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An intensity-duration-frequency curve (IDF curve) is a mathematical function that relates the intensity of an event (e.g. rainfall) with its duration and frequency of occurrence. [1] Frequency is the inverse of the probability of occurrence. These curves are commonly used in hydrology for flood forecasting and civil engineering for urban ...
The rainfall data can be either a user-defined time series or come from an external file. Several different popular rainfall file formats currently in use are supported, as well as a standard user-defined format. The principal input properties of rain gages include: rainfall data type (e.g., intensity, volume, or cumulative volume)
Weather reconnaissance aircraft, such as this WP-3D Orion, provide data that is then used in numerical weather forecasts.. The atmosphere is a fluid.As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future.
An example of 500 mbar geopotential height and absolute vorticity prediction from a numerical weather prediction model Main article: Numerical weather prediction The basic idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of ...
Within the United States, the Hydrometeorological Prediction Center, [20] River Forecast Centers, [1] and local forecast offices within the National Weather Service create precipitation forecasts for up to five days in the future, [21] forecasting amounts equal to or greater than 0.01 inches (0.25 mm). Starting in the mid-to-late 1990s, QPFs ...
ECMWF aims to provide accurate medium-range global weather forecasts out to 15 days and seasonal forecasts out to 12 months. [11] Its products are provided to the national weather services of its member states and co-operating states as a complement to their national short-range and climatological activities, and those national states use ECMWF's products for their own national duties, in ...
Examples of data-driven models include regression techniques, Artificial Neural Networks (ANN), Support Vector Machines (SVM), and tree-based algorithms like Random Forest or XGBoost. Hybrid models combine the strengths of physically-based and data-driven models to enhance flood forecasting accuracy and reliability.
Probabilistic forecasting summarizes what is known about, or opinions about, future events. In contrast to single-valued forecasts (such as forecasting that the maximum temperature at a given site on a given day will be 23 degrees Celsius, or that the result in a given football match will be a no-score draw), probabilistic forecasts assign a probability to each of a number of different ...