Statistical Modelling of the Relationship Between Main Development Region Sea Surface Temperature and \emph{Landfalling} Atlantic Basin Hurricane Numbers
We are building a hurricane number prediction scheme that relies, in part, on statistical modelling of the empirical relationship between Atlantic sea surface temperatures and landfalling hurricane numbers. We test out a number of simple statistical models for that relationship, using data from 1900 to 2005 and data from 1950 to 2005, and for both all hurricane numbers and intense hurricane numbers. The results are very different from the corresponding analysis for basin hurricane numbers.
One possible method for predicting landfalling hurricane numbers is to first predict the number of hurricanes in the basin and then convert that prediction to a prediction of landfalling hurricane numbers using an estimated proportion. Should this work better than just predicting landfalling hurricane numbers directly? We perform a basic statistical analysis of this question in the context of a simple abstract model, and convert some previous predictions of basin numbers into landfalling numbers.
We continue with our program to derive simple practical methods that can be used to predict the number of US landfalling hurricanes a year in advance. We repeat an earlier study, but for a slightly different definition landfalling hurricanes, and for intense hurricanes only. We find that the averaging lengths needed for optimal predictions of numbers of intense hurricanes are longer than those needed for optimal predictions of numbers of hurricanes of all strengths.
In previous work we have analysed the Atlantic basin hurricane number time-series to identify decadal time-scale change points. We now repeat the analysis but for US landfalling hurricanes. The results are very different.
In previous work, we have shown how to combine long and short historical baselines to make predictions of future hurricane numbers. We now ask: how should such combinations change if we are interested in predicting the future number of intense hurricanes?
We consider two ways that one might convert a prediction of sea surface temperature (SST) into a prediction of landfalling hurricane numbers. First, one might regress historical numbers of landfalling hurricanes onto historical SSTs, and use the fitted regression relation to predict future landfalling hurricane numbers given predicted SSTs. We call this the direct approach. Second, one might regress \emph{basin} hurricane numbers onto historical SSTs, estimate the proportion of basin hurricanes that make landfall, and use the fitted regression relation and estimated proportion to predict futur…
We present a simple method for the year-ahead prediction of the number of hurricanes making landfall in the US. The method is based on averages of historical annual hurricane numbers, and we perform a backtesting study to find the length of averaging window that would have given the best predictions in the past.
Annual levels of US landfalling hurricane activity averaged over the last 11 years (1995-2005) are higher than those averaged over the previous 95 years (1900-1994). How, then, should we best predict hurricane activity rates for next year? Based on the assumption that the higher rates will continue we use an optimal combination of averages over the long and short time-periods to produce a prediction that minimises MSE.
We are building a hurricane number prediction scheme based on first predicting main development region sea surface temperature (SST), then predicting the number of hurricanes in the Atlantic basin given the SST prediction, and finally predicting the number of US landfalling hurricanes based on the prediction of the number of basin hurricanes. We have described a number of SST prediction methods in previous work. We now investigate the empirical relationship between SST and basin hurricane numbers, and put this together with the SST predictions to make predictions of both basin and landfalling …
In earlier work we considered methods for predicting future levels of hurricane activity based on the assumption that historical mean activity was at one constant level from 1900 to 1994, and has been at another constant level since then. We now make this model a little more subtle, and account for the possibility of four different levels of mean hurricane activity since 1900.
We study the specific influence of African dust on each one of the categories of Atlantic hurricanes. By applying wavelet analysis, we find a strong decadal modulation of African dust on Category 5 hurricanes and an annual modulation on all other categories of hurricanes. We identify the formation of Category 5 hurricanes occurring mainly around the decadal minimum variation of African dust and in deep water areas of the Atlantic Ocean, where hurricane eyes have the lowest pressure. According to our results, future tropical cyclones will not evolve to Category 5 until the next decadal minimum …
There is significant correlation between main development region sea surface temperature and the number of hurricanes that form in the Atlantic basin. The correlation between the same sea surface temperatures and the number of \emph{landfalling} hurricanes is much lower, however. Why is this? Do we need to consider complex physical hypotheses, or is there a simple statistical explanation?
One possible method for the year-ahead prediction of hurricane numbers would be to make a year-ahead prediction of sea surface temperature (SST), and then to apply relationships that link SST to hurricane numbers. As a first step towards setting up such a system this article compares three simple statistical methods for the year-ahead prediction of the relevant SSTs.
We perform an objective change-point analysis on 106 years of historical hurricane number data. The algorithm we use looks at all possible combinations of change-points and compares them in terms of the variances of the differences between real and modelled numbers. Overfitting is avoided by using cross-validation. We identify four change-points, and show that the presence of temporal structure in the hurricane number time series is highly statistically significant.
The time series of the number of hurricanes per year in the Atlantic basin shows a clear change of level between 1994 and 1995. The time series of the number of hurricanes that make landfall in the US, however, does not show the same obvious change of level. Prima-facie this seems rather surprising, given that the landfalling hurricanes are a subset of the basin hurricanes. We investigate whether it really should be considered surprising or whether there is a simple statistical explanation for the disappearance of this change-point at landfall.
Recent US major landfalling hurricanes Katrina and Rita and last year's four U.S. landfalling major hurricanes have spawned an abundance of questions concerning the role that global warming might be playing in these events. This idea has been given added credence by the September 2005 Science paper of Webster, Holland, Curry and Chang (Vol. 304, pp. 1844-1846) showing that the global number of Category 4-5 hurricanes have increased in the last 15 years (1990-2004) in comparison with the prior 15-year period of 1975-1989. They report 171 Category 4-5 hurricanes in the earlier 15-year period vs.…
There is a clear positive correlation between boreal summer tropical Atlantic sea-surface temperature and annual hurricane numbers. This motivates the idea of trying to predict the sea-surface temperature in order to be able to predict future hurricane activity. In previous work we have used simple statistical methods to make 5 year predictions of tropical Atlantic sea surface temperatures for this purpose. We now compare these statistical SST predictions with SST predictions made by an ensemble mean of IPCC climate models.
Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles
Research Applications Laboratory（研究应用实验室）；NSF National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）；Department of Mathematics, Emory University（埃默里大学数学系）
We are developing schemes that predict future hurricane numbers by first predicting future sea surface temperatures (SSTs), and then apply the observed statistical relationship between SST and hurricane numbers. As part of this overall goal, in this study we compare the historical performance of three simple statistical methods for making five-year SST forecasts. We also present SST forecasts for 2006-2010 using these methods and compare them to forecasts made from two structural time series models.
Tropical cyclones that evolve from a non-tropical origin may pose a special challenge for predictions, as they often emerge at the end of a multi-scale cascade of atmospheric processes. Climatological studies have shown that the 'tropical transition' (TT) pathway plays a prominent role in cyclogenesis, in particular over the North Atlantic Ocean. Here we use operational European Centre for Medium-Range Weather Forecasts ensemble predictions to investigate the TT of North Atlantic Hurricane Chris (2012), whose formation was preceded by the merger of two potential vorticity (PV) maxima, eventual…
A simple study of the relationship between the QBO and the number of hurricanes in the Atlantic, both in the Basin and hitting the U.S. coastline, demonstrates that the QBO is not a particularly useful index to help predict hurricane numbers on five-year time scales. It is shown that there is very little difference between the number of hurricanes following easterly winds in the equatorial stratosphere and the number that follow westerly winds. Given this it is reasonable one would make better predictions just using the mean number of hurricanes in lieu of using the QBO and this is also simply…
Dynamics of East Atlantic seed vortex populations in global km-scale models
University of Leeds（利兹大学）；University of Oxford（牛津大学）；Met Office（英国气象局）；National Centre for Atmospheric Science (NCAS), University of Reading（雷丁大学国家大气科学中心）；Department of Meteorology, University of Reading（雷丁大学气象系）
The proposition that the tropical cyclogenesis increases with the size of the warm pool, the area enclosed by the 26C SST isotherm, is tested by comparing the seasonal variation of the warm pool area with the seasonality of the number of tropical cyclones. A non-linear relationship of high statistical significance is found between the area and the number of cyclones, which may explain both why there is no linear trend in the number of cyclones over time and the recent upturn in the number of Atlantic hurricanes.
National Institute of Standards and Technology（美国国家标准与技术研究院）
The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane properties such as landfall location and wind speed that are consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1-$min$ wind speed at 10 $m$ elevation were created. The trajectory and intensity models…
Bayesian statistical models were developed for the number of tropical cyclones and the rate at which these cyclones became hurricanes in the North Atlantic. We find that, controlling for the cold tongue index and the North Atlantic oscillation index, there is high probability that the number of cyclones has increased in the past thirty years; but the rate at which these storms become hurricanes appears to be constant. We also investigate storm intensity by measuring the distribution of individual storm lifetime in days, storm track length, and Emanuel's power dissiptation index. We find little…
Bayesian statistical models were developed for the number of tropical cyclones and the rate at which these cyclones became hurricanes in the North Atlantic, North and South Indian, and East and West Pacific Oceans. We find that there is small probability that the number of cyclones has increased in the past thirty years. The rate at which these storms become hurricanes appears to be constant. The rate at which hurricanes evolve into category 4 and higher major storms does appear to have increased. We also investigate storm intensity by measuring the distribution of individual storm lifetime in…
Evidence is provided that the global distribution of tropical hurricanes is principally determined by a universal function H of a single variable z that in turn is expressible in terms of the local sea surface temperature and latitude. The data-driven model presented here carries stark implications for the large increased numbers of hurricanes which it predicts for a warmer world. Moreover, the rise in recent decades in the numbers of hurricanes in the Atlantic, but not the Pacific basin, is shown to have a simple explanation in terms of the specific form of H(z), which yields larger percentag…
National Institute of Standards & Technology（美国国家标准与技术研究院）
Fast and accurate prediction of hurricane evolution from genesis onwards is needed to reduce loss of life and enhance community resilience. In this work, a novel model development methodology for predicting storm trajectory is proposed based on two classes of Recurrent Neural Networks (RNNs). The RNN models are trained on input features available in or derived from the HURDAT2 North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models use probabilities of storms passing through any location, computed from historical data. A detailed analysis of model foreca…
This paper demonstrates how principle component analysis can be used to determine the distinct factors that house the terms that explain the variance among the co-dependent variables and how non-response analysis can be applied to model the non-functional relationship that exist in a dynamic system. Moreover, the analysis indicates that there are pumping actions or ebb and flow between the pressure and the water temperature readings near the surface of the water days before a tropical storm forms in the Atlantic Basin and that there is a high correlation between storm conditions and buoy condi…
To enable flexible model coupling in coastal inundation studies, a coupling framework based on ESMF/NUOPC technology under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed. The framework is essentially a software wrapper around atmospheric, wave and storm surge models that enables its components communicate seamlessly, and efficiently run in massively parallel environments. We implemented the coupled application including ADCIRC and unstructured WWAVEWATCHIII caps as well as NUOPC compliant caps to read Hurricane Weather Research and Forecasting Mo…
Tropical cyclones are affected by a large number of climatic factors, which translates into complex patterns of occurrence. The variability of annual metrics of tropical-cyclone activity has been intensively studied, in particular since the sudden activation of the N Atl in the mid 1990's. We provide first a swift overview on previous work by diverse authors about these annual metrics for the NAtl basin, where the natural variability of the phenomenon, the existence of trends, the drawbacks of the records, and the influence of global warming have been the subject of interesting debates. Next, …
Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System…
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error a…
Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict Bayesian intervals in addition to simple point estimates. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating th…
