updated documentation section 1

Dashboard documentation.md

@@ -13,6 +13,12 @@ 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.
+In this section, our primary goal is to provide users with a comprehensive view of tropical cyclones by visualizing both observed and forecasted tracks and crucial additional information. Here, you'll find four main layers: 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)__. 
+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 through 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 regarding the IBTrACS dataset __[here](https://www.ncei.noaa.gov/products/international-best-track-archive)__. 
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+The ECMWF data contains the ensemble forecast tracks of the active cyclones. The information is saved in a BUFR file, which can be imported into Jupyter Notebook as pandas dataframes for detailed analysis and visualization. The dataframe comprises a wealth of information, including the storm identifier and name, ensemble member number, latitude and longitude coordinates of the cyclone center, and crucial temporal details such as the year, month, day, and hour of the forecast initiation. Additionally, each row of the dataframe specifies the time period from the forecast date, i.e., hours passed, allowing users to track cyclone development over time. Key meteorological parameters such as the central pressure in *Pascal* and maximum sustained wind speed in *Metre per second* within the cyclone system are also included. The presence of "false" storms in the BUFR file is worth noting, identifiable by storm identifiers with numbers greater than 70.
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+The IBTrACS data contains the observed track of the active cyclones. These observations are stored in CSV files, providing accessibility and ease of use for users. The CSV file is structured with multiple columns, each contributing essential information for comprehensive analysis. These columns include the storm identifier, year, cardinal number of the storm for that season, basin, subbasin (if applicable), storm name as designated by the relevant agency, timestamp in UTC, storm nature, latitude, and longitude coordinates of the cyclone center. Additionally, the data contains the maximum sustained wind speed reported by the World Meteorological Organization (WMO) for the current location, the responsible reporting agency, track type, distance to the nearest land from the current position, the closest predicted landfall location within the next 6 hours, and an interpolation flag. It's important to note that IBTrACS cyclone track data undergo post-processing after the cyclone dissipates, often resulting in incomplete information, particularly for the latest observations. 
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+It is important to acknowledge that within TropiDash, users may encounter a temporal discrepancy between the latest observed cyclone position in the IBTrACS dataset and the initial forecasted position within the ECMWF data. This divergence arises from the inherent nature of data acquisition and processing. The IBTrACS dataset relies on the meticulous collection and verification of observational data, necessitating time. Consequently, the most recent observations often pertain to roughly 2 to 3 days before the current date. In contrast, the ECMWF forecast data offers a forward-looking perspective, initiating from the present and projecting into the future. This time lag between observational and forecast data underscores the importance of considering the temporal context when interpreting and utilizing cyclone information within TropiDash.