XzV-V-03-001 C2

OCEAN ACOUSTIC HURRICANE DETECTION AND CLASSIFICATION

Nicholas Makris and Kerry Emanuel

MITSG 03-31

~ ~ ~ ~

~ ~ ~ ~ ~ ~ OCEAN ACOUSTIC HURRICANE DETECTION AND CLASSIFICATION

NicholasMakris and Kerry Emanuel

MITSG 03-31

SeaGrant College Program MassachusettsInstitute of Technology Cambridge, 02139

NOAA Grant No.: NA86RG0074 ProjectNo.: RCM-9 Ocean Acoustic Hurricane Detection and Classification Completion Report 2003

Princi le Investi ator:

Nicholas Makris MassachusettsInstitute of Technology phone: 617-258-6104 fax: 617-253-2350 email: makris .mi t,edu

Co-Princi leInvesti ator: Kerry Emanuel MassachusettsInstitute of Technology phone: 617-253-2462 email: emanuel!texmex.mit.edu

Objective: Thepurpose of thisproject is to investigatethe feasibility of usingunderwater acousticsensing techniques to safelyand inexpensively estimate the destructive power of a hurricane. Introduction: We have experimentally demonstratedthat Underwater Acoustic methods may be usedto measurethe wind speedand destructive power of a hurricane,using data from a NOAA hydrophone in the North Atlantic. From this hydrophone data we determined an empiricalrelationship I-V" betweena hurricane'swind speedV and the underwater noiseintensity I it generates.Given this relationshipwe showthat acousticintensity measurementscan be used to accurately estimate hurricane wind speed.To find this relationship, wind speeddata had to be synthesizedfrom satellite and aircraft meteorological measurements.While the results strongly support the use of underwater hydrophone to estimate wind speed,further experiments, with concurrent acoustic and meteorological measurements,are necessarybefore such an acoustic hurricane wind speedmeasurement system could be implemented in practice.

This project also includes theoretical modeling of the generation and propagation of hurricane noise through the ocean wave-guide environment. The theoretical modeling shows that underwater hydrophonescould be used to accurately measurethe wind speeds in a hurricane, and from that measurementclassify the destructive power of the hurricane.A singlehydrophone, deployed such that a hurricanepasses over it, can be usedlike an anemometer,measuring the hurricane'swinds asit movesoverhead. Long arrays of hydrophones, in certain deep ~ater ocean environments, can measurethe destructive power of a hurricane at ranges of severalhundred kilometers.

Hurricanes are one of the most deadly and costly natural disasters.In 1970 a single storm killed more than 300,000 people in Bangladesh Holland 1993! and in 1900 an unnamedhurricane became the mostdeadly disaster in United Stateshistory killing over 6000 people Hebert 1993!. In 1992 hurricane Andrew becamethe most costly natural disaster in the United Statesat an estimated cost of 2S billion dollars Pielke 1998!.Classification of a hurricane'sdestructive power is critical for hurricaneplanning. For example,inaccurate classification can leadto poor forecastingand unnecessary evacuations Emanuel 1999!, which are costly, or missed evacuations,which can result in loss of life. These fatalities and cost can be reduced if the public is given timely and accurateadvanced warning, but this dependson the ability to accurately classify hurricanes while they are still far from land.

Currentlythere are two platformsfor measuringhurricanes while far from land, satellitesand aircraft. While satellitesare useful for detectingand trackinghurricanes they arenot very accuratein determiningthe wind speedand wind power neededto classify a hurricane. The most common method for satellite hurricane classification, the Dvorak method,has been known to yield errorsin wind speedestimates as high as40% Pasch 2000, Franklin 2003, Avila 2001, Stewart 2002!. In the North Atlantic this limitation is overcomethrough the useof reconnaissanceaircraft that are capableof making accuratewind speedmeasurements and hurricane classifications. Unfortunately the expenseof theseaircraft preventstheir useoutside the Holland 1993!. In this project we consider how underwater acoustic measurementsof hurricane wind speedand power may provide an additionaltechnique to augmentsatellite and aircraft measurementsand improve our ability to accuratelyclassify hurricanes.

At frequenciesabove 10 Hz and wind speedsbelow 35 kts, it hasbeen shown that the oceanambient noise field receivedby a singlehydrophone depends significantly on the local surfacewind speed Piggott 1964,Perrone 1970, Farmer 1984,Hodgkiss 1990, Chapman1993!. This relationshiphas previously been used to estimatethe ambientnoise basedon wind speedmeasurements Cato 1997!,or conversely,to estimatethe wind speedbased on ambientnoise measurements Evans 1984,Katz 1984,Nystuen 1997!. In this project we considerestimating the wind speedsin a hurricaneby measuringthe ambientnoise it generates. Experimental Wor k: In 1999NOAA's Pacific Marine EnvironmentalLaboratory PMEL! deployed severalautonomous hydrophones near the Mid-Atlantic Ridge to continuouslyrecord the low frequency,1 to 50 Hz, ambientnoise in the ocean Fox 2001, Smith2002!. While the purposeof this project wasto study seismicevents, on September15 of 1999, hurricaneGert Lawrence2000! passed@rect! y overone of thesehydrophones fortuitously yielding a clearrecording of the underwatersound generated by the hurricane.Figure 1 showsthe spectrogramof the acousticfield measuredby the hydrophoneas the hurricanepassed overhead. This figure showshigh acousticlevels at

around 14:00 as the hurricane was overhead

Spectragramat NOAAhydrophone 9 duringhurricane Gert

~ 20 H~~30 LL$5-Sep-1M909.'02:67 is-sep-'N99914:$8.'l7t5-Ssp-f999!934:58 Time dd-mmm-yyyyHH:MM:SS! Figure 1: Spectrogramof the acousticfield at the NOAA hydrophoneduring the time hurricaneGert passedoverhead. The high levelsat around14:00 are due to the high winds of the hurricane. %e also see a decrease.in the acoustic level at 14:18 as the calm eye of the hurricane passedover the sensor,

Figure 1 showsthe averagepower spectral level SL of the noise,between 10 and50 Hz andaveraged over 10 min time windows,during the hurricane'spassage. No direct measurementsof the wind speedwere takenat the hydrophonelocation so, to correlate spectrallevel with wind speed,we synthesisethe local wind speed record based on availabledata from US Air Forcehurricane hunting aircraft and NOAA hurricane trackingsatellites. Figure 1 showsthe wind speedprofile of thestorm from aircraft measurementson Sept 16th,a dayafter the acousticmeasurements HurricaneResearch Division1999!; clearly shown is thecharacteristic hurricane structure with the powerful winds,up to 47m/s 92kt!, of theeye wall surrounding the calm center or eye.Satellite locationestimates were provided every six hourswith anerror of +/- 28 km National WeatherService 1999!, effectively yielding an ensemble of possibletracks hurricane Gert could havetaken past the hydrophone.The meansatellite estimated track showsthat hurricaneGert passed 11 km to thesouth of thePMEL hydrophone. Each possible track, coupledwith the aircraft measured wind speed profile, can be used to synthesisethe local hurricanewind speed V at thehydrophone location.

Figure3: %ind speedprofile, in mlsat a heightof 10m, of hurricaneGert from US Air Forcereconnaissance aircraft measurements taken on September 16th 1999 Hurricane ResearchDivision 1999!. The low wind speed area at the center is thchurricane's eye surroundedby thehigh winds of theeyc.-wall. The solid linc gives the path of thcPMEL hydrophonerelative to thehurricane based on the likely track showing that hurricane Gertpassed 29 km to thesouth of thePMEL hydrophone.

Foreach possible track the power-law hypothesis is tested by calculatingthe hnear regressionbetween the measured spectral level SL andthe log of thelocal wind speed V. Themost likely hurricane track is theonc where the root-mean-square error RMSE!of thelinear regression between the spectral level and the log of thewind speed is minimised,equalling 0.7. For this likely track, the black line, in Fig3 showsthc path of thePMEL hydrophone relative to thehurricane on Sept 15th, In otherwords hurricane Gertpassed 29 km to thesouth of thePMEL hydrophone; this is withinthe +1- 28 km marginor errorof thesateHite estimated track, Note how the powerful eye wall andthe calm eye of the hurricane passedover the hydrophone. From this likely track Fig 2 shows the local hurricanewind speedabove the hydrophonewith the maximumwinds of the eye wall occurring at 13:30 and 15:30 hours and the minimum winds of the eye at 14:30. ln Fig. 1 we see an exceptional correlation between the measurednoise spectral level and the local wind speed.

Plotting the noise spectral level versus wind speedfrom hurricane Gert Fig. 4! further illustratestheir closecorrelation. The linearregression of this datais alsoplotted and we find a I f!-V'~' or SL=33.6log V!+37.1relationship between wind speedV and noise intensity I f!. While similar relationships have been measuredpreviously at wind speeds below 15 m/s, this is the first datato suggestsuch a relationshipfor the high windsof a hurricane.

This strong relationship between underwater noise and local wind speedsuggests that hydrophonescould be used as 'underwater anemometers' for estimating the surface wind speedsin a hurricane and from the wind speedclassifying the hurricane's destructive power.The powerof a hurricaneis proportionalto the cubeof the hurricane'smaximum wind speed Holland1997! which occursin the eye wall. So a hydrophonedeployed to passunder a hurricane'seye wall could estimatethe hurricane'smaximum wind speed andtotal power. Similar underwateracoustic estimates of wind speedhave been made previouslybut at muchlower wind speeds Evans1984, Katz 1984,Nystuen 1997!. As the NOAA/PMEL hydrophonepassed under the eye wall of hurricaneGert it recordeda maximumspectral level of 93 dB re uPa'/Hzwhich, given our empiricalrelationship, equatesto a maximumwind speedof 46 m/s. This comparesfavourably to the 47 m/s maximumwind speedmeasured by reconnaissanceaircraft, a differenceof only 2%. 30 40 VNndSpeed m/s! Figure4: AverageNoise Spectral Level SL dB re uPa'/Hz!versus surface wind speedV m/s!on a logscale at thePMEL hydrophone circles! based on thelikely track.The linearregression shows a SL= 33.61og V!+37.1relationship between spectral level and log wind speedwith a root-mean squareerror RMSE! of only 0.7 dB.

Now weconsider the likely accuracyof sucha windspeed estimate Figure 3 alsoshows thatthere is only a 0.7dB RMSEbetween the data and the linear regression. This small RMSEis a resultof thelarge 40 Hz bandwidthand long 10min timewindow applied to thisdata Makris1996!. This smallRMSE illustrates the potential accuracy of using underwateracoustic measurements to estimate wind speed. A 0.7dB errorin spectral level,given the empirical relationship between noise intensity and wind speed,yields a 5% error in wind speedestimate. This is much lessthan the 40% error commonin satellitewind speed estimates Pasch2000, Franklin 2003, Avila 2001,Stewart 2002!. In fact on Sept 15th and 16th, the Dvorak satellite method estimated Gert's maximum wind speedto be between59 and64 m/s Lawrence2000! which is 26 to 36% higherthan the 47 and46 m/s estimatesmade by aircraft andacoustic measurements respectively.

The correlationbetween underwater noise level andwind speedfrom hurricaneGert is calculatedyielding a 3.36-powerrelationship between noise intensity and wind speed. Additionally, the high correlation,or small RMSE, suggeststhat estimatesof hurricane wind speedbased on underwaternoise measurements could be very accurate.A hydrophonecould theneffectively act asan 'acousticanemometer', providing an accurateestimate of a hurricane'swind speedsand destructive power. Currentlythere aremany ocean acoustic systems in existence,like the PMEL hydrophonesused in this work andthe U.S. Navy's SOSUSarrays Nishimura1994!, that could be usedfor meteorologicalmeasurements and additional systems could be deployedfrom ships, aircraft,or nearshore at relatively low cost.

While this experunentaldata shows the potentialusefulness of underwatersound measurementsfor estimatinghurricane wind speedand power, the exactre1ationship betweenwind speedand acousticintensity requires further experimentalverification. Sincedirect meastuementsof the hurricanewind speedwere unavailable and the wind speedrecord had to be synthesisedfrom error pronesateHite and delayed aircraft data, the empiricalrelationship between wind speedand acoustic intensity is not conclusive. Furtherexperiments should be conductedwith acousticand meteorological measurementstaken simultaneously. Also, future experimentsshou1d be conductedto determinethe influenceof frequencyand wave-guide environment on the measurement. Theoretical Work: The first part of this projectis the theoreticalmodeling of the acousticfield generatedby a hurricane.In our theoreticalmodeling we will considerusing single hydrophonesand hydrophone arrays to classifyhurricanes through the noise they generate.The field receivedby thesehydrophones is influencedby the wind speedsin the hurricane,the relationship between wind speedand noise generation, the sound speed variation in the oceanwave-guide, the seasurface roughness due to the extremewave heightsin a hurricane,and the frequencydependent attenuation in sea-water.These factorsare explored quantitatively to illustratetheir effect on our abilityto measure hurricanegenerated sound in the ocean.The expectedsound field from a hurricaneis thencompared to theambient noise from thelesser surface winds surrounding the hurricaneand to shippingnoise that becomessignificant below roughly 100Hz. This theoreticalanalysis gives strongevidence to indicatethat underwaterhurricane noise can beenheard above the typical ocean ainbient noise and may be usefulin classifyingthe powerof thehurricane. We find thata singlesensor placed under a hurricanemay accurately measure the local hurricane winds and from this measurementdetermine the hurricane'sdestructive power. We also explore linear arrays, typical of whatmight be deployedby oceanographicor navalvessels, and show how they may be useful in classifyinghurricanes at long range,

Figure 5 showsthe field that would be receivedby a singlehydrophone in the oceanunder a hurricaneas a functionof depthand range from the hurricane'seye, at 50 Hz, for hurricanerich North Atlantic A! and Bay of Bengal 8! oceanenvironments. The wind speedprofile of the modeledhurricane is plotted for comparison.From this figurewe seethat the acoustic intensity under the hurricane closely matches the local hurricanewind speed.So by measuringthe acousticintensity, a hydrophonecan be used as an underwateracoustic anemometer. This hasthe advantagethat a hydrophone hundredsof metersunderwater would be protectedfrom the intensehurricane winds while a conventionalanemometer moored on the seasurface would likely be damaged. 50 6

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3 50 300 i50 200 250 300 Range km!

Figure 5..Acoustic intensity in the oceanwave guideas a functionof depthand range from the hurricane'seye in the North Atlantic A! and the Bay of Bengal B! at 50 Hz. The wind speedprofile of the modelhurricane is plottedfor comparison,In generalthe intensityis depthindependent and closely follows the wind speedprofile. The exception is in the convergencezone structure in the North Atlantic at a depthof 4.7 km andat the surface at a range of 257 km, Wealso find that a hydrophoneanemometer doesn't measure the wind speed at a singlepoint. Due to thepropagation of sound in theocean wave-guide, a single hydrophonereceives sound from a finite areaof the oceansurface, This resultsin a spatiallyaveraged over about 1 km!estimate of thewind speed. Since the acoustic field is a sumof theergodic source contributions over this finite surfacearea, the field receivedby a singlesensor will be Gaussianand long time measurements of this field can yieldvery accurate results less than 10% error in wind speed!.

Figure5 alsoillustrates the difference in thehurricane generated acoustic field betweenthe North Atlantic and the Bay of Bengal.The North Atlantic is deep km! and dueto therefraction of soundin thewave-guide the acoustic field generated by the hurricanecan propagate to longranges. So in theAtlantic Fig.5 A!!we see convergence zones,rays of soundthat propagate from the powerful hurricane eye, at a depthof 4.7km andnear the surface at a rangeof 257lan. We will showlater that these convergence zonesare useful for measuringhiuYicanes atlong ranges using hydrophone arrays. The Bayof Bengalis muchshallower km! andwe don't see any convergence zone propagation.

We haveshown that a singlehydrophone can be usedto measurethe winds in a hurricane,but only if thehurricane passes overhead. This is becausea single sensor primarilymeasures the wind generated surface noise generated directly overhead. Arrays of hydrophones,however, can be usedto spatiallyfilter out the local wind noisesources andmeasure the field Roma hurricaneat longranges. An arraycan also be used in spatiallyfiltering out contamination from other noise sources like shipsor surf.By using longlinear arrays of hydrophones,typical of whatmight be towed from an oceanographicor naval vessel, we find thatit maybe possible to classifya hurricaneat rangesof severalhundred kilometers. Figure 6 showsthe response of broadside arrays in theNorth Atlantic at rangesof 257 A!, 289 B!, and385 km C! for threedifferent hurricanes.The ranges 257 and 385 km correspond to the fourth and sixth convergence zonedistance from the center of thehurricane respectively and 2&9 km is halfway betweenthe fourth and fifth convergencezones. The largest, class 5, hurricanehas a maximumwind speedof 140knots and the smallest,class I, hurricanehas a maximuin wind speedof 64 knots. In Fig. 6 we seethat the three hurricanes are distinguishable from each other by roughly five dB. This five dB difference is what we expect since the acoustic power of the hurricane is proportional to roughly the cube of the hurricane's wind speeds,i.e. 100' / 64' = 5.8 dB and 140'/100'= 4.4 dB. Note that only the larger two hurricanes are detectableover the ambient noise in this example.

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Figure6: Angular spectraldensity in dB! for a 64element horizontal broadside array as a function of steering angle for hurricane generatednoise in the North Atlantic at 100 Hz at rangesof 257 km A!, 289 km B!, and385 km C ! from the eye of the hurricane.257 and385 km correspondto thefourth and sixth convergence zones from thecenter of the hurricane.289 km is exactlyhalfway between the fourth and fifth convergencezones. Curvesare shownfor a large140 kt classfive, a medium100 kt classthree, and a small 64 kt classone hurricane. The angular spectral density due to ambientnoise is plottedfor comparison,A steeringangle of zerodegrees corresponds to thearray steered toward the hurricane. At all threeranges we seean approximately 5 dBchange in theangular spectral densitybetween each hurricane which corresponds to theroughly 5 dBchange in the hurricane'swind power. For arrays A! and C ! in a convergencezone the resolution of thearray is enoughto distinguishthe eye {at zero degrees! and the eye wall {at plus of minus three degrees! of the hurricane.

Theseplots also show the ability of thebroadside horizontal array, when placed in a convergencezone {Figs. 6 {A! and C!!, to resolvethe important features of the hurricanelike theeye and eye wall. At zerodegrees we seethe eye of thehurricane and at plusand minus three degrees we seethe eye wall. This is to beexpected since this 64 elementarray at broadsidehas an angular resolution of 1.8degrees which at a rangeof 257 km correspondsto a spatialresolution of roughly 8 km. This is smallerthan the 20 km diameterof thishurricane's eye. The width of theconvergence zone can also play a roll in our ability to resolvethe eye, however, for thisenvironinent and at thisrange the convergencezone width is roughly 5 km, lessthan the size of the eye. In reality a hurricane'seye diameter can range from tensof la1ometersto over100 km, but,given theability of thesearrays to resolvea small20 km eye,they would also be able to easily resolvea muchlarger hurricaneeye.

So we seethat a horizontalarray can be usedto discriminatebetween hurricanes of differentstrengths and that the acoustic energy density received by thearray is proportionalthe power of thehurricane. Also an array with sufficientlyhigh resolution locatedin a convergencezone can resolve the eye structureof a distanthurricane of at leastmoderate strength. So an array could be used to classifya hurricaneat longranges, however,we do seethat the measurementwould not be as straightforwardas for the single sensorcase Fig. 5!. Conclusions: From theseexperiments and models we find that underwater hydrophones represent an enormous, and currently untapped,potential for hurricane research.The strongrelationship between wind speedand underwater noise means that a single hydrophone can be used as an 'acoustic anemometer', however, unlike a traditional anemometer,it is underwaterand protected from the hurricanewinds. Analysis of data from a NOAA hydrophonein the North Atlantic alsoshows that a singlehydrophone can measurethe acoustic intensity from a hurricane and Rom this estimate the hurricane's wind speedand destructivepower. Currently there are many ocean acoustic systems alreadyin existence,like the PMEL hydrophonesused in this work andthe U.S. Navy's SOSUSarrays Nishimura 1994!,that could be usedfor meteorologicalmeasurements; andadditional systems can be deployedfrom ships,aircraft, or nearshore at relatively low cost.

These acoustic measurementswould not replace satellite methods but would complimentthem. A satellitesstrength lies in its ability to track hurricanesover their life spanas they crossentire oceans.The acousticsystems discussed in this papercould not track a hurricaneover thoseranges but rathercould provide an accuratemeasurement during the time the hurricaneis nearbyor overhead.These local acousticmeasurements could provide a ground truth measurementof the hurricane that would then refine the less accurate satellite estimates.

Furtherexperimental study is requiredto verify the relationshipbetween the hurricanewind speedand acousticintensity and to determinethe possibleinfluence of environment and frequency. Appendix A: Students

JoshuaWilson: Ph.D. Candidate, Massachusetts Institute of Technology.

AppendixB: Publicity

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