Because of global warming, pollution, and many other factors, the environment is changing at an alarming rate. Accurate forecasting can assist people in making appropriate plans for activities such as harvesting, traveling, aviation, etc. Satellites and radar have been increasingly popular in weather forecasting over the previous few decades. The information collected by the satellite and radar can be used to monitor climate movement, track hurricanes, and give barometrical estimations that can be turned into mathematical climate expectation (NWP) models for exact forecasting. Currently, more than 160 meteorological satellites are located in orbit, which generates approximately 80 million observations every day. This chapter discusses several meteorological satellites which are used to extract weather pattern. For the time being, the results of Observation System Simulation Studies (OSSE) utilising satellite information are presented in order to demonstrate the relationship between perceptions from satellite sensors and ground-based sensors.
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A weather satellite system is a kind of satellite which is used to track the temperature and environment of the Earth. Satellites can be polar circling (covering the entire Earth in a non-concurrent manner) or geostationary at times (floating over a comparable spot on the equator). Weather forecast is one of the most important issues in any technical or scientifically disruptive issue (Xie et al., 2022). Rigorous mathematical models were accelerated due to technological advancements in order to produce exact projections. The adoption of largely dependent machine learning models yields higher and better results. Not only are recent meteorological characteristics complex, but they are also numerical. The conditions of the earth's atmosphere change over time. There are seasons such as summer, winter, spring, autumn, Monsoon, and others. The weather conditions can change whenever. Around the world, this type of weather fluctuation is fairly regular and predictable. We can minimise or restrict losses in a range of industries, including agriculture, natural disasters, and many others, if we can effectively predict weather conditions (Rai et al., 2021). So, if we're talking about a particular agricultural region, these techniques can provide us a better view of what is exactly happening, but the technique and amount of data collected is too large for a farmer to absorb and make decisions on in real time. Artificial Intelligence (AI) enables computers to learn how to analyse data more effectively and autonomously, allowing for faster pattern recognition, categorization, and forecasting without the need for human intervention. Space information has turned into an unquestionable part in weather conditions checking and dynamic demonstrating because of late improvements in satellite innovation concerning high goal, multi-spectral band groups including infrared, visible and microwave domains. With the rising exactness of satellite recoveries, upgrades in models could be made, prompting better estimates, especially in the tropics. Since the dawn of human civilization, the importance of weather prediction has been recognised, and initial efforts to estimate the weather were depends upon human experience, intuition, and an understanding of the relationship between the weather and natural cycles. Scientists postulated around the turn of the twentieth century that the atmosphere must satisfy the fundamental principles of physics.
Predicting how the current state of the climate will change is what weather forecasting includes. Ground measurements, measurements from ships, observations from aeroplanes, radio noises, doppler radar, and satellites are all used to determine current weather conditions. This data is forwarded to meteorological centres, which collect, analyse, and present the data in a variety of charts, maps, and graphs. Thousands of observations are transferred onto surface and upper-air maps using modern high-speed computers.
The ability to anticipate the weather requires a sufficiently exact understanding of the on-going state of the climate as well as a sufficiently accurate understanding of the natural principles that regulate the formation of the weather. As a result, meteorological observation networks were seen as equally crucial to the evolution of weather prediction models. Because of the emphasis on atmospheric observations, a ground-based measurement network of surface and upper air weather parameters has been continuously developed. Despite the fact that the ground-based monitoring network is crucial for weather prediction and meteorology, it only delivers limited observations across difficult locations such as mountains, deserts, and extensive marine areas. When compared to observations from a traditional ground-based network, meteorological satellites give observations that are large in coverage, narrowly spaced, representative, and more frequent. As a result, it's no surprise that meteorological satellites have been at the frontier of earth observation. The Television InfraRed Observation Satellite (TIROS-1) was the first meteorological satellite, launched on April 1, 1960. Within the scope of the Global Observing System, the World Meteorological Organization (WMO) created an operational satellite surveillance network of geostationary and polar-orbiting meteorological satellites in 1963 (WMO, 2014). Figure 1 illustrates the Constellation of geostationary and low earth orbiting meteorological satellites.
Figure 1. Constellation of geostationary and low earth orbiting meteorological satellites (WMO, 2014)