1. 1
The effect of the Saharan Air Layer on tropical cyclone intensity
for Hurricanes Katia and Philippe
Andre Turner
SO470C
Capstone Paper
10 APR 15

2. 2
Abstract
The theory behind why the Saharan Air Layer (SAL) impacts tropical cyclone intensity
and formation is clear. Tropical cyclogenesis is dependent upon a moist mid-troposphere and the
SAL located in the mid-troposphere consists of dry, dusty air making the environment not
conducive for intensification. However, there is minimal knowledge to how the SAL influences
tropical cyclone intensification. The purpose of this study is to determine how the SAL along
two hurricane tracks impacts tropical cyclone intensification. This study investigates Hurricane
Katia 2011 experiencing non-SAL conditions and Hurricane Philippe 2011 experiencing SAL
conditions to show the relationship between intensities where the SAL has moderate effect and
where the SAL has minimal effect. The SAL was quantified using relative humidity as an index
value to describe moisture in the mid-troposphere. Dry air/ Saharan Air Layer maps were used to
outline the SAL in the mid-troposphere along the track of Hurricane Katia and Hurricane
Philippe. The intensity and relative humidity were plotted using the data received from AIRS
Level 3 daily Gridded Product and NCEP/NCAR reanalysis. The SAL acted as a reliable
predictor for intensification 25% of the time for Hurricane Katia and 22% of the time for
Hurricane Philippe. When using NCEP/NCAR analysis, relative humidity above 40% was a
reliable predictor for intensification roughly 17% of the time for Hurricane Katia and 24% of the
time for Hurricane Philippe. NCEP/NCAR analysis data contained sampling error due to a large
horizontal resolution of 210 km and did not accurately represent the relative humidity values at
the center of the hurricanes. Future research should consider using operational GFS or other
models with low horizontal resolution to reduce sampling error in relative humidity and produce
a more accurate result.

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1. Introduction
Background
The Saharan Air layer (SAL) is a large mass of dry, dusty air that forms over the Saharan
Desert in North Africa (Pan et al 2011). The SAL is a result of surface heating over the Saharan
Desert. This causes a surface low pressure system to form along with low level convergence over
North Africa (Karyampudi and Pierce 2002). The low level convergence lifts dust and minerals
into the mid-troposphere and mixes it with the dry air. The dry air is due to sensible heating at
the surface which results in an isentropic vertically well mixed layer of air (Braun 2010). The
dry dusty air moves over the tropical North Atlantic Ocean between the spring, summer and
early fall months every three to five days (Dunion and Velden 2004). The SAL is located in the
mid-troposphere around 700 mb or approximately 10,000 feet and consists of mineral dust and
dry air that contains 50% less moisture than an average tropical sounding (Dunion and Velden
2004). The mineral dust travels westward from the Sahara desert due to African Easterly Waves
and the low level African Easterly Jet which is associated with winds ranging from 25-55 mph
and plays a large role in low level wind shear (Dunion and Velden 2004). African Easterly
Waves are characterized by an area of low pressure and are responsible for many of the tropical
cyclones that form in the tropical North Atlantic as the waves move east to west (Karyampudi
and Pierce 2002). At its base around 800-900mb, the SAL consists of warm, stable air associated
with vertical lifting of warm moist water from the tropics and very dry, dusty air throughout the
rest of the column (Dunion and Velden 2004). This column of air from bottom to top tends to
have constant potential temperature and vapor mixing ration as the SAL travels westward as far
as the western Caribbean Sea (Carlson and Prospero 1972).

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The SAL is associated with three dynamical elements, low level temperature inversion,
dry air and increased vertical wind shear (Spells 2006). For convection to take place in the
tropics there must be a considerable amount of warm moist air. The SAL however consists of
very dry air which in return suppresses convection at the surface (Carlson and Prospero 1972).
Dry air intrusion into tropical low pressure systems is often the leading factor in suppressing
hurricane development and intensification (Braun 2010). Additionally, the SAL tends to have
warmer temperatures than the surrounding tropical air (Carlson and Prospero 1972). This is due
to shortwave radiation being absorbed by the dust and minerals during daytime heating. Daytime
heating exceeds the long wave cooling of the SAL, reinforcing the temperature inversion at the
800-900mb level (Dunion and Velden 2004). Also, the SAL is associated with increased vertical
wind shear (Karyampudi and Pierce 2002). Vertical wind shear is the change of wind speed and
direction with height that either enhances or diminishes vertical draft strengths. The SAL is
associated with a low to middle level easterly jet at 700mb that can influence the intensity of the
local wind shear (Dunion and Velden 2004).This jet can have maximum wind speeds of 55 mph.
The SAL typically has wind speeds 10m/s faster than that of the trade winds (Spells 2006).
These winds increase local wind shear and weaken hurricane development and intensification.
Tropical cyclone formation occurs as a result of four necessary but not sufficient
conditions. Necessary but not sufficient means all conditions must be present for tropical
cyclogenesis to occur but even if all conditions do occur it does not always mean tropical
cyclogenesis will occur (Laing and Evans 2011). These conditions are: sea surface temperatures
greater than or equal to 26 degrees Celsius, minimum vertical wind shear less than or equal to 20
knots (10 m/s), positive relative vorticity at low levels, and a moist mid-troposphere (Laing and

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Evans 2011). This study is focused on two of the four necessary but not sufficient conditions as
they relate to the SAL, moist mid-troposphere and minimum vertical wind shear.
Previous studies
Recent studies suggest that the tropical North Atlantic may contain two distinct
soundings, SAL and non-SAL. Previous studies, addresses both of these possibilities by
examining 750 rawinsondes from the tropical North Atlantic during the 2002 hurricane season
(Dunion and Marron 2008). Jordon (1958) compiled a mean tropical sounding for the West
Indies hurricane season. He used rawinsonde data from 1946-1955 and selected three rawinsonde
stations, Miami, Florida, San Juan, Puerto Rico, and Swan Island to generate his statistics. The
researchers used these statistics for reanalysis and found that a two peak bimodal distribution
containing 70% non-SAL and 30% SAL of moisture was present in the tropical North Atlantic in
2002 (Dunion and Marron 2008). This study also suggests the SAL’s influence over humidity,
temperature, wind speed, direction and pressure. There results show that the mixing ratio of the
SAL soundings was 60% drier than the mean non-SAL sounding at 700mb. Finally, it shows
warmer temperatures, higher pressure and stronger easterly winds (Dunion and Marron 2008).
Problem statement
The SAL’s relationship to tropical cyclone activity is very interactive but little research
has been conducted to understand the impact the SAL has on tropical cyclone activity and how it
affects intensification (Braun 2010). This is because it is very difficult to quantify an index value
for the SAL because of its variability with mineral dust. West African dust is comprised of
49.3% quarts, 10.34% Aluminum oxide, 4.4% sodium oxide and traces of titanium oxide,

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magnesium oxide, calcium oxide and diphosphorous hexaoxide (Spells 2006). This makes it
difficult to quantify the SAL to a specific index value. Instead it is identified by a scale from
weaker to stronger (Figure 3). Since the SAL is associated with having dry air, this study is
tailored around the ratio of actual water vapor density to saturation water vapor density. This
ratio is defined as relative humidity, where 100% relative humidity means the air is completely
saturated and moist.
Significance
This study will be conducted by examining the dry air/ Saharan Air Layer maps and the
relative humidity associated with the track of two hurricanes in the tropical Atlantic in 2011,
Hurricane Katia and Hurricane Philippe. The dry air/ Saharan Air Layer maps will be used to
track and outline the SAL while the relative humidity will be used to quantify a value for the
SAL in terms of saturation of water vapor. The purpose of this study is to determine how the
SAL along two hurricane track impacts intensification. If the SAL is associated with a dry mid-
troposphere and a moist mid-troposphere is needed for tropical cyclogenesis then the SAL is
unfavorable for tropical cyclone intensification.
2. Data and Methods
The tropical cyclone data for Hurricane Katia and Hurricane Philippe over the North
Atlantic during 2011 was obtained from NOAA’s National Climatic Data Center (NOAA). This
data includes wind speeds in knots, the latitude and longitude and the time associated with the
wind speed. The data was used to graph wind speed in relation to time to determine the intensity
of the hurricanes along their respective tracks in excel. The intensity was used to show the

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relationship between SAL data and relative humidity data. Hurricane Katia is associated with
non- SAL, where the SAL plays a minimum role in cyclogenesis. Its intensity was observed
between late August and mid-September of 2011. On the other hand, Hurricane Philippe is
associated with SAL conditions, where the SAL played a large role in cyclogenesis. Its intensity
was observed between late September and mid-October of 2011. The intensity was classified by
using the Saffir-Simpson Hurricane Wind Scale (Table 1). The tracks of the two hurricanes in the
North Atlantic basin are shown in Figure 1 and Figure 2.
Saharan Air Layer (CIMSS)
The dry air/Saharan Air Layer maps were obtained through Cooperative Institute for
Meteorological Satellite Studies (CIMSS) tropical cyclone data archive. This data was used to
visually identify the effect of the SAL along both hurricane tracks using the latitude and
longitudes associated with each storm. The latitude and longitude were visually plotted on the
SAL for each day between 0000 and 1800 with 6 hour intervals. The plotted latitudes and
longitudes are the tropical cyclone centers along the track. They were used to determine if SAL
dust was within 2 degrees of the tropical cyclone’s center (Figure 3). If the SAL dust was within
2 degrees of the tropical cyclone center it was marked, yes. If the SAL dust was not within 2
degrees of the tropical cyclone center it was marked, no (Dunion and Velden 2004). A similar
study used GOES SAL- tracking imagery to determine if a tropical cyclone’s center was in close
proximity to the SAL (Dunion and Velden 2004).The researcher used 2 degrees to determine
proximity. The same 2 degree box was decided for this study. This provided a more accurate
picture because the SAL affects convection around the whole storm and not just at the center.
This data was used to indicate when the tropical cyclone was under the influence of the SAL and

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when the tropical cyclone was not under the influence of the SAL (Figure 6 and 7). There is no
data after September 9th
for Hurricane Katia because the storm progressed beyond 35N latitude
which is out of the range of the SAL map.
Relative humidity (AIRS)
Relative humidity was collected using the AIRS Level 3 daily Gridded Product and
NCEP/NCAR Reanalysis product to compare two separate data sets. The AIRS data was used to
obtain satellite observations that represent actual environmental observations (Reale et al 2009).
The AIRS Level 3 daily Gridded Product contains standard retrieval means, standard deviations
and input counts for a 24-hour period coverage of either the descending or ascending orbit
(NASA). The geophysical parameters are averaged and binned into 1 x 1 gridded cells (-180 to
+180 degree longitude and -90 to +90 degree latitude) (NASA). The gridded cells begin at the
international dateline and progresses westward. In the L3 files, two parts of a scan line crossing
the dateline are included to coincide with time (NASA). The gridded product plot produces a
map with 0 degrees longitude at the center, allowing left (West) side and the right (East) side of
the image to contain data farthest apart in time in which the gores between satellite paths
represent lack of coverage for that day (Figure 4). Each gridded map of 4-byte floating-point
mean values corresponds to that of standard deviation and a 2-byte integer grid map counts
(NASA). The counts map contains number of points per bin included in the mean, allowing users
to generate custom multi-day maps from the gridded products (NASA). A Matlab code was
created to extract the relative humidity values for each day at 6 hour intervals. The code
converted three dimensional data into two dimensional data. Each latitude and longitude was
inputted and the relative humidity associated with that coordinate was produced by interpolating

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the figure to get the data at the hurricanes center. No values that fell within the gores between the
satellites paths were recorded. This left many relative humidity values unknown for both
hurricanes. This data was used to quantify the SAL.
Relative humidity (NCEP/NCAR)
Relative humidity was also calculated by using NCEP/NCAR reanalysis. NCEP/NCAR
40-year reanalysis fully utilizes frozen global data assimilation system and database, identical to
NCEP's operationally implemented global system except at horizontal resolution of 210 km
(Kalnay 2015). Its database contains numerous sources of observations unavailable in real time
for operations (Kalnay 2015). Its system encompasses advanced quality control and monitoring
components. Also, various types of output archives are able to be produced (Kalnay 2015).
Additionally, reanalysis information and selected output of four classes, chosen based on the
extent of observations and model's influence, are available online (Kalnay 2015). This reanalysis
allows comparison between recent anomalies and those from earlier times (Kalnay 2015). This
data was used to quantify the SAL using modeled data.
To further explore the relationship between the SAL and intensity, relative humidity was
used as a value to represent the SAL. Previous studies use relative humidity that is less than 40%
as a threshold for identifying the presence of the SAL (Shu and Wu 2009). The same threshold
was used in this study. The intensity for both hurricanes was plotted in relation to relative
humidity collected using both real and modeled data (Figure 8,9,10 and 11).
3. Results
Hurricane Katia’s relationship with the SAL

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Katia developed into a Category 1 hurricane in the first 72 hours at 0000 on September 1st
(Figure 6). The SAL was present between 1800 on August 29th
to 1200 on August 30th
and again
24 hours later at 1200 on August 31st
. The last indication of the SAL was briefly at 1800 on
September 3rd
. Katia remained a Category 1 hurricane until 1200 on September 4th
where it
intensified into a Category 2 (Table 1). Katia then intensified rapidly to a Category 3 in 24 hours
and then to a Category 4 12 hours later where it reached its maximum intensity of 120 kts. 36
hours later, Katia’s intensity dropped to 80 knots and did not intensify again. Katia’s last
recorded intensity was 60 knots.
Hurricane Philippe’s relationship with the SAL
Philippe was a 16 day tropical cyclone with four maximum intensities each one stronger
than the last (Figure 7). Philippe was a tropical depression for a little over 24 hours and
developed into a tropical storm around 1200 on September 24th
with wind speeds of 35 kts. The
SAL was identified for the next 78 hours. The tropical storm continued to develop for the next 48
hours reaching its first peak wind speed of 50 kts with the SAL identified during this period of
intensification. Philippe weakened to a tropical depression reaching minimum wind speeds of 30
kts 24 hours later. Philippe developed back into a tropical storm 12 hours later around 1200 on
September 28th
and continued to intensify for the next 72 hours with a small weakening period
around 0000 on September 30th
. It reached its second peak wind speed of 60 kts around 1200 on
October 1st
. Philippe’s wind speed weakened dramatically 24 hours later to 40 kts. The SAL was
identified briefly for 6 hours as Philippe began to intensify reaching its third peak intensity as a
Category 1 hurricane around 0000 on October 4th
. The SAL was identified shortly after for the
next 24 hours. Philippe weakened to a tropical storm during this period. Once the SAL was no

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longer present Philippe intensified into a Category 1 again with wind speeds at 80 kts. Philippe
weakened a final time for the last 48 hours. During this period the SAL was identified.
Hurricane Katia’s relative humidity using AIRS
Figure 8 shows the relationship between intensity and the relative humidity values
collected from the AIRS data for Hurricane Katia. Relative humidity values greater than 40% are
shaded in yellow and relative humidity values less than 40% are shaded in blue. The gores
between the satellite paths did not produce a relative humidity value. This is indicated by the
gray shading. Hurricane Katia had only a few briefs moment where relative humidity was below
40% and they occurred at the end of every period of prolonged constant intensity. The first
occurrence was at 1200 on September 3rd
after a 66 hour period where Katia was a Category 1 at
65 kts. The second occurrence was from 0000 to 1200 on September 10th
during a 48 hour period
where Katia was a Category 1 at 80 kts. In the beginning, where Katia was developing into a
tropical storm between 0000 on August 29th
and 1800 on August 30th
there is a long period
where relative humidity is above 40% when intensity is increasing from 25 kts to 50 kts. From
there until Katia became a Category 4 at 120 kts there are two brief moments when relative
humidity was above 40% right before intensity dramatically increased. Starting at 1800 on
September 7th
until 0600 on September 9th
there is another long period where relative humidity is
above 40% this time with decreasing intensity. This repeats for a period between 0600 on
September 11th
and 1200 on September 12th
. There are very large gaps of unknown relative
humidity values throughout the life span of Hurricane Katia primarily during the periods of
increasing intensity.

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Hurricane Philippe’s relative humidity using AIRS
Hurricane Philippe experienced one brief moment where relative humidity was below
40% between 0600 and 1200 on October 10th
(Figure 9). Intensity remained constant at 60 kts
throughout the 12 hours relative humidity was below 40%. The intensity decreased to 50 kts 6
hours later when the satellite was in the gores. The remainder of the gridded satellite data shows
relative humidity greater than 40%. Two noticeable occurrences where relative humidity was
greater than 40% are located during two out of the four peak intensities. The first peak was
between 1200 and 1800 on September 26th
and the last peak was between 0600 and 1800 on
October 7th
. Anther noticeable occurrence where relative humidity was greater than 40% was
between 1800 on September 27th
and 1200 on September 28th
. This period experienced a
dramatic decrease in intensity where Philippe weakened to a tropical depression at 30 kts. All
other occurrences were during periods of intensification. Lastly, the satellite gores appeared
more often in Philippe than in Katia and left large areas of unknown relative humidity values.
Hurricane Katia’s relative humidity using NCEP/NCAR reanalysis
Figure 10 shows the relationship between intensity and the relative humidity values
collected from NCEP/NCAR reanalysis. Relative humidity values greater than 40% are shaded
in orange and relative humidity values less than 40% are shaded in blue. Hurricane Katia
experienced a 60 hour period were relative humidity was less than 40%. This period experienced
a dramatic increase in intensity and was also the time of maximum intensity where Hurricane
Katia became a Category 4 (Table 1). This is the only period throughout Katia’s life span where
relative humidity was less than 40%. The remainder of the graph shows relative humidity above
40%.

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Hurricane Philippe’s relative humidity using NCEP/NCAR reanalysis
Hurricane Philippe’s relationship with relative humidity is very similar to Hurricane
Katia’s. Both hurricanes show relative humidity below 40 % for about 3 days in the middle of
their life span. There is also a commonality when relative humidity was below 40% there is a
general trend of increasing intensity. Hurricane Philippe experienced a slight decrease in
intensity between 0000 and 0600 on September 30th
where relative humidity was below 40%
(Figure 11). On the other hand Hurricane Katia’s intensity increased drastically until a few hours
before the end of that period where relative humidity was below 40% (Figure 10). Hurricane
Philippe experienced relative humidity below 40% during its second peak intensity while
Hurricane Katia experienced relative humidity below 40% during its maximum intensity (Figure
10 and 11).
Hurricane Katia’s total results for SAL and non-SAL
The results for the SAL data and the NCEP/NCAR reanalysis data were totaled for both
hurricanes in Table 2. The results for the AIRS data were not totaled because of the large gores
throughout the lifespan of both hurricanes. Hurricane Katia’s intensity increased 5 times with the
SAL present. The intensity stayed the same only 1 time with the SAL present. There were no
occurrences where intensity decreased when the SAL was present. When the SAL was not
present, intensity increased 11 times, stayed the same 21 times, and decreased 6 times. These
results show that Hurricane Katia was not negatively impacted by the SAL which was expected
because Hurricane Katia was deemed a non-SAL storm. The results also show that 11 out of 44
times Hurricane Katia’s intensity increased when the SAL was not present. Therefore the SAL

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acted as a reliable predictor for intensification 25% of the time. Additionally, the SAL acted as
an unreliable predictor for intensification 5 out of 44 times or roughly 11% of the time. During
non- SAL conditions the storms intensity stayed the same 21 out of 44 times and therefore did
not negatively impact intensification roughly 48% of the time.
Hurricane Katia’s total results for relative humidity
Hurricane Katia’s intensity increased 10 times when relative humidity was above 40%
(Table 2). The intensity stayed the same only 34 times when relative humidity was above 40%.
There are 5 occurrences where intensity decreased when the relative humidity was above 40%.
When the relative humidity was below 40%, intensity increased 6 times, stayed the same 1 times
and decreased 3 times for Hurricane Katia. These results show that Hurricane Katia’s intensity
increased 10 out of 59 times when relative humidity was above 40%. Relative humidity above
40% acted as a reliable predictor for intensification roughly 17% of the time. The results also
show that 3 out of 59 times Hurricane Katia’s intensity decreased when the relative humidity was
below 40%. Therefore relative humidity below 40% acted as a reliable predictor for intensity to
decrease roughly 5% of the time. Relative humidity above 40% acted as an unreliable predictor
for intensification 5 out of 59 times or roughly 8% of the time. Relative humidity below 40%
acted as an unreliable predictor for intensity to decrease 6 out of 59 times or roughly 6% of the
times. When relative humidity was above 40% intensity stayed the same 34 out of 59 times and
therefore did not negatively impact intensification roughly 58% of the time.

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Hurricane Philippe’s total results for SAL and non-SAL
Hurricane Philippe’s intensity increased 7 times with the SAL present (Table 2). The
intensity stayed the same 10 time with the SAL present. There are 8 occurrences where intensity
decreased when the SAL was present. When the SAL was not present intensity increased 13
times, stayed the same 15 times and decreased 7 times for Hurricane Philippe. These results
show that Hurricane Philippe was negatively impacted by the SAL 8 out of 60 times or roughly
13% of the time. The results also show that 13 out of 60 times Hurricane Philippe’s intensity
increased when the SAL was not present. Therefore the SAL acted as a reliable predictor for
intensification roughly 22% of the time. The SAL acted as an unreliable predictor for
intensification 7 out of 60 times or roughly 12% of the time. During non- SAL conditions the
storms intensity stayed the same 15 out of 60 times and therefore did not negatively impact
intensification roughly 25% of the time.
Hurricane Philippe’s total results for relative humidity
Hurricane Philippe’s intensity increased 16 times when relative humidity was above 40%
(Table 2). The intensity stayed the same only 20 time when relative humidity was above 40%.
There are 14 occurrences where intensity decreased when the relative humidity was above 40%.
When the relative humidity was below 40% intensity increased 6 times, stayed the same 9 times
and decreased 1 time for Hurricane Philippe. These results show that Hurricane Philippe’s
intensity increased 16 out of 66 times when relative humidity was above 40%. Relative humidity
above 40% acted as a reliable predictor for intensification roughly 24% of the time. The results
also show that 1 out of 66 times Hurricane Philippe’s intensity decreased when the relative
humidity was below 40%. Therefore relative humidity below 40% acted as a reliable predictor

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for intensity to decrease 1.5% of the time. Relative humidity above 40% acted as an unreliable
predictor for intensification 14 out of 66 times or roughly 21% of the time. Relative humidity
below 40% acted as an unreliable predictor for intensity to decrease 6 out of 66 times or 10% of
the time. When relative humidity was above 40% intensity stayed the same 20 out of 66 times
and therefore did not negatively impact intensification roughly 30% of the time.
4. Discussion and Conclusion
Impact of SAL on Hurricane Katia intensity
The SAL seemed to have very little influence over the intensification of Hurricane Katia.
This was expected because Hurricane Katia was selected for its non-SAL conditions and is also
indicated in Table 2 where intensity never decreased when the SAL was present throughout the
entire lifespan of the storm. The SAL was present primarily during the storms stages as a tropical
depression and tropical storm (Figure 6). The National Hurricane Center’s (NHC) report
describes this time period to not be conducive for intensification due to 850-200 mb easterly
vertical wind shear at 20 kts (Stewart 2012). The SAL is associated with a low to middle level
easterly jet that can influence the intensity of the local wind shear (Dunion and Velden 2004), as
in the case of Hurricane Katia (Figure 6). Although this period does not show favorable
condition for intensification in regards to minimum vertical wind shear less than 20 kts, it does
however show favorable conditions in the mid troposphere. The NHC report indicates sufficient
convection causes warm moist air to rise within the air column providing fuel in the form of
moisture to the mid troposphere (Stewart 2012). Vertical wind shear decreased to less than 10 kts
throughout this period and by 0000 on September 1st
, Katia intensified to a Category 1. Katia
did not develop for 72 hours due to an increase in vertical wind shear (Stewart 2012). The red

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shading at 1800 on September 3rd
(Figure 3) indicates a time period where Katia was under the
influence of the SAL. Katia did not develop further until after the tropical cyclone passed
through this region. It then increased its intensity from 65 to 70 kts 6 hours later and by 1200 on
September 4th
Katia developed into a Category 3. The SAL was no longer identified in the time
series after this period and Katia developed into a Category 4 a little over 48 hours after the last
SAL indication. Katia’s intensity decreased rapidly due to a replacement of the eye wall and
continued to decrease as the tropical cyclone moved northward into colder water above latitude
30o
N (Stewart 2012).
Impact of SAL on Hurricane Philippe intensity
The SAL had little to moderate influence over intensification of Hurricane Philippe.
Philippe was negatively impacted by the SAL roughly 13% of the time (Table 2). This
percentage was expected to be much higher because Philippe was selected for this study to show
heavy SAL conditions. The SAL was clearly present in the weakening period – where the wind
speed was decreasing – for the last two peaks during its stages as a Category 1 (Figure 7). The
SAL was also present in the first two peaks. In the first peak, the SAL encompassed the
maximum and weakening period as well as strengthening period of the next peak. Additionally,
it was present in the beginning during the strengthening period prior to the third peak.
Appearance of the SAL in the first peak is in agreement with the National Hurricane
Center’s report which explains increase in vertical wind shear which decreases moisture in the
area. Philippe had moved to mid-level jet by 1200 on September 26th
which marks the first peak
(Berg 2012). There was westerly vertical wind shear that began to increase around that time
which subsequently weakened Philippe. The westerly vertical wind shear further increased on

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September 27th
(Berg 2012). Consequently, at 0000 September 28th
, Philippe weakened to a
tropical depression (Berg 2012).
The second appearance of the SAL was when Philippe was moving away from
strengthening mid-level ridge (Figure 7). It appeared briefly because the increase was not
associated with the SAL. The NHC report does not state anything about local vertical wind shear
increase (Berg 2012).
The third appearance of the SAL is when Phillippe weakened to a tropical storm at 1200
on October 4th
(Berg 2012). Phillippe turned northwest/north over the western Atlantic until the
end of the SAL appearance by early October 6th
(Berg 2012). Lack of the SAL after October 6th
accompanied Philippe with decrease in vertical wind shear. Presence of deep-layer southwesterly
shear increasing to 40 to 50 kt on October 7th
was followed by the hurricane weakening to a
tropical storm on October 8th
at 0600 (Berg 2012). While you would expect the SAL to begin to
appear on October 7th
when the local wind shear begins to increase, yet, its appearance starts at
0000 on October 8th
. Therefore, it is not clear if the SAL was responsible for the last weakening
period.
Analysis using AIRS data
The AIRS Level 3 daily Gridded Product data provides challenges to make for
comparison due to the large gores where there was no coverage from the satellite pass that day
(Figure 4). This would have been the most accurate way to collect the relative humidity data
since it represents real data. There are however a few noticeable instances to mention. During
Katia’s development into a tropical storm on August 28th
through August 30th
, AIRS data show
relative humidity values to be above 40% (Figure 8). This makes sense because tropical cyclones

19. 19
need moisture in order to develop and intensify (Laing and Evans 2011). However when the
AIRS data is compared to the SAL data the opposite relationship is shown (Figure 8 and 6). The
SAL was present during Katia’s development into a tropical storm. This however also makes
sense considering Katia’s location off the west coast of North Africa in close proximity to large
volumes of SAL dust (Figure 1). The NHC report also describes this period to have experienced
easterly vertical wind shear of 20 kts which is also associated with the SAL (Stewart 2012). Both
of these relationships/statements can exist at the same time because the SAL presence was
evaluated within a 2 degree grid from the storms center and the AIRS data collected relative
humidity values from the storms center where relative humidity values are expected to be the
highest (Figure 3 and 4). The area around the storm may have had lower relative humidity values
because of the proximity to the SAL while the storms center remained moist. Another noticeable
instance is briefly on September 3rd
where both the AIRS data and the SAL data agree that
Hurricane Katia experienced relative humidity values that were below 40% and the SAL was
also indicated at that location (Figure 8 and 6). Katia did not develop further until after passing
through this area. This agrees with initial expectations where a dry mid-troposphere and the SAL
are bad for tropical cyclone intensification.
The AIRS data for Hurricane Philippe show similar relationships with the SAL data for
Hurricane Philippe as well. Between September 25th
and September 28th
the AIRS data shows
three areas where relative humidity was above 40% (Figure 9). This makes sense for September
25th
and September 27th
because Philippe was developing into a tropical storm during this period
and required higher relative humidity values. However when the SAL data is compare to the
AIRS data the opposite relationship is shown (Figure 7 and 9), similar to the relationship shown
with the AIRS data and the SAL data for Hurricane Katia (Figure 8 and 6). The SAL data shows

20. 20
the SAL being present during this period. This again makes sense as well because of Philippe’s
close proximity to the west coast of North Africa which has large volumes of SAL dust (Figure
2). The NHC report also describes Philippe’s location moving to a mid-level jet by September
26th
where Philippe experienced westerly vertical wind shear (Stewart 2012). The wind shear
increased around the time Philippe weakened back down to a tropical depression on September
28th
. Both periods experienced conflicting data just as it did with Hurricane Katia. However the
same principle applies as it did with the AIRS data and the SAL data for Hurricane Katia. The
AIRS data is displaying relative humidity values from the storms center while the SAL was
evaluated within a 2 degree by 2 degree grid. The SAL was present, just perhaps not within the
storms center where there were high relative humidity values. Another noticeable instance is on
October 8th
where both the AIRS data and the SAL data agree (Figure 9 and 7). Relative
humidity was below 40% during the weakening period at the end of the storm. The SAL was also
present during this period where 24 hours before the storm experienced 40 to 50 kts of wind
shear (Stewart 2012). Both of these instances make sense due to the dramatic increase in wind
shear that brought SAL dust to the center of the storm.
Analysis using NCEP/NCAR data
The NCEP/NCAR reanalysis data shows the complete opposite relationship with the SAL
for both hurricanes. The NCEP/NCAR reanalysis data for Hurricane Katia shows relative
humidity values below 40% only between September 3rd
and September 6th
and the remainder of
the storm experienced relative humidity values above 40% (Figure 10). Conversely, the SAL is
only present briefly on September 3rd
and not present at all after that and the AIRS data although

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limited shows relative humidity values above 40% on September 4th
(Figure 6 and 8). Hurricane
Katia experienced maximum intensification to a Category 4 during this period.
The same conflicting results are found in Hurricane Philippe. The NCEP/NCAR
reanalysis data for Hurricane Philippe shows relative humidity values below 40% only between
September 28th
and October 1st
and the remainder of the storm experienced relative humidity
values above 40% (Figure 11). Conversely, the SAL data shows the SAL being present between
September 29th
and October 2nd
(Figure 7). The AIRS did not have coverage during this period
(Figure 9). Hurricane Philippe experienced its second peak intensity to 60 kts during this period.
The NCEP/NCAR data conflicts with both AIRS data and SAL data for both hurricanes.
This confliction can best be explained through the idea of representativeness. The NCEP/NCAR
reanalysis data is not representative in either of the hurricanes center and therefore the data
consists of sampling error. NCEP/NCAR reanalysis has a horizontal resolution of 210 km
(Kalnay 2015). This means that one data point in the NCEP/NCAR reanalysis covers a 210 km
by 210 km square. The one data point in the NCEP/NCAR reanalysis may not represent the
actual relative humidity values. The hurricane’s center may have high relative humidity values
but its surrounding environment may have low relative humidity values. In other words, the
average relative humidity within the 44,100 km2
grid may not be representative of the relative
humidity at the hurricane’s center. Therefore, the NCEP/NCAR reanalysis data was not an
effective source to find relative humidity values at the center of the storm.
Future work
A more effective method would have been to use the operational Global Forecast System
(GFS). GFS is a weather forecast model produced by NCEP. This model is used by operation

22. 22
forecaster to help predict weather conditions up to 16 days. It covers the entire globe with a
horizontal resolution of 28 km (NOAA). This means that one data point covers a 28 km by 28km
square. Over the same distance covered by the NCEP/NCAR reanalysis 210 km by 210 km grid
there is roughly 100 data points in the operational GFS. Those 100 points have a much better
chance of giving a realistic idea of the conditions in that box when they are being averaged.
Figure 12 is an example of the relative humidity value for Hurricane Katia collected through
operational GFS at 0000 on September 5th
. The operational GFS value for relative humidity was
80%. Figure 5 is the NCEP/NCAR relative humidity map for Hurricane Katia at 0000 on
September 5th
. The NCEP/NCAR value for relative humidity was 31% at the pink dot. Figure 4
is the AIRS Level 3 daily Gridded Product map for Hurricane Katia at 0000 on September 5th
.
The AIRS relative humidity value is 63% at the pink dot. Figure 3 is the SAL map for Hurricane
Katia at 0000 on September 5th
. The SAL was not present during this period and is indicated by
the green box. The operational GFS gives a more accurate representation of the relative humidity
when compared to NCEP/NCAR reanalysis data and the actual value from the AIRS data.
Further indication of NCEP/NCAR sampling error is displayed in the SAL map where the SAL
is not within 2 degrees from Hurricane Katia’s center (Figure 3).
This data was initially expected to show somewhat of a direct relationship between
intensity and relative humidity. As intensity increases, relative humidity should also increase.
This theory supports the necessary but not sufficient condition in regards to a moist mid-
troposphere being conducive to cycolgenesis. Conversely, as intensity decreases, relative
humidity should also decrease because there is a lack of moisture in the mid-troposphere where
the SAL is located. This was not always the case. When using NCEP/NCAR analysis, relative
humidity above 40% was a reliable predictor for intensification roughly 17% of the time for

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Hurricane Katia and 24% of the time for Hurricane Philippe (Table 2). It was a reliable predictor
for intensity to decrease roughly 5% of the time for Hurricane Katia and 1.5% of the time for
Hurricane Philippe. Additionally, this data contained sampling error due to a large horizontal
resolution of 210 km and does not accurately represent the relative humidity values at the center.
The data collected from the SAL showed the areas where there was a lot of SAL dust along the
track and initially expected correlation with the maximum intensity of the hurricane at that time
and location. Conversely, it outlined the areas where there was minimal SAL dust along the track
and was initially expected to correlate with minimum intensity of the Hurricane at that time and
location. The SAL acted as a reliable predictor for intensification 25% of the time for Hurricane
Katia and 22% of the time for Hurricane Philippe. The SAL data proved to impact intensification
23.5% of the time between both hurricanes and 20.5% of the time using relative humidity as an
indicator. Previous research suggests that the Saharan Air Layer influences relative humidity
(Dunion and Marron 2008). This study concurs with Dunion and Marron’s findings particularly
with the AIRS and SAL results. Future research should consider using operational GFS or other
models with low horizontal resolution to reduce sampling error in relative humidity.

24. 24
References
Berg, R., 2012: Tropical Cyclone Report for Hurricane Philippe. National Hurricane Center,
2011.
Braun, S. A., 2010: Reevaluating the Role of the Saharan Air Layer in Atlantic Tropical
Cyclogenesis and Evolution. Mon. Wea. Rev., 138, 2007–2037.
Carlson, T. N., and J. M. Prospero, 1972: The large-scale movement of Saharan air outbreaks
over the northern equatorial Atlantic. J. Appl. Meteor.,11, 283–297.
Dunion, J.P., and C. S. Marron, 2008: A Reexamination of the Jordan Mean Tropical Sounding
Based on Awareness of the Saharan Air Layer: Results from 2002. J. Climate, 21, 5242–
5253.
Dunion, J.P., and C. S. Velden, 2004: The Impact of the Saharan Air Layer on Atlantic Tropical
Cyclone Activity. Bull. Amer. Meteor. Soc., 85, 353–365.
Kalnay, 2015: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-
470, 1996.
Karyampudi, V. M., and H.F. Pierce, 2002: Synoptic-Scale Influence of the Saharan Air Layer
on Tropical Cyclogenesis over the Eastern Atlantic. Mon. Wea. Rev., 130, 3100–3128.
Laing, A., and J.L. Evans, 2011: Tropical Cyclogenesis. Introduction to Tropical Meteorology,
The Comet Program., Version2, Chapter 8.3.1.
NASA: Global Change Master Directory, cited April 2015: References. [Available online
at http://gcmd.gsfc.nasa.gov/KeywordSearch/Metadata.do?Portal=GCMD&MetadataTyp
e=0&MetadataView=Full&KeywordPath=&EntryId=GES_DISC_AIRX3STD_V006.]
NOAA: National Climatic Data Center, 2015: References. [Available online at
http://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-
gfs.]
Pan, W., L. Wu, and C. L. Shie, 2011: Influence of the Saharan Air Layer on Atlantic tropical
cyclone formation during the period 1-12 September 2003. Advances in Atmospheric
Sciences, D.Lu., Science Press., 28, 1, 16-32.
Reale, O., W. K. Lau, K. Kim, and E. Brin, 2009: Atlantic Tropical Cyclogenetic Processes
during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast
System. J. Atmos. Sci., 66, 3563–3578.
Shu, S., and L. Wu, 2009: Analysis of the influence of Saharan air layer on tropical cyclone
intensity using AIRS/Aqua data, Geophys. Res. Lett., 36, L09809

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Spells, C., 2006: Influence of the Saharan Air Layer on the development and intensity of Atlantic
Hurricanes. Hampton University.
Stewart, S., 2012: Tropical Cyclone Report for Hurricane Katia. National Hurricane Center,
2011.

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Appendix
Table 1. Saffir-Simpson hurricane wind scale.
Category Wind speeds (knots)
Five >= 137
Four 113 - 136
Three 96 - 112
Two 83 - 95
One 64 - 82
Tropical storm 34 - 63
Tropical
depression <= 33
Table 2. Total comparison of the SAL and relative humidity impact on intensity.
Intensity Increased Intensity stayed the
Same
Intensity decreased
SAL (Katia) 5 1 0
Non-SAL (Katia) 11 21 6
Relative humidity
above 40% (Katia)
10 34 5
Relative humidity
below 40% (Katia)
6 1 3
SAL (Philippe) 7 10 8
Non-SAL (Philippe) 13 15 7
Relative humidity
above 40% (Philippe)
16 20 14
Relative humidity
below 40% (Philippe)
3 9 1

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Figure 1. Best track position for Hurricane Katia between August 29th
2011 and September 10th
2011. Source: National Hurricane Center.

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Figure 2. Best track position for Hurricane Philippe between September 24th
2011 and October
08th
2011. Source: National Hurricane Center.

29. 29
Figure 3. SAL tracking satellite imagery for 0000 on September 05th
2011 with overlaid latitude
and longitude of Hurricane Katia’s center. The green box represents 2 degrees from Hurricane
Katia’s center. Source: University of Wisconsin – CIMSS.

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Figure 4. AIRS Level 3 daily Gridded Product for 0000 on September 5th
2011at 700 mb.
Relative humidity values are indicated by colored shading. The white gores between the satellite
paths are the lack of coverage for that day. The pink dot is the latitude and longitude of
Hurricane Katia.

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Figure 5. Relative Humidity from NCEP/NCAR reanalysis data at 700 mb for 0000 on
September 05th
2011. Relative humidity values are indicated by colored shading. The pink dot is
the latitude and longitude of Hurricane Katia.

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Figure 6. SAL data plotted in relation to time and intensity for Hurricane Katia. SAL dust was
marked yes (red) or no (green) if within 2 degrees of the Hurricane Katia’s center to indicate
when Hurricane Katia was under the influence of the SAL and when Hurricane Katia was not
under the influence of the SAL. There is no data for Hurricane Katia after September 9th
because
the storm progressed beyond 35N latitude which is out of the range of the SAL map. This period
is marked as neither (grey).

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Figure 7. SAL data plotted in relation to time and intensity for Hurricane Philippe. SAL dust was
marked yes (red) or no (green) if within 2 degrees of the Hurricane Philippe’s center to indicate
when Hurricane Philippe was under the influence of the SAL and when Hurricane Philippe was
not under the influence of the SAL. There is no data for Hurricane Philippe after October 9th
because the storm progressed beyond 35N latitude which is out of the range of the SAL map.
This period is marked as neither (grey).

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Figure 8. The relationship between intensity and relative humidity values collected from AIRS
Level 3 daily Gridded Product for Hurricane Katia between August 28th
2011 and September 12th
2011. Relative humidity values greater than 40% are shaded in yellow and relative humidity
values less than 40% are shaded in blue. The gores between the satellite paths did not produce a
relative humidity value. This is indicated by the gray shading.

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Figure 9. The relationship between intensity and relative humidity values collected from AIRS
Level 3 daily Gridded Product for Hurricane Philippe between September 23rd
2011 and
September 9th
2011. Relative humidity values greater than 40% are shaded in yellow and relative
humidity values less than 40% are shaded in blue. The gores between the satellite paths did not
produce a relative humidity value. This is indicated by the gray shading.

36. 36
Figure 10. The relationship between intensity and relative humidity values collected from
NCEP/NCAR reanalysis for Hurricane Katia between August 28th
2011 and September 12th
2011. Relative humidity values greater than 40% are shaded in orange and relative humidity
values less than 40% are shaded in blue.

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Figure 11. The relationship between intensity and relative humidity values collected from
NCEP/NCAR reanalysis for Hurricane Philippe between September 23rd
2011 and October 09th
2011. Relative humidity values greater than 40% are shaded in orange and relative humidity
values less than 40% are shaded in blue.

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Figure 12. 5 day forecast based on GFS for Hurricane Katia for 0000 September 05th
2011.
Graph (c) is the relative humidity (green) at 700 mb. Source: NOAA: National Climatic Data
Center (GFS).