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William H. Klein and Harry R. Glahn

Experience over the past decade has shown that objective forecasts of local weather elements can best be obtained by using statistical methods to complement the raw output of numerical prediction models. One of the most successful techniques for accomplishing this is called Model Output Statistics (MOS). The MOS method involves matching observations of local weather with output from numerical models. Forecast equations are then derived by statistical techniques such as screening regression, regression estimation of event probabilities, and the logit model. In this way the bias and inaccuracy of the numerical model, as well as the local climatology, can be built into the forecast system. MOS has been applied by the Techniques Development Laboratory to produce automated forecasts of numerous weather elements including precipitation, temperature, wind, clouds, ceiling, visibility, and thunderstorms. In this paper, the derivation and operational application of MOS forecasts for each of these elements are discussed. Many of the products are transmitted nationwide over facsimile and/or teletypewriter; others are provided for internal use within the National Weather Service. Ultimately, a completely automated, computer-worded, local weather forecast will be produced routinely as part of a program for Automation of Field Operations and Services (AFOS).

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Ashok Kumar, Parvinder Maini, and S. V. Singh

. Proc. Statistical Interpretation of Numerical Weather Prediction Products, Seminar/Workshop, Reading, United Kingdom, ECMWF, 263–310. ——, and D. A. Lowry, 1972: The use of Model Output Statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203–1211. 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2 Klein, W. H., and H. R. Glahn, 1974: Forecasting local weather by means of model output statistics. Bull. Amer. Meteor. Soc., 55, 1217–1227. 10

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Andrew A. Taylor and Lance M. Leslie

-based MOS guidance for maximum/minimum temperature, probability of precipitation, cloud amount, and surface wind. Wea. Forecasting , 5 , 128 – 138 . 10.1175/1520-0434(1990)005<0128:NNBMGF>2.0.CO;2 Kanamitsu, M. , 1989 : Description of the NMC global data assimilation and forecast system. Wea. Forecasting , 4 , 335 – 342 . 10.1175/1520-0434(1989)004<0335:DOTNGD>2.0.CO;2 Klein, W. H. , and Glahn H. R. , 1974 : Forecasting local weather by means of model output statistics. Bull. Amer

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Chermelle Engel and Elizabeth Ebert

and anotated bibliography. Intl. J. Forecasting , 5 , 559 – 583 . 10.1016/0169-2070(89)90012-5 Fritsch, J. M. , Hilliker J. , Ross J. , and Vislocky R. L. , 2000 : Model consensus. Wea. Forecasting , 15 , 571 – 582 . 10.1175/1520-0434(2000)015<0571:MC>2.0.CO;2 Glahn, H. R. , and Lowry D. A. , 1972 : The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor. , 11 , 1203 – 1211 . 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2 Hibon, M

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Harry R. Glahn

cloud amount based on model output statistics. Mon. Wea. Rev., 105, 1565-1572.Glahn, H. R., and J. R. Bocchieri, 1975: Objective estimation of the conditional probability of frozen precipitation. Mon. Wea. R~v., 103, 3-15. , D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11,Acknowledgments. It is obvious that many people 1203-1211.have contributed to the aspect of automation discussed

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Robert L. Vislocky and George S. Young

format for Northern Hemisphere octagonal grid data. NCAR, unpublished.Klein, W. H., 1971: Computer prediction of precipitation probability in the United States. J. Appl. Meteor., 10, 903-915.--, and H. R. G!ahn, 1974: Forecasting local weather by means of Model Output Statistics. Bull. Amer. Meteor. Soc., 55, 1217 1227. , and G. A. Hammons, 1975: Maximum/minimum temperature forecasts based on model output statistics. Mon. Wea. Rev., 103, 796-806.Kruizinga, S., 1983: Statistical medium range

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Mary C. Erickson, J. Paul Dallavalle, and John S. Jensenius Jr.

documentation. NOAA Tech. Memo. NWS NMC-60, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, 68 pp.Glahn, H. R., and D. A. Lowry, 1972: The use of Model Output 'Statistics ( MOS ) in objective weather forecasting. J. Appl. Me teor., 11, 1203-121 I.Heppner, P. O. G., 1992: Snow versus rain: Looking beyond the magic numbers. Wea. Forecasting, 7, 683-691.Hoke, J. E., N. A. Phillips, G. J. Dimego, J. J. Tuccillo, and J. G. Sela, 1989: The regional analysis and forecast

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William D. Bonner

. For seasonal prediction and longer, the most promising approach appearsto be the use of coupled ocean-atmosphere models, inwhich ocean surface conditions do not simply drivethe atmospheric model, but are affected by them aswell. We would expect by the mid 1990s to be making284 WEATHER AND FORECASTING VOLUME4experimental seasonal predictions based upon the output from coupled ocean-atmosphere models. This

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John Bjørnar Bremnes

1. Introduction During the last decades statistical methods that use output from numerical weather prediction (NWP) models have become an important tool in producing quantitative forecasts at sites with observations. One of the reasons is that forecasts can be formulated in terms of probabilities independent of whether the information available is deterministic. Probabilistic forecasts are ideally fully specified probability distributions, but in practice those made by statistical methods are

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Clark Evans, Donald F. Van Dyke, and Todd Lericos

precipitation forecast issued at 0000 UTC 22 August 2008 by the Hydrometeorological Prediction Center (HPC) and kinematic and thermodynamic forecast fields from the 0000 UTC 22 August 2008 cycles of the Global Forecast System (GFS), North American Mesoscale (NAM), and European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic models. Utilizing these data and their subjective interpretation of the forecast event, they were asked to make a 72-h deterministic quantitative precipitation forecast

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