write notes explaining how the concept of probability is used in estimating different weather or climate forecasts? Please answer wisely...
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An Information Statement of the American Meteorological Society
(Adopted by AMS Council on 12 May 2008) Bull. Amer. Meteor. Soc., 89
Summary
This information statement describes the current state of the science of probabilistic weather forecasting.1 Ideally all weather forecasts would include information that accurately quantifies their uncertainty. However, though there are a number of exceptions, most weather forecasts currently do not contain such information. The widespread dissemination of this probabilistic information would likely yield substantial economic and social benefits, because users could make better decisions by explicitly accounting for the uncertainty in weather forecasts.
Producing weather forecasts in probabilistic form for many weather parameters will require improvements in, or the implementation of, techniques for quantifying uncertainty, such as ensemble forecasting. Forecasters will need to be trained not only on how to use probabilistic information in their final forecasts, but also in the diverse requirements of those who use probability forecasts. In addition, users will require information on how to interpret and use probabilistic forecast information, needs that must be met if the communication of uncertainty in weather forecasts is to be effective.
Current Situation
In the last several decades, weather forecasts over most time and space scales have dramatically improved. This success is due to a combination of more accurate and higher-resolution satellite and Earth-observational data and improved “Numerical Weather Prediction (NWP)” forecast models that exploit today’s more powerful computers to more realistically represent the atmosphere. Concurrently, major sectors of the world economy (including agriculture, energy, transportation, and water supply) have made increasing use of weather forecasts, and have become more sophisticated in the integration of these improved forecasts into short- and long-term business planning and decision making. Yet weather forecasts, by their very nature, involve some uncertainty. For the most part, users still do not have ready access to information about this uncertainty, and the information available is often not effectively communicated to them.
Probability Forecasts
A probability forecast includes a numerical expression of uncertainty about the quantity or event being forecast. Ideally, all elements (temperature, wind, precipitation, etc.) of a weather forecast would include information that accurately quantifies the inherent uncertainty. Surveys have consistently indicated that users desire information about uncertainty or confidence of weather forecasts. The widespread dissemination and effective communication of forecast uncertainty information is likely to yield substantial economic and social benefits, because users can make decisions that explicitly account for this uncertainty.
While much progress has been made in developing methods to create probabilistic forecasts, currently only a small fraction of the elements of weather, hydrologic, and climate forecasts are expressed probabilistically. Forecasts of the probability of precipitation occurrence have been made for several decades and are well accepted, even if not always properly interpreted. More recently, in the United States the NWS2 has issued probability forecasts for a variety of weather phenomena, ranging from daily outlooks of tornado hazard and wind-speed fields in tropical storms to weekly and seasonal outlooks for temperature and precipitation.
An Information Statement of the American Meteorological Society
(Adopted by AMS Council on 12 May 2008) Bull. Amer. Meteor. Soc., 89
Summary
This information statement describes the current state of the science of probabilistic weather forecasting.1 Ideally all weather forecasts would include information that accurately quantifies their uncertainty. However, though there are a number of exceptions, most weather forecasts currently do not contain such information. The widespread dissemination of this probabilistic information would likely yield substantial economic and social benefits, because users could make better decisions by explicitly accounting for the uncertainty in weather forecasts.
Producing weather forecasts in probabilistic form for many weather parameters will require improvements in, or the implementation of, techniques for quantifying uncertainty, such as ensemble forecasting. Forecasters will need to be trained not only on how to use probabilistic information in their final forecasts, but also in the diverse requirements of those who use probability forecasts. In addition, users will require information on how to interpret and use probabilistic forecast information, needs that must be met if the communication of uncertainty in weather forecasts is to be effective.
Current Situation
In the last several decades, weather forecasts over most time and space scales have dramatically improved. This success is due to a combination of more accurate and higher-resolution satellite and Earth-observational data and improved “Numerical Weather Prediction (NWP)” forecast models that exploit today’s more powerful computers to more realistically represent the atmosphere. Concurrently, major sectors of the world economy (including agriculture, energy, transportation, and water supply) have made increasing use of weather forecasts, and have become more sophisticated in the integration of these improved forecasts into short- and long-term business planning and decision making. Yet weather forecasts, by their very nature, involve some uncertainty. For the most part, users still do not have ready access to information about this uncertainty, and the information available is often not effectively communicated to them.
Probability Forecasts
A probability forecast includes a numerical expression of uncertainty about the quantity or event being forecast. Ideally, all elements (temperature, wind, precipitation, etc.) of a weather forecast would include information that accurately quantifies the inherent uncertainty. Surveys have consistently indicated that users desire information about uncertainty or confidence of weather forecasts. The widespread dissemination and effective communication of forecast uncertainty information is likely to yield substantial economic and social benefits, because users can make decisions that explicitly account for this uncertainty.
While much progress has been made in developing methods to create probabilistic forecasts, currently only a small fraction of the elements of weather, hydrologic, and climate forecasts are expressed probabilistically. Forecasts of the probability of precipitation occurrence have been made for several decades and are well accepted, even if not always properly interpreted. More recently, in the United States the NWS2 has issued probability forecasts for a variety of weather phenomena, ranging from daily outlooks of tornado hazard and wind-speed fields in tropical storms to weekly and seasonal outlooks for temperature and precipitation.
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