Hurricane Warning: Consume Rainbow Spaghetti with Caution

Most of the United States is well-aware of the dangers of “drinking the Kool-Aid” when it is time to form an opinion on a particular subject. However, the dangers of “eating the rainbow spaghetti” have not yet permeated the consciousness of the general public when interpreting the forecasts of hurricanes and tropical storms (tropical cyclones, or TCs). The spaghetti plot or spaghetti diagram is a visualization tool that shows the predicted paths (tracks) or wind speeds (intensities) of the numerous different TC models. Each potential TC track and intensity is shaded a different color; hence the appearance that the graphic is filled with rainbow spaghetti.

Examples of spaghetti diagrams for track and intensity from Tropical Storm Arthur 2014. (NCAR)

Examples of spaghetti diagrams for track and intensity from Tropical Storm Arthur 2014. (NCAR)

If used correctly, the spaghetti diagram can be a valuable forecasting tool. Viewing all of the potential tracks and intensities of the most realistic TC models helps scientists to understand how each model’s formulation (parameterizations, data assimilation schemes, etc.) can lead to different predicted outcomes. Additionally, the agreement or lack of agreement (commonly referred to as spread) between the models is often related to the confidence one should place in a particular forecast. If the models’ tracks and intensities are grouped together, it is often an indication that the hurricane’s future is more predictable. As a result, the spaghetti diagram can be used as a supplement to the National Hurricane Center’s (NHC) official track and intensity forecast.

When a tropical depression, tropical storm, or hurricane is present in the Atlantic or Eastern Pacific Ocean, the NHC issues an official intensity and track forecast. The intensity forecast is reported as a predicted wind speed but there are no details regarding the uncertainty in the forecast. Instead, ambitious users could look over the error statistics from past years to provide an expectation for the errors of the current storm. However, historical trends are not always the best guide for the intensity errors in individual storms, and errors often vary significantly depending on the situation. The ability to look at a spaghetti diagram and diagnose the spread of the models’ forecasts is helpful for anticipating the reliability of a particular hurricane’s intensity forecast.

(Top Panel) Spaghetti diagram for Tropical Storm Debby at 0600 UTC (2 am EST) on June 24, 2012.  (Bottom Panel) NHC official forecast track cone for Tropical Storm Debby at the same time as the spaghetti diagram. Figures courtesy of NCAR and NOAA.

(Top Panel) Spaghetti diagram for Tropical Storm Debby at 0600 UTC (2 am EST) on June 24, 2012. (Bottom Panel) NHC official forecast track cone for Tropical Storm Debby at the same time as the spaghetti diagram. Figures courtesy of NCAR and NOAA.

Spaghetti diagrams provide a similar advantage for track forecasts. Unlike intensity forecasts, NHC’s track forecasts provide some basic uncertainty information by surrounding the predicted storm path with a forecast cone. Before each hurricane season begins, the size of the forecast cone for the year is calculated based on the NHC official forecast track errors for all storms over the past five years. The same cone is used for the whole hurricane season, no matter how confident the NHC is (see “Forecast Cone Refresher”). By evaluating the spaghetti diagram alongside the forecast cone, it is possible to foresee the situations where the cone is more reliable than others.

The 2012 track forecasts of Tropical Storm Debby are a perfect example of how useful the spaghetti diagram can be. While the NHC forecast cone was showing a developing tropical storm moving westward off the Louisiana coast, half of the model tracks were directed eastward into the panhandle of Florida. Debby eventually migrated eastward and made landfall as a weak tropical storm north of Tampa Bay, Florida. The spaghetti diagram helped reveal the particular forecast cone was less reliable than normal and that there was a possibility the storm could travel in a completely different direction than the forecast cone.

Still, the spaghetti diagram quickly loses value if evaluated by an uninformed eye. With all the cryptic model abbreviations that accompany the diagram, it is hard for the average person to develop any intuition on what models normally perform better than others. Along with the NHC official forecast (shown as OFCI on the spaghetti diagrams), there are four main types of models that are typically included in spaghetti diagrams: trajectory/statistical, statistical-dynamical, dynamical, and consensus. All of these models arrive at their predictions using different methodologies.  The consensus aids are not independent; they are simply averages of other models.  Some of the models you see on spaghetti plots are outlined in the table below, and a more complete list is available here.

A selection of some of the model guidance routinely available to hurricane forecasters. Highlighted sections include very simple trajectory or statistical models (blue), skillful but still relatively simple statistical-dynamical schemes (green),  dynamical models (red), and averages of certain model combinations (tan).

A selection of some of the model guidance routinely available to hurricane forecasters. Highlighted sections include very simple trajectory or statistical models (blue), skillful but still relatively simple statistical-dynamical schemes (green), dynamical models (red), and averages of certain model combinations (tan).

Most spaghetti diagrams for track forecasts will include the models: “BAMS”, “BAMM”, and “BAMD”. These track models are called trajectory models and are much simpler than full dynamical or statistical-dynamical models. Trajectory models use data from dynamical models to estimate the winds at different layers of the atmosphere that are steering the TC but they do not account for the TC interacting with the surrounding atmosphere. Due to this major simplification, trajectory models should rarely be taken seriously but are included on the plots for reference. Averaged over the past five years, these models have track errors that are almost double the best performing model for a particular forecast time.

Statistical models produce track and intensity forecasts that are based solely on climatology and persistence. In other words, these models create a forecast for a TC using information on how past TCs behaved during similar times of the year at comparable locations and intensities (climatology) while also taking into account the recent movement and intensity change of the TC (persistence). Statistical models do not use any information about the atmospheric environment of the TC. As a result, statistical models are outperformed considerably by dynamical, statistical-dynamical, and consensus forecasts and should only be used as benchmarks of skill against the more complex and accurate models. The main track and intensity statistical models included on spaghetti diagrams are respectively CLP5 and SHF5. An even simpler statistical track “model” that is included on some spaghetti diagrams is XTRP (an extrapolation of the future direction of a hurricane solely based on its motion over the past 12 hours).

Statistical-dynamical models are similar to statistical models except that they also use output from the dynamical models on the environmental conditions surrounding the TC and storm-specific details to predict intensity change. The statistical-dynamical models commonly shown on intensity spaghetti diagrams are SHIP, DSHP, and LGEM. SHIP and DSHP are identical except DSHP accounts for the intensity decay of TCs over land and is therefore more accurate than SHIPS. LGEM is the best performing out of the three models. Both LGEM and DSHP are similar in skill to the dynamical models. These models are not capable of predicting rapid changes in intensity, nor are they meant to forecast intensity of weak disturbances.

Dynamical models make track and intensity forecasts by solving the equations that describe the evolution of the atmosphere. There are two main reasons why different dynamical models produce track and intensity forecasts that always differ even though they share a common goal of reproducing the physical processes of the atmosphere. First, even with the growing network of scientific instruments scattered across the globe and space, models have an imperfect picture of the current conditions in the atmosphere. This uncertainty in the current state of the atmosphere cannot be remedied; we do not have the resources to blanket every piece of the Earth and sky with instruments and measure all the necessary atmospheric parameters simultaneously. Additionally, all instruments have inherent measurement errors. Each model uniquely uses the imperfect and sometimes sparse observations available to arrive at slightly different starting points for their forecast. Secondly, even using the most cutting-edge computer systems in the world, the equations that govern the atmosphere cannot be solved for every inch of the atmosphere; it would take too long. Models have to solve equations on a 3-dimensional grid that spans the surface of the Earth and extends upward around 10 miles. Thus, even the finest resolution operational hurricane models have grid points horizontally separated by nearly 2 miles.

Scientists know that this level of detail is not sufficient; there are important physical processes happening within the grid boxes that affect the TC’s evolution. To prevent the weather that is happening at your friend’s house two miles away from being used to describe the weather at your house, modelers often use different “parameterizations”. This fancy word boils down to a variety of approximations used to extrapolate weather at larger scales (at the grid points) to smaller scales (within the grid points). The different dynamical models use a variety of grid sizes and parameterizations to capture some of TC’s small-scale processes, but these approximations ultimately lead to the models developing the TC in different ways.

The simplest dynamical model shown on spaghetti diagrams is the LBAR model, which is only a track model. Analogously to the trajectory models, the approximations used for LBAR lead to large errors and over the long-term, it is one of the worst performing models. The rest of the dynamical models depicted on spaghetti diagrams perform at a higher  level. Most spaghetti diagrams include the “early models” or “early-version” of these dynamical models because they are available to NHC during the forecast cycle. These track and intensity dynamical models often include the GFDI, HWFI, and AVNI/GFSI. These models are called interpolated models (that’s the “I” on the end) because they are adjusted versions of “late models”; the previous run’s forecast is interpolated to the current time because the current run is not available yet.

The fourth class of guidance included on spaghetti diagrams is the consensus model, which is actually not a model at all. Consensus forecasts are a combination of forecasts from a collection of models, usually obtained by averaging them together. For the spaghetti diagrams of intensity forecasts, the consensus models typically included are ICON and IVCN. The consensus models for track forecasts that are normally shown are TCON, TVCE (also known as TVCN), and AEMI.

The dynamical, statistical-dynamical, consensus models, and NHC official forecast all perform at a similar level for track and intensity forecasts, while the trajectory and statistical models have significantly higher errors. Yet when someone sees one of these inferior models deviating from the rest and steering a strong hurricane into their backyard, the natural intuition is to panic. In these situations, it is important to remember which are the more skillful models.

Still, among the skillful models, some perform a little better on average than the others but there is currently no way to foresee the dominant model(s) for a particular scenario. In fact, models will seemingly have good days and bad days, good months and bad months, and even good years and bad years. That is why an informed rainbow spaghetti consumer should not focus too much on an individual noodle but instead use all of the noodles as a side dish to NHC’s forecast cone. So when staring down an approaching hurricane this season, feel free to grab a colorful bowl of spaghetti, just remember to consume with care.

- Kieran Bhatia (PhD candidate in the Department of Atmospheric Sciences)

WHARF Mooring Deployment

Graduate students in the Meteorology and Physical Oceanography department at RSMAS deploy a sub-surface mooring in the Straits of Florida to measure the surface wave field.

So you want to go fishing this weekend to catch a nice big tuna to grill on the BBQ.  What’s the weather like?  You don’t want to get seasick! That means you don’t want the wave heights to be too large, nor the wave period too long (Did you know?…seasickness intensifies with an increasing period of oscillation1 ).

If you want to know what is happening in the waters offshore of Miami, you can take a look at the National Weather Service (NWS) website.  Every day the NWS provides forecasts of the wind and wave conditions over the Straits of Florida, which are used by commercial fisherman, shipping companies and recreational boaters.  The wave forecasts are based on model predictions. However, this region is highly dynamic due to the presence of the fast flowing Florida Current (named the Gulf Stream further north); this current interacts with the wave field and represents a challenge to the wave forecasting models.

Dr. Nick Shay leads the Upper Ocean Dynamics Laboratory at UM Rosenstiel School of Marine and Atmospheric Science, which has operated shore based high frequency (HF) radar systems for over a decade. These radars remotely measure near-real time surface currents across the Straits of Florida with high accuracy2.  HF radar also has the ability to measure the wave field over the surface of the Straits of Florida.  This has attracted increasing interest in recent years, as to provide operational real-time observations of the wave field can help improve the model forecasts.

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But first, the accuracy of the HF radar wave measurements must be evaluated using in-situ observations of the wave field.  This is why the WHARF experiment was conceived.

Mr. Matthew Archer, a PhD student working in Dr. Shay’s lab, is the recipient of a prestigious award to deploy an acoustic wave and current profiler (AWAC) (http://www.nortekusa.com/usa/support/student-equipment-grants/2013-grants/awards).  The AWAC is built by Nortek for long-term deployment in the ocean, to measure the surface wave field and ocean currents.  This instrument will gather data over a 4-month period, during the transition from spring to summer, to measure the in-situ wave heights and currents during different weather conditions.

On April 22nd, the mooring was successfully deployed offshore of Miami Beach, which gave the students experience of working at sea.  The AWAC was attached to a buoy that was moored to the ocean bottom with an anchor – in our case, a train wheel! The instrument, which is moored in 300-m water depth, floats 40-m below the surface, facing upward to measure the surface waves and currents far offshore of the coast, within the Florida Current.

Using this in-situ dataset, the radar system can be calibrated to make sure that the wave data are accurate.  The radar provides data every 20-min, which will be provided in near-real time on the lab website. The results of the WHARF project will provide valuable information that can be used in the further development of the NWS marine forecasts, benefiting shipping and navigation as well as the construction and management of sustainable coastal developments.  It will also give UM Rosentiel scientists data to investigate the relationship between strong currents and the surface wave field, a topic which is not fully understood.

The project was made possible by funding from SECOORA (Southeast Coastal Ocean Observing Regional Association).

 

1 Cheung, B. and A. Nakashima, 2006. A review on the effects of frequency of oscillation on motion sickness. In: Technical Report; No. DRDC-TR-2006-229. Defense research and development Toronto (Canada).

2 Parks, A. B., L. K. Shay, W. E. Johns, J. Martinez-Pedraja, and K.-W. Gurgel, 2009.  HF radar observations of small-scale surface current variability in the Straits of Florida.  In: J. Geophys. Res., 114, C08002, doi:10.1029/2008JC005025.

 

 

The MPO Best Paper Award Goes To…

UM Rosenstiel School Ph.D. student Katinka Bellomo received the Best Paper Award from the Division of Meteorology and Physical Oceanography (MPO) for her research paper recently published in the American Meteorology Society’s Journal of Climate.

“Receiving the MPO best paper award is a huge personal satisfaction,” said Katinka. “This is the first paper of my dissertation and of my life.”

Addu Atoll lagoon at sunset

The paper, titled “Observational and Model Estimates of Cloud Amount Feedback over the Indian and Pacific Oceans,” addressed the largest uncertainty in climate models – cloud feedback – by examining observations of cloud cover taken from ships and satellites from 1954 to 2005. The results of this paper represent the first observational long-term estimate of cloud feedback.

In response to greenhouse gas forcing, the Earth would naturally cool off by emitting more radiation back into space. However, feedback mechanisms, from clouds, can increase or reduce this cooling rate.

“I am satisfied that the paper shows how to handle the uncertainties in observations and provides a methodology to estimate cloud feedbacks from these observations,” said Katinka.

Congrats Katinka!

What Caused the Rapid Intensification of Super Typhoon Haiyan?

Typhoon Haiyan at peak intensity on November 7, 2013. Credit: NASA

Typhoon Haiyan at peak intensity on November 7, 2013. Credit: NASA

During the AMS Hurricane and Tropical Meteorology meeting in San Diego last week, Rosenstiel School professor Nick Shay presented research on the role ocean warming played in the rapid intensification of last year’s devastating Super Typhoon Haiyan in Southeast Asia.

Shay’s study suggests that temperature fluctuations from semi-diurnal internal tides need to be analyzed to fully understand the causes of rapid intensification as the storm went over the warm pool of water in the western Pacific prior to landfall in the Philippines.

Using temperature, salinity and current data collected as Haiyan made a direct hit over the Japan’s Triton buoy (formerly a NOAA TAO buoy), along with satellite-derived data from the SPORTS climatology model (Systematically merged Pacific Ocean Temperature and Salinity developed by Rosenstiel School graduate student Claire McCaskill), Shay’s research team examined ocean warming conditions prior to Haiyan at the thermocline, a distinct ocean temperature layer that is known to fluctuate seasonally due to tides and currents.

 

infrared satellite loop of Typhoon Haiyan in the Philippines. Credit: NOAA

infrared satellite loop of Typhoon Haiyan in the Philippines. Credit: NOAA

Internal tides are known to create large temperature fluctuations. Shay suggests that the upper ocean heat content was important in the rapid intensification of Haiyan similar to what is observed in the Atlantic Ocean basin. While the semidiurnal tides were amplified in the warming thermocline in this regime, they have to be removed from the data to accurately evaluate questions related to the roles climate change and oceanic warming played in the storm’s intensification.

At the Heart of a Hurricane Forecast

One of the many challenges in hurricane forecasting is incorporating observational data into forecast models. Data assimilation, as scientists refer to it, is the process of combining observational data – information obtained by satellites, radars or from instruments deployed into storms by aircraft – into a numerical weather prediction model.

Incorporating real-world temperature, wind, moisture or atmospheric pressure from multiple sources is a core component of hurricane science and vital to provide improved forecasts of both the track and intensity of storms. How to incorporate new types of observational information into a model is at the very heart of hurricane forecasting.

At the American Meteorology Society’s 31st annual Hurricane and Tropical Meteorology meeting in San Diego this week, Rosenstiel School Professor Sharan Majumdar discussed new approaches to improve data assimilation.

Typhoon Sinlaku (2008), as seen from Terra Satellite on September 10 2008. Credit: NASA

Typhoon Sinlaku (2008), as seen from Terra Satellite on September 10 2008. Credit: NASA

Citing the Ph.D. research of Rosenstiel School graduate student Ting-Chi Wu, Majumdar discussed the assimilation of temperature and moisture data obtained from satellite-based advanced Infrared (IR) soundings measured by polar-orbiting satellites of Typhoon Sinlaku during the 2008 Pacific typhoon season. Wu’s studied the period of storm intensification as Sinlaku intensified into a category-2 typhoon. Her conclusion was that the assimilation of temperature and moisture show promise in improving forecasts of hurricane and typhoon intensity, though more work needs to be done to improve their use.

Majumdar also provided an overview of strategies to assimilate observations to improve numerical weather forecasts of the track and structure of storms. He showed that targeted aircraft observations in select areas and satellite observations from both within and outside a tropical cyclone are beneficial.

Explore CARTHE’s Award-Winning Website

The University of Miami was awarded two Outstanding Achievement awards by the Interactive Media Council for excellence in the design, development and implementation of the CARTHE website. Based at the UM Rosenstiel School, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) is a research team dedicated to predicting the fate of the oil released into our environment as a result of future oil spills.

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The CARTHE website launched in August 2013 and serves as a web portal devoted to interactive information and science education for scientists, students, members of the press and the general public. The CARTHE website was created in collaboration with Professor Kim Grinfeder and his team from the UM School of Communications.

“The idea of this website was conceived when I met Kim Grinfeder from the School of Communication at a workshop I attended on main campus,” said Tamay Özgökmen, CARTHE director and Rosenstiel School professor.

“We needed an interactive website to tell our complex scientific story.  Over the course of the next few months, we conceived the website, which had two main interactive elements: An interactive infographic through which visitors can get information about the project, and a main introductory video on the homepage,” said Professor Özgökmen.

The CARTHE videos were developed by Ali Habashi, faculty member in the UM School of Communication’s Department of Cinema and Interactive Media.

“Ali was recommended to me by three different, unrelated people within a week. His name came up repeatedly when I said that it would be most efficient to have a video to tell our complex scientific story within the time span of a few minutes;  people would say “Will Ali do it?” He has a great reputation within the UM community and beyond,” said Professor Özgökmen.

“What I liked most about this project was its cross-disciplinary aspect and how it really shows how different areas of UM can collaborate,” said Kim Grinfeder, associate professor in the UM Department of Cinema and Interactive Media. “CARTHE is an amazing project happening at our university and to learn about their work and to have the opportunity to tell their story was an incredible experience.”

“There are plenty of benefits that can come from bringing faculty members from different schools together, and the CARTHE website is one example,” said Professor Özgökmen.

The judging consisted of various criteria, including design, usability, innovation in technical features, standards compliance and content. The website won in two categories, Science/Technology and Natural Environment/Green. It has received 4,583 visits since its launch in August 2013.

For more information about CARTHE, please visit www.carthe.org or on Facebook at www.Facebook.com/carthe.gomri.

– RSMAS Communications 

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