Search Results
Abstract
Advances in hurricane climate science allow forecasts of seasonal landfall activity to be made. The authors begin with a review of the forecast methods available in the literature. They then reformulate the methods using a Bayesian probabilistic approach. This allows a direct comparison to be made while focusing on a single hindcast of the 2004 season over Florida. The models, including climatology, are estimated using Gibbs sampling. Diagnostic checks verify convergence and efficient mixing of the samples from each of the models. A below average sea level pressure gradient over the eastern North Atlantic Ocean during May and June in combination with an above average tropospheric-averaged wind index associated, in part, with a strengthening of the Bermuda high pressure during July resulted in an above average probability of at least one Florida hurricane. The relatively high hindcast probabilities for 2004 were in marked contrast to the most recent 50-yr empirical probabilities for Florida, but fell short in anticipating the unprecedented level of activity that ensued. Similar results are obtained from hindcasts of total U.S. hurricane activity for 2004.
Abstract
Advances in hurricane climate science allow forecasts of seasonal landfall activity to be made. The authors begin with a review of the forecast methods available in the literature. They then reformulate the methods using a Bayesian probabilistic approach. This allows a direct comparison to be made while focusing on a single hindcast of the 2004 season over Florida. The models, including climatology, are estimated using Gibbs sampling. Diagnostic checks verify convergence and efficient mixing of the samples from each of the models. A below average sea level pressure gradient over the eastern North Atlantic Ocean during May and June in combination with an above average tropospheric-averaged wind index associated, in part, with a strengthening of the Bermuda high pressure during July resulted in an above average probability of at least one Florida hurricane. The relatively high hindcast probabilities for 2004 were in marked contrast to the most recent 50-yr empirical probabilities for Florida, but fell short in anticipating the unprecedented level of activity that ensued. Similar results are obtained from hindcasts of total U.S. hurricane activity for 2004.
Abstract
A flash flood occurred at Milwaukee, Wisconsin on 6 August 1986 as a result of >6 in. (15.2 cm) of rain, much of it falling over a 2-h period. Several possible contributing factors to the excessive rainfall are addressed, as well as a brief overview of the radar imagery and the local National Weather Service (NWS) forecasts issued during the event.
Conventional weather analyses and infrared satellite imagery are used to describe the synoptic-scale weather patterns and cloud features associated with the flash flood. The synoptic patterns are compared with a meteorological composite for heavy rain-producing weather systems associated with relatively warm-topped cloud signatures imbedded in comma-shaped cloud features, as described by Spayd (1982). This composite is referred to as a cyclonic circulation system (CCS). A comparison between the observed synoptic patterns and those predicted by the operational numerical model forecasts is also discussed. A climatological survey is performed to document the frequency of heavy rainfall events associated with weather systems similar to the CCS composite during seven warm seasons.
Results show that the synoptic weather patterns attending the Milwaukee flood were similar in many respects to the CCS composite. While the numerical models were deficient in accurately predicting rainfall amounts, they were more than adequate in forecasting some of the features of the CCS composite. The climatology shows that weather systems resembling the composite appear infrequently on a given day during the warm season. However, rainfall in excess of 5 in. (12.7 cm) occurred in a preferred location of nearly 60% of the cases in which these systems were identified.
This article lends support to the value of pattern recognition from satellite imagery, conventional weather analysis, and forecast model output to alert forecasters to the potential for heavy rainfall.
Abstract
A flash flood occurred at Milwaukee, Wisconsin on 6 August 1986 as a result of >6 in. (15.2 cm) of rain, much of it falling over a 2-h period. Several possible contributing factors to the excessive rainfall are addressed, as well as a brief overview of the radar imagery and the local National Weather Service (NWS) forecasts issued during the event.
Conventional weather analyses and infrared satellite imagery are used to describe the synoptic-scale weather patterns and cloud features associated with the flash flood. The synoptic patterns are compared with a meteorological composite for heavy rain-producing weather systems associated with relatively warm-topped cloud signatures imbedded in comma-shaped cloud features, as described by Spayd (1982). This composite is referred to as a cyclonic circulation system (CCS). A comparison between the observed synoptic patterns and those predicted by the operational numerical model forecasts is also discussed. A climatological survey is performed to document the frequency of heavy rainfall events associated with weather systems similar to the CCS composite during seven warm seasons.
Results show that the synoptic weather patterns attending the Milwaukee flood were similar in many respects to the CCS composite. While the numerical models were deficient in accurately predicting rainfall amounts, they were more than adequate in forecasting some of the features of the CCS composite. The climatology shows that weather systems resembling the composite appear infrequently on a given day during the warm season. However, rainfall in excess of 5 in. (12.7 cm) occurred in a preferred location of nearly 60% of the cases in which these systems were identified.
This article lends support to the value of pattern recognition from satellite imagery, conventional weather analysis, and forecast model output to alert forecasters to the potential for heavy rainfall.
Abstract
The return-flow of low-level air from the Gulf of Mexico over the southeast United States during the cool season is studied using numerical models. The key models are a newly developed airmass transformation (AMT) model and a one-dimensional planetary boundary layer (PBL) model. Both are employed to examine the thermodynamic structure over and to the north of the Gulf. Model errors for predicting minimum, maximum, and dewpoint temperatures at the surface during both offshore and onshore phases of the return-flow cycle are analyzed. PBL model forecasts indicate soil moisture values obtained from the Eta Model improve accuracy. It is shown that forecasts of maximum temperature for coastal locations are sensitive to the soil moisture used in the PBL model. The AMT model performs well in determining boundary layer parameters since it includes horizontal advective processes. The AMT model is also able to predict the regional differences caused by different surface forcing while passing over land or sea. Results lead to a strategy for making predictions during cool-season return-flow events over and around the Gulf of Mexico.
Abstract
The return-flow of low-level air from the Gulf of Mexico over the southeast United States during the cool season is studied using numerical models. The key models are a newly developed airmass transformation (AMT) model and a one-dimensional planetary boundary layer (PBL) model. Both are employed to examine the thermodynamic structure over and to the north of the Gulf. Model errors for predicting minimum, maximum, and dewpoint temperatures at the surface during both offshore and onshore phases of the return-flow cycle are analyzed. PBL model forecasts indicate soil moisture values obtained from the Eta Model improve accuracy. It is shown that forecasts of maximum temperature for coastal locations are sensitive to the soil moisture used in the PBL model. The AMT model performs well in determining boundary layer parameters since it includes horizontal advective processes. The AMT model is also able to predict the regional differences caused by different surface forcing while passing over land or sea. Results lead to a strategy for making predictions during cool-season return-flow events over and around the Gulf of Mexico.
Abstract
The accuracy of track forecasts for tropical cyclones (TCs) is well studied, but less attention has been paid to the representation of track-forecast uncertainty. Here, Bayesian updating is employed on the radius of the 70% probability circle using 72-h operational forecasts with comparisons made to the classical approach based on the empirical cumulative density (ECD). Despite an intuitive and efficient way of treating track errors, the ECD approach is statistically less informative than Bayesian updating. Built on a solid statistical foundation, Bayesian updating is shown to be a useful technique that can serve as a substitute for the classical approach in representing operational TC track-forecast uncertainty.
Abstract
The accuracy of track forecasts for tropical cyclones (TCs) is well studied, but less attention has been paid to the representation of track-forecast uncertainty. Here, Bayesian updating is employed on the radius of the 70% probability circle using 72-h operational forecasts with comparisons made to the classical approach based on the empirical cumulative density (ECD). Despite an intuitive and efficient way of treating track errors, the ECD approach is statistically less informative than Bayesian updating. Built on a solid statistical foundation, Bayesian updating is shown to be a useful technique that can serve as a substitute for the classical approach in representing operational TC track-forecast uncertainty.
