From e167cdf5fc3cfa67e6ba4c2d67c5cad86e9faab3 Mon Sep 17 00:00:00 2001
From: Filippo Dainelli <dainelli.filippo@gmail.com>
Date: Thu, 14 Sep 2023 16:15:31 +0200
Subject: [PATCH] updated section 1 tutorial and documentation

---
 Dashboard documentation.md                    |  4 +++-
 ...l Cyclone Tracks and Characteristics.ipynb | 21 ++++++++++---------
 2 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/Dashboard documentation.md b/Dashboard documentation.md
index 947ecd5b..6a22d234 100644
--- a/Dashboard documentation.md	
+++ b/Dashboard documentation.md	
@@ -13,4 +13,6 @@ To support you for an effective use of the dashboard please also refer to the tu
 
 ## Section 1 - Tropical Cyclone Tracks and Characteristics
 
-In this section, our primary goal is to provide users with a comprehensive view of tropical cyclones by visualizing both observed and forecasted tracks, as well as crucial additional information. Here, you'll find four main layers at your disposal: the Ensemble Forecast Tracks, the Observed Track, the Average Forecast Track, and the Strike Probability Map. These layers collectively offer a comprehensive understanding of cyclone behavior, enabling you to assess the potential impact of these powerful weather phenomena. Whether you're tracking the historical path of a cyclone, examining the consensus of future forecasts, or gauging the likelihood of a strike in a particular area, this section equips you with the tools you need to stay informed about cyclones positions and future developments.
\ No newline at end of file
+In this section, our primary goal is to provide users with a comprehensive view of tropical cyclones by visualizing both observed and forecasted tracks, as well as crucial additional information. Here, you'll find four main layers at your disposal: the Ensemble Forecast Tracks, the Observed Track, the Average Forecast Track, and the Strike Probability Map. These layers collectively offer a comprehensive understanding of cyclone behavior, enabling you to assess the potential impact of these powerful weather phenomena. Whether you're tracking the historical path of a cyclone, examining the consensus of future forecasts, or gauging the likelihood of a strike in a particular area, this section equips you with the tools you need to stay informed about cyclones positions and future developments.
+
+The data for Section 1 is sourced from two datasets. The forecasts are derived from the open dataset provided by the European Centre of Medium-Range Weather Forecasts (ECMWF), accessed with the Azure client throught the ecmwf.opendata library. You can find more information regarding the open-data __[here](https://www.ecmwf.int/en/forecasts/datasets/open-data)__. In parallel, the observed track data is obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, maintained by the National Oceanic and Atmospheric Administration (NOAA). You can find more information regardin the IBTrACS dataset __[here](https://www.ncei.noaa.gov/products/international-best-track-archive)__. 
\ No newline at end of file
diff --git a/tutorials/Section 1 - Tropical Cyclone Tracks and Characteristics.ipynb b/tutorials/Section 1 - Tropical Cyclone Tracks and Characteristics.ipynb
index 5aa469bf..21fb2e05 100644
--- a/tutorials/Section 1 - Tropical Cyclone Tracks and Characteristics.ipynb	
+++ b/tutorials/Section 1 - Tropical Cyclone Tracks and Characteristics.ipynb	
@@ -38,6 +38,7 @@
     "import ipywidgets as widgets\n",
     "import branca.colormap as bc\n",
     "\n",
+    "from ecmwf.opendata import Client\n",
     "from datetime import datetime, timedelta\n",
     "from localtileserver import get_leaflet_tile_layer, TileClient\n",
     "from IPython.display import display\n",
@@ -203,7 +204,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bcb6b511a21547abb72c13a84eb299dc",
+       "model_id": "341a296f7c8c420aa91302ff0fd1e08c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -217,7 +218,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a562d336c93f4ece8ff578323ded0f27",
+       "model_id": "d277ea2441a843aa82652a2194da8936",
        "version_major": 2,
        "version_minor": 0
       },
@@ -260,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -490,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -588,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,13 +655,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "446af78a30d249cd88b58d7cf41e095b",
+       "model_id": "358d6761207a4774ac7a9af485ccf0cf",
        "version_major": 2,
        "version_minor": 0
       },