code/predictability_wavenumbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "753b5b0c-6106-48f1-9868-9adff5656818",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T15:56:37.972491Z",
     "iopub.status.busy": "2022-03-02T15:56:37.971886Z",
     "iopub.status.idle": "2022-03-02T15:56:38.532525Z",
     "shell.execute_reply": "2022-03-02T15:56:38.531353Z",
     "shell.execute_reply.started": "2022-03-02T15:56:37.972438Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0f19cd-0679-4e1c-996d-c93ee576eb83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T15:33:17.581649Z",
     "iopub.status.busy": "2022-03-02T15:33:17.580732Z",
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     "shell.execute_reply": "2022-03-02T15:33:17.598082Z",
     "shell.execute_reply.started": "2022-03-02T15:33:17.581348Z"
    },
    "tags": []
   },
   "source": [
    "From G.J. Boer (2003) Predictability as a function of scale, Atmosphere-Ocean, 41:3, 203-215, DOI: [10.3137/ao.410302](https://doi.org/10.3137/ao.410302)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5082fab7-cb07-4756-8fac-e96ec5c65d2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T15:56:38.780984Z",
     "iopub.status.busy": "2022-03-02T15:56:38.780440Z",
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     "shell.execute_reply.started": "2022-03-02T15:56:38.780935Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "coef = -3/2\n",
    "n = np.arange(1,100)\n",
    "tau = (n/80)**coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f420e21-d666-4031-b705-e5e4146ea08d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T16:24:55.979442Z",
     "iopub.status.busy": "2022-03-02T16:24:55.978929Z",
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     "shell.execute_reply.started": "2022-03-02T16:24:55.979400Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.613086908021586"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a149e025-590b-4a4c-a2d0-635d7427e288",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T15:56:51.491540Z",
     "iopub.status.busy": "2022-03-02T15:56:51.490211Z",
     "iopub.status.idle": "2022-03-02T15:56:51.510058Z",
     "shell.execute_reply": "2022-03-02T15:56:51.508553Z",
     "shell.execute_reply.started": "2022-03-02T15:56:51.491471Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.435429127226919"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau[20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74831565-7eb6-44ba-a04f-79348ab72bb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T16:13:33.849450Z",
     "iopub.status.busy": "2022-03-02T16:13:33.848603Z",
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     "shell.execute_reply": "2022-03-02T16:13:34.058455Z",
     "shell.execute_reply.started": "2022-03-02T16:13:33.849393Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fc063a75eb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n, tau)\n",
    "plt.ylim(0,20)\n",
    "plt.ylabel('predictability [days]')\n",
    "plt.xlabel('wavenumbers')\n",
    "plt.xlim(10,)\n",
    "plt.axhline(1, color = 'gray', ls = 'dashed')\n",
    "plt.axvline(21, color = 'gray', ls = 'dashed')"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}