Weather forecast

Weather forecast

There are two ways for weather forcasting:

Short-range forecasting

Meteorologists can make somewhat longer-term forecasts (those for 6, 12, 24, or even 48 hours) with considerable skill because they are able to measure and predict atmospheric conditions for large areas by computer. Using models that apply their accumulated expert knowledge quickly, accurately, and in a statistically valid form, meteorologists are now capable of making forecasts objectively. As a consequence, the same results are produced time after time from the same data inputs, with all analysis accomplished mathematically. Unlike the prognostications of the past made with subjective methods, objective forecasts are consistent and can be studied, reevaluated, and improved.

Another technique for objective short-range forecasting is called MOS ( Model Output Statistics).This method involves the use of data relating to past weather phenomena and developments to extrapolate the values of certain weather Elements, usually for a specific location and time period. It overcomes the weaknesses of numerical models by developing statistical relations between model forecasts and observed weather. These relations are then used to translate the model forecasts directly to specific weather forecasts. For example, a numerical model might not predict the occurrence of surface winds at all, and whatever winds it did predict might always be too strong. MOS relations can automatically correct for errors in wind speed and produce quite accurate forecasts of wind occurrence at a specific point, such as Heathrow Airport near London. As long as numerical weather prediction models are imperfect, there may be many uses for the MOS technique.

Short-range weather forecasts generally tend to lose accuracy as forecasters attempt to look farther ahead in time. Predictive skill is greatest for periods of about 12 hours and is still quite substantial for 48-hour predictions. An increasingly important group of short-range forecasts are economically motivated. Their reliability is determined in the marketplace by the economic gains they produce.

Long-range forecasting

Extended-range, or long-range, weather forecasting has had a different history and a different approach from short- or medium-range forecasting. In most cases, it has not applied the synoptic method of going forward in time from a specific initial map. Instead, long-range forecasters have tended to use the climatological approach, often concerning themselves with the broad weather picture over a period of time rather than attempting to forecast day-to-day details.

There is good reason to believe that the limit of day-to-day forecasts based on the “initial map” approach is about two weeks. Most long-range forecasts thus attempt to predict the departures from normal conditions for a given month or season. Such departures are called anomalies. A forecast might state that “spring temperatures in Minneapolis have a 65 percent Probability of being above normal.” It would likely be based on a forecast anomaly map, which shows temperature anomaly patterns. The maps do not attempt to predict the weather for a particular day, but rather forecast trends (i.e., warmer than normal) for an extended amount of time, such as a season (i.e., spring).

Prior to the 1980s the technique commonly used in long-range forecasting relied heavily on the analog method, in which groups of weather situations (maps) from previous years were compared to those of the current year to determine similarities with the Atmosphere’s present patterns (or “habits”). An association was then made between what had happened subsequently in those “similar” years and what was going to happen in the current year. Most of the techniques were quite subjective, and there were often disagreements of interpretation and consequently uneven quality and marginal reliability.

Innovative new procedures

In the last quarter of the 20th century the approach of and prospects for long-range weather forecasting changed significantly. Stimulated by the work of Jerome Namias, who headed the U.S. Weather Bureau’s Long-Range Forecast Division for 30 years, scientists began to look at ocean-surface temperature anomalies as a potential cause for the temperature anomalies of the atmosphere in succeeding seasons and at distant locations. At the same time, other American meteorologists, most notably John M. Wallace, showed how certain repetitive patterns of atmospheric flow were related to each other in different parts of the world. With satellite-based observations available, investigators began to study the El Niño phenomenon. Atmospheric scientists also revived the work of Gilbert Walker, an early 20th-century British climatologist who had studied the Southern Oscillation, the aforementioned up-and-down fluctuation of Atmospheric Pressure in the Southern Hemisphere. Walker had investigated related air circulations (later called the Walker Circulation) that resulted from abnormally high pressures in Australia and low pressures in Argentina or vice versa.

Since the mid-1980s, interest has grown in applying numerical weather prediction models to long-range forecasting. In this case, the concern is not with the details of weather predicted 20 or 30 days in advance but rather with objectively predicted anomalies. The reliability of long-range forecasts, like that of short- and medium-range projections, has improved substantially in recent years. Yet, many significant problems remain unsolved, posing interesting challenges for all those engaged in the field.

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Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location and time. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will change.

Weather forecasts are used by a variety of people and organizations, including businesses, farmers, and emergency managers. They can help people plan their activities, protect their property, and stay safe.

There are many different types of weather forecasts, including short-term forecasts for the next few hours or days, medium-range forecasts for the next few days to weeks, and long-range forecasts for the next few weeks to months.

Weather forecasts are made using a variety of methods, including statistical models, numerical weather prediction models, and human judgment. Statistical models use historical data to predict future weather conditions. Numerical weather prediction models use mathematical equations to simulate the atmosphere and predict how it will change. Human judgment is used to interpret the output of numerical weather prediction models and to make adjustments for factors that are not included in the models.

Weather forecasts are not always accurate. There are many factors that can affect the accuracy of weather forecasts, including the complexity of the atmosphere, the limited amount of data that is available, and the uncertainty in the models that are used to make the forecasts.

Despite the challenges, weather forecasting is a very important tool that can help people stay safe and plan their activities. Weather forecasts are constantly being improved, and they are becoming more accurate all the time.

Air quality is a measure of the level of pollutants in the air. Pollutants can come from natural sources, such as Volcanoes-2/”>Volcanoes and forest fires, or from human activities, such as burning fossil fuels. Air Pollution can cause a variety of Health problems, including respiratory problems, heart disease, and cancer.

Clouds are visible masses of water droplets or ice crystals suspended in the atmosphere. Clouds are classified by their shape, height, and appearance. Clouds can affect the weather in a variety of ways. For example, clouds can block sunlight, which can cool the Earth’s surface. Clouds can also produce Precipitation, such as rain or snow.

Climate is the Average weather conditions in a particular place over a long period of time. Climate