Madden julian oscillation and indian monsoon. Mjo.

The Madden–Julian oscillation is the largest element of the intraseasonal variability in the tropical atmosphere. It was discovered in 1971 by Roland Madden and Paul Julian of the American National Center for Atmospheric Research.

What is Mjo in geography?

The MJO can be defined as an eastward moving 'pulse' of clouds, rainfall, winds and pressure near the equator that typically recurs every 30 to 60 days. It's a traversing phenomenon and is most prominent over the Indian and Pacific Oceans.

What is the main source of intra seasonal climate variability in the tropics?

The Madden-Julian oscillation (MJO), sometimes known as the intraseasonal oscillation (ISO), is the leading mode of intraseasonal variability in the tropical climate system.

What causes the Madden-Julian Oscillation?

The wet phase of enhanced convection and precipitation is followed by a dry phase where thunderstorm activity is suppressed. Each cycle lasts approximately 30–60 days. Because of this pattern, the Madden–Julian oscillation is also known as the 30- to 60-day oscillation, 30- to 60-day wave, or intraseasonal oscillation.

How often does the Madden-Julian Oscillation occur?

The Madden-Julian Oscillation (MJO) is the major fluctuation in tropical weather on weekly to monthly timescales. The MJO can be characterised as an eastward moving 'pulse' of cloud and rainfall near the equator that typically recurs every 30 to 60 days.

What is Madden-Julian Oscillation Upsc?

According to India Met Department (IMD), the Arabian Sea arm of south-west monsoon is counting on an itinerant Madden-Julian Oscillation (MJO) wave for normal monsoon. ... The MJO can be defined as an eastward moving 'pulse' of clouds, rainfall, winds and pressure near the equator that typically recurs every 30 to 60 days.

Where is the Madden-Julian Oscillation?

northern Australia

The Madden-Julian Oscillation is moving over northern Australia, encouraging wet conditions. The climate driver usually takes between a month and 60 days to make its way around the tropics. Although this MJO is weak with the La Niña active, it opens the door for rain and storms in the coming weeks.

Why does Indian Ocean have dipole?

The Indian Ocean Dipole (IOD), also known as the Indian Niño, is an irregular oscillation of sea surface temperatures in which the western Indian Ocean becomes alternately warmer (positive phase) and then colder (negative phase) than the eastern part of the ocean.

What is MJO wave?

The MJO is a system of very tall or deep convective clouds (storminess) that travels eastward along the tropical Indian and Pacific Oceans approximately every 30-60 days. The convective region of the MJO has enhanced storms and rainfall, and it is usually sandwiched to the east and west by dry, sunny areas.

What is suppressed convection?

In the suppressed convective phase, winds converge at the top of the atmosphere, forcing air to sink and, later, to diverge at the surface (Rui and Wang, 1990). As air sinks from high altitudes, it warms and dries, which suppresses rainfall.

Where does the MJO start?

Indian Ocean

from Rol Madden and Paul Julian. Typically the convectively active stage of an MJO starts over the equatorial Indian Ocean and moves slowly eastward at 3-5 m/s toward the west and central Pacific Ocean.

Vineesh V
Assistant Professor of Geography,
Government College Chittur, Palakkad
Government of Kerala.

https://vineesh-geography.business.site

Comments

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...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process : - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition. Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Classification of Mixed Pixels

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...

Vineesh V, Geography

Search This Blog