scholarly journals A Consensus Model for Seasonal Hurricane Prediction

2010 ◽  
Vol 23 (22) ◽  
pp. 6090-6099 ◽  
Author(s):  
Thomas H. Jagger ◽  
James B. Elsner
Keyword(s):  
North Atlantic ◽  
Bayesian Model Averaging ◽  
Southern Oscillation ◽  
Model Averaging ◽  
Seasonal Prediction ◽  
Consensus Model ◽  
Probability Models ◽  
Nao Index ◽  
Hurricane Activity ◽  
Hurricane Prediction

Abstract The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.

Dynamics of the ENSO Teleconnection and NAO Variability in the North Atlantic–European Late Winter

2020 ◽  
Vol 33 (3) ◽  
pp. 907-923 ◽  
Author(s):  
Bianca Mezzina ◽  
Javier García-Serrano ◽  
Ileana Bladé ◽  
Fred Kucharski
Keyword(s):  
North Atlantic ◽  
Southern Oscillation ◽  
Reanalysis Data ◽  
Late Winter ◽  
Sea Level Pressure ◽  
The North ◽  
Nao Index ◽  
Eddy Heat Flux ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractThe winter extratropical teleconnection of El Niño–Southern Oscillation (ENSO) in the North Atlantic–European (NAE) sector remains controversial, concerning both the amplitude of its impacts and the underlying dynamics. However, a well-established response is a late-winter (January–March) signal in sea level pressure (SLP) consisting of a dipolar pattern that resembles the North Atlantic Oscillation (NAO). Clarifying the relationship between this “NAO-like” ENSO signal and the actual NAO is the focus of this study. The ENSO–NAE teleconnection and NAO signature are diagnosed by means of linear regression onto the sea surface temperature (SST) Niño-3.4 index and an EOF-based NAO index, respectively, using long-term reanalysis data (NOAA-20CR, ERA-20CR). While the similarity in SLP is evident, the analysis of anomalous upper-tropospheric geopotential height, zonal wind, and transient-eddy momentum flux, as well as precipitation and meridional eddy heat flux, suggests that there is no dynamical link between the phenomena. The observational results are further confirmed by analyzing two 10-member ensembles of atmosphere-only simulations (using an intermediate-complexity and a state-of-the-art model) with prescribed SSTs over the twentieth century. The SST-forced variability in the Northern Hemisphere is dominated by the extratropical ENSO teleconnection, which provides modest but significant SLP skill in the NAE midlatitudes. The regional internally generated variability, estimated from residuals around the ensemble mean, corresponds to the NAO pattern. It is concluded that distinct dynamics are at play in the ENSO–NAE teleconnection and NAO variability, and caution is advised when interpreting the former in terms of the latter.

scholarly journals ENSO’s Impact on Regional U.S. Hurricane Activity

2007 ◽  
Vol 20 (7) ◽  
pp. 1404-1414 ◽  
Author(s):  
Shawn R. Smith ◽  
Justin Brolley ◽  
James J. O’Brien ◽  
Carissa A. Tartaglione
Keyword(s):  
North Atlantic ◽  
East Coast ◽  
Southern Oscillation ◽  
Regional Analysis ◽  
The North ◽  
Hurricane Season ◽  
Hurricane Activity ◽  
National Hurricane Center ◽  
Observational Record ◽  
The U.S

Abstract Regional variations in North Atlantic hurricane landfall frequency along the U.S. coastline are examined in relation to the phase of El Niño–Southern Oscillation (ENSO). ENSO warm (cold) phases are known to reduce (increase) hurricane activity in the North Atlantic basin as a whole. Using best-track data from the U.S. National Hurricane Center, regional analysis reveals that ENSO cold-phase landfall frequencies are only slightly larger than neutral-phase landfall frequencies along the Florida and Gulf coasts. However, for the East Coast, from Georgia to Maine, a significant decrease in landfall frequency occurs during the neutral ENSO phase as compared to the cold phase. Along the East Coast, two or more major (category 3 or above) hurricanes never made landfall in the observational record (1900–2004) during a single hurricane season classified as an ENSO neutral or warm phase.

Incorporating stakeholders' knowledge to stock assessment: Central Baltic herring

2013 ◽  
Vol 70 (4) ◽  
pp. 591-599 ◽  
Author(s):  
Samu Mäntyniemi ◽  
Päivi Haapasaari ◽  
Sakari Kuikka ◽  
Raimo Parmanne ◽  
Maiju Lehtiniemi ◽  
...  
Keyword(s):  
Bayesian Model Averaging ◽  
Stock Assessment ◽  
Model Averaging ◽  
Structured Interview ◽  
Clupea Harengus ◽  
Quantitative Modelling ◽  
Assessment Data ◽  
Probability Models ◽  
The Baltic Sea ◽  
The Baltic

We present a method by which the knowledge of stakeholders can be taken into account in stock assessment. The approach consists of a structured interview process followed by quantitative modelling of the answers. The outcome is a set of probability models, each describing the views of different stakeholders. Individual models are then merged to a large model by applying the techniques of Bayesian model averaging, and this model is conditioned on stock assessment data. As a result, the views of interviewed stakeholders have been taken into account and weighed based on how well their views are supported by the observed data. We applied this method to the Baltic Sea herring (Clupea harengus) stock assessment by interviewing six stakeholders and conditioning the resulting models on stock assessment data provided by the International Council for the Exploration of the Sea.

Hurricane Clusters in the Vicinity of Florida

2012 ◽  
Vol 51 (5) ◽  
pp. 869-877 ◽  
Author(s):  
Thomas H. Jagger ◽  
James B. Elsner
Keyword(s):  
Poisson Distribution ◽  
North Atlantic ◽  
Cluster Model ◽  
Negative Binomial ◽  
Southern Oscillation ◽  
Inverse Gaussian ◽  
Gaussian Models ◽  
The North ◽  
Hurricane Activity ◽  
The North Atlantic Oscillation

AbstractModels that predict annual U.S. hurricane activity assume a Poisson distribution for the counts. Here the authors show that this assumption applied to Florida hurricanes leads to a forecast that underpredicts both the number of years without hurricanes and the number of years with three or more hurricanes. The underdispersion in forecast counts arises from a tendency for hurricanes to arrive in groups along this part of the coastline. The authors then develop an extension to their earlier statistical model that assumes that the rate of hurricane clusters follows a Poisson distribution with cluster size capped at two hurricanes. Hindcasts from the cluster model better fit the distribution of Florida hurricanes conditional on the climate covariates including the North Atlantic Oscillation and Southern Oscillation index. Results are similar to models that parameterize the extra-Poisson variation in the observed counts, including the negative binomial and the Poisson inverse Gaussian models. The authors argue, however, that the cluster model is physically consistent with the way Florida hurricanes tend to arrive in groups.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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