Predicting spring tornado activity in the Central Great Plains by March 1st

Here we illustrate a statistical model for predicting tornado activity in the central Plains by March 1st.  The model predicts the number of tornado reports during April--June using February sea-surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean region (WCA).  The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the non-stationarity in tornado reporting is included as a trend term plus first-order autocorrelation.  Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA).  The model yields a 51% increase in the number of tornado reports per degree C increase in SST over the WCA and a 15% decrease in the number of reports per degree C increase in SST over the GAK.  These significant relationships are broadly consistent with a physical understanding of large scale atmospheric patterns conducive to severe convective storms across the Great Plains.  The SST covariates explain 11% of the out-of-sample variability in observed F1--F5 tornado reports.  The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data.  The research was conducted in the Department of Geography at Florida State University in collaboration with Holly Widen.  It will be published later this year in the American Meteorological Society's Monthly Weather Review.  The code is available from http://rpubs.com/jelsner/4745.

Predicting the distribution of violent tornadoes

Here we illustrate a statistical point process model that uses the spatial occurrence of non-violent tornadoes to predict the distribution of the rare, violent tornadoes during springtime across the U.S. central Great Plains. The average rate of non-violent tornadoes is 55 per 10000 square km per 62 years which compares with an average rate of only 1.5 violent tornadoes per 10000 square km over the same period (less than 3%). Violent tornado report density peaks at 2.6 per 10000 square km (62 yr) in the city to 0.7 per 10000 km in the countryside. 

The risk of a violent tornado is higher by a factor of 1.5, on average, in the vicinity of less violent tornadoes after accounting for the population bias. The model for the occurrence rate of violent tornadoes indicates that rates are lower by 10.3 (3.6, 16.5)% (95% CI) for every 1 km increase in distance from nearest non-violent tornado controlling for distance from nearest city. Model significance and distance-from-nearest non-violent tornado parameter are not sensitive to population threshold or definition of violent tornado. We show that the model is useful for generating a catalogue of touchdown points that can be used as a component to a tornado catastrophe model.

The research was done in collaboration with Richard Murnane, Thomas Jagger, and Holly Widen at Florida State University and will be published later this year in the journal Mathematical Geosciences.

Hurricane Climatology: A Modern Statistical Guide Using R

Our newest book is now available.  Our website provides the code used to produce all the figures.  Learn to code.  Enjoy!

Decreasing Population Bias in Tornado Reports

Tornado-hazard assessment is hampered by a population bias in the available data.  We demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from nearest city center. On average since 1950 reports near cities with populations of at least 1000 in a 5.5 deg latitude by 5.5 deg longitude region centered on Russell, KS exceed those in the country by 70% (54%, 84%) [95% CI].

The model is applied to 10-year moving windows to show that the percentage is decreasing with time (see Figure).  Over the most recent period (2002-2011) the tornado report density in the city is slightly less than 3 reports per 100 square km per 100 years and this value is statistically indistinguishable from the report density in the country. On average the population bias is less pronounced for F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes.  We show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.

The research was done in collaboration with Laura E. Michaels, Kelsey N. Scheitlin, and Ian J. Elsner.  It will be published in the American Meteorological Society's Weather, Climate and Society journal later this year.

Consensus on Climate Trends in Western North Pacific Tropical Cyclones

Research on trends in western North Pacific tropical cyclone (TC) activity is limited by problems associated with different wind speed conversions used by the various meteorological agencies.  Here we use a quantile method to effectively overcome this conversion problem. Following the assumption that the intensity ranks of TCs are the same among agencies, quantiles at the same probability level in different data sources are regarded as having the same wind speed level. Tropical cyclone data from the Joint Typhoon Warning Center (JTWC) and Japan Meteorological Agency (JMA) are chosen for research and comparison. Trends are diagnosed for the upper 45% of the strongest TCs annually. The 27-yr period beginning with 1984, when the JMA began using the Dvorak (1982) technique, is determined to be the most reliable for achieving consensus among the two agencies regarding these trends. The start year is a compromise between including as many years in the data as possible, but not so many that the period includes observations that result in inconsistent trend estimates. The consensus of TC trends between the two agencies over the period is interpreted as fewer but stronger events since 1984, even with the lower power dissipation index (PDI) in the western North Pacific in recent years.  Read more.

The Spatial Pattern of the Sun-Hurricane Connection

We define the spatial response of hurricanes to extremes in the solar cycle. Using an equal-area hexagon tessellation, regional hurricane counts are examined during the period 1851–2010. The response features fewer hurricanes across the Caribbean, Gulf of Mexico, and along the eastern seaboard of the United States when sunspots are numerous. In contrast fewer hurricanes are observed in the central North Atlantic when sunspots are few. The sun-hurricane connection is as important as the El Niño Southern Oscillation in statistically explaining regional hurricane occurrences.  Read more.

Hurricane Sandy and climate change

While the SSTs did not cause Sandy to curve into New Jersey, they quite likely caused Sandy to be stronger. Our new research shows that the limiting intensity of hurricanes (how strong hurricanes can get as a statistical limit) relates to SST at about 8 m/s/C.  With SSTs in the path of Sandy that were 2-3 C warmer than is typical, we would predict a strong hurricane to be twice as strong on average.  Read more.

Maximum wind speeds and U.S. hurricane losses

There is academic, commercial, and public interest in estimating loss from hurricanes striking land and understanding how loss might change as a result of future variations in climate.  We show in a paper in to be published in Geophysical Research Letters that the relationship between wind speed and loss is exponential and that loss increases with wind speed at a rate of 5% per m/s. The relationship is derived using quantile regression and a data set comprising wind speeds of hurricanes hitting the United States and normalized economic losses. We suggest that the “centercepts” for the different quantiles account for exposure-related factors such as population density, precipitation, and surface roughness, and that once these effects are accounted for, the increase in loss with wind speed is consistent across quantiles. An out-of-sample test of this relationship correctly predicts economic losses from Hurricane Irene in 2011. The exponential relationship suggests that increased wind speeds will produce significantly higher losses; however, increases in exposed property and population are expected to be a more important factor for near future losses.   The research was directed by Richard Murnane of the Risk Prediction Initiative.  The code used to obtain the results is available at http://rpubs.com/jelsner/816.

Chasing storms

As part of the undergraduate course, "Chasing Storms," I took four volunteer students on an actual chase. We  embarked on a week-long storm chasing expedition in the Great Plains.

Harvesting video

It's difficult to get a precise measurement of tornado wind speed without actually going inside one. Armed with Blender's new motion tacking features, Ian Elsner tries to fix that.

Sensitivity of hurricane intensity to ocean warmth

The strongest hurricanes are getting stronger as the oceans heat up especially over the North Atlantic. Sensitivity of hurricane intensity to ocean heating is an important variable for understanding what hurricanes might be like in the future, but reliable estimates are not possible with short time-series records. Studies using paired values of intensity and sea-surface temperature (SST) are also limited because most pairs represent hurricanes in an environment that is less than thermodynamically optimal. Here we overcome these limitations using spatial grids and estimate the sensitivity to be 8.2 +/- 1.19 m/s/C (s.e.) for hurricanes over seas hotter than 25C across the North Atlantic. We find that the sensitivity is significantly lower in a high-resolution general circulation model. Results indicate a greater likelihood of more powerful hurricanes during the 21st century as oceans continue to warm over this part of the world, but call into question the usefulness of current GCMs for helping us understand what might happen in the future.

What is Hurricane Climatology?

Hurricane Climatology on Prezi

3rd International Summit Talks

Talks given at the 3rd International Summit on Hurricanes and Climate Change are now available at http://ciquestudios.com/hurricaneclimate/. Enjoy.

Summit summary

Statistical models of regional and clustered tropical cyclone (TC) activity are being developed and tested. High resolution and detailed microphysical models are capable of physically realistic models of TC behavior. General circulation models are still poor at resolving the frequency and intensity of TCs on the inter-annual time scale. There seems to be little, if any, correlation between the skill at forecasting TC frequency and model resolution. Maximum potential intensity (MPI) theory is improved by considering slantwise convection. The MPI theory continues to be important in explaining TC activity, especially in the North Atlantic. Paleotempestology is maturing as a discipline and important new results about basin-wide and global TC activity will likely ensue. New insights about historical hurricanes are possible using synoptic analysis on reanalysis data and weather prediction models. We will convene again in 2013.

3rd International Summit on Hurricanes & Climate Change

June 27-July 2, 2011, Rhodes, GREECE. It promises to be a good one. Check out the program. Props go to Ian Elsner for designing and editing the video.

Crazy guys yelling at a tornado

While hot air swirls on whether climate change is affecting tornadoes, I take a break on May 24, 2011 to chase down a few storms in Oklahoma with my son Ian and his friend Nic Parsons.

Hurricane clusters in the vicinity of Florida

Models that predict annual U.S. hurricane activity assume a Poisson distribution for the counts. This assumption leads to a forecast that under predicts both the number of years without hurricanes and the number of years with three or more Florida hurricanes. The under dispersion in forecast counts arises from a tendency for hurricanes to arrive in groups along this part of the U.S. coastline. We recently developed an extension to our earlier Poisson model that assumes 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 NAO and SOI. Results are similar to models that parameterize the extra-Poisson variation in the observed counts including the negative binomial and the Poisson inverse Gaussian, but we argue that the cluster model is physically consistent with the way Florida hurricanes tend to arrive in groups.

This research is done in collaboration with Thomas H. Jagger (Climatek) and is supported by the Risk Prediction Initiative. It is currently in review with the AMS Journal of Applied Meteorology and Climatology.

Statistical models for tropical cyclone activity

In collaboration with Gabriele Villarini and as part of the U.S. CLIVAR working group on hurricanes, I've written a short summary paper on statistical models for tropical cyclone activity. The paper is available here. The data for the R code are available here. The document was created using Sweave, LaTeX and R. The work is part of a larger project to publish a book on this topic with Oxford University Press. Comments and suggestions are certainly welcome.

3rd International Summit on Hurricanes and Climate Change

Over the past several years the topic of hurricanes and climate change has received considerable attention by scientists, the insurance industry, and the media. Building on the successful 1st and 2nd Summits, I am organizing the 3rd Summit to be held June 27-July 2, 2011 in Rhodes, Greece. The purpose is to bring together leading academics and researchers on various sides of the debate and from all around the world to discuss new research and express opinions about what is happening and what might happen in the future with regard to regional and global hurricane (tropical cyclone) activity. The goals are to address what research is needed to advance the science of hurricane climate and to provide a venue for encouraging a lively, spirited, and sustained exchange of ideas. Please consider joining us.

Spatial grids for hurricane climate research

We demonstrate a new framework for studying hurricane climatology. The framework consists of a spatial tessellation of the hurricane basin using equal-area hexagons. The hexagons are efficient in covering hurricane tracks and provide a scaffolding to combine attribute data from tropical cyclones with spatial climate data. The framework's utility is demonstrated using examples from recent hurricane seasons. Seasons that have similar tracks are quantitatively assessed and grouped. Regional cyclone frequency and intensity variations are mapped. A geographically-weighted regression of cyclone intensity on frequency and SST (results shown here) emphasizes the importance of a warm ocean in the intensification of cyclones over regions where the heat content is greatest. The largest differences between model predictions and observations occur near the coast. The framework would be ideally suited for comparing tropical cyclones generated from different numerical simulations (see U.S. CLIVAR hurricane working group). The hexagons have equal area and are plotted on a map using the Lambert conformal conic projection with standard parallels of 23 and 38 degrees.