadata": {},
"source": [
"## Step 5: Visualize Results\n",
"\n",
"Let's compare the predictions from different strategies by plotting a 300-day sample."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "f4608e3b",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot predictions for the first 300 days of test period\n",
"plot_days = 300\n",
"\n",
"fig, ax = plt.subplots(figsize=(14, 6))\n",
"\n",
"# Plot each strategy\n",
"for strategy, predictions in results.items():\n",
" ax.plot(\n",
" predictions.index[:plot_days],\n",
" predictions.values[:plot_days],\n",
" label=strategy.replace('_', ' ').title(),\n",
" alpha=0.7,\n",
" linewidth=1.5,\n",
" )\n",
"\n",
"# Plot observations\n",
"ax.plot(\n",
" y_test.index[:plot_days],\n",
" y_test.values[:plot_days],\n",
" label='Observed',\n",
" color='black',\n",
" linewidth=2,\n",
" alpha=0.5,\n",
")\n",
"\n",
"ax.set_xlabel('Date')\n",
"ax.set_ylabel('Temperature (°C)')\n",
"ax.set_title('Comparison of Analog Methods (First 300 Days of Test Period)')\n",
"ax.legend(loc='upper right')\n",
"ax.grid(True, alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f5c4f7a4",
"metadata": {},
"source": [
"### Observations\n",
"\n",
"From the plot, you'll notice:\n",
"- **Best Analog**: Can be quite variable, following individual historical days\n",
"- **Sample Analogs**: Similar to best analog but with randomness\n",
"- **Weight Analogs**: Smoother predictions due to weighted averaging\n",
"- **Mean Analogs**: Also smooth, using equal weights\n",
"\n",
"The averaging approaches (weight and mean) tend to be more stable but may miss some variability."
]
},
{
"cell_type": "markdown",
"id": "21b1cbd7",
"metadata": {},
"source": [
"## Step 6: Analog Regression\n",
"\n",
"`AnalogRegression` combines the analog method with linear regression:\n",
"1. Find N analog days\n",
"2. Fit a linear regression using those analog days\n",
"3. Apply the regression to make predictions\n",
"\n",
"This hybrid approach can capture both the analog similarity and systematic relationships."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "b796a604",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting AnalogRegression with 100 analogs...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AnalogRegression RMSE: 2.247°C\n"
]
}
],
"source": [
"# Initialize and fit AnalogRegression\n",
"n_analogs_reg = 100 # Use more analogs for regression\n",
"\n",
"print(f'Fitting AnalogRegression with {n_analogs_reg} analogs...')\n",
"analog_reg = AnalogRegression(n_analogs=n_analogs_reg)\n",
"analog_reg.fit(X_train, y_train)\n",
"\n",
"# Generate predictions (extract only the 'pred' column)\n",
"predictions_reg_full = analog_reg.predict(X_test)\n",
"predictions_reg = predictions_reg_full[['pred']] # Keep only prediction column\n",
"\n",
"# Calculate RMSE\n",
"rmse_reg = np.sqrt(np.mean((predictions_reg.values - y_test.values) ** 2))\n",
"print(f'AnalogRegression RMSE: {rmse_reg:.3f}°C')"
]
},
{
"cell_type": "markdown",
"id": "5feeeb58",
"metadata": {},
"source": [
"## Step 7: Compare All Methods\n",
"\n",
"Let's create a comprehensive comparison including AnalogRegression."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "b3b1a392",
"metadata": {},
"outputs": [
{
"data": {
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u3bvk22+Xyo4d2wM6DpQwtN29e7eceGL2xFcejRs3loSEhOLuDseoypEvpGrKx37LrO4j4nZlisNVzh92a2TRtnNmBc029xGJjlwj4i64q63VlSS768w++syivR/yDW0BAAAAAABCUSBD24yMDPn44w9l5cpf5JNPPpIjR3J8s7sc0B6hlELbs846S2bP9gRp2TfXu+++K2eeeWZxd4dj4HK6xOpKMT8eEc7/pGb6exKdubaQHrFlwFbE0NZnYJlOl+lTm3N5QZOPEc4CAAAAAIBwE8jQ9p9/NojDkdXO0uVyyZ9//lHuYyC0zVtRv9fu9eSTT8rtt98u3333ndjtdnn22Wdl69atkp6eLpMnTy7u7nAMtv5xUA5XG5JreUpEW3FE1JO49O/K9bq6i1ppe7QCWNs3pLu0yrbwClnf4tq8QtsaiW/IoSqDxW2tWowRAwAAAAAABIdAhrYa1PrSfA8hGto2b95cFi1aJF9++aX8+++/4nQ6pWvXrnLZZZdJ5cqVy2aUKPTD7LTWkjTrCeZxUqXLyveKWXP0k82PO6s9gssdIalSqQgvsHhDWw1soyPseWyj+8zaKFKsYj/aN5emtgAAAAAAIBQEMrS15sh0tm3bGrCxoIShrYqOjpazzz5bqlSpYiYjO+WUUwhsy1HOz7LF7RCXNU4sR5c7rdXL/qB+Ayhilw1tiSAi9ojjJdN2qdicBf9S0nOKSzpXX2FaKbRo9K+sWZsujoiG2YfOb0ieiwEAAAAAABDEcla7lifN9XzFx+8v9zHQHqGUQtsDBw7I0KFDZfXq1VK1alVzY6WkpMgFF1wgr732mglyUbbcOSYaq3JkpiRW6R8Cl93THkEnFfNfml/4GpVxnIj8Zx5H2FwS6djuF9q6xZZnaGyxZlX1AgAAAAAABLNAVtrabEX89jSCfyKyYcOGSWRkpCxZskR++eUXWblypSxcuND0vBg+fHjZjBIFfpid1iplPkmXpcDZzYp4bGdWkGrJcdsV/GrPcS1mO5sz3m9tclx/cVvyaLVQtpcDAAAAAAAg5ENbhFGl7a+//iqffvqpNGrUyLusSZMmZoKy6667rrTHh7yE6Gc5KdEtCdHdJS2iRZFf4zsR2dEl/vusUktiUnMntBFRNM4GAAAAAACBY7GE3kRkgUB7hFKqtG3cuLFs3Lgx1/Ldu3dLw4bZX1tHGcrxYXZZa/kVluasRi1rWuV7wGqXeGtGgdtt3SySHnFqMQths891y97GklLZf5K1zEirHKkUIY4IizgrZ/0NonLqYqnWYEcxzwIAAAAAAKBiBafOo9+KDiRC21KqtL3yyivl2WeflbVr10qrVq0kIiJC1q9fL1OnTpV+/frJ7NmzvdteccUVxd09isCd48OcFtPOOwmZWW+JloM7UqRW47hyuZ5pmU5ZGJPVqLqH1PUutx3cKNYjeyXz+E7m+aFjyJK9p2WxSHxSzdzrLQ4T3NojbRIrWb9ojlTqIdHpCcd0LgAAAAAAAOUrkJW2gQ9tUUqh7QcffGAmG1u0aJH58ahcubLfMk3JCW0Dw2WtKv8s3yvnNT6pXI63YuvhPJfH/vWu+dcT2lpt7mL/QorOaCD2Jifm+wq31SVHs1pxuLM7+5Z1j18AAAAAAICCFLXrQUVpj3DddTfIRx9NK7fjVbjQ9ptvvimbkaDoivBZdma6yu2gyRkOv+fWpJ3iqtrI/7eUxSJ+ExIW8feRSyLF3qynROxdJe7cDW5NpW32tiLMeQgAAAAAAIJBUcPYQIa25dkeQb+tnxfaI5RSaKs2bNggW7ZsEbvdnmsd1bVlr9Fp1WTtV8HzgXflOFTk7hWSUfUqn8E4RSwRYvFJaotaB5ud01ryfJXFHeHdq2+mm7i7aZHHDwAAAAAAUDF72pbfsS2WYk+tVaEVO7R95ZVX5J133pFatWpJdHS03zpaIpSPw06H2JyHxWmr4V2m7QB8Q1G/JrdlzFpYAutyiFizbjW3+d+xqRqbIiI5+tqanWUNwPejH7i/UQEAAAAAAIRGpW15HttqJbQt09D2k08+kZEjR5oJyRAYOxPSxOLWADM7tM2lXFu6Zn/otrpSpUHOD/zRptZuV3Zga3UlS6W0H+VIpYsL3HMlyf7DwEkNtsnvG/xDW0dkitgyspZVdtskraSnAgAAAAAAUGFC2/KstM07rKI9Qt6KHXHrJGRnnHFGcV+GUhaXmj0JXF6Vtfl8Do5dAR9i32PNcOySP9J35Biaf89bZXMeEpvzYJ77i7avlcqpC6Va8lSp1Wyt9yAxkXaJyVjpt60jMlm21d8gCVXi5fh6m4p5UgAAAAAAAIEObaWCVNoyaXyZVto++uij8txzz8k999wjDRs2zFXarMtQthyJdrFHnphjqX7IfG7+MvocOFxu2ZecLg2rxnj/EuIJbeOSnXLGr4lyqMfBPANf39zXYp7k/YvBZY2T6MwNWY9trhwn5H9iydU2yp6asbKn7la5NlErbn0mQAMAAAAAAAjyQLS8etru2bNbFi36SmJiYqR37z5StWq1cu2nm197BCptSym0TU9Pl7Vr18qNN97od1H1RtTn69evL+4uUQzaYuDAj4fFFdu5sC2970mpOPp7ZvGG/bJ6V6Jc0+o4ObF2ZbPMc4wTN2VITKpbojbEi3TJ2v7I4VT5cdnv0q57F7HZsn9ZRTq2iEVyV+Ca/bnSsx8X0p22sdUpm48eP9KW6buXYzxRAAAAAACA8Kt2nTPnC0lKSjKPv/vuG7nssr7lWmlLOFvGoe3LL78s/fv3Nz+azCNY6F8rsj9oiRmZ8uLXm+SW9k2kXhX/CeOOTda+/z2Qav49kGL3hrYF2b3oX4mImit/ndZaqldzyn7HHnHY6prxOmyNpVLaMkmN7eR/JEuEuCVKLGIXixTyF59I7e1bReyWWHHEVCvJCQIAAAAAAIRtT1tPYKs2bFhf7qEtE5GVcU9bu90uAwcOlBNPPFGOO+64XD/HQvfZp08f+eWXX7zLRowYIaeccorfz/Tp049p/+En9wfK4s4KUz32p2aYf//Zn1JKh/Qcs/APs1bH/vPzTDmYqqFr1mtSMpzSonmaVD3yicRmLJfU2M5ypFK3owGuv8zIE+VIpe6SEW3NrrQ9Wk2bFmsVZ44eKG7dymKRnWfeIlZX1lRktih7Sc8YAAAAAACgBIoaiAauqW15tkfIv9KWb0uXSqXt4MGD5e2335annnpKoqNLXsGZkZEhDz74oGza5D+J1L///muW9+3b17ssLi6uxMcLefncx25LJbH4/HXkSP2jy0vtwDn25Ns+N4+/yli2LJHtIlLVYjHB65GMTHE7XUX+W0F83QjZe1wlOcOmfWqzbWvqllrxVqmS7MxakFl4tS8AAAAAAEBF72mbl/Jtj5B3HtSmTdtyG0NYh7Y//fSTrF69WmbPni21a9cWm83mt37p0qVF3tfmzZtNMJvXDaKh7S233CJ16tQp7hDDWl5/laiculBSKl+cvcDtFFd0Of6lxHNYaSCJ0adKtcxEiZPkrHVmpVuczqMhay55/3Jw2ETSY62yNu486eNzFHt0jknO0uqJu0ZWVTF/mAEAAAAAAMEi2Noj5H3swE5E1qVLN6lX72jlIUoW2vbr18/8lIZff/1V2rVrJ/fff7+cffbZ3uUpKSmyb98+adq0aakcJ9y43DkD0NxBrqcXbOl97t35fqDdR9c55SpJixBJT4yWWrXm+o3O4nJJerpbDlW712+89qiWeR/u6CZOS6TfYpcta5Iym1sreEUsmVp9fTS0FZFo+5/itkSKrfpZJTpbAAAAAACA8hDY0DawE5Gdey5VtqUW2vq2K0hMTJQqVaqYi34sM8Bdd911eS7XKlvd31tvvSXff/+9VK9eXW6++Wa/Y1dkma4M8Y0y7ZEn+a2PdOwQi9vTX7iUPnxHP8TxjvXyn+snaZs5WERqmGVWt1uqHsodHetLkiKOk7SI+uJyOGX1Op24Lr1oh8vVjiFr7xmxB8y/EXq0CKuIWyu9s9a5HC5xWauL22Kj6BYAAAAAAARUaFTaMhFZ2IS2+mZqmPr+++9LcnKyLFq0SF5//XWpVKmSPPnkkxIVFVXiQW3ZssWEts2aNTOTnq1cudL00NWett27dy/x/sONTtzlYXPuk2j7GolIbSdS8rfCR9aHeJ/zT/Pv/gztWJsVFtv+OSDNdtwobotuc3TqMbdFNm67VCIqZ0qGJUose9Nlf3Km3x6dkVZxiVsiM4v/C8IVd/TkLG5xWY626HCLZES1MA8jUkv15AEAAAAAAMKup215Hjuv9ggoxdB2woQJMn/+fHnppZdMWwOlFbBPP/20jB492gS3JXXFFVdI586dTYWtatGihWzdulVmzJiRb2gbGWnzFGOGPVumI991Tls9SancRyodsEpCTavYIqwSFeXfd7g4rLasixqlAWtU1jXWxgQRPvs9GN9QrJbUrBeYAthUSTzSRNLEIZEWp4lxXfF2yVn/qtWybrMsj363GgBbsj7QehxrpC1rLKYnQnZJfcTR0FbHFBXl8+F3S4nOG+UnIoL3CaGP+xjhgPsY4YJ7GeGA+xjhoqLfyzabRWy2woNK34ylbMfjPxY9pi4ryhhLQ3R0RJ5jCHYRAbqPix3azpo1ywS2bdq08QZnF1xwgYwaNUruvffeUgltdb+ewNZDq25XrFiR72syM/Ob6Cr8WPKd1Ev8+s7qX0syM11itx/7tYlyZv1VKNPuEKfdKS53VusChyN7vy6LW7wfObM+q6duxBHH0ccirhSn6UNb4Hn5NHOo4oqQLmm15ae4rOPYnFaJ0LG4tQ/uQXGnZTWpPl70GFmvdBwdqxmGU0p03ihfvFcIB9zHCAfcxwgX3MsIB9zHCBcV+V7WrMrpLLyS1W53lMt1yjkWPaYeuyhjLA0OhzvPMYQCewDGWewo/eDBg1K3bt1cy6tWrSqpqUerLUtI2y0MGjTIb9mGDRtMcAuRiJjkXJfBnc+C8uhMkjUlmP+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