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50-551: [REDACTED] Look up qpf in Wiktionary, the free dictionary. QPF may refer to: Quantitative precipitation forecast Quebec Police Force , the Sûreté du Québec Queensland Poetry Festival Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with the title QPF . If an internal link led you here, you may wish to change

100-405: A consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error. However, regardless how small the average error becomes with any individual system, large errors within any particularly piece of guidance are still possible on any given model run. Professionals are required to interpret the model data into weather forecasts that are understandable to

150-424: A forecast increases with time as errors decrease. There comes a point when the errors are so large that the forecast has no correlation with the actual state of the atmosphere. Looking at a single forecast model does not indicate how likely that forecast is to be correct. Ensemble forecasting entails the production of many forecasts to reflect the uncertainty in the initial state of the atmosphere (due to errors in

200-419: A matter of hours. Radar imagery forecasting techniques show higher skill than model forecasts within 6 to 7 hours of the time of the radar image. In the past, the forecaster was responsible for generating the entire weather forecast based upon available observations. Today, meteorologists' input is generally confined to choosing a model based on various parameters, such as model biases and performance. Using

250-400: A particular resolved scale state. Instead of predicting the most likely sub-grid scale motion, a stochastic parametrisation scheme represents one possible realisation of the sub-grid. It does this through including random numbers into the equations of motion. This samples from the probability distribution assigned to uncertain processes. Stochastic parametrisations have significantly improved

300-409: A set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo analysis . The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by

350-437: A single model-based approach. When the models within a multi-model ensemble are adjusted for their various biases, this process is known as "superensemble forecasting". This type of a forecast significantly reduces errors in model output. When models of different physical processes are combined, such as combinations of atmospheric, ocean and wave models, the multi-model ensemble is called hyper-ensemble. The ensemble forecast

400-414: A single number. In a perturbed parameter approach, uncertain parameters in the model's parametrisation schemes are identified and their value changed between ensemble members. While in probabilistic climate modelling, such as climateprediction.net , these parameters are often held constant globally and throughout the integration, in modern numerical weather prediction it is more common to stochastically vary

450-399: Is linear regression , often known in this context as model output statistics . The linear regression model takes the ensemble mean as a predictor for the real temperature, ignores the distribution of ensemble members around the mean, and predicts probabilities using the distribution of residuals from the regression. In this calibration setup the value of the ensemble in improving the forecast

500-540: Is resolution. This is an indication of how much the forecast deviates from the climatological event frequency – provided that the ensemble is reliable, increasing this deviation will increase the usefulness of the forecast. This forecast quality can also be considered in terms of sharpness , or how small the spread of the forecast is. The key aim of a forecaster should be to maximise sharpness, while maintaining reliability. Forecasts at long leads will inevitably not be particularly sharp (have particularly high resolution), for

550-628: Is expected. The red warning indicates rainfall amounts of 50 millimetres (2.0 in) per hour are anticipated. The black warning indicates that rainfall rates of 70 millimetres (2.8 in) are possible. Within the United States, the Hydrometeorological Prediction Center , River Forecast Centers, and local forecast offices within the National Weather Service create precipitation forecasts for up to five days in

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600-415: Is known as probability of precipitation . These probabilities can be derived from a deterministic forecast using computer post-processing. The Bureau of Meteorology began a method of forecasting rainfall using a combination, or ensemble, of different forecast models in 2006. It is termed The Poor Man's Ensemble (PME). Its forecasts are more accurate over time than any of the individual models composing

650-399: Is large, this indicates more uncertainty in the prediction. Ideally, a spread-skill relationship should exist, whereby the spread of the ensemble is a good predictor of the expected error in the ensemble mean. If the forecast is reliable , the observed state will behave as if it is drawn from the forecast probability distribution. Reliability (or calibration ) can be evaluated by comparing

700-399: Is then that the ensemble mean typically gives a better forecast than any single ensemble member would, and not because of any information contained in the width or shape of the distribution of the members in the ensemble around the mean. However, in 2004, a generalisation of linear regression (now known as Nonhomogeneous Gaussian regression ) was introduced that uses a linear transformation of

750-432: Is usually evaluated by comparing the ensemble average of the individual forecasts for one forecast variable to the observed value of that variable (the "error"). This is combined with consideration of the degree of agreement between various forecasts within the ensemble system, as represented by their overall standard deviation or "spread". Ensemble spread can be visualised through tools such as spaghetti diagrams, which show

800-506: Is zero. With tropical cyclones which impact the United States, the GFS global forecast model performed best in regards to its rainfall forecasts over the last few years, outperforming the NAM and ECMWF forecast models. Ensemble forecasting Ensemble forecasting is a method used in or within numerical weather prediction . Instead of making a single forecast of the most likely weather,

850-434: The chaotic nature of the evolution equations of the atmosphere, which is often referred to as sensitive dependence on initial conditions ; and (2) errors introduced because of imperfections in the model formulation, such as the approximate mathematical methods to solve the equations. Ideally, the verified future atmospheric state should fall within the predicted ensemble spread , and the amount of spread should be related to

900-516: The chaotic nature of the fluid dynamics equations involved. Furthermore, existing observation networks have limited spatial and temporal resolution (for example, over large bodies of water such as the Pacific Ocean), which introduces uncertainty into the true initial state of the atmosphere. While a set of equations, known as the Liouville equations , exists to determine the initial uncertainty in

950-530: The EDA perturbations are more active in the tropics. The NCEP ensemble, the Global Ensemble Forecasting System, uses a technique known as vector breeding . Model uncertainty arises due to the limitations of the forecast model. The process of representing the atmosphere in a computer model involves many simplifications such as the development of parametrisation schemes, which introduce errors into

1000-535: The US are also generated by the US Navy and Air Force . There are various ways of viewing the data such as spaghetti plots , ensemble means or Postage Stamps where a number of different results from the models run can be compared. As proposed by Edward Lorenz in 1963, it is impossible for long-range forecasts—those made more than two weeks in advance—to predict the state of the atmosphere with any degree of skill owing to

1050-399: The benefit of society, the economy and the environment. It establishes an organizational framework that addresses weather research and forecast problems whose solutions will be accelerated through international collaboration among academic institutions, operational forecast centres and users of forecast products. One of its key components is THORPEX Interactive Grand Global Ensemble (TIGGE),

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1100-486: The current state of the atmosphere, together with its past evolution. There are a number of ways to generate these initial condition perturbations. The ECMWF model, the Ensemble Prediction System (EPS), uses a combination of singular vectors and an ensemble of data assimilations (EDA) to simulate the initial probability density . The singular vector perturbations are more active in the extra-tropics, while

1150-501: The dispersion of one quantity on prognostic charts for specific time steps in the future. Another tool where ensemble spread is used is a meteogram , which shows the dispersion in the forecast of one quantity for one specific location. It is common for the ensemble spread to be too small, such that the observed atmospheric state falls outside of the ensemble forecast. This can lead the forecaster to be overconfident in their forecast. This problem becomes particularly severe for forecasts of

1200-657: The ensemble probability distribution was a representative sample of the probability distribution in the atmosphere. It was not until 1992 that ensemble forecasts began being prepared by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP). There are two main sources of uncertainty that must be accounted for when making an ensemble weather forecast: initial condition uncertainty and model uncertainty. Initial condition uncertainty arises due to errors in

1250-414: The ensemble spread in this way varies, depending on the forecast system, forecast variable and lead time. In addition to being used to improve predictions of uncertainty, the ensemble spread can also be used as a predictor for the likely size of changes in the mean forecast from one forecast to the next. This works because, in some ensemble forecast systems, narrow ensembles tend to precede small changes in

1300-479: The ensemble spread to give the width of the predictive distribution, and it was shown that this can lead to forecasts with higher skill than those based on linear regression alone. This proved for the first time that information in the shape of the distribution of the members of an ensemble around the mean, in this case summarized by the ensemble spread, can be used to improve forecasts relative to linear regression . Whether or not linear regression can be beaten by using

1350-471: The ensemble. The PME is quick to produce, and is available through their Water and the Land page on their website. The Hong Kong Observatory generates short term rainstorm warnings for systems which are expected to accumulate a certain amount of rainfall per hour over the next few hours. They use three levels of warning. The amber warning indicates that a rainfall intensity of 30 millimetres (1.2 in) per hour

1400-444: The estimate of the starting conditions for the forecast, both due to limited observations of the atmosphere, and uncertainties involved in using indirect measurements, such as satellite data , to measure the state of atmospheric variables. Initial condition uncertainty is represented by perturbing the starting conditions between the different ensemble members. This explores the range of starting conditions consistent with our knowledge of

1450-430: The forecast process at some point in the future, there is currently still a need for human intervention. The forecasting of the precipitation within the next six hours is often referred to as nowcasting . In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model. A human given

1500-430: The forecast. Several techniques to represent model uncertainty have been proposed. When developing a parametrisation scheme, many new parameters are introduced to represent simplified physical processes. These parameters may be very uncertain. For example, the ' entrainment coefficient' represents the turbulent mixing of dry environmental air into a convective cloud , and so represents a complex physical process using

1550-536: The future, forecasting amounts equal to or greater than 0.01 inches (0.25 mm). Starting in the mid-to-late 1990s, QPFs were used within hydrologic forecast models to simulate impact of rainfall on river stages. Rainfall forecasts can be verified a number of ways. Rain gauge observations can be gridded into areal averages, which are then compared to the grids for the forecast models. Weather radar estimates can be used outright, or corrected for rain gauge observations. Several statistical scores can be based on

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1600-517: The inevitable (albeit usually small) errors in the initial condition will grow with increasing forecast lead until the expected difference between two model states is as large as the difference between two random states from the forecast model's climatology. If ensemble forecasts are to be used for predicting probabilities of observed weather variables they typically need calibration in order to create unbiased and reliable forecasts. For forecasts of temperature one simple and effective method of calibration

1650-406: The latest radar, satellite and observational data will be able to make a better analysis of the small scale features present and so will be able to make a more accurate forecast for the following few hours. However, there are now expert systems using those data and mesoscale numerical model to make better extrapolation, including evolution of those features in time. The detail that can be given in

1700-645: The lay person. Professionals can use knowledge of local effects which may be too small in size to be resolved by the model to add information to the forecast. As an example, terrain is considered in the QPF process by using topography or climatological precipitation patterns from observations with fine detail. Using model guidance and comparing the various forecast fields to climatology, extreme events such as excessive precipitation associated with later flood events lead to better forecasts. While increasing accuracy of forecast models implies that humans may no longer be needed in

1750-458: The link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=QPF&oldid=933072972 " Category : Disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Quantitative precipitation forecast Algorithms exist to forecast rainfall based on short term radar trends, within

1800-444: The mean, while wide ensembles tend to precede larger changes in the mean. This has applications in the trading industries, for whom understanding the likely sizes of future forecast changes can be important. The Observing System Research and Predictability Experiment (THORPEX) is a 10-year international research and development programme to accelerate improvements in the accuracy of one-day to two-week high impact weather forecasts for

1850-415: The model initialization, the equations are too complex to run in real-time, even with the use of supercomputers. The practical importance of ensemble forecasts derives from the fact that in a chaotic and hence nonlinear system, the rate of growth of forecast error is dependent on starting conditions. An ensemble forecast therefore provides a prior estimate of state-dependent predictability, i.e. an estimate of

1900-510: The observations and insufficient sampling). The range of different forecasts produced can then assess the uncertainty in the forecast. Ensemble forecasts are increasingly being used for operational weather forecasting (for example at European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and the Canadian Forecasting Center). Ensemble mean forecasts for precipitation have

1950-401: The observed and forecast fields. One, known as a bias , compares the size of the forecast field to the observed field, with the goal of a score of 1. The threat score involves the intersection of the forecast and observed sets, with a maximum possible verification score of 1. The probability of detection , or POD, is found by dividing the overlap between the forecast and observed fields by

2000-492: The probabilities of certain rainfall amounts being met. This allows the forecaster to assign the degree of uncertainty to the forecast. This technique is considered to be informative, relative to climatology. This method has been used for years within National Weather Service forecasts, as a period's chance of rain equals the chance that 0.01 inches (0.25 mm) will fall in any particular spot. In this case, it

2050-478: The same problems associated with their use in other fields, as they average out more extreme values, and therefore have limited usefulness for extreme events. In the case of the SREF ensemble mean, used within the United States, this decreasing usefulness starts with values as low as 0.50 inches (13 mm). In addition to graphical rainfall forecasts showing quantitative amounts, rainfall forecasts can be made describing

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2100-470: The situations in the past when a 60% probability was forecast, on 60% of those occasions did the rainfall actually exceed 1 cm. In practice, the probabilities generated from operational weather ensemble forecasts are not highly reliable, though with a set of past forecasts ( reforecasts or hindcasts ) and observations, the probability estimates from the ensemble can be adjusted to ensure greater reliability. Another desirable property of ensemble forecasts

2150-427: The size of the observed field: the goal here is a score of 1. The critical success index, or CSI, divides the overlap between the forecast and observed fields by the combined size of the forecast and observed fields: the goal here is a score of 1. The false alarm rate , or FAR, divides the area of the forecast which does not overlap the observed field by the size of the forecasted area. The goal value in this measure

2200-417: The skill of weather forecasting models, and are now used in operational forecasting centres worldwide. Stochastic parametrisations were first developed at the European Centre for Medium Range Weather Forecasts . When many different forecast models are used to try to generate a forecast, the approach is termed multi-model ensemble forecasting. This method of forecasting can improve forecasts when compared to

2250-508: The standard deviation of the error in the ensemble mean with the forecast spread: for a reliable forecast, the two should match, both at different forecast lead times and for different locations. The reliability of forecasts of a specific weather event can also be assessed. For example, if 30 of 50 members indicated greater than 1 cm rainfall during the next 24 h, the probability of exceeding 1 cm could be estimated to be 60%. The forecast would be considered reliable if, considering all

2300-430: The types of weather that might occur, given inevitable uncertainties in the forecast initial conditions and in the accuracy of the computational representation of the equations. These uncertainties limit forecast model accuracy to about six days into the future. The first operational ensemble forecasts were produced for sub-seasonal timescales in 1985. However, it was realised that the philosophy underpinning such forecasts

2350-498: The uncertainty (error) of the forecast. In general, this approach can be used to make probabilistic forecasts of any dynamical system , and not just for weather prediction. Today ensemble predictions are commonly made at most of the major operational weather prediction facilities worldwide, including: Experimental ensemble forecasts are made at a number of universities, such as the University of Washington, and ensemble forecasts in

2400-526: The value of the parameters in time and space. The degree of parameter perturbation can be guided using expert judgement, or by directly estimating the degree of parameter uncertainty for a given model. A traditional parametrisation scheme seeks to represent the average effect of the sub grid-scale motion (e.g. convective clouds) on the resolved scale state (e.g. the large scale temperature and wind fields). A stochastic parametrisation scheme recognises that there may be many sub-grid scale states consistent with

2450-402: The weather about 10 days in advance, particularly if model uncertainty is not accounted for in the forecast. The spread of the ensemble forecast indicates how confident the forecaster can be in his or her prediction. When ensemble spread is small and the forecast solutions are consistent within multiple model runs, forecasters perceive more confidence in the forecast in general. When the spread

2500-523: Was also relevant on shorter timescales – timescales where predictions had previously been made by purely deterministic means. Edward Epstein recognized in 1969 that the atmosphere could not be completely described with a single forecast run due to inherent uncertainty, and proposed a stochastic dynamic model that produced means and variances for the state of the atmosphere. Although these Monte Carlo simulations showed skill, in 1974 Cecil Leith revealed that they produced adequate forecasts only when

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