The Use of Model Output Statistics (MOS) in Objective Weather Forecasting

Glahn, Harry R.; Lowry, Dale A.

doi:10.1175/1520-0450(1972)011<1203:tuomos>2.0.co;2

Cited by 874 publications

(508 citation statements)

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“…In the 1970s and 1980s two post-processing schemes were operationally used to solve this problem: Perfect Prognosis (PP or Perfect Prog) (Klein et al 1959) and Model Output Statistics (MOS) (Glahn & Lowry 1972). …”

Section: Introductionmentioning

confidence: 99%

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

Boi¹

2004

Meteorological Applications

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Section: Introductionmentioning

confidence: 99%

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

Boi¹

2004

Meteorological Applications

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“…At a basic level, a multivariate statistical regression model known as model output statistics (MOS) is applied to remove biases (Glahn and Lowry 1972). More complex methods (such as ANN, autoregressive models and others) are also used to provide nonlinear corrections to models Giebel and Kariniotakis 2007;Pelland et al 2013).…”

Section: Blending the Forecasts And Predicting Powermentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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“…Further the uncertainties in the forecasts are generally greater than that associated with the observations. Statistical post-processing methods have been widely used to deal with the errors in QPFs and QTFs (Glahn and Lowry, 1972;Krzysztofowicz and Sigrest, 1999;Schaake et al, 2007). Data assimilation methods have been widely used to reduce the uncertainty associated with the initial conditions (ICs) used in the hydrological models.…”

Section: Uncertainties In Hydrological Forecastingmentioning

confidence: 99%

Hydrological monitoring and seasonal forecasting: Progress and perspectives

et al. 2016

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Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment, preparedness and mitigation. In recent decades, developments in ground and satellite measurements have made the hydrometeorological information readily available, and advances in information technology have facilitated the data analysis in a real-time manner. New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution. These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status, and earlier warning of extreme hydrological events such as droughts and floods. This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting, reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting, and briefly introduces the current monitoring and forecasting practices in China. The grand challenges and perspectives for the near future are also discussed, including acquiring and extracting reliable information for monitoring and forecasting, predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity, and filling the gap between numerical models and the end user. We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.

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The Use of Model Output Statistics (MOS) in Objective Weather Forecasting

Cited by 874 publications

References 10 publications

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

Short-Range Forecasting for Energy

Hydrological monitoring and seasonal forecasting: Progress and perspectives

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