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Model Output Statistics (MOS) is a multiple linear regression technique in which predicands, often near-surface quantities, such as 2-meter air temperature, horizontal visibility, and wind direction, speed and gusts, are related statistically to one or more predictors. The predictors are typically forecasts from a numerical weather prediction (NWP) model, climatic data, and, if applicable, recent surface observations. Thus, output from NWP models can be transformed by the MOS technique into sensible weather parameters that are familiar to the "person on the street".
Contents
Background
Output directly from the NWP model's lowest layer(s) generally is not used by forecasters because the actual physical processes that occur within the Earth's boundary layer are crudely approximated in the model (i.e., physical parameterizations) along with its relatively coarse horizontal resolution. Because of this lack of fidelity and its imperfect initial state, forecasts of near-surface quantities obtained directly from the model are subject to systematic (bias) and random model errors, which tend to grow with time.
In the development of MOS equations, past observations and archived NWP model forecast fields are used with a screening regression to determine the 'best' predictors and their coefficients for a particular predictand and forecast time. By using archived model forecast output along with verifying surface observations, the resulting equations implicitly take into account physical effects and processes which the underlying numerical weather prediction model cannot explicitly resolve, resulting in much better forecasts of sensible weather quantities. In addition to correcting systematic errors, MOS can produce reliable probabilities of weather events from a single model run. In contrast, despite the enormous amount of computing resources devoted to generating them, ensemble model forecasts' relative frequency of events—often used as a proxy for probability—do not exhibit useful reliability. Thus, ensemble NWP model output also requires additional post-processing in order to obtain reliable probabilistic forecasts.
United States
MOS was conceived and planning for its use began within the U.S. National Weather Service’s (NWS’s) Techniques Development Laboratory (TDL) in 1965 and forecasts first issued from it in 1968. Since then, TDL, now the Meteorological Development Laboratory (MDL), continued to create, refine and update MOS equation sets as additional NWP models were developed and made operational at the National Meteorological Center (NMC) and then the Environmental Modeling Center or EMC.
Given its multi-decadal history within the U.S. NWS and its continuous improvement and superior skill over direct NWP model output, MOS guidance is still one of the most valuable forecast tools used by forecasters within the agency.
United States
Currently there are eight sets of MOS guidance available from MDL, operational and experimental, covering the span of time from the next hour out to 10 days for the United States and most of its territories.
Initially, MOS guidance was developed for airports and other fixed locales where METARs (or similar reports) were routinely issued. Therefore, MOS guidance was and continues to be provided in an alphanumeric 'bulletin' format for these locations. Here is an example of a short-range MOS forecast for Clinton-Sherman Airport, Oklahoma (KCSM) based on the EMC's Global Forecast System model output.
The short-range GFS MOS bulletin is described here.
With the availability of private- and government-owned weather mesonets, new objective analysis and interpolation techniques, gridded GFS MOS guidance became available in 2006.
Advantages and disadvantages
The advantage of MOS forecast guidance as developed in the United States allowed for
These points, while greatly desired by forecasters, do come at a price. From its very beginnings, the development of robust MOS equations for a particular NWP model required at least two years' worth of archived model output and observations, during which time the NWP model should remain unchanged, or nearly so. This requirement is necessary in order to fully capture the model's error characteristics under a wide variety of meteorological flow regimes for any particular location or region. Extreme meteorological events such as unusual cold- or heat-waves, heavy rain and snowfall, high winds, etc., are important in the development of robust MOS equations. A lengthy model archive has the best chance of capturing such events.
From the 1970s and into the 1980s, this requirement was not very onerous since EMC (then NMC) scientists, being relatively constrained by computational resources at the time, could only make relatively minor, incremental improvements to their NWP models. However, since the 1990s, NWP models have been upgraded more frequently, oftentimes with significant changes in physics and horizontal and vertical grid resolutions. Since MOS corrects systematic biases of the NWP model its based on, any changes to the NWP model's error characteristics affects MOS guidance, usually in a negative way.
In the case of a major upgrade to a NWP model, the EMC will run the newer version of model in parallel with the operational one for many months to allow for direct comparison of model performance. In addition to parallel real-time runs, EMC also runs the newer model to examine past events and seasons, i.e., retrospective forecasts.
All of these runs from the upgraded model allows the National Weather Service, Weather Prediction Center, National Hurricane Center, and Storm Prediction Center to evaluate its performance prior to the decision to either accept or reject it for operational use. MDL scientists have taken advantage of these runs to evaluate and reformulate the MOS equations as needed to avoid deterioration in guidance quality.
Other weather centers
Royal Netherlands Meteorological Institute developed a MOS system to forecast probabilities of (severe) thunderstorms in the Netherlands.
Scientists from the Meteorological Service of Canada developed a post-processing system called Updateable MOS (UMOS) that quickly incorporates changes to their regional NWP model without the need for a lengthy model archive. The Canadian UMOS system generates a 2-day forecast of temperatures, wind speed and direction and probability of precipitation (POP). UMOS temperature and wind forecasts are provided at 3-h intervals, and POP at 6-h intervals.
Scientists at the Kongju National University have also implemented a UMOS system to create forecasts of air temperatures over South Korea. It is unclear as to whether it is used operationally at the Korean Meteorological Administration.