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Quantitative precipitation forecast

Quantitative precipitation forecast is a earth science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Quantitative precipitation forecast rather than just read about it. In short: The quantitative precipitation forecast (abbreviated QPF) is the expected amount of melted precipitation accumulated over a specified time period over a specified area. Terrain is considered in QPFs by use of topography or based upon climatological precipitation patterns from observations with fine detail.

Quantitative precipitation forecast — main illustration
Quantitative precipitation forecast — illustration

Key takeaways

  • Quantitative precipitation forecast belongs to earth science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Quantitative precipitation forecast to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Quantitative precipitation forecast from memory before moving on to harder problems.

Reference excerpt

The quantitative precipitation forecast (abbreviated QPF) is the expected amount of melted precipitation accumulated over a specified time period over a specified area. Terrain is considered in QPFs by use of topography or based upon climatological precipitation patterns from observations with fine detail. Starting in the mid-to-late 1990s, QPFs were used within hydrologic forecast models to simulate impact to rivers throughout the United States. Forecast models show significant sensitivity to humidity levels within the planetary boundary layer, or in the lowest levels of the atmosphere, which decreases with height. QPF can be generated on a quantitative basis, forecasting amounts, or a qualitative basis, forecasting the probability of a specific amount. Radar imagery forecasting techniques show higher skill than model forecasts within 6 to 7 hours of the time of the radar image. The forecasts can be verified through use of rain gauge measurements, weather radar estimates, or a combination of both. Various skill scores can be determined to measure the value of the rainfall forecast.

Use of radar

Algorithms exist to forecast rainfall based on short term radar trends, within 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.

Use of forecast models

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 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 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 the forecast process at some point in the future, there is currently still a need for human intervention.

Nowcasting

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 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.

Ensemble forecasting

The detail that can be given in 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 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 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).

Probability approach

In addition to graphical rainfall forecasts showing quantitative amounts, rainfall forecasts can be made describing 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 is known as probability of precipitation. These probabilities can be derived from a deterministic forecast using computer post-processing.

Entities which generate rainfall forecasts

Australia 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 the ensemble. The PME is quick to produce, and is available through their Water and the Land page on their website.

Hong Kong 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 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.

… excerpt ends here. Continue reading the full article.

Illustrations

Quantitative precipitation forecast: Example of a five-day rainfall forecast from the Hydrometeorological Prediction Center
Example of a five-day rainfall forecast from the Hydrometeorological Prediction Center
Quantitative precipitation forecast: Table showing probabilities of certain rainfall amounts in various blocks of time
Table showing probabilities of certain rainfall amounts in various blocks of time
Quantitative precipitation forecast: 24 hours rain accumulation on the Val d'Irène radar in Eastern Canada. Notice the zones without data in the East and Southwest caused by radar beam blocking from mountains. (Source: Environment Canada)
24 hours rain accumulation on the Val d'Irène radar in Eastern Canada. Notice the zones without data in the East and Southwest caused by radar beam blocking from mountains. (Source: Environment Canada)

Worked examples

Example 1 — a first encounter with Quantitative precipitation forecast

Start with the simplest possible case. Write down what Quantitative precipitation forecast claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In earth science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Quantitative precipitation forecast before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Quantitative precipitation forecast ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Quantitative precipitation forecast

In research
Quantitative precipitation forecast appears in earth science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Quantitative precipitation forecast in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Quantitative precipitation forecast is common in secondary-school and first-year university syllabi. It links to neighbouring topics Hydrology, Weather forecasting, so understanding it makes those chapters shorter.
In everyday life
Look for Quantitative precipitation forecast outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Quantitative precipitation forecast in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Quantitative precipitation forecast means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Quantitative precipitation forecast out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Quantitative precipitation forecast in simple terms?

The quantitative precipitation forecast (abbreviated QPF) is the expected amount of melted precipitation accumulated over a specified time period over a specified area. Terrain is considered in QPFs by use of topography or based upon climatological precipitation patterns from observations with fine…

Why does Quantitative precipitation forecast matter?

Because it connects several earth science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Quantitative precipitation forecast?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Quantitative precipitation forecast.

Tags

  • Hydrology
  • Weather forecasting

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