Skip to main content

Cyclone


1. Low-Pressure System

A low-pressure system is an area where the atmospheric pressure is lower than its surroundings. These systems are associated with rising warm air, which leads to cloud formation and precipitation. They are the primary drivers of weather disturbances like cyclones and storms.

  • Concept: Warm air rises, creating a region of lower pressure at the surface. As air converges to fill this void, it starts to rotate due to the Coriolis effect.
  • Example: A monsoon low-pressure system forming over the Bay of Bengal, leading to heavy rains in eastern India.

2. Depression

A depression is a more developed form of a low-pressure system with a well-defined circulation. It brings moderate to heavy rainfall and gusty winds.

  • Concept: When a low-pressure system intensifies with wind speeds between 31-49 km/h, it is classified as a depression.
  • Example: The depression over the Arabian Sea that causes heavy rainfall in Mumbai during the monsoon season.

3. Deep Depression

A deep depression is a further intensification of a depression with stronger winds and heavier rainfall.

  • Concept: A depression becomes a deep depression when wind speeds increase to 50-61 km/h. This stage is a precursor to a tropical storm or cyclone.
  • Example: A deep depression forming over the Bay of Bengal, later developing into Cyclone Yaas in 2021.

4. Cyclone

A cyclone is a large-scale air mass that rotates around a strong center of low atmospheric pressure. It is classified into different categories based on wind speed.

  • Concept: A cyclone forms when a deep depression further intensifies, with wind speeds exceeding 62 km/h. Warm ocean waters provide energy, causing rapid intensification.
  • Types of Cyclones:
    • Tropical Cyclones: Form over warm ocean waters (e.g., Cyclone Amphan in 2020).
    • Extratropical Cyclones: Occur outside tropical regions and are associated with frontal systems (e.g., Nor'easters in the U.S.).

5. Storm

A storm is a general term for a disturbed state of the atmosphere that can bring strong winds, heavy rain, thunder, lightning, and sometimes snow.

  • Concept: When wind speeds reach 62-88 km/h, the system is called a "Cyclonic Storm." If it intensifies further, it may become a Severe Cyclonic Storm (89-117 km/h) or even a Super Cyclone (>222 km/h).
  • Example: Cyclone Fani (2019) was classified as an Extremely Severe Cyclonic Storm with wind speeds exceeding 250 km/h, causing significant destruction in Odisha, India.

Wind Speed Classifications

System TypeWind Speed (km/h)
Low Pressure< 31
Depression31-49
Deep Depression50-61
Cyclonic Storm62-88
Severe Cyclonic Storm89-117
Very Severe Cyclonic Storm118-165
Extremely Severe Cyclonic Storm166-221
Super Cyclone> 222

Comments

Post a Comment

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...
Post a Comment
Read more

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...
Post a Comment
Read more

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...
Post a Comment
Read more

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...
Post a Comment
Read more