{ "cells": [ { "cell_type": "markdown", "id": "57b22edd-ff30-4e05-8b54-9cd6ba52755c", "metadata": {}, "source": [ "# Flip-Flop Index" ] }, { "cell_type": "markdown", "id": "3023fa66-1f96-4961-afbc-c4cf91fe232a", "metadata": {}, "source": [ "The Flip-Flop Index quantifies the stability of a forecast without penalising a trend toward more accurate values at shorter lead times. The Flip-Flop Index has the same units as the forecast, with smaller values indicating greater stability (fewer \"flip-flops\"). The Flip-Flop Index cannot be used to verify a forecast. Indeed, it does not use the verifying observation in its calculation. However, it describes an (arguably important) characteristic of a sequence of forecasts for an event. \n", "\n", "Read about the Flip-Flop Index at: \n", "\n", "- Griffiths D. et al. (2019). Flip-Flop Index: Quantifying Revision Stability for Fixed-Event Forecasts, \n", "*Meteorological Applications*, 26, 30-35. [https://doi.org/10.1002/met.1732](https://doi.org/10.1002/met.1732) \n", "- Griffiths D. et al. (2021). Circular Flip-Flop Index: quantifying revision stability of forecasts of direction, *Journal of Southern Hemisphere Earth Systems Science*, 71, 266–271. [https://doi.org/10.1071/ES21010](https://doi.org/10.1071/ES21010)\n", "\n", "The two functions demonstrated here are `flip_flop_index` which calculates the index for individual forecast revision series, and `flip_flop_index_proportion_exceeding` which summarises the Flip-Flop Index values over many forecast revision series by reporting the frequency with which it exceeded values of interest." ] }, { "cell_type": "code", "execution_count": null, "id": "91abca47", "metadata": { "ExecuteTime": { "end_time": "2023-11-09T04:58:29.280799Z", "start_time": "2023-11-09T04:58:29.275796Z" } }, "outputs": [], "source": [ "from scipy.stats import skewnorm\n", "import numpy as np\n", "import xarray as xr\n", "import pandas as pd\n", "import plotly.express as px\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from scores.continuous import mse\n", "from scores.continuous import flip_flop_index_proportion_exceeding, flip_flop_index" ] }, { "cell_type": "code", "execution_count": 2, "id": "41766974-8695-4361-9eb5-c52d82109713", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(flip_flop_index) # Uncomment this to see the help message" ] }, { "cell_type": "code", "execution_count": 3, "id": "d8bea123-8e4e-4006-b9f6-7df5e27f4b74", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(flip_flop_index_proportion_exceeding) # Uncomment this to see the help message" ] }, { "cell_type": "code", "execution_count": 4, "id": "dcf1849b-c5f7-4631-9685-a0277c945c14", "metadata": {}, "outputs": [], "source": [ "# Create two synthetic forecast revision series.\n", "# These might represent a forecast of 28 degrees a week in advance of the event, updated daily with a forecast of 21 degrees the day before the event.\n", "# Inspection (and the graph below) shows that the unstable_fcst jumps around while the stable_fcst has a cooler trend each day.\n", "unstable_fcst = xr.DataArray(data=[21., 23, 18, 20, 26, 25, 28], dims=\"lead_day\", coords={\"lead_day\": np.arange(1, 8)})\n", "stable_fcst = xr.DataArray(data=[21., 23, 23, 24, 25, 26, 28], dims=\"lead_day\", coords={\"lead_day\": np.arange(1, 8)})" ] }, { "cell_type": "code", "execution_count": null, "id": "3917ba0e-6db2-4ef5-b268-20f1fa8884da", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "", "legendgroup": "", "marker": { "color": "#636efa", "symbol": "circle" }, "mode": "markers", "name": "", "orientation": "v", "showlegend": false, "type": "scatter", "xaxis": "x", "yaxis": "y" }, { "mode": "lines", "name": "unstable forecast", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7 ], "y": [ 21, 23, 18, 20, 26, 25, 28 ] }, { "mode": "lines", "name": "stable forecast", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7 ], "y": [ 21, 23, 23, 24, 25, 26, 28 ] } ], "layout": { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter()\n", "fig.add_scatter(x=unstable_fcst['lead_day'], y=unstable_fcst, mode=\"lines\", name=\"unstable forecast\")\n", "fig.add_scatter(x=unstable_fcst['lead_day'], y=stable_fcst, mode=\"lines\", name=\"stable forecast\").update_layout(xaxis_title=\"Lead Day\", yaxis_title=\"T (C)\")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 6, "id": "e7066009-c847-46ae-bd04-1bf6d755685b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray ()>\n",
       "array(1.8)\n",
       "Attributes:\n",
       "    sampling_dim:  lead_day
" ], "text/plain": [ "\n", "array(1.8)\n", "Attributes:\n", " sampling_dim: lead_day" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The Flip-Flop Index of 1.8 for the noisy forecast quantifies the lack of stability.\n", "flip_flop_index(unstable_fcst, \"lead_day\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "697c1104-54ea-490e-b5b5-7ea0ff23be87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray ()>\n",
       "array(0.)\n",
       "Attributes:\n",
       "    sampling_dim:  lead_day
" ], "text/plain": [ "\n", "array(0.)\n", "Attributes:\n", " sampling_dim: lead_day" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The Flip-Flop Index for the stable forecast is 0, reflecting no flip-flopping.\n", "flip_flop_index(stable_fcst, \"lead_day\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "ac3ef136", "metadata": { "ExecuteTime": { "end_time": "2023-11-09T04:51:28.537658Z", "start_time": "2023-11-09T04:51:28.521157Z" } }, "outputs": [], "source": [ "# Working with real data requires several forecast issuances, which is a very large amount of gridded data. \n", "# We choose to work with synthetic data to keep this example tightly focused.\n", "# We will start with synthetic observations and add perturbations to create synthetic forecasts.\n", "\n", "# Create synthetic temperature observations for 100 days and 100 locations (so, 10,000 observations)\n", "# The values of the observations are between 0 and 40.\n", "obs = 40 * np.random.random((100, 100))\n", "obs = xr.DataArray(\n", " data=obs, \n", " dims=[\"time\", \"location number\"],\n", " coords={\"time\": pd.date_range(\"2023-01-01\", \"2023-04-10\"), \"location number\": np.arange(0, 100)}\n", ")\n", "\n", "# Create forecasts for 7 lead days\n", "# Each observation has 7 forecast values, issued between 1 and 7 days in advance.\n", "# We create two such forecasts, initially matching the observations, then adding some noise to simulate a forecast\n", "fcst1 = xr.DataArray(data=[1]*7, dims=\"lead_day\", coords={\"lead_day\": np.arange(1, 8)})\n", "fcst1 = fcst1 * obs\n", "fcst2 = fcst1.copy()\n", "\n", "# fcst1 is 70,000 forecasts, with 10,000 forecast revision sequences.\n", "# Each forecast revision sequence is predicting one specific obeservation.\n", "# Similarly for fcst2.\n", "\n", "# Create some noise to add to the forecasts to make them interesting and more realistic.\n", "# The noise increases with lead-day reflecting less accurate forecasts at longer lead days.\n", "noise = skewnorm.rvs(4, size=(1, 100, 100))\n", "noise_for_fcst1 = noise.copy()\n", "noise_for_fcst2 = noise.copy()\n", "for lead_day in np.arange(1, 7):\n", " next_lead_day_noise = (1+lead_day/7)*skewnorm.rvs(4, size=(1, 100, 100))\n", " noise_for_fcst1 = np.concatenate((noise_for_fcst1, next_lead_day_noise))\n", " if lead_day % 2 == 0:\n", " noise_for_fcst2 = np.concatenate((noise_for_fcst2, -1*next_lead_day_noise))\n", " else:\n", " noise_for_fcst2 = np.concatenate((noise_for_fcst2, next_lead_day_noise))\n", "\n", "# Add the noise to get the final forecasts.\n", "fcst1 += noise_for_fcst1\n", "fcst2 += noise_for_fcst2" ] }, { "cell_type": "code", "execution_count": 9, "id": "fadab7f3", "metadata": { "ExecuteTime": { "end_time": "2023-11-09T04:57:44.034837Z", "start_time": "2023-11-09T04:57:44.020020Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (lead_day: 7)>\n",
       "array([0.99, 1.32, 1.66, 2.04, 2.48, 2.88, 3.42])\n",
       "Coordinates:\n",
       "  * lead_day  (lead_day) int64 1 2 3 4 5 6 7
" ], "text/plain": [ "\n", "array([0.99, 1.32, 1.66, 2.04, 2.48, 2.88, 3.42])\n", "Coordinates:\n", " * lead_day (lead_day) int64 1 2 3 4 5 6 7" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the Mean Square Error to confirm that the forecasts are less accurate at longer lead days\n", "mse(fcst1, obs, preserve_dims=[\"lead_day\"]).round(2)" ] }, { "cell_type": "code", "execution_count": 10, "id": "ef993c82", "metadata": { "ExecuteTime": { "end_time": "2023-11-09T04:57:45.759437Z", "start_time": "2023-11-09T04:57:45.739361Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (lead_day: 7)>\n",
       "array([0.99, 1.32, 1.66, 2.04, 2.48, 2.88, 3.42])\n",
       "Coordinates:\n",
       "  * lead_day  (lead_day) int64 1 2 3 4 5 6 7
" ], "text/plain": [ "\n", "array([0.99, 1.32, 1.66, 2.04, 2.48, 2.88, 3.42])\n", "Coordinates:\n", " * lead_day (lead_day) int64 1 2 3 4 5 6 7" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# By design, fcst2 has the same absolute errors as fcst1, and hence the same mean square errors.\n", "mse(fcst2, obs, preserve_dims=[\"lead_day\"]).round(2)" ] }, { "cell_type": "code", "execution_count": 11, "id": "5cf3660e-5f90-4942-ad54-a6ef6397e455", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "array([[1.09, 0.29, 0.15, ..., 0.25, 1.27, 0.59],\n",
       "       [0.63, 0.44, 0.96, ..., 1.35, 1.39, 0.23],\n",
       "       [0.97, 0.47, 0.19, ..., 1.39, 0.91, 0.97],\n",
       "       ...,\n",
       "       [0.62, 0.57, 1.09, ..., 0.97, 1.52, 0.33],\n",
       "       [0.84, 0.58, 0.53, ..., 0.25, 0.33, 0.63],\n",
       "       [0.71, 1.37, 1.13, ..., 0.64, 0.58, 0.62]])\n",
       "Coordinates:\n",
       "  * time             (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-04-10\n",
       "  * location number  (location number) int64 0 1 2 3 4 5 6 ... 94 95 96 97 98 99\n",
       "Attributes:\n",
       "    sampling_dim:  lead_day
" ], "text/plain": [ "\n", "array([[1.09, 0.29, 0.15, ..., 0.25, 1.27, 0.59],\n", " [0.63, 0.44, 0.96, ..., 1.35, 1.39, 0.23],\n", " [0.97, 0.47, 0.19, ..., 1.39, 0.91, 0.97],\n", " ...,\n", " [0.62, 0.57, 1.09, ..., 0.97, 1.52, 0.33],\n", " [0.84, 0.58, 0.53, ..., 0.25, 0.33, 0.63],\n", " [0.71, 1.37, 1.13, ..., 0.64, 0.58, 0.62]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-04-10\n", " * location number (location number) int64 0 1 2 3 4 5 6 ... 94 95 96 97 98 99\n", "Attributes:\n", " sampling_dim: lead_day" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can use the flip_flop_index to calculate the Flip-Flop Index for the revision sequence for each date and location.\n", "flip_flop_index(fcst1, \"lead_day\").round(2)" ] }, { "cell_type": "code", "execution_count": 12, "id": "f59e3633-941e-4564-a33c-1bd2bea5c287", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.1854]\n", "[0.848]\n" ] } ], "source": [ "# We expect fcst2 to be less stable (have more flip-flopping) than fcst1.\n", "# Calculating the frequency with with the Flip-Flop Index exceeds 1 confirms that\n", "# the Flip-Flop Index for exceeds 1 only about 20% of the time for fcst1, but well over 80% of the time for fcst2.\n", "# The exact values depend on the random noise generated each time this notebook is run.\n", "print(flip_flop_index_proportion_exceeding(fcst1, \"lead_day\", [1]).values)\n", "print(flip_flop_index_proportion_exceeding(fcst2, \"lead_day\", [1]).values)" ] }, { "cell_type": "code", "execution_count": 13, "id": "20ade6c3", "metadata": { "ExecuteTime": { "end_time": "2023-11-09T05:07:17.160703Z", "start_time": "2023-11-09T05:07:16.955346Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Lead Days 1-2-3-4-5-6-7')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can display a graph showing the proportion of times the Flip-Flop exceeds the x-value as is done here.\n", "# We are calculating `flip_flop_index_proportion_exceeding` each of 0.1, 0.2, 0.3 etc.\n", "# The lower line represents the comparative stability fcst1 with the higher line representing the jumpiness of fcst2 as a system.\n", "# Many of the fcst2 revision sequences have a Flip-Flop Index exceeding 2 but barely any of fcst1.\n", "flip_flop_index_proportion_exceeding(fcst1, \"lead_day\", np.arange(0.1, 5, 0.1)).plot()\n", "flip_flop_index_proportion_exceeding(fcst2, \"lead_day\", np.arange(0.1, 5, 0.1)).plot()\n", "plt.legend(['fcst1', 'fcst2'])\n", "plt.xlabel(\"Flip-Flop Index\")\n", "plt.ylabel(\"Proportion exceeding\")\n", "plt.title('Lead Days 1-2-3-4-5-6-7')" ] }, { "cell_type": "code", "execution_count": 14, "id": "e13ffd83-2c78-4f79-8440-d374d09b48fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'fcst1')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Considering just fcst1, we can compare the Flip-Flop Index of three forecasts in the revision series.\n", "# We see here that the shorter lead-day forecasts flip-flop slightly less than the longer range forecasts.\n", "flip_flop_index_proportion_exceeding(fcst1, \"lead_day\", np.arange(0.1, 4, 0.1), days123=[1, 2, 3]).days123.plot()\n", "flip_flop_index_proportion_exceeding(fcst1, \"lead_day\", np.arange(0.1, 4, 0.1), days456=[4, 5, 6]).days456.plot()\n", "plt.legend(['lead-days 1, 2, 3', 'lead-days 4, 5, 6'])\n", "plt.xlabel(\"Flip-Flop Index\")\n", "plt.ylabel(\"Proportion exceeding\")\n", "plt.title('fcst1')" ] }, { "cell_type": "markdown", "id": "34d3fd9d-4f7c-4f73-a932-d1d158fb0404", "metadata": {}, "source": [ "Next Steps:\n", "\n", "- Try `flip_flop_index` on some real data of interest.\n", "- Try on some forecasts of direction using the argument `is_angular=True`\n", "- For `flip_flop_index_proportion_exceeding` try the `reduce_dims` or `preserve_dims` to report data conditioned by something of interest. For example, if your data includes `season` or `forecast zone` you can report exceedence values for those.\n", "- It is not appropriate to consider the Flip-Flop Index from forecast revision sequences of different lengths. Do you agree?" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.2" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }