Hurricane Earl (2010)


AVHRR/MVISR Composite Image of Earl on September 2, 2010 as it tracked north off the coast of the Carolinas. Image courtesy CIMSS.

Introduction

Hurricane Earl was a Cape Verde Islands hurricane that swept a path across the Atlantic in late August and early September 2010. It brushed the Leeward Islands before curving north, tracking the Atlantic coastline and making landfall in Nova Scotia. It reached peak strength as a Category 4 storm.

In this article, I will be discussing Earl's steering environment, its seedling easterly wave, the conditions on August 28-29 just before attaining hurricane status, and an eye wall replacement cycle. I will conclude with a discussion of the self-development processes of hurricanes.

Body

Steering Environment

The image below shows Earl's general track during its life in the late summer of 2010.

Track of Hurricane Earl. Map courtesy The Weather Underground.

Earl followed a relatively classic pattern for an Atlantic hurricane, sweeping west-northwest towards the Caribbean before veering north and then recurving northeast, before hitting Canada's Maritimes. The vector wind map below will show why this track occurred. 

500mb vector winds from August 25 - September 4, 2010. The approximate track of Earl is indicated in red. It is quite apparent how the steering winds affected Earl's path, with 500mb vector winds serving as a proxy for the steering wind layer. Image courtesy ESRL.

The steering winds guided Earl; the easterlies in the mid-Atlantic pushed it westward. The lighter southwesterlies over the Caribbean curved it eastward, while the stronger southwesterlies and westerly winds off the mid-Atlantic seaboard caused the recurving towards a more northeasterly direction. Earl first became a tropical storm at about 15N, 35W, where it was being pushed along by fairly strong easterlies. By the time it reached about 16N, 62W, it had reached hurricane force. Except for a brief period of weakening off the mid-Atlantic coast, it maintained hurricane strength as the steering winds guided it up the seaboard towards landfall in Canada.

Easterly Wave

Earl had its genesis in a tropical wave which left the African coast on August 23, 2010. 

 This image from 1800Z on August 23, 2010, shows the convective cluster associated with the easterly wave that would provide the seed for Earl. The red ellipse highlights the convection; in and just offshore of Africa, convergence and convection tends to occur ahead of the easterly wave, so in this image the wave itself would be just east of the convective cluster. Channel 10 image from Meteosat SEVRI archive courtesy of Dundee Satellite Receiving Station.

This image shows the convective cluster associated with the wave; the wave itself was likely located just to the east of this cluster, as upward motion in waves near Africa tends to precede the wave itself. This pattern reverses itself in the central Atlantic and Caribbean. Once the wave reaches the eastern flank of the mid-Atlantic trough, the coupling of upward motion from the wave and the eastern flank of the trough reinforces upward motion, encouraging development. 

Favorable conditions for Strengthening

For a tropical cyclone to develop and strengthen, there are six fundamental criteria: (a) sufficient sea surface temperatures (SST); (b) a preexisting disturbance; © at least 5 degrees of latitude away from the equator; (d) relatively low mid-tropopsheric wind shear; (e) a relatively moist mid troposphere; and (f) a troposphere that is unstable with respect to moist ascent.

In the easterly wave, we have (b) and © accounted for. Let's now turn to some of the other necessary ingredients, looking at the time period (August 28) just before Earl reached hurricane strength. Earl was found to be at hurricane strenght by 1200Z on August 29 by a USAFR reconnaissance aircraft.

Sea Surface Temperatures

First, sea surface temperatures must be in excess of 80F/26.5C/299K to sustain further development, ideally with a deep, warm layer beneath the surface. A SST image (below) shows that Earl's path took it over water with SSTs sufficient to sustain development, and it maintained such SSTs all the way until it reached the waters off the coast of New Jersey. 

SST map for August 28, 2010, the day before Earl reached hurricane status. SSTs of greater than 301.5K (28.5C) supported development in the area where Earl was located as well as along its future track parallel to the Atlantic seaboard. SSTs of greater than about 299K (80F/26C) support strengthening, and these existed as far north as the New Jersey coast. Image courtesy ESRL.

TCHP imagery below also shows that the waters surrounding Earl's location on August 28 provided sufficient deep warm water to sustain further development but not so much as to encourage rapid intensification.

 TCHP image from August 28, 2010, with Earl's approximate position noted in red. TCHP values in the range of 60-70. This levels do not suggest the likelihood of sudden intensification, but when combined with the SST data, do suggest that there is sufficient warm water to sustain development. Image courtesy NOAA/AOML.

Midtropospheric Moisture

Another criterion favoring development is sufficient moisture in the mid-trophosphere. The water vapor image below shows Earl shortly before attaining hurricane status.

Water vapor image from GOES East on August 29, at 1200Z, shortly before Earl became a hurricane. Earl is embedded in an area of relatively moist middle and upper tropospheric water vapor, as shown by the gray shading. Image courtesy Dundee Satellite Receiving Station.

Earl at this time is embedded in an area of the atmosphere which is indicated (via gray shading)  to have sufficient moisture in the mid-troposphere, to sustain further development.  Indeed, the future path of Earl will keep it in this area of relatively moist air; the dark black patch to the north, off the Atlantic seaboard, is the nearest area of dryer air.

Vertical Wind Shear

A third criterion for further development is benign vertical wind shear.

Deep layer shear image from 1200Z on August 29, just as Ear was becoming a hurricane. Earl was embedded in an area of relatively low shear. Image courtesy CIMSS.

This image shows that on August 28, earl was in an area of minimal deep layer vertical wind shear, with strengths in the range of 5 to 10 m/s. Shear values of below 10 m/s are considered low enough for tropical storms to intensify. Earl was thus in an area favorable for further strengthening, shortly before it reached hurricane strength on August 29.

On August 28 (shortly before attaining hurricane strength on August 29), Earl was in an area that supported further strengthening, using our 'recipe' for tropical storms.

Eye-wall replacement cycle

 

MIMIC imagery from August 31, 2010, showing an eye wall replacement cycle. Imagery courtesy CIMSS.

The MIMIC imagery shown above as an animated GIF file is the result of a digital manipulation of microwave imagery. Microwave imagery is well suited to showing the thunderstorms that exist in the eyewall of a hurricane as well as those in the spiral bands. In areas without sufficient radar coverage, microwave imagery can help serve as a substitute, and combining multiple images as done by CIMSS above helps us study the dynamics of eye wall replacement cycles.

The image above documents the eye wall replacement cycle which occurred in the early morning hours (GMT) on August 31. The dark red returns of the eye wall thunderstorms that show at the beginning of the loop can be seen to fade away as the replacement cycle occurs that morning.  By 0800Z the inner eye wall thunderstorms disappear; meanwhile a new eye wall, further away from the center, can be seen to be organizing.

An eye wall replacement has the effect of initially weakening the storm, as the radius of maximum winds increases. After the new eye wall organizes and contracts, the storm can again gain strength, as the radius of maximum wind decreases as the eye wall contracts around the center.

Conclusion

Tropical cyclones strengthen through a process of positive feedback. The 'ingredients' of tropical cyclone development point to conditions which encourage deep convection to form and persist. We looked at three of these criteria. Warm SSTs encourage a high rate of evaporation of moisture from the sea surface; additionally, by warming the atmosphere above the relatively warm water, it encourages upwards motion and thus convection.  The warm-SST fed evaporation feeds moisture to the middle troposphere; dry air entraining into a thunderstorm causes downdrafts to develop as a result of evaporational cooling, which tends to disrupt convection. Therefore a moist middle troposphere, free of dryer air, encourages convection. Finally, relatively strong vertical wind shear disrupts the convection around the center of the storm; weak vertical wind shear allows this convection to persist.

In such conditions, a positive feedback cycle can exist for the cloud clusters, known as conditional instability of the second kind (CISK). As a convective cluster develops, a large amount of latent heat is released, promoting deep convection. This encourages compressional warming in the descending air surrounding the convective cluster. This descending air feeds moisture and air necessary to promote the low-level convergence needed to cause further convection.  As long as conditions such as SST, shear and moisture permit this cycle to persist uninterrupted, further convection will occur. 

 

E-Portfolio #2: The El Ñino of 1982-1983 and the Subtropical Jet


A cartoon I clipped and saved several years ago from the great cartoon strip Shoe. In fact, you CAN "argue with that" as long as you have a proper understanding of El Ñino. Source: http://www.shoecomics.com/

Introduction

El Ñino is a climate pattern that occurs periodically in the Pacific Ocean that is is characterized by abnormally-warm sea surface temperatures (SST) in the Eastern Pacific Ocean, defined as when the three-month average of sea-surface temperatures in a strip between latitudes five degrees north and south and longitudes 170 degrees west and 120 degrees west exceeds the long-term mean by 0.5 degrees Celsius. 

This image shows (top frame) the SST anomaly as El Ñino started to build in 1982; the red colors indicate warmer-than-average sea surface temperatures. The warm SST interacts with the atmosphere overlying it, resulting in numerous effects on the weather, on a local, regional and global scale. Image courtesy ESRL.

Typically building in strength from March through June, El Ñino has the greatest effect on weather in North America in the December - February time frame. The abnormally-warm SSTs cause a disruption in the normal Walker circulation, bringing more convection to the eastern Pacific and, through teleconnection, affecting weather throughout North America. Although there are several theories as to the cause of El Ñino, there is no consensus yet.  Regardless of cause, its effect is clear. 

I will be examining the El Ñino that occurred in 1982-1983 and the impacts it had on the subtropical jet stream (STJ)  and on seasonal temperature and precipitation in the Gulf Coast States in the United States. The 1982-1983 El Ñino was the strongest since records on these patterns have been kept (1958). This strong alteration in the SST had a significant impact on the STJ and on weather in the US Gulf Coast.

Body

El Ñino and the Subtropical Jet

The subtropical jet stream is a narrow ribbon of high speed winds, found on the equatorward side of the subtropical front. It is characterized by high speed winds increasing with altitude from about 400mb to 200mb or so, where they reach their maximum. 

 The 200mb Vector Wind climatology image above shows the STJ as a band of unusually-fast west winds around 25N-35N. The STJ tends to stay around 30N because of the tendency of air parcels to conserve angular momentum in the upper branches of Hadley Cells. This image shows the long-term average, smoothing the effects of ENSO and normal climatology. STJ is strongest in winter months. The STJ is higher on average over western Asia, Japan and the east Pacific than it is anywhere else, largely due to the influence of the Himalayan plateau. Image courtesy of ESRL.

Note that the typical STJ  tends to fade over the central and eastern Pacific before regaining some strength over Mexico and the southern US. The STJ's root can be traced to temperature gradients at about 400mb. The tropics are generally devoid of significant horizontal temperature gradients at the surface, but at 400mb a relatively strong north-south gradient forms as a result of Hadley circulation. It is this gradient that is the root of the STJ, which itself is strongest at 200mb.

Now let's examine the STJ in the winter of the 1982-83 El Ñino event. 

Mean wind vectors at 200mb during the winter period of 1982-83 El Ñino event. Note the annotated areas of unusally-strong STJ winds at 200mb during the El Ñino. Compare this to the climatology image, above; the same areas show significantly slower wind speeds during normal, non El Ñino, time periods. Image courtesy of ESRL

In the winter of 1982-83, the band of strong winds extending off of Japan extended much further into the Pacific, across the date line and almost to California. Additionally, the "gap" in the STJ that normally exists over the eastern Pacific has nearly disappeared, with a larger-than-normal pocket of higher-speed STJ from south of Baja California extending to off the coast of the Carolinas. What caused this enhanced STJ during the winter of 1982-83?

The 400mb temperature anomaly map shows the pool of anomalously-warm water over the central and east Pacific, particularly east of Hawaii. This sets up a very strong temperature gradient compared to the waters in the north Pacific. It has the effect of strengthening the STJ, which develops as a result of temperature gradients at about 400mb and reaches maximum strength at about 200mb. Image courtesy of ESRL.

During the 1982-83 El Ñino, there was a pool of significantly-warmer-than-normal air over the eastern equatorial Pacific, off of South America, and a commensurately-cool pocket of air over the North Pacific. This sets up an anomalously-strong horizontal temperature gradient along the path of the STJ. The temperature anomaly contributed to a significant height anomaly. Rembering that the root of the STJ is at about 400mb, we can see why this resulted in increased STJ at 200mb extending further east than normal climatology would suggest.

400mb geopotential height anomaly, as a result of temperature anomaly. Image courtesy ESRL.

Geopotential height anomalies at 200mb, which also contributed to the strong STJ. Image courtesy ESRL.

 

What caused these anomalies? The abnormally-warm SST during an El Ñino causes the overlying atmosphere to also become unusually warm in the central and eastern Pacific. As this warm air rises, forming the Hadley circulation, the north-south temperature gradient at 400 mb becomes stronger than in normal years. The 1982-1983 El Ñino was particularly strong, and thus SSTs were quite high. The effect of this was enhanced baroclinicity, starting at the surface and extending all the way up to 200mb, resulting in a strengthening of the STJ.  

Now that we understand how El Ñino serves to enhance the STJ, we turn to the regional effects of it on weather in the US.

Seasonal Impacts of El Ñino

The extended El Ñino-influenced STJ over North America affects weather patterns over the Gulf states of the US, including precipitation and temperature.

Precipitation
Surface precipitation rate anomaly chart for the winter of 1982-83. Note the particularly wet weather in the Pacific Northwest as well as the southeastern US. This is a typical patter for El Ñino. Image courtesy ESRL.

Precipitation in the Gulf states  is significantly higher in our El Ñino event than normal. The STJ plays an important role in cyclogenesis and transporting moisture.  The STJ and jet streaks enhance upper-air divergence, which helps intensify surface lows.

Zoomed in view of 200mb wind vectors in the winter of 1982-83, showing left-exit regions of the jet streaks. Image courtesy of ESRL.The divergence in the left-exit regions of these jet streaks can be seen to contribute to the precipitation in the Pacific Northwest of the US as well as the area off coastal Carolina. These divergence-rich area are rich grounds for cyclogenesis and accompanying precipitation. Furthermore, the fast and strong STJ serves to transport moisture from the maritime environment in the eastern Pacific and the Gulf into the adjoining Gulf states of the US.

Temperature

 The surface temperature anomaly map is shown below for the winter of the 1982-1983 El Ñino. 

We can see from this that the 1982-1983 El Ñino did not have significant effects on temperature anomalies in the southeast. However, a pattern of warmer-than-normal surface temperatures did exist over the North Central, Great Lakes and Northeastern states. This is typical of an El Ñino year. 

Average DJF temperature rankings during El Ñino years. It shows a pattern of warmer than normal temperatures over the north central part of the country, with normal or cooler-than-normal temperatures in the southeast. This is fairly consistent with the pattern we see in 1982-1983. Image courtesy of the Climate Prediction Center.Why was it that the Gulf Coast states were, if anything, warmer than typical in an El Ñino? I suggest that this is because the strong El Ñino of 1982-83 had a strong STJ, which transported warmer air from the Pacific across Mexico into the region. This would have the effect of moderating temperatures in the Gulf area, and perhaps moving those states back towards climatological norms. Let's compare this to the map showing strong El Ñino's.


The map shows precipitation distributions in strong El Ñino events, including the 1982-83 El Ñino. Image courtesy CPC.We can see that in strong El Ñino years, the Gulf coast's temperatures tend to moderate (note the absence of blue CWAs in the Florida panhandle.). 

We can look to a specific day as an example, with the caveat that ENSO cannot predict specific weather on specific days. Looking at 0600Z on December 12, 1982, we can see that a significant trough existed in the central part of the country.  

Trough of low pressure set up across central part of country. Image courtesy ESRL.At the same time, a jet streak existed as part of the enhanced STJ flowing across Texas and Louisiana.

200mb Wind vectors showing the jet streak. The approximate location of the left exit region is indicated with a circle. Image courtesy of ESRL.This jet streak is part of the anomalous STJ that existed during the 1982-83 El Ñino. Going back to our earlier conversation, the normal STJ during non-ENSO years doesn't pick up steam until further east, towards the eastern seaboard. In El Ñino years, the STJ's strength over Mexico and Texas is stronger, and the setup on December 12, 1982, is consistent with this. This positioned the divergence-rich left exit region of the jet streak over Mississippi, Alabama and Georgia. 

The result of this was precipitation 'downstream' towards the Carolinas. 

 Precipitation rate, showing significant precipitation downstream of the divergence-rich left exit region of the STJ streak. Image courtesy ESRL.Additionally, we should note that the STJ is further north than the average for the 1982-83 DJF time period. As was noted in lesson 4, when the STJ is carried north, particularly when there is a strong jet streak over the eastern half of the US, there is a favorable breeding ground for storms in the coastal Atlantic. That is what seems to have happened on December 12, 1982. Indeed, on the map below, we can see the polar jet stream and STJ interacting:

Approximate positions of polar jet and subtropical jet as shown. They are interacting in the area where the significant precipitation is occurring. Image courtesy ESRL. 

Conclusion

Although the precise causes of El Ñino are not known, it is fairly clear that the seasonal changes to the STJ in the 1982-83 cold season are an effect of El Ñino, not the cause.  

El Ñino is a warming of the SSTs of the central and eastern equatorial Pacific. As we demonstrated above, the warming of the SSTs causes a warming of the atmosphere. The enhanced baroclinicity related to this warming is the primary cause of the enhancement to the STJ during El Ñino episodes. There is anomalously-large north-south temperature gradient, which in turn contributes to similar height anomalies. This is the root cause of the extension of the STJ eastward from it’s normal location as shown my climatology.

Of course, general patterns such as the enhanced STJ during an El Ñino cannot be said to cause specific storms or specific localized weather conditions. Instead, the large-scale patterns established can help us establish conditional probabilities on specific weather patterns such as local or regional temperatures or precipitation. The teleconnections established by El Ñino in strong episodes, such as 1982-83, are fairly strong themselves and thus the probabilities can be forecast with greater definition.  However, even the effects of strong ENSO episodes interact with many other atmospheric conditions and forces, and thus the exact impact cannot be predicted far in advance.

E-Portfolio #1: Hurricane Ike (2008)

Introduction

AVHRR/MCVISR Composite image of Ike on September 9, 2008, at 1848Z. At the time, Ike was a Category 1 storm, though it was Category 4 when it made landfall in Cuba on September 7, two days earlier. Courtesy CIMSS-Univ. of Wisconsin.

Hurricane Ike was one of the costliest hurricanes ever to make landfall in the United States. By some estimates it caused over $27 billion dollars in damage and 112 deaths in Texas, Louisiana and Mississippi.  Ike made landfall near Galveston, TX as a Category 2 storm on September 13, 2008 (though earlier in its life it was as strong as a category 4 storm). Even the remnants of Ike were strong; one of the reasons I picked it was because of the extensive flooding caused in the Midwest, where I live. 

 Track of Hurricane Ike, September 2008. The storm made landfall as a Category 2 storm in Texas, though earlier in its life it was Category 4. Its strength varied widely as shown on the track. After losing its tropical character, Ike continued to track up through the Mississippi valley into the Midwest, bringing heavy rainfall throughout the region. http://tropic.ssec.wisc.edu/storm_archive/2008/storms/ike/IKE.track.gif, http://tropic.ssec.wisc.edu/storm_archive/2008/storms/ike/ike.html

This article will examine Ike's intensity shortly before landfall on the Texas coast, focusing on observations made around 1200Z on September 12, 2008.

Analysis: Three Data Sources

We will be examining three separate types of data, all taken from observations on September 12, 2008, at the same time around 1200Z. We will examine a Vortex data message (VDM)  from a "Hurricane Hunter" flight, a multiplatform satellite surface wind analysis, and a HRD Wind Analysis, with the purpose of better understanding how these types of data allow us to determine tropical cyclone (TC) intensity.

Vortex Data Message

VDMs are transmitted by Hurricane Hunter flights from the USAFR's 53rd Weather Reconnaisance Squadron. The 53rd's WC-130Js are tasked with flying through hurricanes such as Ike and transmitting data to the National Hurricane Center (NHC). These messages provide data on the current strength and character of the storm. In addition to data gathered from various sensors on the WC-130J, the VDM contains impressions from the flight meteorologist on board the plane, an important eyewitness and reality check to the sensor data.

On September 12, 2008, at 1142Z, a Hurricane Hunter transmitted the following VDM: 

Vortex Data Message from Hurricane Hunter flight on September 12, 2008, at 1142Z. VDMs are issued when recon planes passes through the center of the TC. It reports conditions in and around the eye of the storm. VDMs are relied upon by NHC forecasters in determining current storm intensity and organization. VDM courtesy of NHC.

Below is an annotated VDM, interpreting each line (PDF).

Interpreted VDM. The header blocks (no letters) indicate the mission ID, storm ID - e.g. AL092008 is the 9th TC in the Atlantic in 2008. Click on PDF link above for an easier-to-read version.

Besides the obvious data points such as maximum recorded flight level winds, estimated minimum sea-level pressure and estimated surface winds, the VDM provides a number of other data points which help us analyze storm intensity. For example, item I provides the temperature outside the eye, and item J provides the temperature inside the eye.  

Table of Select VDMs - Items I & J

 

Date Time Outside Temp (I) Inside Temp (J) Temp Difference Pressure (H)
5-Sep 1710Z 10 15 5 959 mb
6-Sep 0655Z 10 14 4 963 mb
7-Sep 0754Z 13 15 2 948 mb
8-Sep 1803Z 10 12 2 970 mb
9-Sep 0618Z 11 14 3 NA mb
10-Sep 0553Z 11 14 3 964 mb
11-Sep 0541Z 13 18 5 947 mb
12-Sep 0409Z 12 17 5 956 mb
12-Sep 1142Z 13 15 2 954 mb
12-Sep 2325Z 10 17 7 954 mb
13-Sep 0235Z 10 16 6 953 mb
13-Sep 0631Z 11 16 5 953 mb

 

All temps in C. Data source 

An eye warmer than the atmosphere surrounding it is characteristic of a strong TC, with a greater differential indicating a stronger storm. The fluctuations in the temperature differential shown above are also generally consistent with the fluctuations in the strength of Ike as it passed westward across the islands, Cuba and the Gulf towards landfall. The correlation of higher temperature to lower surface pressure, while not perfect, is there.

In summary, the VDM indicates a maximum intensity (surface wind speed) of 65 kts from Line D of the VDM. However, see the conclusion below for an important caveat. 

Multiplatform Satellite Surface Wind Analysis

The multiplatform satellite surface wind analysis ("multiplatform analysis"), as its name suggests, relies upon the input of several remote sensors (satellites), which are then combined into a single analysis.  Below is the analysis for 1200Z on September 12, 2008, shortly after the VDM above.

 Multiplatform Satellite Surface Wind Analysis for Ike from September 12, 2008, at 1200Z. VMAX is the maximum wind speed, shown at 91 kts. In the lower left of the image is a chart showing the distance in NM from the center at which certain wind speeds may be found. For example, in the NW quadrant, winds in excess of 64kts (hurricane force) can be found at 60 miles from the center of the storm. Image courtesy RAMMB-CIRA. http://rammb.cira.colostate.edu/products/tc_realtime/image_mpsatwnd.asp?storm_identifier=AL092008&product_filename=2008AL09_MPSATWND_200809121200

The image above is built from several building blocks, including data derived from AMSU-A, data from cloud-drift winds based on IR and water vapor imagery, surface winds derived from cloud temperatures on IR imagery, and scatterometry data from the Quickscat satellite

In particular, scatterometry data from the QuikSCAT satellite was used and its contribution can be seen here:

Scatterometry data from RAMMB-CIRA. Scatterometry provides little data at or close to the center of the storm. QuikSCAT operates in the K-band, a relatively high frequency and thus smaller wavelength band. Areas of heavy precipitation cause us to be concerned about the quality of data in such areas because the raindrops attenuate the signal strength such that the date is not reliable. The areas of heavy precipitation in the eyewall and surrounding areas would make scatterometry data from a QuikSCAT image of dubious value, and thus it is not shown on this map ("Black flagged"). The convention on RAMMB-CIRA is to show data from QuikSCAT in blue. With the loss of QuikSCAT, this image (had it been of a storm today) would have no scatterometry data as no ASCAT pass appears (ASCAT is shown in red). The "black flagging" of data close to the center of the storm can be seen as well (see caption). 

The approximate maximum wind shown with icons is in the range of 80-95 kts to the northeast of the center of the storm. The black isotachs are spaced at 15 kt increments. VMax is shown on the data block as 91 kts.

HRD Wind Analysis

The third source of data we will examine is a wind analysis from the Hurricane Research Division (HRD) of NOAA.  Here is the 1330Z image from September 12, 2008, approximately 90 minutes after the time of the vortex data message and multiplatform analysis discussed above.

HRD Wind analysis from 1300Z on September 12, 2008. This is the 4 degree view, which shows a reasonable amount of detail and still provides enough coverage to understand the structure of the storm and the context. Shown above is the image with a 4 degree coverage on the map. Larger scale 2 degree and smaller scale 8 degree coverage is also available at the source. Source: http://www.aoml.noaa.gov/hrd/Storm_pages/ike2008/wind.html

The HRD wind analyses combine data from several sources, including ships, buoys, SFMR measurements and dropsonodes from NOAA research aircraft and USAFR C-130s, QuikScat and TRMM satellites, and GOES cloud drift winds. These analyses help bridge the gap between subjective and objective estimates of wind speed by applying objective analysis to as much data as possible.

Data sources which were inputs into the HRD analysis. The SFMR data in particular provided wide coverage over the 4 degree area of coverage; the alpha patterns of the flights are clearly identifiable. Data: HRD. ftp://ftp.aoml.noaa.gov/hrd/pub/hwind/2008/AL092008/0912/1330/AL092008_0912_1330_dataCoverage04.png

Looking at the data source image provided by HRD, above, as well as the caption of the analysis itself, we can see that the HRD wind analysis we are looking at relied heavily on SFMR imagery from recon flights (in yellow), GPS dropsonodes (in pink/maroon and green) as well as a fortuitous moored buoy NW of the center. These may be easier to see in the 2 degree image here.

In summary, the HRD wind analysis suggests maximum surface winds of 93 kts.

Conclusion

The three tools we looked at suggested different maximum surface wind speeds: the vortex message suggested 65kts, the multiplatform analysis suggests 91 kts, and the HRD wind analysis suggests max wind of 93 kts. The outlier clearly is the VDM; the other two analyses are consistent with each other. The VDM and HRD analysis have the advantage of combining in-situ and remote sensing to corroborate data. Why is the VDM so far "off" of the other two data sources?

To explain this anomaly, we can look to the other VDMs from approximately the same time and mission.  VDMs from the same flight (AF301) show estimated max wind speeds (item D, the inflight meteorologists’ estimate of surface wind speed) of 88kts at 1326Z, 81 kts at 1505Z, and 63 kts at 1721Z. The subsequent flight (AF304) showed speeds varying 76-89kts  from 2038Z to 0235Z the next day. The 65kts of my chosen VDM seems to be an outlier, while the majority of the other observations around a similar time clump in the mid-80s. When plotted on the HRD wind map (below), the fix for the VDM shows as being well south of the area of maximum wind, thus leading to the lower wind speed estimate. This shows why it is important to use as much data as is available, including remote sources and in-situ ones; had we relied only on the one VDM, we would have gotten an erroneous estimate of intensity. 

Annotation showing approximate point of fix for VDM. This is east of the eye but still well SW of the area of maximum surface wind shown on the HRD Wind Analysis. This likely led to the SFMR identifying the lower wind speed shown in the VDM. Annotation showing approximate location of fix in VDM on multiplatform satellite surface wind analysis. The fix was likely taken on the NW to SE pass shown via yellow SFMR fixes.

The maximum surface wind is somewhere in the range of 88-93 kts at 1200Z on September 12. This compares favorably with the 90kts given in Table 1 of Ike's Tropical Cyclone Report.

Excerpt from Table 1 shows maximum surface wind of 90 kts for 1200Z on September 12, 2008 for Ike. This is consistent with all our data except my chosen VDM, but an analysis of other contemporaneous VDMs lends support to this maximum surface wind speed. The HRD wind analysis is probably the best "match", since it incorporates into its model a wide variety of remote and in situ data sources. It has the advantage over the VDM of being able to incorporate observations from these sources over a period of time, and it has an advantage over the multiplatform analysis in using in situ data sources as opposed to solely satellite sources. These in situ sources can help act as a reality check on remotely-sensed data. For full report, click link above.