Weather forecasting in India blends numerical models and machine learning for accurate predictions. With its diverse climate, the India Meteorological Department (IMD) utilizes supercomputers to run atmospheric simulations, factoring in historical data, satellite inputs, and sensors. Machine learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) capture intricate spatial and temporal patterns. For instance, CNNs analyze satellite images to predict cloud cover, while RNNs handle time series data for temperature and precipitation forecasting. This hybrid approach ensures a dynamic understanding of India's complex weather conditions, combining traditional physical models with advanced machine learning to enhance forecast accuracy.

Dr Kanhaiya Sharma thank you for your contribution to the discussion

Weather Forecasting in old time is carried out by hand, using changes in barometric pressure, current weather conditions, and sky condition or cloud cover, weather forecasting now relies on computer-based models that take many atmospheric factors into accounting now relies on computer-based models that take many. The forecast process is roughly the same regardless of the type of weather. Our scientists thoroughly review current observations using technology such as radar, satellite and data from an assortment of ground-based and airborne instruments to get a complete picture of current conditions. Polar orbiting satellites provide the information most useful for long-term weather forecasting. These satellites use instruments to measure energy, called radiation, emitted by the Earth and atmosphere. This information is incorporated into weather models, which in turn leads to more accurate weather forecasts. So, an accurate forecast of weather is essential for ocean navigation, fishing activity, and aviation schedules. Agriculture, especially in India, is dependent on weather conditions. Rainfall, temperature, and humidity guide the cropping pattern and cropping seasons of a place. Thus, a weather map that depicts atmospheric conditions at a given time is a synoptic chart to a meteorologist. In order to have an average view of the changing pattern of weather, a meteorological centre prepares a series of synoptic charts every day.The forecast accuracy is over 80% in a 24-hour period and more than 60% over a five-day period, he added. According to the IMD, it relies on human intervention to assess models, since dependence on technology alone can leave data open to interpretation in some cases. There are four main types of weather prediction we're going to discuss in this lesson: short-range, medium-range, long-range, and hazardous weather forecasting. With the most complete global real-time and historical data, most robust database of forecast models, most advanced forecast engine globally, proprietary patents, and comprehensive validation results, AccuWeather is the most accurate weather company worldwide. Apart from tracking satellite data, IMD collaborates with ISRO for ground-based observations from the Automatic Weather Stations (AWS), the Global Telecommunication System (GTS) that measure temperature, sunshine, wind direction, speed and humidity. The methods include persistence, climatologic, looking at the sky, use of barometer, now casting, use of forecasting models, analogue and ensemble forecasting. Forecasting could be applied in air traffic, severe weather alerts, marine, agriculture, utility companies, private sector and military application. Most people know that high and white clouds suggest a bright and sunny day, low black clouds are a sign of rain, and a gray veil in the sky means a big storm is imminent. The bottom of clouds can also help predict the weather. The more ragged the bottom of a cloud, the more likely it will rain.NN using time series along with a linear SVC and a five-layered neural network is used to predict the weather. In weather forecasting, prediction models use deep learning primarily to process images from weather satellites, and our model is no exception. The satellite data we receive is combined with radar measurements and numerical model forecasts to generate a precipitation map. Traditional weather forecasting systems typically rely on physical models that involve millions of equations attempting to accurately represent the complex phenomena occurring in the atmosphere. In contrast, ML uses statistical models to make predictions. The prediction is made based on sliding window algorithm. The month wise results are being computed for three years to check the accuracy. The results of the approach suggested that the method used for weather condition prediction is quite efficient with an average accuracy of 92.2%.We can use that knowledge in our project of Rainfall Prediction System as it will help a lot of people. Various Machine Learning algorithms such as Logistic Regression, Decision Tree, K-Nearest Neighbor, and Random Forest are compared to find the most accurate model.Deep learning is an AI technology that aims to replicate how humans process things like images. In weather forecasting, prediction models use deep learning primarily to process images from weather satellites, and our model is no exception. Deep learning models can be built to find weather patterns of cloud behavior by training it with satellite imagery. This means a model does not try to reproduce entire weather systems via simulations, but instead is trained to focus its compute power on seeing visual patterns from a mosaic of pixels.