Enhanced Fujita Scale

```wiki

REDIRECT Enhanced Fujita scale

Introduction

The Template:Short description is an essential MediaWiki template designed to provide concise summaries and descriptions for MediaWiki pages. This template plays an important role in organizing and displaying information on pages related to subjects such as Binary Options, IQ Option, and Pocket Option among others. In this article, we will explore the purpose and utilization of the Template:Short description, with practical examples and a step-by-step guide for beginners. In addition, this article will provide detailed links to pages about Binary Options Trading, including practical examples from Register at IQ Option and Open an account at Pocket Option.

Purpose and Overview

The Template:Short description is used to present a brief, clear description of a page's subject. It helps in managing content and makes navigation easier for readers seeking information about topics such as Binary Options, Trading Platforms, and Binary Option Strategies. The template is particularly useful in SEO as it improves the way your page is indexed, and it supports the overall clarity of your MediaWiki site.

Structure and Syntax

Below is an example of how to format the short description template on a MediaWiki page for a binary options trading article:

Parameter	Description
Description	A brief description of the content of the page.
Example	Template:Short description: "Binary Options Trading: Simple strategies for beginners."

The above table shows the parameters available for Template:Short description. It is important to use this template consistently across all pages to ensure uniformity in the site structure.

Step-by-Step Guide for Beginners

Here is a numbered list of steps explaining how to create and use the Template:Short description in your MediaWiki pages: 1. Create a new page by navigating to the special page for creating a template. 2. Define the template parameters as needed – usually a short text description regarding the page's topic. 3. Insert the template on the desired page with the proper syntax: Template loop detected: Template:Short description. Make sure to include internal links to related topics such as Binary Options Trading, Trading Strategies, and Finance. 4. Test your page to ensure that the short description displays correctly in search results and page previews. 5. Update the template as new information or changes in the site’s theme occur. This will help improve SEO and the overall user experience.

Practical Examples

Below are two specific examples where the Template:Short description can be applied on binary options trading pages:

Example: IQ Option Trading Guide

The IQ Option trading guide page may include the template as follows: Template loop detected: Template:Short description For those interested in starting their trading journey, visit Register at IQ Option for more details and live trading experiences.

Example: Pocket Option Trading Strategies

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Recommendations and Practical Tips

To maximize the benefit of using Template:Short description on pages about binary options trading: 1. Always ensure that your descriptions are concise and directly relevant to the page content. 2. Include multiple internal links such as Binary Options, Binary Options Trading, and Trading Platforms to enhance SEO performance. 3. Regularly review and update your template to incorporate new keywords and strategies from the evolving world of binary options trading. 4. Utilize examples from reputable binary options trading platforms like IQ Option and Pocket Option to provide practical, real-world context. 5. Test your pages on different devices to ensure uniformity and readability.

Conclusion

The Template:Short description provides a powerful tool to improve the structure, organization, and SEO of MediaWiki pages, particularly for content related to binary options trading. Utilizing this template, along with proper internal linking to pages such as Binary Options Trading and incorporating practical examples from platforms like Register at IQ Option and Open an account at Pocket Option, you can effectively guide beginners through the process of binary options trading. Embrace the steps outlined and practical recommendations provided in this article for optimal performance on your MediaWiki platform.

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- Financial Disclaimer**

The information provided herein is for informational purposes only and does not constitute financial advice. All content, opinions, and recommendations are provided for general informational purposes only and should not be construed as an offer or solicitation to buy or sell any financial instruments.

Any reliance you place on such information is strictly at your own risk. The author, its affiliates, and publishers shall not be liable for any loss or damage, including indirect, incidental, or consequential losses, arising from the use or reliance on the information provided.

Before making any financial decisions, you are strongly advised to consult with a qualified financial advisor and conduct your own research and due diligence.

Template:Infobox weather

Template:Infobox weather is a standardized template used on Wikipedia and other MediaWiki-based wikis to consistently display key meteorological data for specific weather events, locations, or phenomena. It provides a structured and visually appealing way to present information like temperature, precipitation, wind speed, atmospheric pressure, and humidity. This article provides a comprehensive guide for beginners on how to understand, use, and customize the `Infobox weather` template.

Purpose and Benefits

The primary purpose of the `Infobox weather` template is to standardize weather-related information across articles. This offers several benefits:

Consistency: Ensures a uniform look and feel for weather information throughout the wiki, improving readability and user experience.
Organization: Presents data in a structured format, making it easier for readers to quickly find specific information.
Accessibility: Facilitates data comparison between different weather events or locations.
Maintainability: Simplifies updates and modifications to weather information. Changes to the template automatically propagate to all articles using it.
Data Integration: Enables potential integration with external weather data sources in the future.

Basic Usage

To use the `Infobox weather` template, you simply need to copy and paste the template code into the relevant article and fill in the appropriate parameters with the corresponding data. A basic example is shown below:

```wiki Template loop detected: Template:Infobox weather ```

This code will generate an infobox displaying the specified weather data. Let's break down each parameter:

location: The name of the place where the weather event occurred. This is a required parameter.
date: The date of the weather event in YYYY-MM-DD format. Also a required parameter. Consider using Help:Dates and times for formatting.
time: The time of the weather event, usually in UTC (Coordinated Universal Time).
temperature: The temperature value.
unit_temperature: The unit of temperature (e.g., °C, °F, K).
precipitation: The amount of precipitation.
unit_precipitation: The unit of precipitation (e.g., mm, in).
wind_speed: The wind speed value.
unit_wind_speed: The unit of wind speed (e.g., km/h, mph, m/s, knots).
wind_direction: The wind direction (e.g., N, S, E, W, NW, SE).
pressure: The atmospheric pressure value.
unit_pressure: The unit of atmospheric pressure (e.g., hPa, mmHg, inHg).
humidity: The relative humidity value.
unit_humidity: The unit of humidity (e.g., %).
image: The filename of an image to display in the infobox.
image_caption: A caption for the image.

Available Parameters

The `Infobox weather` template offers a wide range of parameters to accommodate various weather phenomena and data types. Here's a comprehensive list:

location: (Required) The location of the weather event.
date: (Required) The date of the weather event (YYYY-MM-DD).
time: Time of the observation.
temperature: Temperature value.
unit_temperature: Unit of temperature (°C, °F, K).
precipitation: Precipitation value.
unit_precipitation: Unit of precipitation (mm, in).
snowfall: Snowfall value.
unit_snowfall: Unit of snowfall (cm, in).
wind_speed: Wind speed value.
unit_wind_speed: Unit of wind speed (km/h, mph, m/s, knots).
wind_direction: Wind direction (N, S, E, W, NW, SE, etc.).
wind_gust: Wind gust value.
unit_wind_gust: Unit of wind gust (km/h, mph, m/s, knots).
pressure: Atmospheric pressure value.
unit_pressure: Unit of atmospheric pressure (hPa, mmHg, inHg).
humidity: Relative humidity value.
unit_humidity: Unit of humidity (%).
visibility: Visibility distance.
unit_visibility: Unit of visibility (km, mi).
uv_index: UV index value.
image: Image filename.
image_caption: Image caption.
source: Source of the weather data. Consider linking to Wikipedia:Reliable sources.
accessdate: The date the data was accessed. Use the Help:Dates and times format.
notes: Additional notes or comments.
event: Type of weather event (e.g., Hurricane, Blizzard, Heatwave). Can be linked to a relevant article like Tropical cyclone.
severity: Severity of the event (e.g., Category 3 Hurricane).
fatalities: Number of fatalities caused by the event.
damage: Estimated damage caused by the event.
area_affected: Geographical area affected by the event.
rainfall_rate: Rainfall rate (mm/h, in/h).
unit_rainfall_rate: Unit of rainfall rate.
hail_size: Hail size (mm, in).
unit_hail_size: Unit of hail size.
lightning_frequency: Lightning frequency (flashes/minute).

Advanced Customization

Beyond the basic parameters, the `Infobox weather` template allows for more advanced customization:

Units: Ensure consistent use of units. Always specify the `unit_` parameter for each value.
Conditional Formatting: Using Help:Conditional expressions, you can dynamically change the appearance of the infobox based on certain conditions. For example, you could display a warning message if the temperature is below freezing.
Multiple Values: For parameters like precipitation, you can specify multiple values separated by a comma (e.g., `precipitation = 5, 2, 1`). However, this might not be ideal for all situations, as it can clutter the infobox.
External Data: While direct integration with external weather data sources is not built-in, you can use tools like AWB or bots to automatically update the infobox with data from APIs. This requires programming knowledge.
Custom Labels: You can change the labels displayed in the infobox by modifying the template code itself. However, this should be done with caution, as it can affect the consistency of the infobox across the wiki. Always discuss changes with other editors first.

Best Practices

Accuracy: Always ensure the accuracy of the data you enter. Cite your sources and verify the information before adding it to the infobox.
Consistency: Use consistent units and formatting throughout the article and the infobox.
Completeness: Fill in as many relevant parameters as possible to provide a comprehensive overview of the weather event.
Conciseness: Keep the infobox concise and avoid unnecessary details.
Image Selection: Choose an image that is relevant to the weather event and of high quality. Ensure you have the necessary rights to use the image.
Accessibility: Provide alt text for images to make the infobox accessible to users with visual impairments.
Source Citation: Always include a `source` parameter and cite your sources using proper citation templates like Template:Cite web.

Common Issues and Troubleshooting

Infobox Not Displaying: Check for syntax errors in the template code. Ensure all required parameters are present.
Incorrect Units: Verify that the `unit_` parameters are correctly specified.
Image Not Showing: Ensure the image filename is correct and the image file exists on the wiki.
Formatting Issues: Use the `{{{ }}}` syntax to prevent variables from being interpreted as wiki code. For example, use `{{{temperature}}}` instead of `temperature`.
Template Conflicts: If the infobox is not displaying correctly, there might be a conflict with other templates on the page. Try removing other templates to see if that resolves the issue. Consult the Help:Templates page for more information.

Strategies, Technical Analysis, Indicators, and Trends (Related to Weather and its Impacts)

While the infobox itself displays data, understanding the *implications* of that data requires knowledge from various fields. Here are links to concepts that are relevant when analyzing weather information and its effects:

**Risk Assessment:** [1] Assessing the potential impact of weather events.
**Disaster Preparedness:** [2] Strategies for preparing for and responding to severe weather.
**Climate Change Modeling:** [3] Understanding long-term weather trends.
**Statistical Forecasting:** [4] Using statistical methods to predict future weather conditions.
**Ensemble Forecasting:** [5] Using multiple forecasts to improve accuracy.
**Analog Forecasting:** [6] Comparing current weather patterns to past events.
**Trend Analysis (Weather Patterns):** [7] Identifying long-term changes in weather patterns.
**Seasonal Forecasting:** [8] Predicting weather conditions for the upcoming season.
**El Niño-Southern Oscillation (ENSO):** [9] Understanding the impact of ENSO on global weather patterns.
**North Atlantic Oscillation (NAO):** [10] Understanding the impact of NAO on European and North American weather.
**Atmospheric River:** [11] Understanding the role of atmospheric rivers in precipitation.
**Severe Weather Outlooks:** [12] Assessing the risk of severe weather events.
**Radar Interpretation:** [13] Understanding weather radar imagery.
**Satellite Imagery Analysis:** [14] Interpreting satellite images to track weather systems.
**Meteorological Modeling:** [15] The process of creating and using computer models to predict weather.
**Nowcasting:** [16] Short-term weather forecasting.
**Probability Forecasting:** [17] Expressing forecasts in terms of probabilities.
**Verification Techniques:** [18] Assessing the accuracy of weather forecasts.
**Hydrological Modeling:** [19] Predicting the impact of precipitation on water resources.
**Impact-Based Decision Support Services (IDSS):** [20] Providing weather information tailored to specific user needs.
**Geospatial Analysis (Weather Data):** [21] Using GIS to analyze weather data.
**Remote Sensing (Weather):** [22] Using satellites and other remote sensors to collect weather data.
**Machine Learning in Weather Forecasting:** [23] Applying machine learning techniques to improve weather predictions.
**Data Assimilation:** [24] Incorporating observations into weather models.
**Stochastic Weather Forecasting:** [25] Utilizing randomness in weather prediction.

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The Enhanced Fujita Scale (EF Scale) is a scale used to rate the intensity of tornadoes in the United States and Canada, as well as other countries. It is an update to the original Fujita scale (also known as the F-Scale), which was developed in 1971 by Dr. Tetsuya Theodore Fujita. The EF Scale was implemented on February 1, 2007, and is a set of wind speeds and damage degrees. It's crucial to understand that the EF Scale is *not* based on observed wind speeds (measuring wind speed *within* a tornado is exceptionally difficult and rarely done accurately); instead, it's based on the *damage* caused by the tornado. This damage is then correlated to estimated wind speeds.

History and Development

The original Fujita Scale was revolutionary for its time, providing a standardized way to assess tornado intensity. However, it had limitations. It was based largely on subjective assessments of damage, and there was a growing realization that the wind speed estimates associated with certain damage levels were often too high. Furthermore, it didn’t adequately account for variations in construction quality. A poorly built structure would suffer more damage at a given wind speed than a well-built one.

In the 1990s, a joint effort by the National Weather Service (NWS) and Environment Canada began to address these shortcomings. This led to extensive research, including detailed engineering analyses of damage caused by numerous tornadoes. The research involved:

**Damage Reconnaissance:** Teams of engineers and meteorologists meticulously documented damage after tornadoes, categorizing the type and extent of damage to various structures.
**Wind Tunnel Testing:** Structures and building materials were tested in wind tunnels to determine the wind speeds required to cause specific types of damage.
**Statistical Analysis:** Data from damage surveys and wind tunnel tests were statistically analyzed to establish more accurate correlations between damage and wind speed.

The result was the Enhanced Fujita Scale, which aimed to improve the accuracy and consistency of tornado intensity ratings. The key improvements included:

**28 Damage Indicators (DIs):** The EF Scale uses 28 different damage indicators, categorized by structure type (e.g., single-family residences, mobile homes, large buildings, trees). Each DI has five degrees of damage (DOS), ranging from light damage to complete destruction.
**Degree of Damage (DOS) Values:** Each combination of DI and DOS is assigned a range of estimated wind speeds.
**Improved Damage Descriptions:** The EF Scale provides more detailed and specific descriptions of damage for each DOS.
**Consideration of Construction Quality:** The EF Scale takes into account the quality of construction when assessing damage. For example, a well-built, code-compliant structure will require higher wind speeds to sustain the same level of damage as a poorly built structure.

How the EF Scale Works

Assessing a tornado's EF rating is a multi-step process. It’s important to remember that the rating is assigned *after* the tornado has passed, based on the damage left behind. Here's a breakdown of the process:

1. **Damage Path Survey:** A team of trained storm spotters and damage surveyors follows the tornado's path, documenting the damage to various structures and objects. They identify the type of structures damaged (the Damage Indicator) and the extent of the damage (the Degree of Damage). This includes detailed photographs and notes. 2. **Damage Indicator and Degree of Damage Assignment:** For each damaged structure, the team determines the appropriate Damage Indicator and Degree of Damage. For example, a single-family residence with a missing roof might be assigned a DI of "Single-Family Residence" and a DOS of "Moderate." 3. **Estimated Wind Speed Range Determination:** Once the DI and DOS are assigned, the corresponding estimated wind speed range is determined from the EF Scale charts. Each DI/DOS combination has a range – not a single wind speed. 4. **EF Rating Assignment:** The EF rating is based on the *highest* estimated wind speed observed along the tornado's path. The entire path isn't necessarily one EF rating; it can vary. The EF rating is then assigned based on the following categories:

   *   **EF0:**  65–85 mph (105–137 km/h) – Light damage.
   *   **EF1:**  86–110 mph (138–177 km/h) – Moderate damage.
   *   **EF2:**  111–135 mph (178–217 km/h) – Significant damage.
   *   **EF3:**  136–165 mph (218–266 km/h) – Severe damage.
   *   **EF4:**  166–200 mph (267–322 km/h) – Devastating damage.
   *   **EF5:**  Over 200 mph (322 km/h) – Incredible damage.

5. **Path Variability:** It’s vital to note that a single tornado can have different EF ratings along its path. A tornado might start as an EF0, intensify to an EF3, and then weaken back to an EF1. The highest rating observed along the path is the official EF rating for that tornado.

Damage Indicators (DIs)

The 28 Damage Indicators are a core component of the EF Scale. They represent different types of structures and objects that are commonly damaged by tornadoes. Some examples of Damage Indicators include:

**Single-Family Residences:** A common DI, focusing on the damage to the roof, walls, and foundation.
**Mobile Homes:** These are highly vulnerable to tornado damage and are often assigned higher EF ratings for a given level of damage.
**Large Buildings:** Including commercial buildings, schools, and hospitals.
**High-Rise Buildings:** Damage to cladding, windows and structural elements.
**Trees:** Damage to trees can provide valuable clues about tornado intensity, especially in areas with limited structures.
**Power Poles:** The degree of damage to power poles (bent, broken, snapped) is another useful indicator.
**Vehicles:** Damage to vehicles (overturned, thrown, crushed) can also be used to estimate wind speeds.
**Railroads:** Damage to railcars and tracks.

Each Damage Indicator has five Degrees of Damage (DOS), ranging from:

**DOS 0:** No Damage
**DOS 1:** Light Damage
**DOS 2:** Moderate Damage
**DOS 3:** Considerable Damage
**DOS 4:** Severe Damage
**DOS 5:** Complete Destruction

Challenges and Limitations

Despite its improvements over the original Fujita Scale, the EF Scale still has limitations:

**Subjectivity:** While the EF Scale provides more detailed guidelines, some subjectivity is still involved in assessing damage and assigning DOS values. Different surveyors might interpret damage differently.
**Construction Variability:** Even with consideration for construction quality, variations in building codes and construction practices can make it difficult to accurately estimate wind speeds.
**Non-Tornado Winds:** Damage can be caused by straight-line winds (downbursts, microbursts) that are *not* associated with tornadoes. It's crucial to differentiate between tornado damage and non-tornado damage. This is a common source of error in assessing tornado intensity.
**Sparse Data:** In sparsely populated areas, there may be limited structures to assess damage, making it difficult to accurately determine the tornado’s intensity.
**Debris Balls:** The presence of a "debris ball" (a swirling mass of debris lofted into the air by a tornado) can complicate damage assessment. It's difficult to determine the source of the debris and the wind speeds required to lift it.

Relation to other scales and concepts

Understanding the EF Scale is helpful when learning about other meteorological concepts. Here are some related topics:

**Severe Weather**: Tornadoes are a type of severe weather event.
**Supercells**: The most powerful tornadoes typically form within supercell thunderstorms.
**Mesocyclones**: A rotating updraft within a supercell, often a precursor to tornado formation.
**Tornado Watch**: A forecast indicating conditions are favorable for tornado development.
**Tornado Warning**: An alert issued when a tornado has been sighted or indicated by radar.
**Storm Chasing**: The pursuit of severe weather, often involving documentation of tornadoes.
**Radar Meteorology**: The use of radar to detect and track tornadoes.

Future Developments

Research continues to refine the EF Scale and improve tornado intensity assessment. Potential future developments include:

**Incorporating Doppler Radar Data:** Using Doppler radar data to estimate wind speeds within tornadoes more accurately. However, this is technically challenging.
**Developing More Sophisticated Damage Indicators:** Creating more specific and nuanced Damage Indicators that better reflect the vulnerability of different structures.
**Improving Construction Quality Databases:** Developing comprehensive databases of building codes and construction practices to better account for construction variability.
**Artificial Intelligence and Machine Learning:** Utilizing AI and machine learning algorithms to automate damage assessment and improve the accuracy of EF ratings.

The EF Scale is a crucial tool for understanding and classifying tornado intensity. While it's not perfect, it represents a significant improvement over previous scales and provides valuable information for researchers, emergency managers, and the public. It’s important to remember that the EF Scale is a post-event assessment tool; its primary purpose is to provide a historical record of tornado intensity and to help improve our understanding of these dangerous weather events.

Fujita scale Tornado Severe thunderstorm National Weather Service Storm spotter Damage assessment Wind speed Meteorology Extreme weather Thunderstorm Atmospheric pressure Weather forecasting Severe weather climatology Doppler radar Storm shelter Tornado safety Tornado alley Enhanced Fujita scale (Canada) Tornado outbreak Supercell thunderstorm Mesocyclone Downburst Microburst Storm chasing Severe weather preparedness Weather modification Climate change and tornadoes Radar meteorology Numerical weather prediction Atmospheric dynamics Convective instability Wind shear

Technical Analysis of Tornado Trends Strategies for Tornado Preparedness Indicator for Severe Weather Trend Analysis of Tornado Activity Forecasting Tornado Intensity: Strategies Risk Management in Tornado Zones Damage Assessment Indicators EF Scale Calibration Techniques Predictive Modeling of Tornado Paths Statistical Analysis of Tornado Frequency Correlation between EF Scale and Economic Impact Long-Term Trends in Tornado Severity Geographical Distribution of Tornado Intensity Impact of Climate Change on Tornado Activity Advanced Radar Techniques for Tornado Detection Public Awareness Campaigns for Tornado Safety Community Resilience to Tornado Disasters The Role of Emergency Management Agencies Building Codes and Tornado Resistance Insurance Coverage for Tornado Damage Post-Tornado Recovery Strategies The Psychology of Tornado Survivors Comparative Analysis of Tornado Scales Future Directions in Tornado Research Data Visualization of Tornado Events Spatial Analysis of Tornado Clusters Temporal Analysis of Tornado Seasons Developing Early Warning Systems ```

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Enhanced Fujita Scale

Contents

Introduction

Purpose and Overview

Structure and Syntax

Step-by-Step Guide for Beginners

Practical Examples

Example: IQ Option Trading Guide

Example: Pocket Option Trading Strategies

Related Internal Links

Recommendations and Practical Tips

Conclusion

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Purpose and Benefits

Basic Usage

Available Parameters

Advanced Customization

Best Practices

Common Issues and Troubleshooting

Related Templates and Articles

Strategies, Technical Analysis, Indicators, and Trends (Related to Weather and its Impacts)

See Also

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History and Development

How the EF Scale Works

Damage Indicators (DIs)

Challenges and Limitations

Relation to other scales and concepts

Future Developments

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