hurricane irene case study

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Storm Practices: Lessons Learned from Hurricane Irene

Even Ann Keyes learned a lesson from Hurricane Irene about the importance of reliable communications and being prepared for the worst.

“Have a Plan C for your Plan B,” advises the Washington County emergency management director who has 25 years of experience under her belt. During the storm, Keyes’ Internet connection went down and her pre-tested air cards — devices that give users mobile Internet access using their cellular data service — did not work.

“I relied on good relationships with my partners, so that I could get into the store and get new cards,” she recalls.

Keyes shared her Irene experiences with more than 150 other emergency managers, meteorologists, public information officers, emergency responders and university researchers at the third annual N.C. Division of Emergency Management-East Carolina University Hurricane workshop, co-sponsored by North Carolina Sea Grant. The May event highlighted the role of emergency communications and the lessons learned from Hurricane Irene in 2011.

The hurricane taught North Carolina emergency managers and their communities the importance of accurate and detailed communications before, during and after the storm. They depend upon early forecasting from the National Weather Service, or NWS, and clear communications to residents and visitors to make sure that people are able to evacuate vulnerable areas.

The National Hurricane Center found that effective communications are just as important as the development of accurate weather models in forecasting hurricanes. Improved strategies include using maps and graphics, and collaborating closely with news media to inform the public.

Many people had not expected that Irene, a Category 1 storm when it made landfall, would do so much damage. During the storm, areas of Hyde County, which includes Ocracoke Island, experienced a 7-foot storm surge, major flooding and sustained high winds for hours. Eighteen homes were destroyed, and 803 residences and businesses were affected.

However, the hardest part has been rebuilding. “Most people did not expect the recovery process to take as long as it did. It is like switching from a sprint to an endurance race,” says Justin Gibbs, Hyde County’s director of emergency services, who also attended the workshop.

FORECASTING IRENE

The impacts of Hurricane Irene, especially in areas surrounding the Pamlico Sound, surprised many people. Sustained high winds and major flooding from storm surge caused greater damage than most people anticipated. NWS forecasts were accurate but people still were caught off guard.

“People just did not think that the impacts from a Category 1 would have been so substantial,” says John Cole, warning meteorologist with the NWS in Newport/Morehead City.

Although meteorologists correctly forecasted the track and rainfall amounts for the hurricane, people reported that they had no idea that the flooding problems would be so severe. Was this because the experts or the news media were not doing a good job at conveying potential risk? Or were people overly optimistic that they would not be personally affected by severe storms?

To understand the public perception of the threats posed by Hurricane Irene and find out how people responded to the weather forecasts, the NWS held public meetings in some of the communities hardest hit by the storm. At December meetings in Dare, Pamlico and Beaufort counties, participants were surveyed about their experiences and perceptions.

In addition, Rich Bandy, lead meteorologist at the NWS Newport/Morehead City office, presented comparisons of the forecasts for wind, inland flooding and storm surge with observations during and after the storm.

Meteorologists forecasted a low threat of tornadoes. However, there were three confirmed tornadoes in the state. The strongest, near Columbia, had maximum winds of 130 mph and caused severe local damage.

Wind impacts were projected to be extreme, with possible sustained winds of 115 mph for much of the coastal area. In actuality, “there were gusts of 90 to 100 mph in the corridor where the eye made landfall,” Bandy notes.

In comparing the forecast of inland flooding with actual rainfall impacts, Bandy says that the predictions “were pretty close.” Although Irene dropped about the same amount of rainfall as Hurricane Floyd in 1999, river flooding was not as significant because of low water levels prior to the storm.

Four days before landfall, researchers from the Coastal and Inland Flooding Observation and Warning project, or CI-FLOW, began running models, and making river-flooding and storm-surge inundation predictions for the area. The experimental system was developed after Hurricanes Dennis and Floyd to improve real-time water level predictions prior to and during tropical storm events.

The modelers and forecasters were pleased with the results from CI-FLOW. The model forecasted maximum water levels in the Tar-Pamlico and Neuse river basins, which came within 1 foot of the observed water levels in the selected test sites. The NWS forecasters had access to CI-FLOW to evaluate how well the system represented the interaction between storm-surge, sound and river water, and its impact on the coast.

Jack Thigpen, Sea Grant extension director, notes, “Although CI-FLOW is experimental and not a replacement for the standard forecasting models traditionally used by the forecasters, it adds another source of information to help the experienced forecaster make better predictions and reduce potential for loss of life and property damage.”

Coastal flooding due to storm surge in many areas was catastrophic and unanticipated by the public. The NWS forecast 18 hours prior to landfall was for extreme coastal flooding, meaning 8 feet or more of water above ground level, in parts of Carteret, Pamlico, Beaufort and Hyde counties. Other areas — including the entire barrier island chain north of Cape Lookout, and the Pamlico and Neuse river areas — were warned of high storm surge, with an expected 5 to 7 feet of flooding.

After the storm, measurements showed that about 4 to 6 feet of storm surge occurred above the ground level in areas of the Pamlico River. In the Neuse River, similar surge levels were measured, although some local areas had as much as 8 feet of water.

The forecast of coastal flooding due to storm surge was close to the observed levels but many residents were still unprepared.

NWS is improving forecasting, and striving to provide the public and emergency managers with better accuracy and longer lead time for emergency preparations. However, getting people to evacuate often is difficult. The experts focus on communicating danger with graphics, but even that information might not get people to leave.

“What we cannot convey with our warning maps is what it really means to stay behind,” Bandy notes. “It is not just still water. There are waves and it is extremely dangerous.”

WARNINGS IN 140 CHARACTERS

Many municipalities across the state are using Twitter, Facebook and other social media to expand their communications. Hurricane Irene was the first time several counties used these new communication tools for emergency management.

Donna Kain, faculty member in the technical and professional communications program at ECU, surveyed emergency managers about social media. She found that 55 percent are using social media and another 24 percent are likely to start using it in the near future.

“Social media has been adopted by larger county emergency management departments,” says Kain, who participated in a Sea Grant-funded study on risk perceptions and emergency communication effectiveness in coastal zones. “Most of the departments prepare the messages themselves, in addition to their other duties.”

Roberta Thuman, Town of Nags Head’s public information officer, spent Hurricane Irene in the emergency operation center listening in on calls from people who wanted to be rescued during the storm. This was the first emergency in which she was using Twitter to provide information to the public about the storm and the conditions in the area.

“I was the person sending out the tweets and I had to keep my emotions in check,” Thuman says. She started tweeting before the hurricane — and her Twitter followers grew exponentially during the storm. She was grateful that many of her posts were re-tweeted, especially ones that alerted people to rising waters and recommended staying inside.

In New Hanover County, emergency manager Warren Lee finds that social media is a good way to reach out to young people. The county uses Facebook, Twitter, Flickr and YouTube, in addition to traditional media such as newspapers or television.

“During Irene we had a 300 percent increase in Facebook followers with over 76,000 views and 204 tweets. In addition, we produced six YouTube videos showing the activities of the county’s Emergency Operations Center,” Lee says.

While social media is a way to reach people who do not rely on traditional media, adoption has been slow because many municipalities do not allow employees to access the sites on work computers. One of the lessons from Irene for emergency communicators has been that policies need to be developed about the use of social media, including how it will be staffed when a crisis occurs.

“The immediacy of Twitter and other social media is wonderful, but it is also a big challenge. You have to keep that in mind,” Thuman says.

HYPERLOCAL INFORMATION

Traditional news media, particularly television news, played an important role during Hurricane Irene. Local and national media turned to emergency managers and the NWS for information, but getting the local information people need often is challenging.

Irene showed emergency communicators the power of social media to share real-time and precise information. Social media plays an important role in filling the gaps for local information and offers an outlet for the news media to reach additional audiences.

Skip Waters, chief meteorologist at WCTI-TV, never had a desire to communicate via social media. With Hurricane Irene, he has seen its value. “The stuff that really resonates for people is the hyperlocal,” he says.

Hyperlocal is very specific in both location and time. It is the kind of information that Waters has from driving around his region, 19,000 miles each year — and the kind of knowledge that helps when using social media. There is value in “knowing every little landmark, every little store, every store that used to be there, every place that has a good cheeseburger,” he says.

During Irene, Travis Morton, the news station’s intern, adapted Waters’ on-air weather reports and descriptions to Facebook and Twitter to increase their communications reach. Tweeting or creating Facebook posts also can allow people to share what they are experiencing in their neighborhoods. For example, comments from viewers via social media provided verification of storm impacts, often in real time.

However, some of the biggest communications challenges occurred during the recovery, when many communities were without power for extended periods. Because of the hurricane, Waters says that the station evolved from just sharing information to understanding how people without power were getting much of their weather information through the station’s social media outlet via smart phones or other devices.

One viewer was thankful for the social media updates during the storm when she was without power for three hours. “Travis was my hero,” she told Waters.

VOLUNTEER WEATHER OBSERVING

Local information is sometimes difficult for the forecasters to obtain with limited numbers of weather stations around the state. That is where the Community Collaborative Rain, Hail and Snow Network, also known as CoCoRaHS, comes in. It is a volunteer weather-observing program that trains citizens to collect precipitation data and send it daily to the NWS.

“It is a good way for folks to get involved,” notes Brian Efland, coastal business specialist with Sea Grant, who is helping recruit volunteers. “Some counties in the region do not have any observers, so Sea Grant has funded 36 rain gauges for volunteers in rural locations along the coast.”

To be involved, participants must have a high-capacity, 4-inch diameter rain gauge and register with the CoCoRaHS website. Then each morning, the volunteer empties the rain gauge and records the amount of precipitation through the website.

During storms like Hurricane Irene, the CoCoRaHS volunteers provide detailed data that become part of tropical storm statements for NWS. Rainfall amounts also are incorporated into CI-FLOW and other models to help improve rainfall forecasting.

“One of the pluses is that the rain gauges do not rely on electricity, so they are great for storm totals and fill in the gaps between automated gauges. The CoCoRaHS network was also critical in ground truthing a new radar system put into place by the NWS just prior to Hurricane Irene,” says David Glenn, state coordinator for the project and NWS meteorologist.

NO ‘JUSTA’ STORM

Bill Read, recently retired director of the National Hurricane Center, spent his 35-year career studying hurricanes. One of his most recent efforts has been the Hurricane Forecast Improvement project, a 10-year program that began in 2009. While forecasts of the hurricane track have improved greatly, forecasts of intensity have not.

Irene was an example of how difficult it is to predict hurricane strength and speed. “A lesson learned from Irene is one I have seen in many storms. Even a good forecast has uncertainty. Getting locked into specific times and locations, especially before 48 hours, can cause misunderstanding,” Read says.

Hurricane Irene reminded meteorologists and emergency managers that there is no “justa” tropical storm or “justa” Category 1 hurricane. Each storm is different. In a severe thunderstorm, 60-mph winds may last seconds or minutes, but in a tropical storm the size of Irene, these winds can last hours.

“If I could rewrite history, I would not have any kind of scale,” Read remarks, referring to the categories used for hurricanes. “Mother Nature is a continuum.”

• Information on hurricane preparedness is available at: readync.org and www.ncdhhs.gov/hurricanes . Many county and town websites also have good resources.
• For research on weather preparedness see: www.ecu.edu/riskcomm .
• See a video describing the Coastal and Inland Flooding Observation and Warning Project at: https://ciflow.nssl.noaa.gov/ .
• Find out if you are at risk for flooding at: www.ncfloodmaps.com.
• To volunteer to be a CoCoRaHS observer in North Carolina, contact David Glenn at: [email protected]. In the coastal region, contact Brian Efland at: [email protected]. For more on CoCoRaHS, visit: www.cocorahs.org .

This article was published in the  Autumn 2012  issue of Coastwatch.

For contact information and reprint requests, visit  ncseagrant.ncsu.edu/coastwatch/contact/ .

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Newport/Morehead City, NC

Weather Forecast Office

Hurricane Irene August 26-27, 2011

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Event Overview 

Hurricane Irene was a large and powerful Atlantic hurricane that left extensive flood and wind damage along its path through the Caribbean, the United States East Coast and as far north as Canada (Figure 1). Irene made landfall near Cape Lookout, North Carolina at around 7:30 AM EDT on August 27, 2011 as a strong category 1 storm (Figure 2). Irene caused 5 deaths in North Carolina. On the evening of August 26, well ahead of landfall, Hurricane Irene also spawned several tornadoes. One EF-2 tornado near Columbia in Tyrrell County demolished at least 4 homes and overturned cars.

Precipitation totals associated with Irene were particularly high (Figure 3) with totals ranging from around 5 inches over the Northern Outer Banks, to around 15 inches in Beaufort County. Bunyan in Beaufort County reported 15.66 inches. Doppler Radar estimated totals of over 17 inches in portions of Beaufort, Craven and Pamlico Counties (Figure 4). Extensive storm surge and wind damage also occurred with Irene. The peak wind gust recorded was 115 mph at the Cedar Island Ferry Terminal in Carteret County as the eye was moving ashore (Figure 5). Trees were down throughout eastern North Carolina and thousands were left without electricity. Ocean and Sound overwash created numerous breaches of Highway 12 along the Outer Banks.

Figure 1. Track of Hurricane Irene, August 20 through 29, 2011. (Source: National Hurricane Center)

Figure 2. Eye of Hurricane Irene making landfall near Cape Lookout, NC at 749 AM EDT, August 27, 2011

Figure 3. Rainfall Totals from Hurricane Irene, August 26-27, 2011

Figure 4. Newport/Morehead City Doppler Radar Estimated Rainfall for Hurricane Irene. Note the large white area of over 17 inches over Beaufort, Craven and Pamlico Counties.

Figure 5. Peak Wind Gusts from Hurricane Irene, August 27, 2011.The peak wind gust recorded was 115 mph at the Cedar Island Ferry Terminal in Carteret County.

Evolution and Impacts 

Hurricane Irene evolved from a tropical wave that exited the African coast on August 15, 2011. With a favorable environment ahead of the wave, a Tropical Depression formed on August 20 as the wave approached the Lesser Antilles. By 23Z on August 20, the depression was upgraded to Tropical Storm Irene. On August 21, the surface center reformed closer to the deepest convection, as an anticyclone aloft provided supportive outflow over the cyclone. With the improved structure, as well as light wind shear and high sea surface temperatures, Irene was forecast to strengthen to near hurricane force prior to landfall in Hispaniola. Over the subsequent day (August 22), while passing near the island of Saint Croix in the U.S. Virgin Islands, Irene moved toward Puerto Rico, more northward than initially expected, where it underwent a considerable increase in strength and organization. Hours later, Irene moved ashore, approaching from the southeast at landfall near Punta Santiago, Puerto Rico, with estimated sustained winds of 70 mph. Despite the storm's interaction with land, radar imagery showed a ragged eye-like feature, and Doppler radar data indicated wind speeds in excess of hurricane force. Just after its initial landfall, Irene was accordingly upgraded to a Category 1 hurricane, the first of the 2011 Atlantic hurricane season.

After briefly weakening on August 23, Irene began to develop a distinct eye encircled by an area of deepening convection the next morning. Moving erratically through the southeast Bahamas over very warm waters, Irene quickly expanded as its outflow aloft became very well established. The hurricane intensified into a Category 3 major hurricane as it recurved toward the northwest along a weakness in the subtropical ridge. Irene would gradually weaken to a strong Category 1 storm as it approached the North Carolina coast as dry air wrapped into the hurricane.

Tropical-storm force winds began to affect the Outer Banks and Crystal Coast during the early evening hours of August 26. Additionally, Irene spawned several tornadoes during the late evening hours of August 26, producing significant damage in Tyrrell, Washington and Beaufort Counties. Figure 6 shows the strong rotational couplet associated with an EF-1 tornado near Creswell, North Carolina around 1055 pm August 26.

Figure 6. Strong Rotational Couplet in the Doppler velocities from Creswell, NC around 1055 pm August 26. This couplet produced an EF-1 tornado.

Irene made landfall between 7:30 and 8 AM EDT Saturday August 27 near Cape Lookout, North Carolina. As the eye wall moved ashore, wind gusts to 115 mph were observed at the Cedar Island Ferry Terminal. The lowest pressure observed with the landfall of Irene was at Beaufort (Figure 7) with 28.11 inches of mercury or around 951 millibars at 8:56 AM. Strong winds and driving rains pounded most of Eastern North Carolina into the mid-afternoon hours on Saturday. As the eye moved inland, strong westerly winds on the backside of the storm gusted to near 100 mph at Atlantic Beach around 10:30 AM. Torrential rainfall amounts in excess of 10 inches were widespread. Storm surge levels of over 10 feet were observed at Ocracoke and several breaches of Highway 12 were noted all along the Outer Banks. A total of 5 people were killed in North Carolina as the result of Irene.

Figure 7. Barometric Pressure Graph from Beaufort, NC August 26-27, 2011. (Courtesy: National Ocean Service)

PUBLIC INFORMATION STATEMENT

NATIONAL WEATHER SERVICE NEWPORT/MOREHEAD CITY NC

550 PM EDT SUN AUG 28 2011

...PUBLIC INFORMATION STATEMENT...

THE FOLLOWING ARE UNOFFICIAL OBSERVATIONS TAKEN DURING HURRICANE IRENE.

APPRECIATION IS EXTENDED TO HIGHWAY DEPARTMENTS...COOPERATIVE

OBSERVERS...SKYWARN SPOTTERS...COCORAHS OBSERVERS AND MEDIA FOR

THESE REPORTS. THIS SUMMARY IS ALSO AVAILABLE ON OUR HOME PAGE AT

WEATHER.GOV/NEWPORT/

***********************PEAK WIND GUST***********************

LOCATION             MAX WIND     TIME/DATE   COMMENTS              

                         GUST            OF

                          MPH   MEASUREMENT     

NORTH CAROLINA

...BEAUFORT COUNTY...

   WASHINGTON              45   435 AM  8/27  KOCW-DOWN AT 455 AM (8/27)

   AURORA                  90   845 AM  8/27  PCS PHOSPHATE

...CARTERET COUNTY...

   CEDAR ISLAND           115   750 AM  8/27  CEDAR ISLAND FERRY TERM                 

   BEAUFORT                70  1003 AM  8/27  KMRH-DOWN AT 1003 AM(8/27)

   FORT MACON              92  1110 AM  8/27  WEATHERFLOW

   CAPE LOOKOUT            78   400 AM  8/27  WEATHERFLOW

   MOREHEAD CITY           78   329 AM  8/27  MHC POLICE DEPT

   MOREHEAD CITY           70  1130 AM  8/27  WEATHERFLOW-DOWN AT 1103 AM(8/27)         

   NEWPORT                 64   849 AM  8/27  NWS NEWPORT

   STACY                   93   408 AM  8/27  TRAINED SPOTTER

   BEAUFORT                77   641 AM  8/27  TRAINED SPOTTER

   NEWPORT                 74  1015 AM  8/27  TRAINED SPOTTER

   BEAUFORT                76  1101 AM  8/27  TRAINED SPOTTER   

   ATLANTIC BEACH         101  1035 AM  8/27  TRAINED SPOTTER  

...CRAVEN COUNTY...

   CHERRY POINT            75  1154 PM  8/27  KNKT                 

   NEUSE RIVER             59   125 AM  8/27  WEATHERFLOW-DOWN AT 135 AM (8/27)

   NEW BERN                74   751 AM  8/27  KEWN-DOWN AT 849 AM (8/27)                 

...DARE COUNTY...

   MANTEO                  74   755 AM  8/27  KMQI                 

   FRISCO WOODS            76   610 AM  8/27  WEATHERFLOW-DOWN AT 1010 PM(8/28)

   HATTERAS HIGH           68   710 AM  8/27  WEATHERFLOW-DOWN AT 1020 PM(8/27)

   HATTERAS                88   851 AM  8/27  KHSE                 

   BUXTON                  79   930 AM  8/27  WEATHERFLOW-DOWN AT 920 PM (8/27)

   MANTEO CSWY             69  1118 AM  8/27  WEATHERFLOW

   ALLIGATOR BRIDGE        66   847 AM  8/27  WEATHERFLOW

   OREGON INLET            78   856 AM  8/27  WEATHERFLOW

   AVON (OCEAN SIDE)       77   725 AM  8/27  WEATHERFLOW-DOWN AT 903 AM (8/26)

   AVON (SOUND SIDE)       67   635 AM  8/27  WEATHERFLOW-DOWN AT 836 AM (8/27)

   WAVES                   57   713 AM  8/27  WEATHERFLOW

   SALVO                   69   755 AM  8/27  WEATHERFLOW

   BUXTON                  79   600 AM  8/27  WEATHERFLOW          

   PAMLICO SOUND           58  1050 AM  8/27  WEATHERFLOW-DOWN AT 1130 PM(8/26)

   REAL SLICK              77  1020 AM  8/27  WEATHERFLOW-DOWN AT 654 PM (8/27)

   WAVES                   63   725 AM  8/27  WEATHERFLOW-DOWN AT 921 AM (8/27)

   OREGON INLET            77   210 PM  8/27  WEATHERFLOW-DOWN AT 618 PM (8/27)

   MANTEO                  74   435 PM  8/27  WEATHERFLOW-DOWN AT 537 PM (8/27)

   MANTEO CSWY             68  1015 AM  8/27  WEATHERFLOW-DOWN AT 537 PM (8/27)

   JOCKEYS RIDGE           69   400 PM  8/27  WEATHERFLOW-DOWN AT 646 PM (8/27)

   PEA ISLAND              83   950 AM  8/27  KPEI

   DUCK                    85  1036 AM  8/27  DUCK COE PIER

...DUPLIN COUNTY...

   2 N KENANSVILLE         58   915 AM  8/27  KDPL                  

...HYDE COUNTY...

   OCRACOKE                69   439 AM  8/27  WEATHERFLOW-DOWN AT 616 AM (8/27)

...JONES COUNTY...

   WYSE FORKS              70  1131 AM  8/27  TRAINED SPOTTER

...ONSLOW COUNTY...

   JACKSONVILLE            50   355 AM  8/27  KOAJ-DOWN AT 415 AM (8/27)                  

   NEW RIVER               61   356 AM  8/27  KNCA-DOWN AT 429 AM (8/27)                 

   JACKSONVILLE            94   615 AM  8/27  TRAINED SPOTTER

...PITT COUNTY...

   GREENVILLE              64   835 AM  8/27  KPGV   

   GREENVILLE              73   700 AM  8/27  PITT EOC

...TYRRELL COUNTY...  

   ALLIGATOR BRIDGE        71  1050 AM  8/27  WEATHERFLOW-DOWN AT 328 PM (8/27)

...NDBC BUOY...

   DUCK PIER               79   512 PM  8/27  DUKN7

   OREGON INLET MARINA     80   400 PM  8/27 

   HATTERAS                79   536 AM  8/27

   CAPE LOOKOUT            78   400 AM  8/27  CLKN7

   BEAUFORT                67   612 AM  8/27  BFTN7

   41036                   69   820 AM  8/27 

***********************BAROMETRIC PRESSURE***********************

LOCATION     LOWEST PRESSURE    DATE/       COMMENTS

                      INCHES    TIME

HATTERAS       28.65           8/27  135 PM    KHSE

BEAUFORT       28.11           8/27  856 AM    KMRH

CHERRY POINT   28.18           8/27  854 AM    KNKT

GREENVILLE     28.84           8/27 1235 PM    KPGV

...NDBC BUOYS...

41036          28.25           8/27  620 AM  

44014          29.20           8/27 1150 AM

CLKN7          28.15           8/27  800 AM

ORIN7          28.80           8/27 1218 PM

*******************STORM TOTAL RAINFALL REPORTS***********************

LOCATION            TOTAL PRECIP      COMMENTS

...BEAUFORT...

   BUNYAN             15.66           RAWS - BNYN7

   WASHINGTON         13.11           HYDRO - TRAN7

   CROATAN FOREST     11.13           RAWS - NPTN7

   NEWPORT            10.41           WFO MHX  

   NEWPORT             9.41           NEWPORT 0.2 SW                

   MOREHEAD CITY       8.11           MOREHEAD CITY 6.0 WNW

   NEWPORT             7.81           NEWPORT 2.0 WSW 

   BEAUFORT            7.00           BEAUFORT 5.3 N   

   CEDAR ISLAND        6.68           RAWS - TS788

   MOREHEAD CITY       6.65           MOREHEAD CITY 0.6 NW

   BEAUFORT            6.31           ASOS - KMRH

   NEW BERN           14.79           NEW BERN 1.3 NNE   

   NEW BERN           12.86           RAWS - NBRN7

   HAVELOCK           10.70           HAVELOCK 1.4 SW   

   NEW BERN            7.79           ASOS - KEWN

   TRENT WOODS         5.94           TRENT WOODS 1.3 SSE

   PERRYTOWN           5.04           COOP - PYTN7 (8/27)

   DARE BOMB RANGE     7.57           RAWS - STCN7

   HATTERAS            6.77           ASOS - KHSE

   KILL DEVIL HILLS    2.43           KILL DEVIL HILLS 0.9 WNW

   BEAULAVILLE         9.61           BEAULAVILLE 0.5 NE   

   MOUNT OLIVE         5.68           COOP - MTON7

   MOUNT OLIVE         4.66           MOUNT OLIVE 2.4 SW

   FAIRFIELD           7.61           RAWS - TS161

   OCRACOKE            2.72           COOP - OCRN7 (8/27)

...LENOIR COUNTY...

   KINSTON             9.53           KINSTON 3.7 WNW

...MARTIN COUNTY...

   WILLIAMSTON        14.27           WILLIAMSTON 0.9 SSW

   JACKSONVILLE       11.70           JACKSONVILLE 5.5 WSW

   SWANSBORO           9.76           SWANSBORO 3.3 NW              

   CAMP LEJUENE        9.57           HYDRO - CLJN7

   HUBERT              9.36           HUBERT 4.3 SE

   HUBERT              9.12           HUBERT 4.9 SE

   RICHLANDS           8.87           RICHLANDS 2.8 SSE

   JACKSONVILLE        8.36           JACKSONVILLE 1.7 W

   HOFMANN FOREST      7.95           RAWS - HFMN7

   JACKSONVILLE        6.80           COOP - JEON7

...PAMLICO COUNTY...

   BAYBORO            15.74           COOP - BAYN7

   GRIFTON            12.69           HYDRO - GRTN7

   GREENVILLE         12.32           GREENVILLE 7.1 SSE

   GREENVILLE         11.64           COOP - GREN7

   GREENVILLE         10.57           GREENVILLE 8.2 SSE

   GREENVILLE         10.50           HYDRO - PGVN7

   GREENVILLE         10.25           GREENVILLE 1.1 S

   WINTERVILLE         9.74           WINTERVILLE 3.5 W

   GREENVILLE          8.69           GREENVILLE 1.4 SE

   GREENVILLE          7.23           AWOS - KPGV

...WASHINGTON COUNTY...

   POCOSIN LAKES      11.20           HYDRO - POCN7

Sources –

National Hurricane Center

National Ocean Service

GR2 Analyst Software

Damage Pictures from Eastern North Carolina  Photos courtesy of: Storm Survey Team

Case Study Team: MHX Case Study Team

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US Dept of Commerce National Oceanic and Atmospheric Administration National Weather Service Newport/Morehead City, NC 533 Roberts Rd Newport, NC 28570 252-223-5737 Comments? Questions? Please Contact Us.

Hurricane Irene: Here, Gone, and Back Again

Since 2005, healthcare facilities have begun to place a higher priority on the connection between planning and action in their preparedness measures. This article provides a unique example of how education for emergency managers in exercise design can enable hospitals to improve their disaster preparedness. In looking at the design of a hyper-realistic hurricane response exercise in 2009, one hospital was able to take specific actions that enhanced its response to an actual hurricane incident in 2011. In fact, the design of the 2009 exercise was comprehensive enough that it came very close to mirroring the real 2011 event. The outcomes of this exercise and the response that followed demonstrate the importance of exercise design courses for emergency managers. In addition, this case study illustrates the benefits of using exercises to build working relationships among responder groups and agencies. Finally, the use of lessons learned from exercises and jointly analyzed hazard vulnerabilities enable a robust and all-bases-covered response to actual critical incidents.

Kevin C. Thomas

Director, Healthcare Emergency Management Program Boston University School of Medicine

John J. Burke

Chief, Sandwich (MA) Fire Department

Nina Shaafi Kabiri

Research Scientist, Department of Anatomy and Neurobiology at Boston University School of Medicine

Alanna Cote

Doctoral Student, Boston University, Graduate School of Biomedical Sciences

Teija K. Corse

Emergency Preparedness Coordinator, Pacific Gas and Electric Company

Emily A. Winter

Project Manager for Outreach for the Healthcare Emergency Management Program, Boston University School of Medicine

Anthony Abruzzese

Adjunct Professor, Boston University School of Medicine Healthcare Emergency Management Program

Read This Article

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• Published: 08 March 2016

Stratified coastal ocean interactions with tropical cyclones

• S. M. Glenn 1 ,
• T. N. Miles 1 ,
• G. N. Seroka 1 ,
• R. K. Forney 1 ,
• H. Roarty 1 ,
• O. Schofield 1 &
• J. Kohut 1  

Nature Communications volume  7 , Article number:  10887 ( 2016 ) Cite this article

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• Atmospheric dynamics
• Physical oceanography

Hurricane-intensity forecast improvements currently lag the progress achieved for hurricane tracks. Integrated ocean observations and simulations during hurricane Irene (2011) reveal that the wind-forced two-layer circulation of the stratified coastal ocean, and resultant shear-induced mixing, led to significant and rapid ahead-of-eye-centre cooling (at least 6 °C and up to 11 °C) over a wide swath of the continental shelf. Atmospheric simulations establish this cooling as the missing contribution required to reproduce Irene’s accelerated intensity reduction. Historical buoys from 1985 to 2015 show that ahead-of-eye-centre cooling occurred beneath all 11 tropical cyclones that traversed the Mid-Atlantic Bight continental shelf during stratified summer conditions. A Yellow Sea buoy similarly revealed significant and rapid ahead-of-eye-centre cooling during Typhoon Muifa (2011). These findings establish that including realistic coastal baroclinic processes in forecasts of storm intensity and impacts will be increasingly critical to mid-latitude population centres as sea levels rise and tropical cyclone maximum intensities migrate poleward.

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Introduction.

Tropical cyclones are among the most destructive weather phenomena on Earth 1 . Declines in hurricane related mortalities 2 reflect improvements in global atmospheric and ensemble modelling approaches 3 that have reduced hurricane track forecast errors by factors of 2–3 (ref. 4 ). Despite two decades of progress in hurricane track prediction, improvements in hurricane-intensity forecast skill have lagged significantly 4 . The predictions, public response and unexpected devastation patterns related to Hurricane Irene exemplify this dichotomy. Accurate track forecasts days in advance provided time for preparations and coastal evacuations, but Irene’s official forecast maximum wind speeds along the Mid-Atlantic coast were consistently ∼ 5 m s −1 too high 5 . Irene instead caused catastrophic inland flooding because of heavy rainfall 5 , making it the eighth costliest cyclone to hit the United States since 1900 (ref. 6 ), with damages of ∼ $16 billion (ref. 5 ). These intensity forecast uncertainties have significant negative consequences, ranging from unnecessary preparation costs to future public skepticism 7 .

Improved tropical cyclone intensity predictions include dependencies on the rapid space–time evolution of the atmosphere–ocean responses and feedbacks 8 . Coupled atmosphere–ocean models demonstrate that small shifts in sea surface temperature (SST) and stratification, even on small (100 km) horizontal scales, can have significant impacts on storm intensity 9 , 10 , 11 . Several studies have noted 12 , 13 , 14 , 15 , 16 the relationship between warm and cold mesoscale features in the deep ocean and rapid changes in intensity, but the coastal ocean has received much less attention.

Here, utilizing an ocean observing network to inform ocean and atmospheric model simulations, the role of baroclinic processes on a stratified coastal ocean and their impact on the intensity of Hurricane Irene was quantified. The high percentage of ahead-of-eye-centre 14 , 17 , 18 cooling (76–98%) observed in Irene is not reproduced by standard open ocean models that exclude these coastal baroclinic processes. Atmospheric model sensitivity studies indicate that intense in-storm sea surface cooling over a strongly stratified coastal regime is the missing contribution required to reproduce the rapid decay of Hurricane Irene’s intensity. The 30-year historical buoy record shows an average of 73% of the in-storm cooling occurs ahead-of-eye-centre on the Mid-Atlantic Bight (MAB) in the stratified season. A Yellow Sea buoy observed up to 85% of in-storm cooling ahead-of-eye-centre during Super Typhoon Muifa (2011). The results demonstrate the importance of rapid ahead-of-eye-centre vertical shear-induced mixing processes and the ensuing ocean–atmosphere feedbacks for generating more accurate simulations of storm intensity.

Synoptic conditions

Hurricane Irene formed east of the Caribbean’s Windward Islands on 22 August 2011 and made initial United States landfall in North Carolina as a Category 1 hurricane on 27 August. It re-emerged over the ocean in the MAB before a second landfall in New Jersey as a tropical storm on 28 August (ref. 5 ), closely following the historical northeastward tracks of hurricanes along the northeast United States 19 . Irene accelerated and lost intensity as it crossed the MAB, moving parallel to the coast with the eye over inner-continental shelf waters ( Fig. 1a ). Propagation was rapid at 30–40 km h −1 , requiring only ∼ 9.5 h to cross from North Carolina to New Jersey landfall. Cloud bands extended over 600 km from the eye centre, obscuring the ocean from satellite infrared SST sensors during passage. Differencing 3-day composites of cloud-free satellite imagery before (24–26 August) from after (29–31 August) Irene reveals the regional pattern of MAB sea surface cooling ( Fig. 1a and Supplementary Fig. 1A,B ). The largest cooling (5–11 °C) was observed to the right of the eye centre over the MAB’s middle to outer shelf. Inner shelf cooling was slightly less, with averages of 3–5 °C of cooling within the 25-km radius eye wall ( Supplementary Fig. 1C ). Cooling was much less significant on the shelf seas to the south of the MAB, in the deep ocean to the east and, as previously noted in other hurricanes 20 , along the very shallow unstratified coast, bays and sounds.

( a ) SST difference map post-Irene (8/31) minus pre-Irene (8/26) with NHC best track (black dots connected by dashed line labelled with August date and UTC time), weather buoys/stations (coloured diamonds), underwater glider RU16 location during storm (yellow square) and bathymetry at 50 m (dotted magenta) and 200 m (solid magenta). ( b – d ) Buoy/station observed SST (blue) and air temperature (red) with vertical black dashed line/label indicating the time/value of minimum air pressure ( b , c ), and time of eye passage according to NHC best track data ( d ). The individual SST three-day composite maps for 24–26 August and 29–31 August are provided in Supplementary Fig. 1A,B .

Observations

National Data Buoy Center (NDBC) buoys 44009 and 44065 recorded peak wind speeds ( Supplementary Fig. 2 ) near 20 m s −1 from offshore as Irene approached. At these NDBC buoys and at 44100, water temperatures dropped rapidly by 3.8–6.3 °C ahead of eye centre passage ( Fig. 1b–d ), representing 82–98% of the in-storm cooling at these locations ( Supplementary Fig. 3 ). At Irene’s fast propagation speed, the eye was still 150–200 km to the south after the most rapid cooling was complete. As the ocean surface cooled, observed air temperatures were greater than SSTs, indicating air–sea-sensible heat fluxes were from the atmosphere into the ocean.

Atmospheric conditions ( Fig. 2a ) were recorded just inshore of a Slocum autonomous underwater glider 21 , 22 measuring subsurface ocean conditions 23 during Irene at the location shown in Fig. 1a (see Supplementary Fig. 4 for a plot of the complete glider track well before, during and after the storm). Winds initially from offshore (90°), with speeds near 20 m s −1 ahead of the eye, rotated rapidly to blow from onshore (270°) after the eye passed. Glider-observed subsurface temperatures ( Fig. 2b ) indicate that initially, typical MAB summer stratification 24 was present, with a seasonally warmed surface layer ( ∼ 24 °C) above the MAB Cold Pool 25 (<10 °C) separated by a sharp (<8 m thick) thermocline. Significant cooling of the surface layer (5.1 °C) and deepening of the thermocline (>15 m) was observed under the leading edge of the storm. Little change in thermocline depth and much less cooling (1.6 °C) of the upper layer was observed after eye passage. Thus, ahead-of-eye-centre cooling represents 76% of in-storm cooling observed at the glider ( Fig. 2b ). Both the glider and buoy data suggest that much of the satellite observed SST cooling (over ∼ 100,000 km 2 of continental shelf) occurred ahead-of-eye-centre.

( a ) Tuckerton WeatherFlow, Inc. station 10 m wind speed (orange) and direction from (black) with vertical black dashed line/label indicating the time/value of the minimum air pressure corresponding to landfall time on 28 August at 935 GMT. ( b ) Temporal evolution and vertical structure of the glider temperature during storm conditions with lines indicating top (black) and bottom (magenta) of thermocline. ( c ) Cross-shelf currents (positive onshore, negative offshore) for the surface layer (red) from CODAR HF Radar, depth-averaged (green) from the glider and bottom layer (blue) calculated from the depth-weighted average of the HF radar and glider velocities. ( d ) Same as c but for along-shelf currents (positive up-shelf northeastward and negative down-shelf southwestward).

Ocean surface currents measured by a CODAR high-frequency (HF) radar 26 network 27 illustrated the rapid response of the thin surface layer ( Supplementary Fig. 5 ) to the changing wind direction ( Fig. 2a ). Time-series of the cross-shelf components of the currents ( Fig. 2c ) at the glider location, with positive values towards land, indicate that the onshore surface currents began building before the eye entered the MAB, increasing to a peak value >50 cm s −1 towards the coast before the eye passage. Along-shelf currents throughout the water column were weak ( Fig. 2d ). After the eye, the winds changed direction and within a few hours, the cross-shelf surface currents switched to offshore. Despite the strong observed surface currents, the depth-averaged current ( Fig. 2c ) reported by the glider remained small during the storm’s duration, with peaks barely exceeding 5 cm s −1 . As in deep water, the current response is baroclinic 28 , 29 , but the low depth-averaged current implies a strong offshore flow in the bottom layer. These bottom layer currents were estimated based on the relative layer thicknesses and the requirement that the combined surface and bottom layer-averaged currents matched the glider-observed dead-reckoned depth-averaged current. The estimated bottom layer currents accelerated in the offshore direction as the eye approached, causing significant shear between the two layers at the same time the surface layer was deepening and cooling.

Ocean model simulations

Coastal ocean three-dimensional (3D) model simulations of Irene using the Regional Ocean Modeling System (ROMS) in the MAB 30 , 31 successfully reproduced the thermocline deepening and surface layer cooling ( Fig. 3a ) similar to the glider observations ( Fig. 2b ). The modelled cross-shelf velocity component ( Fig. 3b ) also has similarities to the combined glider and HF radar data ( Fig. 2c ). The surface layer flow accelerated shoreward for 12 h until eye passage, while the bottom layer responded more slowly with an offshore counter-flow. A few hours after eye passage, the cross-shelf flows reversed, also consistent with observations. The dominant terms in the cross-shelf momentum balance ( Fig. 3g ) indicate that the surface wind stress increased as the eye approached and decreased as it receded. Before the eye centre arrival, the presence of a coastline produced an offshore-directed pressure gradient that nearly balanced the wind stress and accelerated the offshore jet in the bottom layer. After the storm passage, the cross-shelf surface current switched to offshore; the cross-shelf pressure gradient also switched sign and was redirected towards the coast. At this point in the storm, the dominant cross-shelf momentum balance was nearly geostrophic ( Fig. 3g ) with a northward along-shelf surface current ( Fig. 3d ).

ROMS ocean simulation results at the glider location during the storm period, with first vertical black dashed line indicating initiation of the coastal baroclinic response and second vertical black dashed line indicating eye passage. ( a ) Temperature with top (black) and bottom (magenta) of thermocline as in Fig. 2b . ( b ) Cross-shelf velocity (red/yellow onshore; blue offshore). ( c ) Eddy viscosity. ( d ) Along-shelf velocity (red/yellow northward; blue southward). ( e ) Log 10 (Richardson number) with black contour indicating Richardson number of 0.25. ( f ) Vertical diffusion temperature diagnostic equation term, showing warming (positive, red/yellow) and cooling (negative, dark blue). ( g ) Dominant depth-averaged cross-shelf momentum balance terms (positive onshore and negative offshore) from wind stress (wstress, magenta), Coriolis force (coriolis, red), pressure gradient (press, cyan) and bottom stress (bstress, blue). ( h ) Same as g but for along-shelf momentum balance terms (positive northward, negative southward).

The subsurface cross-shelf circulation within the two-layer coastal ocean had a significant influence on vertical mixing as illustrated by the Richardson number ( Fig. 3e ) and the vertical eddy viscosity ( Fig. 3c ). The Richardson number and the eddy viscosity show that the surface layer deepened to meet the stratification at the top of the thermocline as the surface layer accelerated with the approaching storm. As the offshore counter current accelerated in the bottom boundary layer, the lower layer Richardson number also decreased and eddy viscosity increased until the two layers interacted. The most rapid ahead-of-eye-centre cooling and deepening of the surface layer occurred when the small Richardson numbers and large vertical eddy viscosities from the surface and bottom boundary layers overlapped. The model’s temperature diagnostic equation indicates that vertical diffusion ( Fig. 3f ) was the dominant term ( Supplementary Fig. 6 ) acting to deepen the thermocline and cool the surface layer during the event.

Atmospheric model simulations

Atmospheric model simulations of Irene used the Weather Research and Forecasting (WRF) 32 model as applied to the US East Coast for tropical cyclone forecasting 33 . Typical surface boundary approaches in uncoupled atmospheric models use satellite SSTs over water that remain fixed when new data is not available because of cloud cover. A matrix of over 130 simulations revealed ahead-of-eye-centre cooling of the ocean’s surface layer has a significant impact on intensity as reflected in the hurricane pressure ( Fig. 4 ) and wind fields ( Supplementary Fig. 7 ). Examining the ensemble of simulations with track errors less than one eye-wall radius, the largest wind and pressure intensity sensitivities were generated using fixed warm pre-storm and cold post-storm SST boundary conditions ( Supplementary Figs 8,9 ). The sea level pressure (SLP) fields at landfall indicate the warm ( Fig. 4a ) versus the cold ( Fig. 4b ) SST changed the centre SLP by 7–8 hPa, with the maximum wind speed reduced by >5 m s −1 due to the cooler SST ( Supplementary Fig. 7 ). The minimum SLP time history ( Fig. 4c ) of selected model runs can be compared with the National Hurricane Center (NHC) best track parameters. The best track central pressure remains constant near 952 hPa until the eye enters the MAB (28 August at about 00 h), followed by a steady increase in the central pressure to 965 hPa 13 h later as the eye leaves the MAB. Once Irene’s eye entered the MAB, the cold SST air–sea flux parameterization sensitivities all produce a reduction in intensity that cluster with the best track analysis, while the warm SST air–sea flux parameterization sensitivities maintain a lower minimum SLP with little change nearly until landfall.

( a ) WRF model SLP (with surface flux option 2) at landfall (red star) for the warm SST boundary condition with NHC best track drawn as in Fig. 1a . ( b ) Same as a but for the cold SST. ( c ) Minimum SLP for NHC best track (black), and WRF’s three air–sea flux parameterization options isftcflx=0 (thin line); 1 (dotted line); and 2 (thick line) for the warm (red) and cold (blue) SST. Vertical grey and black dashed lines indicate eye enters MAB, makes landfall and leaves MAB. ( d ) Model SLP sensitivity to SST (black, warm minus cold SST for isftcflx=2), and to flux parameterizations (isftcflx=1 minus isftcflx=0) for warm (red) and cold (blue) SST. ( e ) Box and whisker plots of SLP deviations from NHC best track when eye is over MAB for warm (red) and cold (blue) SST.

The top three model sensitivities are quantified by the envelope width for the minimum SLP ( Fig. 4d ). For both warm and cold SSTs, sensitivities to the three standard WRF air–sea flux formulations range from 0 to 2 hPa for the 13 h after the eye entered the MAB. The sensitivity to warm and cold SST begins growing as the storm nears the MAB, climbing steadily to 5 hPa as it leaves the MAB. Statistical comparisons of each model run to the NHC best track over the MAB are quantified by the box and whisker plots ( Fig. 4e ) showing the median, inter-quartile range and outliers. The three warm SST air–sea flux sensitivities consistently over-predict the intensity with minimum SLPs that are too low, while the three cold SST air–sea flux sensitivities more accurately reflect the intensity reduction for all of the air–sea flux options.

Using Hurricane Irene as a diagnostic case study, a new feedback mechanism on storm intensity in the coastal ocean has been identified. The strong onshore winds occurring ahead-of-eye-centre in tropical cyclones and the coastal wall set up a down-welling circulation that limits the storm surge and results in significant shear across the thermocline. This shear leads to turbulent entrainment of abundant cold bottom water and mixing with warmer surface water. The resulting ocean cooling reduces surface heat fluxes to the atmosphere, weakening the storm.

Rapid tropical cyclone intensity changes over the deep ocean have been correlated with storm passage over warm and cold core eddies 12 , 13 , 14 , 15 , 16 , 34 . Also in the deep ocean, SST changes of as little as 1 °C are noted to significantly impact storm intensity 9 , 35 . During Hurricane Irene, ahead-of-eye-centre cooling of 3.8–6.3 °C was observed with nearshore buoys ( Supplementary Fig. 3 ) and 5.1 °C was observed with a mid-shelf glider ( Fig. 2 ). Storm-induced cooling in deep water is often equally distributed between the front and back half of the storm 36 . Deep ocean simulations of Irene with both a 1D ocean mixed layer model and the 3D Price–Weller–Pinkel 37 model produced 32 and 56% of the in-storm cooling ahead-of-eye-centre, respectively. In Hurricane Irene, 76% (glider) to 98% (buoy 44100) of the in-storm cooling occurred ahead-of-eye-centre, indicating that coastal baroclinic processes are enhancing the percentage of ahead-of-eye-centre cooling in Irene.

To verify that enhanced ahead-of-eye-centre coastal ocean cooling is not unique to Irene, 30 years of historical nearshore buoy data throughout the MAB were investigated. During that time period, ahead-of-eye-centre cooling was observed in all 11 tropical cyclones that tracked northeastward over the MAB continental shelf during the highly stratified summer months (June–August) 24 , 38 ( Table 1 and Supplementary Figs 10–12 ). The maximum continental shelf buoy observed ahead-of-eye-centre cooling for these 11 storms averages 2.7±1.3 °C, representing an average of 73% of the in-storm cooling.

An 11-year global satellite climatology 39 reveals that the shallow mid-latitude Yellow Sea and northern East China Sea also experience a large 20 °C seasonal SST cycle, similar to the MAB but over three times larger in area. A 1986 Yellow Sea shipboard conductivity temperature and depth survey reports surface to bottom temperature differences approaching 15 °C (ref. 40 ), also similar to the stratified summer MAB. Maps of western Pacific typhoon tracks ( coast.noaa.gov/hurricanes ) indicate 26 typhoons have tracked across the northern East China Sea and Yellow Sea during June–August since 1985. Like Irene, the landfalling intensity of Super Typhoon Muifa (2011) was over-predicted by standard models 41 . Satellite SST maps indicate Muifa caused significant in-storm cooling (up to 7 °C) across ∼ 300,000 km 2 of the continental shelf 41 . Nearshore buoy observations show cooling of 4.1 °C (85% of the in-storm cooling observed at that location) was ahead-of-eye-centre ( Table 1 , Supplementary Fig. 13 ).

Globally, over the past 30 years, tropical cyclone maximum intensities have migrated poleward 42 . In the North Atlantic, hurricane intensities have increased since the early 1980s and are projected to continue to increase as the climate warms 43 , 44 , 45 , 46 . Combined with rapid sea level rise 47 , mid-latitude population centres will experience heightened vulnerability to storm surge and inundation from increasingly powerful storms. To mitigate these risks, improved forecasting of tropical cyclone intensity over mid-latitude stratified coastal seas is vital, and will require realistic 3D ocean models to forecast enhanced ahead-of-eye-centre cooling.

Data source

The Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) is a sustained regional component of the US Integrated Ocean Observing System (IOOS) 48 . Its integrated observation network of satellites, buoys, coastal meteorological stations, HF radar and autonomous underwater gliders provided the data used in this study 49 .

Satellite remote sensing

National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High-Resolution Radiometer (AVHRR) satellite data ( Supplementary Fig. 1 ) were acquired through a SeaSpace TeraScan L-Band satellite ground station at Rutgers University. AVHRR data are converted to SST using the multi-channel SST algorithm 50 . To specifically map areas of rapid cooling, a ‘coldest-dark-pixel’ composite technique is used to identify and remove bright cloud covered pixels while retaining the darker ocean pixels. This is accomplished through the following series of tests performed on AVHRR channels 4 and 2 scans. Pixels are considered contaminated by clouds and removed if (1) AVHRR channel 4 (10.3–11.3 μm) temperatures are <5 °C (3.5 °C) in summer (winter); or (2) near infrared albedo in daytime AVHRR Channel 2 (0.725–1 μm) exceeds 2.3% (an empirically derived threshold specific to the MAB). Further tests are performed on 3 × 3 km grid boxes to account for large changes in temperature over short distances typical of cloud edges. Centre pixels are flagged as potential cloud edges and removed if (1) temperature changes in AVHRR channel 4 scans are >1 °C across the centre point of each 3 × 3 grid data; or (2) the change in infrared albedo across the centre of each 3 × 3 grid box is >0.15%. After declouding is performed, the resulting 3 days of scans between 12:00 to 17:00 GMT are composited with the NASA (National Aeronautics and Space Administration) short-term Prediction Research and Transition centre (SPoRT) 2 km blended 7-day SST product. At each pixel the coldest value is retained between all daytime AVHRR scans for the past 3 days and the SPoRT SST product for that day to ensure retention of coastal upwelling zones and regions that underwent rapid mixing. Consistent with real-time processing protocols, the date assigned to each composite corresponds to the final day of the data window.

Meteorological observations

Meteorological observations were obtained from NOAA NDBC buoys, coastal towers and pier stations, and a WeatherFlow Inc. meteorological tower located in Tuckerton, New Jersey ( Fig. 1a ). Buoys 44009 (38.461° North and 74.703° West) and 44065 (40.369° North and 73.703° West) included wind speed and direction measured at a height of 5 m, air temperature at a height of 4 m and ocean temperatures at 0.6 m depth. Buoy 44100 (36.255° North and 75.591° West) is a Waverider buoy managed by Scripps Institution of Oceanography that measured ocean temperatures at 0.46 m depth. Station DUKN7 (36.184° North and 75.746° West) is a coastal station that measures air temperature at 15.68 m above mean sea level. The Tuckerton WeatherFlow Inc. meteorological tower (39.52° North and 74.32° West) measured wind speed and direction at 12 m. Meteorological data is plotted at the standard frequencies and averaging intervals reported by these stations.

High frequency radar

A network of over 40 CODAR Ocean Sensors SeaSonde HF Radar stations 26 are deployed along the MAB coast by a consortium of institutions coordinated through MARACOOS 27 . The stations transmit HF radio waves that are scattered off the ocean surface waves and then received back on shore. The Doppler shift in the Bragg peaks of the received signal are used to map the radial components of the total surface velocity field in front of each station 51 . Radial components from multiple stations are combined using an optimal interpolation scheme 52 to produce 1 h centre-averaged hourly surface current maps 53 with a nominal 6 km spatial resolution ( Supplementary Fig. 5 ).

Autonomous underwater gliders

Teledyne Webb Research Slocum gliders are buoyancy-driven underwater vehicles that act as mobile sensor platforms 22 . These instrument platforms adjust small amounts of buoyancy in order to glide through the water column at 20–30 cm s −1 in a sawtooth pattern. At pre-programmed intervals the gliders come to the surface and transfer data back to Rutgers University in near real-time. The glider used in this study, RU16, was equipped with an un-pumped Seabird conductivity temperature and depth sensor that logged data every 4 s on downcasts and upcasts. Depth- and time-averaged velocity calculations were performed using a dead-reckoning technique typical for such platforms 22 , 54 , 55 . The measured pitch angle, fall velocity and a model of glider flight to estimate angle of attack are used to calculate an underwater horizontal displacement during each dive segment. The difference between the calculated horizontal displacement from the final pre-dive location and the actual surfacing location divided by the time underwater provides an estimate of depth- and time-averaged velocity.

A combination of dead-reckoned depth-averaged glider currents and HF radar surface currents are used to estimate bottom currents along the glider track ( Fig. 2c ). The following algorithm assumes that the HF radar surface currents are representative of the surface layer above the thermocline (defined as the maximum vertical temperature gradient along each profile) and requires that the depth-weighted average surface and bottom layer currents must equal the total depth-averaged glider current:

where H s and H b are the layer thicknesses above and below the thermocline, respectively, U g and V g are along- and cross-shelf depth-averaged currents, respectively, from glider dead-reckoning, U s and V s are surface layer-averaged currents from HF radar, and U b and V b are the calculated bottom layer-averaged currents ( Fig. 2 ).

ROMS model setup

The numerical simulations were conducted using the ROMS 31 , a free-surface, sigma coordinate, primitive equation ocean model (code available at http://www.myroms.org ) that has been widely used in a diverse range of coastal applications. The ESPreSSO (Experimental System for Predicting Shelf and Slope Optics) model 56 covers the MAB from the centre of Cape Cod southward to Cape Hatteras, from the coast to beyond the shelf break and shelf/slope front. Gridded bathymetric data is used to construct a model grid with a horizontal resolution of 5 km ( Supplementary Fig. 4 ) and 36 vertical levels in a terrain-following s-coordinate system. The initial conditions were developed from the same domain ROMS run with strong constrained four-dimensional variational (4D-Var) data assimilation 57 . The meteorological forcing is from the North American Mesoscale (NAM) model 12 km 3-hourly forecast data. Reanalyses of surface air temperature, pressure, relative humidity, 10 m vector winds, precipitation, downward longwave radiation and net shortwave radiation were used to specify the surface fluxes of momentum and buoyancy based on the COARE bulk formulae 58 . Boundary conditions are daily two-dimensional surface elevation, as well as three-dimensional velocity, temperature, and salinity fields from the Hybrid Coordinate Ocean Model Navy Coupled Ocean Data Assimilation forecast system. Inflows for the seven largest rivers are from daily average United States Geological Survey discharge data. Tidal boundary conditions are from the The ADvanced CIRCulation tidal model. The general length scale method k-kl type vertical mixing scheme 59 , 60 is used to compute vertical turbulence diffusivity.

ROMS momentum balance analysis

We extracted depth-averaged momentum balance terms from ROMS ( Fig. 3g–h ) at the glider sampling location in order to diagnose the dominant forces during the storm, where the acceleration terms are balanced by a combination of horizontal advection, pressure gradient, surface and bottom stresses and the Coriolis force (horizontal diffusion was small and neglected in this case):

where u and v are the along-shelf and cross-shelf components of velocity respectively, t is time, P is pressure, ρ o is a reference density, τ s and τ b are surface and bottom stresses, h is water column depth and f is the latitude-dependent Coriolis frequency.

ROMS heat balance analysis

Heat balance analysis . The general conservation expression for the temperature budget in ROMS is given by

with the following surface and bottom boundary conditions:

The ROMS conservation of heat equation was used to diagnose the relative contributions of the different terms responsible for the modelled temperature change. Time-series of the vertical temperature diagnostic terms were investigated along the glider track with emphasis on the temperature evolution between the top of the thermocline depth (the shallowest location where the vertical temperature gradient exceeded 0.4 °C m −1 , black contour in Fig. 3a and Supplementary Fig. 6 ) and the transition layer depth (the deepest location where the vertical temperature gradient exceeded 0.7 °C m −1 , magenta contour in Fig. 3a and Supplementary Fig. 6 ). Term-by-term analysis of equation 5 offered additional insights on the temperature source and sink terms. Supplementary Fig. 6A shows the temperature rate of change, which is the sum of the vertical diffusion term ( Supplementary Fig. 6B ) and advection term ( Supplementary Fig. 6C ), in which the advection term is separated into along-shelf advection ( Supplementary Fig. 6D ), cross-shelf advection ( Supplementary Fig. 6E ) and vertical advection ( Supplementary Fig. 6F ). The horizontal diffusion term’s order of magnitude is much smaller than other terms and is not plotted. The dominant term influencing the surface mixed layer temperature change was the vertical diffusion, which is plotted in Fig. 3f .

WRF-ARW model setup

The Weather Research and Forecasting Advanced Research (WRF-ARW) dynamical core (code available at http://www.wrf-model.org ) 32 , Version 3.4 was used for the atmospheric simulations in this study. WRF-ARW is a fully compressible, non-hydrostatic, terrain-following coordinate, primitive equation atmospheric model. Our WRF-ARW domain extends from South Florida to Nova Scotia ( Supplementary Fig. 14 ), with grid resolution of 6 km in the horizontal and 35 vertical levels. Lateral boundary conditions used are from the Global Forecast System (GFS) 0.5° initialized at 06 UTC on 27 August 2011.

Our simulations begin at 06 UTC on 27 August 2011 when Hurricane Irene was south of North Carolina (NC) over the South-Atlantic Bight (SAB) and end at 18 UTC on 28 August 2011 as the storm moved into New England. Simulation results shown ( Fig. 4c,d and Supplementary Fig. 7C,D ) begin at 12 UTC on 27 August 2011, at NC landfall time, after the model has 6 h to adjust to vortex initialization. WRF’s digital filter initialization (DFI) was run to determine the sensitivities to different realizations of the GFS initializations. DFI deepened the initial vortex central pressure by over 10–960 hPa, which matches GFS initial central pressure ( Supplementary Fig. 15 ). However, downstream sensitivity to DFI beyond 2 h was minimal.

For our control run, the following are used: longwave and shortwave radiation physics were both computed by the Rapid Radiative Transfer Model-Global scheme; the Monin–Obukhov atmospheric layer model and the Noah Land Surface Model were used with the Yonsei University planetary boundary layer scheme; and the WRF Double-Moment 6-class moisture microphysics scheme was used for grid-scale precipitation processes.

WRF sensitivity to SST

The model was run over 130 times to compare the sensitivity of certain parameter tuning. All sensitivities were compared to the control run (described above), which for surface boundary conditions over the ocean, that is, SST, used the Real-Time Global High-Resolution (RTG HR) SST analysis from 00 UTC on 27 August 2011 fixed throughout the simulation. This is the warm pre-storm SST, and has temperatures across the model domain similar to the AVHRR coldest-dark-pixel composite a day earlier ( Supplementary Fig. 1A ). By having the control run use Real-Time Global High-Resolution SST fixed throughout the simulation, we are consistent with what the operational NAM 12 km model used for bottom boundary conditions over the ocean.

To show the maximum impact of the ahead-of-eye-centre SST cooling on storm intensity, we compared our control run with a simulation using observed cold post-storm SST. For this, we used our AVHRR coldest-dark-pixel composite, which includes data from 29 to 31 August 2011 ( Supplementary Fig. 1B ). According to underwater glider and NDBC buoy observations along Irene’s entire MAB track, almost all of the SST cooling occurred ahead of Irene’s eye centre ( Fig. 1b–d ). NDBC buoy observations near Irene’s track in the SAB (41013, 41036, 41037) also show ahead-of-eye-centre SST cooling, but values are on the order of 1 °C or less ( Fig. 1a ). Because our model simulations include only 6 h of storm presence over the SAB before NC landfall, and SST cooling in the SAB was significantly less than observed in the MAB ( Fig. 1 ), we can conclude that the main result from our SST sensitivity is due to the ahead-of-eye-centre cooling in the MAB.

WRF sensitivity to air-sea flux parameterizations

The equations for the momentum (τ), sensible ( H ) and latent heat fluxes ( E ) are as follows:

where ρ is density of air, C D is drag coefficient, U is 10 m wind speed, c p is specific heat capacity of air, C H is sensible heat coefficient, θ 2 m is potential temperature at 2 m and θ sfc is potential temperature at the surface, L ν is enthalpy of vaporization, C Q is latent heat coefficient, q 2m is specific humidity at 2 m and q sfc is interfacial specific humidity at the surface.

Three options exist in WRF-ARW Version 3.0 and later for air–sea flux parameterizations (WRF namelist option isftcflx=0, 1, and 2; see (ref. 61 ) for more details). These parameterization options change the momentum ( z 0 ), sensible heat ( z T ) and latent heat roughness lengths ( z Q ) in the following equations for drag ( C D ), sensible heat ( C H ) and latent heat ( C Q ) coefficients:

where K is the von Kármán constant and z ref is a reference height (usually 10 m).

Therefore, our SST sensitivity effectively changes the variables θ sfc and q sfc in equations 8, 9, 10 above, while our air–sea flux parameterization sensitivities change the equations for the momentum, sensible heat and latent heat coefficients (equations 11, 12, 13) going into the respective flux equations 8, 9, 10.

For our air–sea flux parameterization sensitivities in this study, we ran isftcflx=0, 1, and 2 with both the warm (control) and cold SST boundary conditions.

Additional WRF sensitivities

We have discussed SST and air–sea flux parameterizations. WRF-ARW was run over 130 times in total, with various model configuration and physics options turned on and off.

We examined the ensemble of simulations with space/time track errors <25 km (one eye-wall radius) from available NHC best track positional data. Only preserving those simulations with accurate tracks is important because Hurricane Irene tracked close to and parallel to the Mid-Atlantic coast. The remaining sensitivities are shown in central pressure ( Supplementary Fig. 8 ) and maximum winds ( Supplementary Fig. 9 ). These are cumulative hourly sensitivities during Irene’s presence over the MAB and NY Harbor (28 August 00-13 UTC). Supplementary Table 1 shows a list of these sensitivities, with the WRF namelist option number alongside its name (control run listed last for each sensitivity).

The sensitivity titled ‘latent heat flux <0 over water’ requires a brief explanation. In the WRF surface layer scheme code, there is a switch that disallows any latent heat flux less than 0 W m −2 (similarly, there is a switch that disallows any sensible heat flux less than −250 W m −2 ). WRF convention for negative heat flux is downward, or atmosphere to land/water. We run WRF after removing the line of code disallowing negative latent heat flux, and compare to the control run. This switch removal only changes latent heat flux and allows it to be negative over water, as the subsequent WRF land surface scheme modifies fluxes and allows for negative latent heat flux over land.

Ahead-of-eye-centre and in-storm cooling calculations

Ahead-of-eye-centre cooling ( Table 1 ) at NDBC buoys ( Supplementary Figs 10–12 ) and the Yellow Sea buoy ( Supplementary Fig. 13 ) was calculated by taking the difference between the maximum water temperature as the winds increased above 5 m s −1 and the minimum water temperature before or at the minimum observed SLP. In-storm cooling was determined as the difference between the same maximum water temperature as the winds increased above 5 m s −1 and the minimum water temperature while winds remained above 5 m s −1 after the pressure minimum. To calculate the average and standard deviation of cooling for the 11 storms passing through the MAB since 1985, we selected the one buoy on the continental shelf that recorded wind speed, pressure and water temperature and exhibited the greatest ahead-of-eye-centre cooling. For completeness we show Irene cooling statistics ( Table 1 ) and time-series ( Supplementary Fig. 3 ) for buoys 44065 and 44100 used in Fig. 1 .

Data availability

Buoy meteorological data used in this study are available through the National Data Buoy Center. Glider and HF Radar data can be found through the MARACOOS THREDDS server at http://maracoos.org/data . Tuckerton meteorological data are supported by WeatherFlow Inc. and can be made available upon request to the corresponding authors. WRF and ROMS model simulations are stored locally at the Rutgers Department of Marine and Coastal Sciences and will be made available upon request to the corresponding authors. The Yellow Sea buoy data are stored at the Institute of Oceanology, Chinese Academy of Sciences.

Additional information

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Acknowledgements

Support was provided by the National Oceanic and Atmospheric Administration (NOAA) led Integrated Ocean Observing System (IOOS) through the Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS, NA11NOS0120038), the Environmental Protection Agency (EP-11-C-000085), the New Jersey Department of Environmental Protection (WM13-019-2013) and Board of Public Utilities (2010RU-COOL) and the NOAA Cooperative Institute for the North Atlantic Region (CINAR, NA13OAR4830233), Disaster Recovery Act. The authors thank Teledyne Webb Research and Rutgers University for student support, and the NOAA National Centers for Environmental Prediction for student engagement. We further thank David Titley, Jim Price and an anonymous reviewer for their useful comments.

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Contributions

S.M.G. synthesized and analysed the multiple data sets and wrote the manuscript in collaboration with the other authors. T.N.M. assisted in the synthesis of the in situ oceanographic data. G.N.S. contributed the atmospheric and storm sensitivity studies. Y.X. contributed the ocean simulations and analysis. R.K.F. performed historical buoy data and storm track analysis. F.Y. provided plots of buoy data beneath Super Typhoon Muifa. H.R. provided the observational data from the HF Radars. O.S. was involved in data collections and involved in analysis and manuscript preparation. J.K. contributed the Slocum data and was involved in analysis and manuscript preparation. All authors reviewed and edited this manuscript.

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Using Value Modeling to Evaluate Social Media Messages: The Case of Hurricane Irene

Freberg, Karen, Saling, Kristin, Vidoloff, Kathleen, G., & Eosco, Gina (2013). Using value modeling to evaluate social media messages: The case of Hurricane Irene. Public Relations Review, 39(3), 185-192.

Summary Advances in social media have opened a world of opportunities for crisis communication professionals and public affairs specialists for sharing information across public and private sectors and disseminating necessary information about a crisis among various stakeholder groups. Emerging technology communication platforms are transforming how crisis communicators reach their audiences and partner agencies in a variety of situations. These transformations and adoptions not only change how individuals and organizations communicate during a crisis, but also how others perceive their actions and behaviors and the overall reputation of brands or corporations involved in the situation. Through an analysis of social media crisis messages and the integration of qualitative and quantitative value modeling techniques, the researchers propose a set of best practices and a simple baseline model for determining what comprises a “good” crisis message, using data collected regarding Hurricane Irene in 2011 to demonstrate a proof-of-concept model. Implications of this research include the construction of guidelines for effective crisis communication and reputation management monitoring using social media platforms.

Method Social mention was used to collect 2,157 updates appearing on social media platforms from August 22, 2011 to September 1, 2011 from more than 100 social media sites.

Key Findings 1) Twitter was one of the primary resources for information related to this particular crisis. 2) While some traditional hashtags were used consistently, there were some geographical differences (i.e., North Carolina had #ncirene and Maryland had #mdirene). 3) It was unclear whether hashtags differences were effectively communicated across media platforms or agencies. Government agencies were all using some of the same hashtags, but other sources were not. 4) Some agencies added idiosyncratic tags to their updates. 5) All of the top updates were associated with Twitter, yet only four updates used a hashtag. 6) The updates that received the highest scores were more conversational than official in tone of voice, so this should be an area of consideration for crisis communication professionals operating in social media.

Implications for Practice Individuals want both textual and visual information in times of crisis as well as references to a credible source. Organizations and agencies need to be strategic in monitoring and assigning hashtags for specific events for others to monitor, follow, and respond to if need be. Proper training in this practice for crisis communications professionals using social media needs to be explored.  Project EPIC from the University of Colorado has initiated this type of research with their “Tweak the Tweet” application to use in disasters. Most of the updates collected from Social Mention focused on addressing breaking news about Hurricane Irene, but they were framed to be more conversational than official compared to traditional media. Practitioners should explore whether conversational tone versus official may be more beneficial.

Article Location The full article is available for purchase at: http://www.sciencedirect.com/science/article/pii/S0363811113000386

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Experts review the lessons learned from Hurricane Irene

Did forecasters, policymakers and media types overhype Hurricane Irene ? It's not just a meteorological question: The debate over whether the outlook for damage was overhyped, or hyped just right, touches upon issues of risk perception and even the climate change debate. Like most natural disasters, Irene's deadly sweep over the U.S. East Coast has left behind some important lessons for researchers as well as regular folks.

Here are some of the lessons that Monday-morning commentators are chewing over:

What was right and wrong about storm prediction?

The computer models, and the meteorologists who wielded them, put in a "gold medal" performance when it came to predicting Irene's track — but there was much more uncertainty about the intensity of the storm. That's typical for tropical storms, said Frank Marks, director of the Atlantic Oceanographic and Meteorological Laboratory's Hurricane Research Division. "Irene really exemplified the issues that we've been trying to tackle," he told me.

Hurricanes typically follow a pattern in which an outer ring of storms will tighten up to replace an inner ring surrounding the hurricane's eye, intensifying the storm system in the process. In Irene's case, that pattern (known as eyewall replacement ) was interrupted, and the storm didn't gather as much strength as most of the models suggested. "Some of the models did represent it well," Marks said, but there wasn't enough confidence in those models to change the storm forecast.

Researchers have been working to reduce the error rate for hurricane track and intensity forecasts through the Hurricane Forecast Improvement Project , with the goal of a 50 percent reduction from 2008 levels by 2018. The University of Washington's Cliff Mass, an expert on weather modeling, said Irene showed that much more progress still has to be made on predicting a storm's intensity.

"The classic is good forecast for track, bad forecast for intensity," he told me. "Let's face it: This happens all the time. ... To get the intensity right, you have to be able to predict the inner workings of the storm, and that's what we don't do well yet."

But Mass said "we didn't even need the models" to know that Irene would become less intense as it moved up the coast, through the increasingly cool waters of the Atlantic. In fact, Mass contends in a blog post today that "there is really no reliable evidence of hurricane-force winds at any time the storm was approaching North Carolina or moving up the East Coast."

He argued that the National Weather Service should have downgraded the storm much more quickly than it did. "There's a tendency to be conservative," he told me. "We have to learn to be more nimble."

Did forecasters overhype the storm?

In his blog posting, Mass addresses the hype surrounding Irene: "Considering the tendency for media to hype storms, it is crucial for meteorologists to stick to the exact story and not overwarn in the hope of encouraging people ot take effective action. If the storm was known not to be a hurricane earlier, might the mayor of NY have held off closing the city down, thus saving billions of dollars?"

Marks said that the storm was assessed based on readings taken from above as well as on the surface, and that the National Oceanic and Atmospheric Administration followed the standard procedures for those assessments. But he acknowledged that Irene was a tough storm to classify, in part because of its breadth. "From Cape Cod all the way inland to Pennsylvania — just think about the energy," he said. "It's really the energy of the storm, it's not the peak wind."

He said spin control isn't part of NOAA's mission. "We provide as much information as we can, based on what we know," Marks said. "What the public and decision makers do with that information is something that's out of our purview."

Marks acknowledged that some of the reports made the storm sound scarier than it really was. "If you looked at those scenarios that the media was getting ... the disaster scenario was extreme. That was for a major hurricane coming straight at them, not a weakening storm coming up the coast," he said.

How much was lost in translation?

So was this a case of journalists and policymakers making too much of the storm? Maybe so, said David Ropeik, a consultant on risk perception,  Big Think blogger and author of the book "How Risky Is It, Really?"  But maybe that's not so bad.

"Yes, the information the media presented was wrapped up in breathless alarmism," Ropeik, a former msnbc.com contributor, told me. "But we forget two things: First, surveys show that the public knows that about the media. And second, under all the alarmism was really important information that helped people stay safe: storm track timing, tips for preparedness , evacuation routes. It was alarmist in voice, but an informative tool. And that probably helped more than it hurt. ... There was no panic, there was no hysteria."

Ropeik said government officials also did the right thing: "In my opinion, they were overly precautionary, but most people want them to do that. One can only measure the accuracy of their precaution in hindsight, and you don't want to err on the wrong side. ... The evacuation, the closing of the subways, you don't want to make a mistake on that in the wrong direction."

There were political considerations, to be sure. Just ask New York City Mayor Michael Bloomberg, who faced harsh criticism over the lack of preparedness for last winter's snowstorms — or former President George W. Bush, who was similarly criticized in the wake of Hurricane Katrina almost exactly six years ago.

But beyond the politics, the storm's toll —  more than 30 dead , plus an estimated $7 billion in property damage — clearly demonstrates that Irene was more than just hype.

"I daresay the people who are saying there was overreaction are not those who are still without power, or who suffered property losses, or who lost loved ones," Ropeik said. "Risk is a matter of perception. It depends on who you ask."

Some commentators worry that hyping hurricanes will lead folks to disregard future warnings as a case of "crying wolf," but Ropeik said the public response to the warnings about Irene "puts the lie to that."

"Other storms have been hyped, and have not panned out, and yet people still took reasonable precautions this time," he said. "The 'cry-wolf' thing didn't happen."

Do more big storms lie ahead?

The concerns about Irene's effects could hint at the shape of climate debates to come.

Research published last year in the journal Nature Geoscience suggested that global warming was likely to produce fewer but stronger tropical storms . This year, a study in the journal Science came to a similar conclusion.

Such projections have sparked strong debate, as most claims about climate effects have done. It's impossible to link any single event, such as Irene or Katrina, to long-term climate trends. But in a posting to his Desmog Blog , science writer Chris Mooney argues that Hurricane Irene should get people thinking about what lies ahead :

"... Irene focuses our attention on our serious vulnerability, and we need to seize that moment — because too often our default position is to act like nothing bad is going to happen.

"There are several places in the United States, besides New Orleans, where a strong hurricane landfall could be absolutely devastating. These include the Florida Keys, the Miami-Fort Lauderdale area, Tampa Bay/St. Petersburg and Houston/Galveston. But they also include some East Coast locations, and chief among these is New York/Long Island. ...

"So what are our major coastal cities doing to protect themselves? That's the question we should all be asking right now."

What questions are you asking? Share them as a comment below, and we'll see if we can get a discussion going.

Update for 5:30 p.m. ET: One of the first Irene-related research projects to come to light focuses on whether big storms could actually counteract the effects of greenhouse-gas emissions.

Scientists at the Stroud Water Research Center and the University of Delaware are sampling the storm runoff at sites along creeks in Delaware to measure how much carbon is being transported. In a news release , the National Science Foundation says the project could reveal how much of a role soil erosion plays in sequestering carbon to prevent it from re-entering the global carbon cycle.

"The bigger the storm, the greater the disproportionate load, so you might have a single 100-year storm event move 25 percent of the material for an entire decade," said Anthony Aufdemkampe, a scientist at the Stroud Water Research Center. "This is important, because fresh waters and the carbon they transport play a major role in the global cycling of greenhouse gases."

Update for 6:30 p.m. ET: Climate Progress' Joe Romm notes that rising sea levels, which some see as an effect of global climate change, would heighten the destructive effect of coastal storms such as Irene because the storm surge would come on top of those higher seas. (The state of global sea levels is another subject of scientific discussion .)

More about Irene's aftermath:

• Twitter's top lessons from Hurricane Irene
• Readers capture Hurricane Irene
• Hurricane Irene spawns baby boom
• Stocks close sharply higher after Irene passes

Connect with the Cosmic Log  community by "liking" the log's Facebook page , following @b0yle on Twitter  or adding me to your Google+ circle . You can also check out "The Case for Pluto," my book about the controversial dwarf planet and the search for other worlds.

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In the path of the Hurricane: impact of Hurricane Irene and Tropical Storm Lee on watershed hydrology and biogeochemistry from North Carolina to Maine, USA

• Published: 02 February 2018
• Volume 141 , pages 351–364, ( 2018 )

Cite this article

• Philippe Vidon   ORCID: orcid.org/0000-0001-7470-843X 1 ,
• Diana L. Karwan 2 ,
• A. Scott Andres 3 ,
• Shreeram Inamdar 4 ,
• Sujay Kaushal 5 ,
• Jonathan Morrison 6 ,
• John Mullaney 6 ,
• Donald S. Ross 7 ,
• Andrew W. Schroth 8 ,
• James B. Shanley 9 &
• Byungman Yoon 10  

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Although many climate predictions suggest that the frequency and intensity of large storm events might increase in the coming decades, few studies document the full impact of such events along their path. Here, we synthesize information on the impact of Hurricane Irene (formed August 21 2011) and Tropical Storm Lee (formed August 30, 2011) on erosion and sediment transport, lake metabolism, riparian hydrology and biogeochemistry, and stream water quality, from North Carolina to Maine. In almost all cases, these storms generated unprecedented changes in water quality (concentrations, loads), from tenfold increases in DOC and 100-fold increases in POC in Maryland, to 100-fold increases in TSS concentrations in Pennsylvania. Overbank flooding and up to 200-year streamflow events were recorded in New York and Vermont. In many cases, particulate loads (e.g. POC, PP, TSS) occurring during Irene and Lee represented more than 30% of the annual load. The dominance of particulate exports over solutes during Irene and Lee is consistent with the mobilization of normally immobile sediment pools, and massive erosion as reported at many locations across the Northeastern US. Several studies reported long lasting (> 1 year) effects of Irene and Lee on cyanobacterial blooms, erosion, or stream suspended sediment concentrations. However, this review also highlighted the lack of a consistent strategy in terms of methods, and measured water quality parameters. This strongly hinders our ability to fully assess the large-scale impact of such events on our environment, and ultimately their impact on our economy and society.

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Acknowledgements

This review is the result of a collaboration effort amongst the authors of this study that started at the AGU Chapman Conference on Extreme Climate Events held in San Juan Puerto Rico in January 2017. We would like to thank the USDA (award # 2016-67019-25280), NSF-EPSCoR (#1641157), USGS, National CZO office, and the US Forest Service IITF for funding this AGU Chapman conference on Extreme Climate Events in San Juan, Puerto Rico and providing travel funds to the attendees. Dr. Peter Groffman provided data for the BES LTER site online for Baisman Run. Significant funding for collection of these data and other data in this paper was provided by the National Science Foundation Long-Term Ecological Research Program (NSF DEB-0423476 and DEB-1027188) and the Critical Zone Observatory Program (CRB CZO - NSF EAR 1331856). White Clay Creek sampling and analysis was enabled by an NSF postdoctoral fellowship to Dr. Diana Karwan (NSF EAR 1144760). The contributions of Andrew Schroth and Donald Ross were supported by the National Science Foundation under VT EPSCoR Grant No. NSF OIA 1556770 and IIA-133046. Laura Medalie of the US Geological Survey provided WRTDS output and useful insight towards its analysis and limitations. She also provided constructive comments on an earlier version of the paper. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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The State University of New York College of Environmental Science and Forestry (SUNY-ESF), Syracuse, NY, 13104, USA

Philippe Vidon

Department of Forest Resources, University of Minnesota, Saint Paul, MN, 55108, USA

Diana L. Karwan

Delaware Geological Survey, University of Delaware, Newark, DE, 19716, USA

A. Scott Andres

University of Delaware, Newark, DE, 19716, USA

Shreeram Inamdar

University of Maryland, College Park, MD, 20742, USA

Sujay Kaushal

U.S. Geological Survey, East Hartford, CT, 06108, USA

Jonathan Morrison & John Mullaney

Department of Plant and Soil Science, University of Vermont, Burlington, VT, 05405, USA

Donald S. Ross

Department of Geology, University of Vermont, Burlington, VT, 05405, USA

Andrew W. Schroth

U.S. Geological Survey, Montpelier, VT, 05602, USA

James B. Shanley

Yale University, New Haven, CT, 06511, USA

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Vidon, P., Karwan, D.L., Andres, A.S. et al. In the path of the Hurricane: impact of Hurricane Irene and Tropical Storm Lee on watershed hydrology and biogeochemistry from North Carolina to Maine, USA. Biogeochemistry 141 , 351–364 (2018). https://doi.org/10.1007/s10533-018-0423-4

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Received : 27 September 2017

Accepted : 25 January 2018

Published : 02 February 2018

Issue Date : December 2018

DOI : https://doi.org/10.1007/s10533-018-0423-4

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• Introduction
• When Safety Is in Harm's Way
• Emergency Preparedness Man
• Hanging in the Balance
• Our Plan Doesn't Work

Put to the Test: Hurricane Irene

• Lessons Learned
• Lesser of Two Evils
• Download this case as a PDF

Hurricane Irene Photo from NASA.gov

For the next four years, LIJ thankfully faced no major natural or manmade disasters. But late on Saturday, August 20, 2011, Tropical Storm Irene formed over the Atlantic Ocean 120 miles east of the Caribbean. On Wednesday, August 24, weather forecasts indicated a high probability that Irene—then a Category 3 hurricane just north of Haiti—would hit the New York metropolitan area. North Shore-LIJ activated its Emergency Operations Center in Syosset, NY and began preparing for the storm.

Solazzo, Romagnoli, Mahoney and the other members of North Shore-LIJ's Incident Command team prepared for the possibility of evacuating one or more hospitals. They alerted the system's employees to prepare for the hurricane and initiated a rapid discharge throughout the system to open as many beds as possible. They canceled all elective procedures. They reviewed evacuation plans for Southside Hospital and the two Staten Island University Hospitals. "It was really tense," recalls Romagnoli.

We had never evacuated before. As the storm gets closer and the track continues to hold and the winds continue to be sustained at hurricane force, we are coming quickly to the realization that we have to evacuate.

Mandatory. On Thursday, August 25, the City of New York ordered that low-lying coastal areas—Flood Zone A—be evacuated by 8 p.m. on August 27. [18] The order was mandatory. The city determined flood zones using the federal government's Sea, Lake and Overland Surge from Hurricanes (SLOSH) Model. A storm surge was a temporary rise in sea level caused by a storm's winds. The wider the storm and the stronger the winds, the higher the storm surge. Coastal geography also played an important role. Storm surges that hit the New York metropolitan area were boosted in size and speed by the natural funnel formed by northern New Jersey and Long Island. Zone A marked areas at risk of flooding from the surge of a Category One hurricane. [19]

North Shore-LIJ's Staten Island hospitals were in Zone A. Because of the logistical challenges of evacuating hospitals on short notice, the city had no choice but to give healthcare facilities the option—unavailable to other individuals or businesses—to shelter in place. Romagnoli, as chief of protective services, recommended that LIJ evacuate those hospitals. That night, as EVP/COO Solazzo weighed his decision, the three vulnerable North Shore-LIJ hospitals moved to the next stage of the evacuation process and began moving mechanically dependent patients, including those on ventilators.

The Incident Command team determined that Staten Island University North was the hospital most vulnerable to the storm surge, predicted to be five feet. They made the assessment that if the surge went that high, the hospital was right at the line of being able to maintain generator power. However, floodwater knocking out backup power wasn't the only risk Irene posed. "You’ve got to understand all the threats to your institution," says Solazzo.

Staten Island University Hospital - North Site Image courtesy of North-Shore LIJ

There was a significant chance that Irene's storm surge would breach Staten Island's sewage treatment facility. If that happened, sewage could be forced back into the hospitals. "We tried to get from our engineers and from the city engineers their best estimate, and it became basically a 50-50 shot," says Solazzo. "I was not comfortable riding out a 50-50 and not having any backup plan if that happened." The team considered vertical evacuations within the Staten Island facilities, but in the end determined that the risk was too great and a full evacuation was warranted. "So we pulled the trigger on Staten Island at that point," says Solazzo. "That was a little over 48 hours to the storm."

The decision. It was the first time hospitals in the region were evacuated in advance of a hurricane. It was a decision "none of us thought we would make," says Romagnoli.

It’s hundreds of people. And I have to tell you, it’s tough. The thing that makes it hard is you have to make [the decision] so far out. You have to make it while you leave yourself a nice window of time that you can complete the evacuation prior to the storm.

Confirms Solazzo: "It’s probably one of the most significant, most difficult decisions I’ve made in my career. After you make the decision, you’re holding your breath."

North Shore-LIJ's two Staten Island hospitals were evacuated the next day: Friday, August 26. It was a sunny day. Patients were brought to a staging area at each hospital. An official checked each patient's identification wristband to confirm that the patient was going to the right facility in the right type of vehicle. Each hospital maintained a list of evacuees and periodically sent the list to the Emergency Operations Center. The patients were moved on ambulances and buses. A few were moved by helicopter. [20] That afternoon, the city expanded the evacuation order to include portions of the Rockaways in Zone B, and moved the deadline to 5 p.m. on the 27th. [21]

Click here to see a map of the three hospitals in relation to the evacuation zones.

The North Shore-LIJ Incident Command team weighed their options for the third vulnerable facility, Southside Hospital, on the evening of the 26th. It appeared that the storm surge was not likely to reach the hospital from the shore, however, a creek flowed behind the facility and some of the company's engineers were concerned that it could channel the storm surge into the hospital. Solazzo determined the risk was sufficient to warrant evacuating the hospital. They began a phased evacuation that evening and completed it by the middle of the next day. In all, North Shore-LIJ evacuated 947 patients from three at-risk hospitals long before Irene's winds lashed the buildings. Despite the evacuations, the three hospitals remained open with sufficient staff to treat emergency cases. "You can’t have a closed sign on the door," says Romagnoli.

North Shore-LIJ's evacuation plan worked smoothly. It also turned out to be unnecessary. At 5:35 a.m. on Sunday, August 28, 2011, Irene made landfall near Atlantic City, New Jersey. Irene weakened shortly before hitting the shore, losing some of her punch. Winds dropped from 86 to 69 miles per hour. The storm's rainfall produced devastating floods in Vermont and upstate New York, but Irene's winds and 4-foot storm surge brought relatively little damage to the coast.

[18] New makes it hard is you have to make [the decision] so far out. You have to York City press release PR-308-11. See: http://www.nyc.gov/html/om/html/2011b/pr308-11_alt.html

[19] New York City Natural Hazard Mitigation Plan, Coastal Storms: Multi-Hazard Analysis for New York City. See: http://www.nyc.gov/html/oem/downloads/pdf/hazard_mitigation/section_3f_coastal_storm_hazard_analysis.pdf

[20] “A Hospital System's Response To A Hurricane Offers Lessons, Including The Need For Mandatory Interfacility Drills,” Health Affairs , 31, no.8 (2012):1814-1821

[21] New York City press release PR-308-11. See: http://www.nyc.gov/html/om/html/2011b/pr308-11_alt.html

Biographies

• James Romagnoli
• Mark Solazzo

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North Carolina Mitigation Project after Irene Proves its Worth

DURHAM, N.C. – After Hurricane Matthew roared through coastal North Carolina in October 2016, damage in the state will total as much as $4 billion, according to the National Oceanic and Atmospheric Administration. Proactive steps taken by Pamlico County following 2011’s Hurricane Irene spared this North Carolina County from significant impacts.

Officials say the lack of damage can be credited to the county’s diligence in collaborating with North Carolina Emergency Management (NCEM) and Federal Emergency Management Agency (FEMA) in the years following another devastating storm from five years earlier - Hurricane Irene.

Pamlico County encompasses 337 square miles of land and sits nearly surrounded by water at the junctions of the Pamlico and Neuse rivers and Pamlico Sound. The population of approximately 13,000 is disbursed among nine incorporated municipalities, beach homes and vacation getaways.

Hurricane Irene had a devastating impact on the sound-side communities of eastern North Carolina. In some areas, the inundation from the hurricane was as high as a 500-year flood.

FEMA brought in approximately 300 manufactured housing units to support displaced Hurricane Irene residents, whose homes, schools and churches flooded with as much as 20 feet of water.

The Goose Creek Volunteer Fire Department was flooded with nearly 5 feet of water and Oriental’s Town Hall suspended operations and moved to a trailer on higher ground. As a whole, North Carolina recorded more than $1.2 billion in damage from Irene.

Four months after the 2011 hurricane, the county, state and FEMA entered into the Pamlico County Hazard Mitigation Grant Pilot Program to assist survivors with either elevating their homes or selling them to the county, which would then demolish them. (More)

Three subgrants from the Hurricane Irene Hazard Mitigation Grant Program totaling $13 million funded the project.

“More than 300 people were interested and applied within three days,” said Nicholas Burk, section manager for hazard mitigation grants, North Carolina Department of Public Safety Division of Emergency Management. “Residents were enthusiastic for the assistance.”

Of the 300 applicants, 115 qualified. Half of those owners decided to elevate and the rest opted for demolition. In this pilot program, FEMA paid 75 percent of the costs and the state picked up the remaining 25 percent.

Whichever option homeowners selected, it took some time before the work began. Before demolition, the properties were sold to the county and went through traditional sale procedures – appraisal, title search, surveys and closing. As the owner, the county was required to place a restrictive deed on each property to preserve it as open space with no future development.

If owners opted for elevation, engineers were hired to certify that the structures were suitable to raise. If they were suitable, their foundations were retrofitted, and the homes were raised above the 100-year base flood elevation. Also included was utility relocation and retrofitting as well as modifications to steps/porches/decks to meet current building code.

Homeowners paid nothing for the work, except for any emergency repairs that were needed before the pilot project began. They were not reimbursed for those costs.

Also during the pilot program, the county, NCEM and FEMA assembled the first-of-its kind Hazard Mitigation Disaster Recovery Center. The state opened the center and staffed it with experts who could explain the processes and procedures for each option.

“We leveraged the name from the FEMA Individual Assistance Disaster Recovery Center because the name resonated with survivors,” Burk said.

Because the concept was successful following Hurricane Irene, the state has now scaled the Hazard Mitigation Disaster Recovery Center concept to eligible communities that were heavily damaged by Hurricane Matthew, including Princeville and Lumberton.

For more information on North Carolina’s recovery, visit fema.gov/disaster/4285 and readync.org. Follow FEMA on Twitter at @femaregion4 and North Carolina Emergency Management @NCEmergency .

Disaster recovery assistance is available without regard to race, color, religion, nationality, sex, age, disability, English proficiency or economic status. If you or someone you know has been discriminated against, call FEMA toll-free at 800-621-3362 or TTY at 800-462-7585.

FEMA’s mission is to support our citizens and first responders to ensure that as a nation we work together to build, sustain, and improve our capability to prepare for, protect against, respond to, recover from and mitigate all hazards. Follow FEMA on Twitter at @femaregion4. Download the FEMA app with tools and tips to keep you safe before, during and after disasters.

Dial 2-1-1 or 888-892-1162 to speak with a trained call specialist about questions you have regarding Hurricane Matthew; the service is free, confidential and available in any language. They can help direct you to resources. Call 5-1-1 or 877-511-4662 for the latest road conditions or check the ReadyNC mobile app, which also has real-time shelter and evacuation information. For updates on Hurricane Matthew impacts and relief efforts, go to ReadyNC.org or follow N.C. Emergency Management on Twitter and Facebook. People or organizations that want to help ensure North Carolina recovers can visit NCdisasterrelief.org or text NCRecovers to 30306.

The U.S. Small Business Administration (SBA) is the federal government’s primary source of money for the long term rebuilding of disaster-damaged private property. SBA helps homeowners, renters, businesses of all sizes, and private nonprofit organizations fund repairs or rebuilding efforts and cover the cost of replacing lost or disaster-damaged personal property. These disaster loans cover losses not fully compensated by insurance or other recoveries and do not duplicate benefits of other agencies or organizations. For more information, applicants may contact SBA’s Customer Service Center by calling (800) 659-2955 , emailing [email protected] , or visiting SBA’s website at www.sba.gov/disaster . Deaf and hard-of-hearing individuals may call (800) 877-8339 .