Satellite imagery, weather balloons, and radar are all important sources of data for weather forecasting. Satellite imagery provides a global view of weather patterns, while weather balloons provide vertical profiles of temperature, humidity, and wind speed. Radar is used to track the movement and intensity of precipitation.

The GFS (Global Forecast System) is the primary computer model used by the National Weather Service to predict future weather conditions. It is a global model that runs four times a day and produces forecasts out to 16 days.

Numerical Weather Prediction (NWP) is the process of using computer models to predict future weather conditions. NWP models solve the equations that govern the atmosphere to simulate the evolution of weather patterns.

Statistical Weather Forecasting (SWF) is a weather forecasting technique that uses past weather data to predict future weather conditions. SWF models use statistical relationships between past weather patterns and future weather conditions to make predictions.

Analog Weather Forecasting (AWF) is a weather forecasting technique that uses similar weather patterns from the past to predict future weather conditions. AWF models identify past weather patterns that are similar to the current weather pattern and then use those past patterns to predict future weather conditions.

Ultra-long-range forecasts are not a type of weather forecast. Short-range forecasts cover the next few days, medium-range forecasts cover the next few weeks, and long-range forecasts cover the next few months.

Observational Weather Forecasting (OWF) is a weather forecasting technique that uses observations from weather stations, satellites, and other sources to make predictions. OWF models use current weather conditions and trends to make short-term forecasts.

Temperature forecasts, precipitation forecasts, and wind forecasts are all common types of weather forecasts.

Ensemble Weather Forecasting (EWF) is a weather forecasting technique that uses ensemble modeling to make predictions. EWF models run multiple simulations of the same weather forecast model with slightly different initial conditions. The results of these simulations are then combined to produce a more accurate forecast.

The chaotic nature of the atmosphere, the lack of data, and the computational cost of running weather models are all challenges in weather forecasting.

Machine Learning Weather Forecasting (MLWF) is a weather forecasting technique that uses artificial intelligence to make predictions. MLWF models use historical weather data and machine learning algorithms to learn the relationships between different weather variables. These models can then be used to make predictions about future weather conditions.

Severe weather forecasts, tropical weather forecasts, and air quality forecasts are all types of weather forecasts.

Satellite Weather Forecasting (SWF) is a weather forecasting technique that uses satellite imagery to make predictions. SWF models use satellite images to identify weather patterns and track their movement. These models can then be used to make predictions about future weather conditions.

Hourly forecasts, daily forecasts, and weekly forecasts are all types of weather forecasts.

Radar Weather Forecasting (RWF) is a weather forecasting technique that uses radar data to make predictions. RWF models use radar images to identify precipitation patterns and track their movement. These models can then be used to make predictions about future weather conditions.