diff --git a/example.ipynb b/example.ipynb
index fdf136506775e43f56a6b0db09c59479df6d0ee9..341f00b78fe1337be705d19f096a750b137c3145 100644
--- a/example.ipynb
+++ b/example.ipynb
@@ -2,12 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 204,
    "id": "e4da13a3-d820-485d-8150-7a1ab7f32312",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Подключаем требуемые библиотеки/модули (достаточно один раз в начале всей программы)\n",
+    "import wget\n",
     "import math\n",
     "import netCDF4 as nc\n",
     "import numpy as np\n",
@@ -18,6 +19,46 @@
     "import cartopy.io.img_tiles as cimgt"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "c8746951-68b2-4b0a-9e95-5e9d4e8da2a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading file 1:\n",
+      "100% [....................................................] 12468848 / 12468848\n",
+      "Loading file 2:\n",
+      "100% [....................................................] 74764060 / 74764060"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'p-levels_t_u_v_geopotential.nc'"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Загружаем NetCDF-файлы с метеоданными из облачного хранилища НИВЦ МГУ\n",
+    "# Функция wget.download скачивает файл по ссылке из первого аргумента и записывает его по пути и названию из второго аргумента\n",
+    "print('Loading file 1:')\n",
+    "wget.download('http://kibel.srcc.msu.ru:8080/share.cgi?ssid=5743fc3a82a14733a325cf8c68398db2&fid=5743fc3a82a14733a325cf8c68398db2&filename=surface_t2m_precip.nc&openfolder=forcedownload&ep=', \n",
+    "              out = 'surface_t2m_precip.nc')\n",
+    "print('\\nLoading file 2:')\n",
+    "wget.download('http://kibel.srcc.msu.ru:8080/share.cgi?ssid=1d8db488ae30499f9971a287560ed203&fid=1d8db488ae30499f9971a287560ed203&filename=p-levels_t_u_v_geopotential.nc&openfolder=forcedownload&ep=',\n",
+    "              out = 'p-levels_t_u_v_geopotential.nc')\n",
+    "# Если файл с таким названием в папке назначения уже есть, то новый будет записан с добавкой \"(1)\" к имени \n",
+    "# (а если есть и такой, то будет с добавкой \"(2)\" и т.д.)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e399b549-30d1-486c-ac9c-f46a141af07d",
@@ -293,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 166,
    "id": "8db0a988-46e1-4a1b-86b4-f4ae3abd0251",
    "metadata": {},
    "outputs": [
@@ -322,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 167,
    "id": "9ba521c3-c469-407f-b2ae-0f4bf080c15a",
    "metadata": {},
    "outputs": [
@@ -353,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 168,
    "id": "9b204341-9b5a-41e3-8b66-b98f51fdcbbe",
    "metadata": {},
    "outputs": [],
@@ -370,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 169,
    "id": "8b3fe572-bda4-4b9e-ae5e-1bdd65e23154",
    "metadata": {},
    "outputs": [
@@ -389,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 170,
    "id": "edd82722-d488-4c28-8da6-e98c6fd0a8df",
    "metadata": {},
    "outputs": [
@@ -535,13 +576,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 202,
    "id": "ee0e1074-fe8a-4181-a704-10a64e8302a4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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yBAEARI4gAIDIEQQAEDmCAAAiRxAAQOQIAgCIHEEAAJEjCAAgcgQBAESOIACAyBEEABA5ggAAIkcQAEDkCAIAiBxBAACRIwgAIHIEAQBEjiAAgMgRBAAQOYIAACJHEABA5AgCAIgcQQAAkSMIACByBAEARI4gAIDIEQQAEDmCAAAiRxAAQOQIAgCIXFVBYGZfMrM+M9thZvclVRQAID0VB4GZ1Uv6D0l/LGmFpDvMbEVShQEA0lHNEcHVkna4+/vufkrSBkm3JlMWACAtDVW8t1XSrrzX/ZL+cOhKZtYlqSv78qSZ9VbxmUCtzJN0YLSLAIpor+XGqwkCK7DMhy1wXy9pvSSZ2RvuvqaKzwRqgn0TY5mZvVHL7Vdzaqhf0qK8122S9lRXDgAgbdUEweuSlprZpWY2SdI6Sc8mUxYAIC0Vnxpy99Nm9g1JP5NUL+kRd99S5m3rK/08oMbYNzGW1XT/NPdhp/UBABGhsxgAIkcQAEDkEgsCM/tlUtsCaqHcPmpmGTNjCikmBDP7ykjv9pBYELj7tUltC6gF9lFE5isKt/8pK8kjggEz6zSz5/KW/cDM7so+32lm/2Rmb5pZj5ktzy6famaPmNnrZvaWmXGbCtREuX00b9nXzezBvNd/aWbfT7FUTDBm9r9m1m1mW7J3W5CZDeT9++1m9sPs8yVmtjH7O/GfB9cr8/v1e2a21cw2m9kDZnatpFsk/ZuZbTKzJaXqS/sawQF3/31J/ynpb7LL/kHSy+7+B5KuUyh8asp1Afk2SLrFzBqzr78m6b9HsR6Mf3e7+2pJayTdY2ZzS6z7kKSHsr8TyzbpmtkcSX8iaaW7XynpX9z9lwp9Xd9x91Xu/l6pbaQdBE9nH7slXZJ9fqOk+8xsk6SMpMmSLkq5LuB33P2YpJclfTl75Nro7j2jXBbGt3vM7G1JGxXuyLC0xLprJf04+/x/RrDtI5I+lfSwmf2ppOPnW1w19xoq5LTODZfJQ/79ZPbxTN5nm6Tb3L0v4VqAQsrto4MelvT3kt4RRwOogpl1SvqCpLXuftzMMgr7XX4TV7H9MF/BfTfb3Hu1pBsU7vDwDUnXn0+NSR8R/EbSCjNrMrOZ2cLK+Zmkb5qZSZKZXZVwTUC+Ee2j7v6awl9ufy7p8RTrw8QzU9LH2RBYLuma7PJ9Zna5mdUpnNoZtFHSbdnn6/KWF9x3zWyapJnu/rykb0lalV3/qKTpIykwySBwd98l6QlJmyU9JumtEbzvfkmNkjZnb1F9f4I1AfnOdx99QtL/ufvHaRSHCeunkhrMbLPC77eN2eX3SXpO4TTk3rz1vyXpr8zs15IWSvpEkkrsu9MlPZfd/s8lfTu7fIOk72Qn4ZS8WJzILSayFz7edPeLq94YUAOV7KPZGRoPuvtLtasMOJeZNUs64e5uZusk3eHuNZ1NWfU1AjO7UOEi7wNVVwPUwPnuo2Y2S9KvJb1NCGAUrJb0g+zp8sOS7q71B3LTOQCIHPcaAoDIEQQAEDmCAAAiRxAAQOQIAgCI3P8D5omLF9ru37gAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -553,16 +594,20 @@
     }
    ],
    "source": [
-    "# В качестве последнего примера построим изменение скорости ветра в зависимости от месяца в среднем\n",
-    "# на всех трёх высотах и по всем широтам и долготам\n",
+    "# В качестве последнего примера построим зависимость скорости ветра от высоты (уровня давления) в среднем\n",
+    "# за всё лето по всем широтам и долготам\n",
     "wind_monthly_mean = np.mean(data_windspeed_1, axis = (0, 2, 3))\n",
     "\n",
     "# Построим график месячного хода\n",
-    "plt.plot(['june', 'july', 'august'], wind_monthly_mean, linewidth = 2, color = 'red')\n",
+    "plt.plot(wind_monthly_mean, p_level, linewidth = 2, color = 'red')\n",
     "\n",
     "# Выставим границы области рисования\n",
-    "plt.xlim([0, 2]) # Хоть координаты графика по X у нас заданы названиями, они имеют индексы от 0 до N-1, где N - число месяцев\n",
-    "plt.ylim([0, 10])\n",
+    "plt.ylim([1000, 700]) # Границы для графика по оси Y указаны в обратном порядке, потому что нам нужно перевернуть ось\n",
+    "                      # (иначе высокое давление будет наверху, а низкое внизу)\n",
+    "plt.xlim([0, 7])\n",
+    "plt.ylabel('Pressure level [hPa]')\n",
+    "plt.xlabel('Wind speed [m/s]')\n",
+    "plt.title('Mean wind speed for June - August 2021')\n",
     "\n",
     "# Рисуем сетку координат на графике\n",
     "plt.grid()\n",