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- Author or Editor: Peter Guttorp x
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Abstract
Ranking years based on statistical estimates of regional and temporal averages is subject to uncertainty. This uncertainty can in fact be quite substantial and can be described by the rank distribution of an ensemble of such averages. The authors develop a method for estimating it using simulation. The effect of temporal correlation is quite limited in the case studied in this paper: the contiguous United States' annual-mean temperature. The method also allows assessment of derived quantities such as the probability of a given year being one of the 10 warmest in the historical record.
Abstract
Ranking years based on statistical estimates of regional and temporal averages is subject to uncertainty. This uncertainty can in fact be quite substantial and can be described by the rank distribution of an ensemble of such averages. The authors develop a method for estimating it using simulation. The effect of temporal correlation is quite limited in the case studied in this paper: the contiguous United States' annual-mean temperature. The method also allows assessment of derived quantities such as the probability of a given year being one of the 10 warmest in the historical record.
Abstract
Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional on the current atmospheric pattern (termed a “weather state”), rain gauge stations in a network could be considered spatially independent. For a spatially dense network, this assumption is not tenable. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the conditional probability of rainfall given the weather state. Methods for fitting the parameters, assessing the goodness of fit of the model, and generating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washington State.
Abstract
Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional on the current atmospheric pattern (termed a “weather state”), rain gauge stations in a network could be considered spatially independent. For a spatially dense network, this assumption is not tenable. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the conditional probability of rainfall given the weather state. Methods for fitting the parameters, assessing the goodness of fit of the model, and generating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washington State.
Abstract
Tornadic activity in four U.S. regions is stochastically modeled based on data on tornado counts over the years 1953–98. It is shown that tornadic activity on a given day is mostly affected by the activity on the previous day. Hence, the process can be modeled as a Markov chain. A parametric nonhomogenous Markov chain model is developed based on the well-known increase of tornadic activity in the spring and summer months. This model, with only eight parameters, describes tornadic activity quite well. The interpretability of the estimated parameters allows a diagnosis of the regional differences in tornadic activity. For instance, a comparison of the values of the parameters for the four regions suggests that in the South tornado persistence is specific mostly to the early part of the year. Finally, within the framework of probabilistic forecast verification, it is shown that the Markov chain model outperforms the climatological model, even though the former is far simpler in terms of the number of parameters (8 and 366, respectively). The superior performance of the model is confirmed in terms of several measures of performance in all four regions. The exception is the southern Tornado Alley, where the reliability of the model forecasts is nonsignificantly inferior to that of the climatological ones.
Abstract
Tornadic activity in four U.S. regions is stochastically modeled based on data on tornado counts over the years 1953–98. It is shown that tornadic activity on a given day is mostly affected by the activity on the previous day. Hence, the process can be modeled as a Markov chain. A parametric nonhomogenous Markov chain model is developed based on the well-known increase of tornadic activity in the spring and summer months. This model, with only eight parameters, describes tornadic activity quite well. The interpretability of the estimated parameters allows a diagnosis of the regional differences in tornadic activity. For instance, a comparison of the values of the parameters for the four regions suggests that in the South tornado persistence is specific mostly to the early part of the year. Finally, within the framework of probabilistic forecast verification, it is shown that the Markov chain model outperforms the climatological model, even though the former is far simpler in terms of the number of parameters (8 and 366, respectively). The superior performance of the model is confirmed in terms of several measures of performance in all four regions. The exception is the southern Tornado Alley, where the reliability of the model forecasts is nonsignificantly inferior to that of the climatological ones.
Abstract
Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular–circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.
Abstract
Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular–circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.
Abstract
The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000–2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
Abstract
The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000–2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
