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

By H. Glahn, D. Lowry. 1972

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Citations in the corpus

Simulation-assisted machine learning (2018, positive): “model output statistics from weather forecasting, where low level data from primary simulations are used as inputs to a multiple regression model which outputs human-friendly weather predictions (Glahn and Lowry, 1972).”
Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting (2002, methodology): “The MOS approach was developed and implemented in an operational environment by the Meteorological Development Laboratory (MDL, formerly known as the Techniques Development Laboratory) of the National Oceanic and Atmospheric Administration (NOAA; Glahn and Lowry 1972).”
Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance (2014, background): “Luckily some of these errors are systematic and can be corrected with statistical postprocessing, often also referred to as model output statistics (MOS; Glahn and Lowry 1972).”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, methodology): “The predominant approach is based on regression, using model output statistics (MOS) schemes based on multiple linear regression for linear variables, such as temperature or pressure, and logistic regression for binary variables, such as precipitation occurrence or freezing (Glahn and Lowry 1972; Wi”
A skill score based on economic value for probability forecasts (2001, background): “The forecasts consist of objective statistical forecasts from Model Output Statistics (MOS) (Glahn & Lowry, 1972), and the corresponding subjective probability forecasts (SUB) made by US National Weather Service forecasters (who generally had access to the MOS forecasts before formulating their…”
Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures (2014, methodology): “For these reasons, the model output statistics (MOS) approach (Glahn and Lowry 1972) has been successfully used to improve upon model output through bias removal and statistical correction and provide location-specific forecasts from model guidance.”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, methodology): “These biases may be due to model errors, insufficient resolution (Weisman et al. 1997; Mullen and Buizza 2002; Szunyogh and Toth 2002; Buizza et al. 2003) or suboptimal parameteriza- tions, suboptimal methods for generating the initial conditions (Barkmeijer et al. 1998, 1999; Hamill et al.”
Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application (2006, background): “This approach is, of course, well established, being the essence of model output statistics (MOS) techniques (Glahn and Lowry 1972; Carter et al. 1989).”
Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging (2007, methodology): “Regression techniques such as model output statistics (MOS) can be used to generate probabilities of exceeding thresholds (Glahn and Lowry 1972; Klein and Glahn 1974; Bermowitz 1975; Charba 1998; Antolik 2000), or to generate quantiles of expected precipitation (Bremnes 2004; Friederichs and Hense…”
Distributional regression forests for probabilistic precipitation forecasting in complex terrain (2018, background): “…meteorological quantities and hence attention in the weather forecasting literature has been shifting from classical linear deterministic models (Glahn and Lowry 1972) towards probabilistic models such as the non-homogeneous Gaussian regression (NGR) of Gneiting, Raftery, Westveld III, and…”
Wind power forecasting : state-of-the-art 2009. (2009, background): “D three dimensional AC Alternating Current ACE Area Control Error AEC Adaptive Exponential Combination AESO Alberta Electric System Operator AGC Automatic Generation Control ANFIS Adaptive Neural Fuzzy Inference System ANN Artificial Neural Network AR Autoregressive ARIMA Autoregressive Integrated M”
Linear and Nonlinear Statistical Downscaling for Rainfall Forecasting over Southeastern Brazil (2006, methodology): “A statistical method of downscaling involving multivariate linear regression (MLR) methods is that of model output statistics (Glahn and Lowry 1972), which is used by many NWP centers worldwide.”