Model output statistics
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".
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 (i.e., physical parameterizations) in the model along with its relatively coarse horizontal resolution. Because of this lack of fidelity, 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 can 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 deterministic model run. In contrast, despite the enormous amount of computing resources devoted to generating them, ensemble model forecasts of relative frequency of events—often used as a proxy for probability—do not exhibit useful reliability.[1] Ensemble NWP model output also requires additional post-processing in order to obtain reliable probabilistic forecasts.[2][3]
History
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 its forecasts first issued in 1968.[4] 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.[5]
Implementation of MOS guidance
United States
Currently there are six 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.
Name | Forecast Period | Update frequency |
---|---|---|
Localized Aviation MOS Program (LAMP) | 1-25 hrs | Every hour |
North American Mesoscale (NAM) MOS | 6-84* hrs | Every twelve hours |
Global Forecast System (GFS) MOS | 6-84* hrs | Every six hours |
GFS Extended MOS | 12-192 hrs | Twice per day |
North American Ensemble Forecast System MOS | 12-240 hrs | Twice per day |
ECMWF MOS† | 12-192 hrs | Once per day |
*In the alphanumeric text bulletins, the short-range forecasts goes to 72 hours only.
† Access to ECMWF MOS is restricted to the NOAA organization
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.
KCSM GFS MOS GUIDANCE 8/06/2014 1200 UTC |
---|
DT /AUG 6/AUG 7 /AUG 8 /AUG 9 HR 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 06 12 N/X 71 101 74 104 72 TMP 90 96 94 84 78 74 72 84 95100 98 87 82 78 75 88 98102 99 80 73 DPT 65 62 62 63 63 63 64 65 63 60 60 62 63 63 64 65 63 60 61 63 63 CLD CL FW CL CL BK BK CL CL CL CL CL CL FW CL CL CL CL CL CL OV FW WDR 21 20 19 16 16 18 19 22 32 07 11 12 16 18 19 22 22 20 20 19 21 WSP 14 15 13 11 13 10 10 08 06 06 10 08 10 10 10 14 12 15 15 08 07 P06 2 9 6 1 2 4 2 4 2 6 5 P12 14 5 4 10 12 Q06 0 0 0 0 0 0 0 0 0 0 0 Q12 0 0 0 0 0 T06 29/27 38/21 22/ 6 8/ 2 26/14 24/ 8 16/ 5 12/ 4 27/18 20/ 7 T12 58/31 24/ 6 39/16 29/ 6 44/25 CIG 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 VIS 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 OBV N N N N N N N N N N N N N N N N N N N N N |
The short-range GFS MOS bulletin is described here.
With the availability of private and government owned weather mesonets,[6] new objective analysis and interpolation techniques,[7] gridded GFS MOS guidance became available in 2006.[8][9]
Advantages and Disadvantages
The advantage of MOS forecast guidance as developed in the United States allowed for
- the removal of NWP model bias,
- prediction of weather elements not forecasted by the NWP model, e.g., horizontal visibility, cloud ceiling height,
- reliable probabilities of binary events, e.g. probability of precipitation and thunderstorms,
- reliable probabilities of categorical events, e.g., probability of clear, scattered, broken or overcast skies.
These points, while highly 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 was 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. Rare meteorological events such as unusual cold or heat, flash flooding, 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 1970's and into the 1980's, 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. Since the 1990's,[10][11] however, 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.[12][13] In case of major upgrades to the NWP model, the EMC re-runs the new model to allow MDL and other National Centers, e.g. WPC, NHC, SPC, to examine past events and seasons and evaluate its performance prior to acceptance and operational implementation. MDL scientists have taken advantage of these 'reforecasts' to evaluate and reformulate the MOS equations as needed to avoid deterioration in operational guidance.[14]
Other Weather Centers
Royal Netherlands Meteorological Institute developed a MOS system to forecast probabilities of (severe) thunderstorms in the Netherlands.[15][16]
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.[17] 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.[18] It is unclear as to whether it is used operationally at the Korean Meteorological Administration.
References
- ^ Rudack, David; Ghirardelli, Judy (August 2010). "A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecast of Ceiling Height and Visibility". Weather and Forecasting. 25 (4): 1161–1178. doi:10.1175/2010WAF2222383.1.
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(help) - ^ Wilks, Daniel S.; Hamill, Thomas M. (June 2007). "Comparison of Ensemble-MOS Methods Using GFS Reforecasts". Monthly Weather Review. 135 (6): 2379–2390. doi:10.1175/MWR3402.1.
- ^ Glahn, Bob (January 2009). "MOS Uncertainty Estimates in an Ensemble Framework". Monthly Weather Review. 137 (1): 246–268. doi:10.1175/2008MWR2569.1.
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(help) - ^ Glahn, Harry R.; Dallavalle, J. Paul (January 2000). "TDL Office Note 00-1:MOS-2000" (PDF). Internal publication. Silver Spring Maryland USA: Techniques Development Laboratory: 179. Retrieved 9 August 2014.
- ^ Unpublished. "Annual MDL Users Survey 2011" (PDF). pp. 25–27. Retrieved 3 August 2014.
- ^ NOAA, Earth Systems Research Laboratory. "MADIS Surface Network Information". Retrieved 7 August 2014.
- ^ Glahn, Bob; Im, J.S. (January 2011). "Algorithms for effective objective analysis of surface weather variables". 24th Conf. on Weather and Forecasting/20th Conf. on Numerical Weather Prediction (J19.4). Retrieved 7 August 2014.
- ^ Glahn, Bob; Gilbert, Kathryn; Cosgrove, Rebecca; Ruth, David; Sheets, Kari (April 2009). "The Gridding of MOS". Weather and Forecasting. 24 (2): 520–529. doi:10.1175/2008WAF2007080.1.
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(help) - ^ National Weather Service. "National Digital Guidance Database".
- ^ Environmental Modeling Center, Mesoscale Modeling Branch. "Mesoscale Branch Web Page Reference List". Retrieved 7 August 2014.
- ^ Environmental Modeling Center, Global Branch. "[GFS/GDAS] Changes since 1991". Retrieved 7 August 2014.
- ^ Erickson, Mary C. (March 1991). "Evaluating the Impact of RAFS Changes on the NGM-Based MOS Guidance". Weather and Forecasting. 6 (1): 142–147. doi:10.1175/1520-0434(1991)006<0142:ETIORC>2.0.CO;2.
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(help) - ^ Erickson, Mary C.; Dallavalle, J. Paul; Carroll, Kevin L. (January 2002). "The new AVN/MRF MOS development and model changes: a volatile mix?". 16th Conference on Probability and Statistics in the Atmospheric Sciences. Retrieved 5 August 2014.
- ^ Antolik, Mark; Baker, Michael (2 June 2009). "On the ability to develop MOS guidance with short dependent samples from an evolving numerical model" (PDF). 23rd Conference Weather Analysis and Forecasting/19th Conference Numerical Prediction. 6A.1. Retrieved 9 August 2014.
- ^ Schmeits, Maurice J.; Kok, Kees J.; Vogelezang, Daan H. P. (April 2005). "Probabilitic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics". Weather and Forecasting. 20 (2): 134–148. doi:10.1175/WAF840.1.
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(help) - ^ van Gastel, Valentijn. "Investigating MSG-SEVIRI data as an additional predictor source in the KNMI probabilistic (severe) thunderstorm forecasting system" (PDF). Koninklijk Nederlands Meteorologisch Instituut. Royal Netherlands Meteorological Institute (KNMI) publication. Retrieved 9 August 2014.
- ^ Wilson, Laurence; Vallee, Marcel (April 2002). "The Canadian Updateable Model Output Statistics (UMOS) System: Design and Development Tests". Weather and Forecasting. 17 (2): 206–222. doi:10.1175/1520-0434(2002)017<0206:TCUMOS>2.0.CO;2.
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(help) - ^ Kang, Jeon-Ho; Suh, Myoung-Seok; Hong, Ki-Ok; Kim, Chansoo (February 2011). "Development of updateable model output statistics (UMOS) system for air temperature over South Korea". Asia-Pacific Journal of Atmospheric Sciences. 47 (2): 199–211. doi:10.1007/s13143-011-0009-8.