Weather forecasting
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What is weather forecasting?
Weather forecasting predicts weather through physics, statistics, and empirical methods. It predicts atmospheric phenomena and their effects on Earth's surface, like snow cover, storm tides, and floods. Modern forecasting uses tools to measure temperature, pressure, wind, and humidity, analyzed via computer models.
Since the mid-20th century, digital computers have objectively calculated changes in atmospheric conditions using weather models, though these tools are not perfect. Meteorologists track multiple conditions across vast areas, accounting for interacting variables. This data collection has become more accurate through technology, such as satellites and radar.
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Why is it important to predict the weather?
Weather forecasting is important for numerous reasons, including agriculture, aviation, and public safety. Farmers use weather forecasts for planting and harvesting, and to protect crops and livestock. The aviation industry relies on weather forecasts to plan safe and efficient flight routes. Weather warnings protect human life by providing alerts for threatening weather events like hurricanes, tornadoes, and severe thunderstorms. These warnings allow for timely mobilization of resources and enable people to take necessary precautions. Economically, accurate weather predictions help businesses optimize operations, manage resources, and minimize losses from weather-related events.
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What tools do meteorologists use to forecast the weather?
Meteorologists use a variety of tools to forecast the weather, combining traditional observations with advanced technology. They measure atmospheric conditions using tools such as radiosondes, which are balloon-borne instruments that transmit data on temperature, humidity, and wind speed.
Radar systems, including Doppler radar, help monitor precipitation and wind speeds, while satellite observations provide a global view of weather patterns. Numerical weather prediction models use computers to calculate changes in atmospheric conditions based on physical equations. These models, along with techniques like nowcasting and Model Output Statistics (MOS), enable more accurate and objective forecasts.
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How do satellites help with weather forecasting?
Weather satellites have revolutionized forecasting by providing global views of weather conditions, including temperature, cloud cover, moisture, ice cover, and soil moisture.
There are two main types of weather satellites:
* Polar Orbiters These satellites orbit Earth at low altitudes (500-1,000 km) in a north-south direction, providing high-resolution data twice daily. They are crucial for high-latitude locations.
* Geostationary Orbiters Positioned about 36,000 km above the Equator, these satellites maintain a constant view of a large area. They offer continuous monitoring of rapid weather changes in thunderstorms, hurricanes, and fronts, making them invaluable for both forecasting and research.Before satellites, medium-range forecasts (five to seven days) were impossible, especially over the oceans. Today, global forecasting models use satellite data routinely.
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What are weather models and how do they work?
Weather models are objective calculations of changes to the weather map based on physics-based equations. These models mathematically and objectively calculate changes in atmospheric conditions.
Here's how they work:
1. Data is collected to specify initial conditions.
2. Model equations are solved for segments of the weather map to calculate how much conditions are expected to change in a set time.
3. These changes are added to the initial conditions to generate a new map.
4. The process repeats, using the new map as a new set of initial conditions, to generate a forecast.Numerical forecasts have improved as computing power has grown, allowing for more complex models with greater detail. New observations from satellites, radar systems, and weather balloons are incorporated into the models as improved initial synoptic maps.
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What challenges do scientists face when making weather forecasts?
Weather forecasting presents several challenges for scientists. Meteorologists must simultaneously monitor numerous variables across large areas, understanding that these variables interact, influencing one another.
Even with sophisticated tools, predicting weather remains difficult. Since the mid-20th century, digital computers have enabled objective calculations of changing atmospheric conditions using evolving weather models. Despite these advancements, forecasting remains imperfect.
Furthermore, atmospheric movements may be chaotic, which limits the range of weather forecasting, especially concerning detailed predictions.
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News •
Weather forecasting, the prediction of the weather through application of the principles of physics, supplemented by a variety of statistical and empirical techniques. In addition to predictions of atmospheric phenomena themselves, weather forecasting includes predictions of changes on Earth’s surface caused by atmospheric conditions—e.g., snow and ice cover, storm tides, and floods.
Measurements and ideas as the basis for weather prediction
The observations of few other scientific enterprises are as vital or affect as many people as those related to weather forecasting. From the days when early humans ventured from caves and other natural shelters, perceptive individuals in all likelihood became leaders by being able to detect nature’s signs of impending snow, rain, or wind, indeed of any change in weather. With such information they must have enjoyed greater success in the search for food and safety, the major objectives of that time.
In a sense, weather forecasting is still carried out in basically the same way as it was by the earliest humans—namely, by making observations and predicting changes. The modern tools used to measure temperature, pressure, wind, and humidity in the 21st century would certainly amaze them, and the results obviously are better. Yet, even the most sophisticated numerically calculated forecast made on a supercomputer requires a set of measurements of the condition of the atmosphere—an initial picture of temperature, wind, and other basic elements, somewhat comparable to that formed by our forebears when they looked out of their cave dwellings. The primeval approach entailed insights based on the accumulated experience of the perceptive observer, while the modern technique consists of solving equations. Although seemingly quite different, there are underlying similarities between both practices. In each case the forecaster asks “What is?” in the sense of “What kind of weather prevails today?” and then seeks to determine how it will change in order to extrapolate what it will be.
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Because observations are so critical to weather prediction, an account of meteorological measurements and weather forecasting is a story in which ideas and technology are closely intertwined, with creative thinkers drawing new insights from available observations and pointing to the need for new or better measurements, and technology providing the means for making new observations and for processing the data derived from measurements. The basis for weather prediction started with the theories of the ancient Greek philosophers and continued with Renaissance scientists, the scientific revolution of the 17th and 18th centuries, and the theoretical models of 20th- and 21st-century atmospheric scientists and meteorologists. Likewise, it tells of the development of the “synoptic” idea—that of characterizing the weather over a large region at exactly the same time in order to organize information about prevailing conditions. In synoptic meteorology, simultaneous observations for a specific time are plotted on a map for a broad area whereby a general view of the weather in that region is gained. (The term synoptic is derived from the Greek word meaning “general or comprehensive view.”) The so-called synoptic weather map came to be the principal tool of 19th-century meteorologists and continues to be used today in weather stations and on television weather reports around the world.
Since the mid-20th century, digital computers have made it possible to calculate changes in atmospheric conditions mathematically and objectively—i.e., in such a way that anyone can obtain the same result from the same initial conditions. The widespread adoption of numerical weather prediction models brought a whole new group of players—computer specialists and experts in numerical processing and statistics—to the scene to work with atmospheric scientists and meteorologists. Moreover, the enhanced capability to process and analyze weather data stimulated the long-standing interest of meteorologists in securing more observations of greater accuracy. Technological advances since the 1960s led to a growing reliance on remote sensing, particularly the gathering of data with specially instrumented Earth-orbiting satellites. By the late 1980s, forecasts of the weather were largely based on the determinations of numerical models integrated by high-speed supercomputers—except for some shorter-range predictions, particularly those related to local thunderstorm activity, which were made by specialists directly interpreting radar and satellite measurements. By the early 1990s a network of next-generation Doppler weather radar (NEXRAD) was largely in place in the United States, which allowed meteorologists to predict severe weather events with additional lead time before their occurrence. During the late 1990s and early 21st century, computer processing power increased, which allowed weather bureaus to produce more-sophisticated ensemble forecasts—that is, sets of multiple model runs whose results limit the range of uncertainty with respect to a forecast.
