Search results
Results from the WOW.Com Content Network
In weather forecasting, model output statistics (MOS) is a multiple linear regression technique in which predictands, often near-surface quantities (such as two-meter-above-ground-level air temperature, horizontal visibility, and wind direction, speed and gusts), are related statistically to one or more predictors.
However, direct output from computer simulations of the atmosphere needs calibration before it can be meaningfully compared with observations of weather variables. This calibration process is often known as model output statistics (MOS). The simplest form of such calibration is to correct biases, using a bias correction calculated from past ...
The output of forecast models based on atmospheric dynamics is unable to resolve some details of the weather near the Earth's surface. As such, a statistical relationship between the output of a numerical weather model and the ensuing conditions at the ground was developed in the 1970s and 1980s, known as model output statistics (MOS).
Model output statistics differ from the perfect prog technique, which assumes that the output of numerical weather prediction guidance is perfect. [61] MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases.
In the vertical, the model is divided into 127 layers and extends to the mesopause (roughly ~80 km). It produces forecast output every hour for the first 120 hours, [1] three hourly through day 10, and 12 hourly through day 16. The output from the GFS is also used to produce model output statistics.
These statistical models are collectively referred to as model output statistics (MOS), [76] and were developed by the National Weather Service for their suite of weather forecasting models by 1976. [77] The United States Air Force developed its own set of MOS based upon their dynamical weather model by 1983. [78]
The dispersion models vary depending on the mathematics used to develop the model, but all require the input of data that may include: Meteorological conditions such as wind speed and direction, the amount of atmospheric turbulence (as characterized by what is called the "stability class" ), the ambient air temperature, the height to the bottom ...
It is a continually updated globally gridded data set that represents the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output from 1948 to present.