Prediction

Plot description
Most recent tropical cyclone forecasts from each of the forecasting centers
LEGEND
UNIVERSITY
PRIVATE ENTITY
GOVERNMENT AGENCY
CURRENT SEASON
AVERAGE PREDICTION
FORECAST FILTERS
 
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NS
 
MH
 
ACE
Hurricanes Predictions
FORECAST FILTERS
 
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NS
 
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ACE
Hurricanes Predictions
 
 
14
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0
 
 
28
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7
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210
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High activity
Normal activity
Low activity
High activity
Normal activity
Low activity
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Normal activity
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Normal activity
Low activity
No seasonal forecast available yet.
 
No seasonal forecast available yet.
 
No seasonal forecast available yet.
 
No seasonal forecast available yet.
 
FORECAST EXPLANATION
 
 

NewForecasters

FORECASTERS
AccuWeather has a global long-range forecast team that studies current and projected tele-connections and climate relationships and creates a set of analog years that closely match observed and climate model output weather trends. Forecast ideas are submitted by each team member and a consensus is then reached.
Coastal Carolina University (CCU) has issued North Atlantic Ocean Basin seasonal Tropical Cyclone and Hurricane outlooks since 2013. The model system predicts family genesis numbers along with the probability of hurricane land-falls on U.S. coastlines. The forecasts utilize historical data to develop statistical and empirical blended mathematical models, which have demonstrated significant levels of skill in the prediction of hurricane seasons back to 1950. An interactively coupled model suite also forecasts the track, intensity and coastal surge, flood and inundation of any incoming hurricane.
Colorado State University (CSU) has issued Atlantic basin seasonal hurricane forecasts since 1984, primarily using statistical techniques. These forecasts utilize historical data to develop statistical models which show significant levels of skill in the prediction of past hurricane seasons. Their models also utilize currently-available dynamical model forecasts for large-scale atmosphere/ocean conditions as additional predictions into CSU's seasonal hurricane outlooks.
The Cuban Institute of Meteorology (InsMet) developed its first statistical seasonal forecast model in 1995, and the issuance of these forecasts began in 1996. This methodology is based on the solution of a linear regression equation set, and the evaluation of a discriminant function, according to climatological and analogical criteria. This scheme considers ENSO as the fundamental modulator of TC activity in the North Atlantic and also considers the atmospheric circulation during March and April in the North Atlantic and the SSTs in the tropical Atlantic and the Caribbean Sea.
MDA Weather Services (EarthSat Weather) has been issuing North Atlantic tropical seasonal forecasts to its energy and agriculture customers since the early 2000s. The forecast system we leverage is a combination of inputs from physical/dynamical weather and climate models as well as inputs from statistical pattern recognition/matching algorithms. The forecast system is dynamically tuned to capture signals around the world that have proven to show skill as leading indicators to tropical activity (number of events and magnitude of events). These signals originate from time lagged indicators of observed weather as well as indicators from the expected patterns sourced from the physical/dynamical models. The statistical algorithms then weight these various inputs based on historical performance over a variety of categories to ultimately produce the blended output that is then used for the annual Official MDA North Atlantic Tropical Seasonal Forecast.
The interdisciplinary group of North Carolina State University (NCSU) Department of Statistics and Department of Marine, Earth and Atmospheric Sciences has been releasing annual hurricane prediction forecasts since 2005. The seasonal outlook is made by using a Poisson regression model, along with variable selection techniques. The model is trained and validated using historical data. The main predictors in the model include January-February measures of sea surface temperature in the Atlantic and forecasted ENSO index measures.
The forecast from the North American Multi-Model Ensemble (NMME) is based on a multiple regression relationship between the observed Atlantic hurricane season activity and two predicted circulation variables. The two predictors used in this model are the mean forecasted August to October vertical wind shear over the main development region and the preseason observed North Atlantic sea surface temperature.
The National Oceanic and Atmospheric Administration (NOAA) has been issuing Atlantic hurricane season outlooks since August 1998. These outlooks are based on predictions of the main climate factors known to influence seasonal Atlantic hurricane activity. Prediction tools involve statistical regression and climate-based analogues, dynamical model predictions from the NOAA Climate Forecast System (CFS) and the NOAA Geophysical Fluid Dynamics Lab (GFDL) models FLOR-FA and HI-FLOR, and statistical/ dynamical hybrid forecast tools which are also based on the CFS.
Penn State University's (PSU) Earth System Science Center has been issuing annual seasonal tropical cyclone forecasts for the North Atlantic basin since 2007. Seasonal forecasts are made by using a multivariate Poisson regression model, trained on corrected historical tropical cyclone counts and climate predictors, and published in the Journal of Geophysical Research in 2012. The primary variables considered by the statistical model in these forecasts include sea surface temperatures in the main development region and anticipated El Nino conditions.
Mexican National Meteorological Service (description not available).
StormGeo has been issuing hurricane seasonal outlooks starting in 2007 after devastating 2005 Atlantic tropical cyclone season. We use a blend of both statistical and dynamical models that incorporate current and projected trends of surface and upper air pressure anomalies associated with forecast water temperature anomalies over the Pacific and Atlantic Oceans. A key part of this process is to identify analog seasons that best represent current and projected atmospheric pressure trends over the Atlantic Basin and apply a weighting factor for each analog in order to derive the seasonal forecast.
Tropical Storm Risk (TSR), based at University College London in the UK, has issued seasonal outlooks for North Atlantic hurricane activity since December 1998. TSR predicts basin-wide tropical cyclone (TC) activity (numbers of storms of different strengths and the ACE index), U.S. landfalling TC activity, and Caribbean Lesser Antilles landfalling TC activity. The TSR prediction models are statistical in nature and are underpinned by atmospheric and oceanic predictors that have sound physical links to contemporaneous TC activity.
The University of Arizona (UA) forecasts the mean number and a range of hurricanes based on a statistical method relying on observational measurements from March to May over the Atlantic and Pacific. The predictors considered include the April–May multivariate ENSO index (MEI) conditioned upon the Atlantic multidecadal oscillation (AMO) index, the average zonal pseudo–wind stress across the North Atlantic in May and the average March–May tropical Atlantic sea surface temperature.
The UK Met Office (UKMO) has been issuing seasonal forecasts of Atlantic tropical storm activity since 2007. Forecasts are created using the Met Office global seasonal forecasting system, GloSea. This system uses current observations of the ocean, land and atmosphere and simulates these over the next 7 months to provide a forecast of tropical storm activity. Multiple forecasts are created and combined to produce a best-estimate forecast as well as a forecast range.
Weatherbell issues three seasonal forecasts (March, May, August) by combining analog years with model output. Beside the standard metrics of hurricane activity, we also quantify the seasonal forecasts using a power and impact scale, which we feel is more representative than the usual Saffir-Simpson scale. Seasonal forecasts are made available to a general audience, but customers of Weatherbell are given exclusive access one week prior to the public release.
The Weather Company (WSI-TWC) has been issuing seasonal tropical forecasts for the North Atlantic basin since 2006, using an optimized blend of dynamical models and proprietary statistical models. The statistical models have been built using historical values of various relevant earth-atmosphere indices along with observed historical activity levels in the tropical Atlantic. The skill of both the dynamical and statistical models has been evaluated and weights are assigned to each model in producing the final blended forecast.
WeatherTiger's seasonal hurricane forecasts are created using a proprietary statistical modeling engine, TigerTracks. This ensemble scheme is trained using a temporally and spatially expansive set of tropical cyclone, atmospheric, and oceanic historical observations, yielding probabilistic guidance for both measures of overall activity and seasonal landfall risk. TigerTracks employs rigorous checks against overfitting, with uncertainty ranges for all predictands derived from the out-of-sample statistical ensemble.
Wilkens Weather Technologies (WWT) Atlantic hurricane season forecast incorporates at a minimum factors such as the El Niño Southern Oscillation in the equatorial Pacific Ocean, sea surface temperature anomaly patterns in the months leading up to hurricane season, the Arctic Oscillation, and the Atlantic Multi-decadal Oscillation. Previous seasonal records with similar patterns in place are used as a proxy to determine whether a below-, near-, or above-normal season is expected. Finally, the individual contributions of the latest climate signals lead to the determination of the expected frequency of systems and intensity spectrum.
The Oceans and Climate Lab at Colorado University (CU) Boulder issues predictions of named storms based on the spatial pattern and intensity of outgoing longwave radiation (OLR) over Africa. The scientific basis and prediction methods used are published in the peer-reviewed journal Geophysical Research Letters (Karnauskas 2006; Karnauskas and Li 2016).