Skip to main content

Weather and Climate.

Good morning.
Good day.
This is Vineesh.
Welcome to our our class.

Today's topic of discussion is about Weather and Climate, a simple and interesting topic. 
We all know that Geography is Spatial science deals with Man, earth and environment. Now a days we people are studying about Different spatial process and patterns.

Weather.

Weather is the state of the atmosphere, describing for example the degree to which it is hot or cold, wet or dry, calm or stormy, clear or cloudy. On Earth, most weather phenomena occur in the lowest layer of the planet's atmosphere, the troposphere, just below the stratosphere. 

Weather is the mix of events that happen each day in our atmosphere. Weather is different in different parts of the world and changes over minutes, hours, days and weeks. Most weather happens in the troposphere, the part of Earth's atmosphere that is closest to the ground.

Weather refers to day-to-day temperature and precipitation activity, whereas climate is the term for the averaging of atmospheric conditions over longer periods of time. CLIMATE: the weather conditions prevailing in an area in general or over a long period.

Types of weather include sunny, cloudy, rainy, windy, and snowy.

Climate.

Climate is the average weather in a given area over a longer period of time. A description of a climate includes information on, e.g. the average temperature in different seasons, rainfall, and sunshine. Also a description of the (chance of) extremes is often included.

Climate is the long-term pattern of weather in a particular area. Weather can change from hour-to-hour, day-to-day, month-to-month or even year-to-year. A region's weather patterns, usually tracked for at least 30 years, are considered its climate.

Climate is the average of that weather. For example, you can expect snow in the Northeast in January or for it to be hot and humid in the Southeast in July. This is climate. The climate record also includes extreme values such as record high temperatures or record amounts of rainfall.

Let's conclude today's session,
Whereas weather refers to short-term changes in the atmosphere, climate describes what the weather is like over a long period of time in a specific area. Different regions can have different climates.

Thanks for attending the class,
Come with your feedback and questions.

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

Comments

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...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

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...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...