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Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging
"... We introduce two ways to produce locally calibrated gridbased probabilistic forecasts of temperature. Both start from the Bayesian model averaging (BMA) statistical postprocessing method, which can be globally calibrated, and modify it so as to make it local. The first method, geostatistical model ..."
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Cited by 7 (3 self)
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We introduce two ways to produce locally calibrated gridbased probabilistic forecasts of temperature. Both start from the Bayesian model averaging (BMA) statistical postprocessing method, which can be globally calibrated, and modify it so as to make it local. The first method, geostatistical model averaging (GMA), computes the predictive bias and variance at observation stations and interpolates them using a geostatistical model. The second approach, Local BMA, estimates the parameters of BMA at a grid point from stations that are close to the grid point and similar to it in elevation and land use. We give results of these two methods applied to the eightmember University of Washington Mesoscale Ensemble (UWME) for the 2006 calendar year. GMA was calibrated and sharper than Global BMA, which has constant predictive bias and variance across the domain, with prediction intervals that were 8 % narrower on average. Examples using a sparse and dense training network of stations are shown. The sparse network illustrates the ability of GMA to draw information from the entire training network, while Local BMA performs well in the dense training network due to the availability of nearby stations that are similar to the grid point
Uncertainty quantification in complex simulation models using ensemble copula coupling
 Statist. Sci
, 2013
"... ar ..."
2012: A method for calibrating deterministic forecasts of rare events
 Wea. Forecasting
"... Convectionallowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convectionallowing models are still unrefined. This paper proposes a method ..."
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Cited by 4 (2 self)
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Convectionallowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convectionallowing models are still unrefined. This paper proposes a method of deriving calibrated probabilistic forecasts of rare events from deterministic forecasts by fitting a parametric kernel density function to the model’s historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts. 1.
2630 MONTHLY WEATHER REVIEW VOLUME 139 Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging
, 2010
"... The authors introduce two ways to produce locally calibrated gridbased probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias and variance across the domain, and modify it to make ..."
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The authors introduce two ways to produce locally calibrated gridbased probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias and variance across the domain, and modify it to make it local. The first local method, geostatistical model averaging (GMA), computes the predictive bias and variance at observation stations and interpolates them using a geostatistical model. The second approach, Local BMA, estimates the parameters of BMA at a grid point from stations that are close to the grid point and similar to it in elevation and land use. The results of these two methods applied to the eightmember University of Washington Mesoscale Ensemble (UWME) are given for the 2006 calendar year. GMA was calibrated and sharper than Global BMA, with prediction intervals that were 8 % narrower than Global BMA on average. Examples using sparse and dense training networks of stations are shown. The sparse network experiment illustrates the ability of GMA to draw information from the entire training network. The performance of Local BMA was not statistically different from Global BMA in the dense network experiment, and was superior to both GMA and Global BMA in areas with sufficient nearby training data. 1.
Enhancing Air Quality Forecasts over Catalonia (Spain) Using Model Output Statistics
, 2015
"... Model Output Statistics (MOS) is a wellknown technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtaine ..."
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Model Output Statistics (MOS) is a wellknown technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen Dioxide (NO2), Particulate Matter (PM10) and Ozone (03) have been the pollutants considered and the methodology has been applied on statistical values of these pollutants according to regulatory levels. Four MOS algorithms have been developed, characterized by different approaches in relation with seasonal stratification and stratification according to the measurement stations considered. Algorithms have been compared among them in order to obtain a MOS that reduces the forecast uncertainties. Results obtained show that the best MOS designed increases the accuracy of NO2 maximum 1h daily value forecast from 71 % to 75%, from 68 % to 81 % in the case of daily