




































    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 5; September-October 2025; 

ISSN: 2837-2964 

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1 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

MANAGEMENT OF THE IMPACTS OF SUDDEN AND SLOW ONSET 

WEATHER EVENTS ON AVIATION OPERATIONS IN SOUTHEASTERN 

NIGERIA  
 

1Ogu, Eucharia Chinyere, 2Ozakpo, Ogaga Akpode, 3Kpang, MeeluBari Barinua Tsaro, 
4Nwagbara, Moses Okemini 
1’2’3Department of Geography and Environmental Management, University of Port Harcourt, Port Harcourt, 

Rivers State, Nigeria 
4Department of Water Resources Management and Agrometeorology, Michael Okpara University of Agriculture, 

Umudike, Abia State, Nigeria 

DOI: https://doi.org/10.5281/zenodo.17182818 

 

Abstract  

This study examined the impact of sudden and slow onset weather events on aviation operations in south-eastern 

Nigeria. The ex post facto and cross-sectional research designs were adopted for in this study. Data were sourced 

from both primary and secondary sources and the secondary data were hourly data on sudden weather parameters 

(wind, cloud cover, visibility, and precipitation) and slow weather parameters (minimum and maximum 

temperature) were collected from the archives of Nigerian Meteorological Agency (NiMet) for a period of 10 

years. On the other hand, air traffic data were collected from the airliners while primary data were derived with 

the aid of distributed copies of questionnaire. Descriptive statistics, statistical diagrams and tables were used to 

present the data and analysis of variance (ANOVA) test, was used for analysis. The results showed that rainfall, 

temperature, wind and cloud cover established remarkable variation annually and monthly.  Also, rainfall showed 

lack of consistency in most of the years, but 2013 had a similar pattern across the region whereas temperature 

ranged between 30⁰C and 35⁰C with Enugu recorded the highest maximum temperature. There was substantial 

evidence of slow and sudden weather events effects on flight operations which spatially varied across the cities 

investigated. The revealed effects of the sudden and slow events were delays, cancelation of flights and additional 

cost for travelers. There was a significant spatial variation in the sudden and slow weather events in the region at 

p<0.05. The study recommended training and retraining programmes acquisition of automated instruments for 

real time and accurate data collection, replacement of aging aircraft and weather monitoring facilities and 

sensitization of passengers on coping techniques with sudden and slow weather events.  

Keywords: Aviation, Weather, Visibility, Precipitation, Operations, Disruption 

 

 

 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 5; September-October 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

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2 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

1. INTRODUCTION 

Weather has a significant impact on airport operations and the performance of the whole aviation network. Delayed 

operations can be caused by airport capacity constraints due to severe weather conditions (Glass, Davis & Watkins-

Lewis, 2022). The prediction of aircraft processes along their whole trajectories is required to achieve punctual 

operations (Montlaur, Delgado & Prats, 2023). Delays in departure at one airport can affect the network, resulting 

in system-wide far reaching effects. As documented in the year 2016, reactionary delays were the main cause of 

delays, followed by turn around delays which accounted for 46% of departure delays (Reitmann & Schultz, 2022). 

When transport systems are interrupted, delays emerge, introducing uncertainty regarding travelers’ arrival time 

(Wang, Jin & Sun, 2022). Also, delays directly lead to extra travel time. Furthermore, in reaction to uncertain 

travel times, travelers may adjust their travelling schedule for potential delays leading to further cost on travel 

(Kumar & Khani, 2023). Generally, delays emerge when interactions between air transport players (i.e., carriers, 

airports and air traffic control entities) and external factors (i.e., adverse weather conditions, strikes and other 

incidents) lead to airport congestion (Lin, 2023).The empirical literature has analyzed two main determinants of 

airport congestion inherent to the air transport system - congestion externalities in general and how the structure 

of the airport network allows for diverse responses to these externalities. The congestion externality argument 

explains the emergence of delays as a consequence of airlines' failure to internalize the effect their scheduling 

decisions have on other airlines (Ren, 2023).  At hub airports (i.e., airports with one or a few dominant carriers) 

two contrary forces affect delays. Thus, there is a tradeoff between providing additional connections and rising 

marginal congestion costs (i.e., increase in delays and connecting times) due to higher traffic numbers (Gnutzmann 

& Śpiewanowski, 2023). Again, hub airlines have leeway in their scheduling decisions, which allows to partially 

offset the increased congestion (Porter, 2023).  

Modern societies are characterized by a high degree of mobility of goods, services and people. Supply chains are 

interlinked across countries and continents and people increasingly travel for business and private reasons (Sytch, 

Kim & Page, 2022). As population grows and income increases, the demand for mobility is growing as well 

(Ahmed, Le & Shahzad, 2022). Overall, the increasing demand for mobility leads to a high dependence on 

transport systems and their services, with high costs when mobility suddenly is restricted, delayed, or canceled 

(Deng et al., 2023). Adverse weather conditions are an important external factor affecting delays in the air traffic 

system as well (Hutter & Pfennig, 2023). Depending on the year and month, they account for up to 50 percent 

of air traffic delays within the air space (Abdelghany, Guzhva, & Abdelghany, 2023). The impact of weather-

related extremes on delays in the aviation system are only scarcely covered in recent empirical literature, 

due in part to the acclaimed improvements in the aviation systems and fleets. EXisting literature suggests 

that the general impact of adverse weather conditions on airport and airline operations is substantial and 

cannot be ignored (Voskaki, Budd, & Mason, 2023). Borsky & Unterberger, 2019) analyzed the impact of 

different weather shocks on airline operations at the Atlanta Hartsfield International Airport for one airline. 

He finds that annually over 165,000 min of delay are attributable to adverse weather conditions. Bertness, 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 5; September-October 2025; 

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3 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

(1980) shows that at the end of the 1970s, rainfall substantially increased the number of departures with a 

delay above 30minutes at Chicago O'Hare airport. Li, Jing & Dong, (2023) examined the impact of thunderstorms 

on delays at Frankfurt Airport during 1997 and 1998. They discover that thunderstorms significantly increase 

delays by a factor of 6.3 in 1997 and by 1.1 in 1998. From the forgoing, the aviation industry and associated 

operations are significantly influenced by weather.  Aviation safety, efficiency and capacity are sensitive to 

weather, and adverse weather can have negative impacts on the sector (Dalal et al., 2023).  The increase in aviation 

demand can push airport's capacity to its limits, and even a small weather change can lead to a reduction of the 

airport capacity (Dönmez, 2023). Weather conditions affect all aspects of aerodrome operations such as aircraft 

fueling, cleaning, baggage handling, catering, aircraft maintenance, and the actual scheduled flights.  The 

operational capacity of airports, and even a region’s entire airspace, can be significantly reduced due to bad 

weather, resulting in delays, diversions and cancellations of flights (Dalmau & Gawinowski, 2023). The problem 

being envisaged in finding solutions to the rampant air disaster in Nigerian airspace by the aviation authority is 

the negligence in addressing the weather factors identified among the causative factors (Dempsey, 2002). It was 

reported by Diamond, (2017) that the civil aviation practice in Nigeria has come to the front burners in recent 

years because of the fear to fly, as a result of the countless plane crashes that had drummed up public debate on 

the safety of lives and property. Delays are highly sensitive to the time of day affected by adverse weather, as the 

greatest amount of delays occur during the highest demand periods (Chen & Wang, 2019).  It is against this 

background, that this study therefore seeks to examine the impact of sudden and slow onset weather events on 

flights’ departure delays, overall operability and accidents in south-eastern Nigeria.  

2. MATERIALS AND METHODS 

2.1 Study area 

Southeastern Nigeria is located within latitudes 4° 47' 35'' N and 7° 7' 44'' N, and longitudes 7° 54' 26'' E and 8° 

27' 10'' E. The area comprises nine States namely; Abia, Anambra, Ebonyi, Enugu, Imo, Cross-River, Akwa-

Ibom, Rivers and Bayelsa (Onyishi & Ofualagba, 2021). The area covers about 29095 km2 which accounts for 

3.19 % of the total area of Nigeria. The area is bounded to the North by Kogi and Benue States, to the east the 

area is bounded by Cross-River State and bounded to the south and west by Rivers and Delta States respectively 

(see figure 1). 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 5; September-October 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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4 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 1: Southeastern Nigeria Showing the selected Airports 

The region has a tropical climate with humidity and rainfall decreasing from the coast inland, and characterized 

by uniformly high temperature and a seasonal distribution of bimodal rainfall (Ezemonye & Emeribe, 2012). The 

rainfall of southern Nigeria generally is heavy and ranges from over 2500 mm in the southern most region towards 

the Atlantic to about 1500 mm annually  around  River  Benue  in  the  northern  borders.  The vegetation  stretches  

from  the  mangrove  swamp  in  the  coast through to the derived savanna in the interior (Ezemonye & Emeribe, 

2012)  but  the  region  lies  in  the  lowland  rainforest  natural vegetation belt with evergreen trees in the south 

and gradually gives way northward to rainfall-savanna forest characterized by trees interspersed with grass.The  

underlying  geology  consists  of  heterogeneous  materials namely  basement  complex,  beach  sands,  coastal  

plain  sands, mangrove swamp deposits, sandstones, shale, sombrero Warri- deltaic deposits, recent and sub-

recent alluvium (Okereke & Emeribeole, 2020). The soils of the southeastern Nigeria is heterogeneous in nature 

comprising of loose red-earth with sands, sandstones, clayey-loam with or without ferric properties underlain by 

shale formation (Okereke & Emeribeole, 2020). Also reported by Isagba et al. (2021) the soils are derived from 

shale and sandstone parent materials which are deep, porous, and acidic with low organic  content  as  a  result  

of  leaching  from  rainfall  activity.  

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5 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

2.2 Population/Sample size/Sampling Technique 

The target population for this study includes airline managers for local airlines and passengers of the airports 

(Akwa-Ibom {Victor Attah International airport} Calabar {Margaret Ekpo International airport}, Imo {Sam 

Mbakwe} Rivers {Port Harcourt International airport} and Enugu (Akanu-Ibiam airport) that have existed in the 

area for up to 10 years.  In all by consulting the manifest, the researcher found that the total annual passenger 

traffic for the listed airports were 489235, added to the managers of the airlines that ply the airports, it brings the 

population to 489247.  The weather data for these states were collected for a period of 10 years on hourly scale 

(synoptic). The reason for sampling on hourly scale was to help the researcher knit an analysis between the days 

of interruption of flight operations with the prevailing weather circumstance at the time of interruption, whether 

sudden or slow events. Therefore, the period of sample is 10 years (2011-2020). The ten years’ period was 

designed to eliminate hasty conclusions in the findings of the study. Secondly, the study was looking are the 

erraticism of sudden and slow weather events and how it affects aviation and Ayoade (2004), advised that using 

data from 2-10 years is sufficient for such inquiry. The researcher employed the Taro Yamane Equation to this 

population and a total of 400 respondents were derived. See equation 1: 

𝑛 =  
𝑁

1 + 𝑁(𝑒)2) 
                   … … … … … … … … … … … … … (1) 

Therefore, total sample size is 400.  

2.3 Sources/Method of Data Collection/Analytical Technique 

The data used for study was obtained mainly from secondary sources. Hourly data on sudden weather parameters 

(wind, cloud cover, visibility, precipitation) and slow weather parameters (minimum and maximum temperature) 

were collected from the Nigerian Meteorological Agency (NiMet) and for a period of 10 years (2011-2020). 

Similarly, air traffic data (such as delays and cancellation, accidents and reasons for delay and cancellation) were 

collected from the archives of airliners (Arik, Air peace, Aero contractors and Ibom Air). Data on the perception 

of passengers view of impacts of sudden and slow onset weather events on aviation were derived with the aid of 

a questionnaire. Descriptive statistics, statistical diagrams and tables were used for the presentation of data while 

means and standard deviation were used for description and summarization of data. On the other hand, Analysis 

of variance (ANOVA) test was used to analyze the hypothetical statement that the effects of sudden-slow weather 

events on flight operations in the south east is not significantly same. All analyses were done in the statistical 

package for the social sciences (SPSS) version 26 and Micro-soft Excel 2016 environment. ANOVA Equation is 

expressed as follows: 

ANOVA Equation  

TSS = ∑ 𝑥2 −
(∑ 𝑥)2

𝑁
  ----  ----  ----  (2) 

BSS = 
(∑ 𝑥1)2

𝑛1
+

(∑ 𝑥2)2

𝑛2
+

(∑ 𝑥3)2

𝑛3
+

(∑ 𝑥4)2

𝑛4
−

(∑ 𝑥)2

𝑁
… ..   … … ..              ….       (3) 

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6 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

WSS = TSS – BSS  ----  ----  …. …….  (4) 

Where: TSS = Total Sum of Squares  

 BSS = Between Sample Sum of Squares  

 WSS = Within Sample Sum of Squares  

 n1 … n3 = Number of Samples means being compared 

 N = Total items of all groups. 

3. DATA PRESENTATION 

3.1 The sudden and slow weather event characteristics over the years in South Eastern Nigeria  

The sudden and slow weather events are those weather events that come up suddenly in the day and can disrupt 

anthropogenic activities including aviation operations. These sudden and slow events were monitored within the 

data available in the data archive of the Nigerian meteorological agency. The weather characteristics in the south 

eastern region of Nigeria are presented in Figures 1-6.  

 
Figure 2: Precipitation characteristics in South Eastern Nigeria (2012-2021) 

In figure2, the rainfall amounts and distribution for the whole region is displayed. In 2012, the highest rainfall 

amount for the region was recorded in Uyo with annual rainfall amount of 3585 mm of rainfall. The nearest to this 

amount of rainfall was recorded in Calabar (3495mm). These two locations are due to the fact that they are close 

to each other, having similar environmental and forcing factors. As such they are expected to have similar rainfall. 

Although, Enugu and Port Harcourt had similarity in rainfall amounts (1500mm and 1575mm respectively). 

Although the rainfall amount for Port Harcourt is fairly higher than that of Enugu with about 75mm of rainfall.       

In 2013 the pattern is similar, although the rainfall amount for all the locations were higher than that of the year 

2012. For example, the rainfall amount for Uyo in 2013 was over 4500mm compared to the 3585mm posted in the 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

R
ai

n
fa

ll 
A

m
o

u
n

t 
in

 m
m

Year

Portharcourt Enugu Owerri Calabar Uyo

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7 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

year 2012 for the same location.  This same pattern can be reported therefrom. However, in 2020 whereas other 

locations were experiencing lowered amount in rainfall, Port Harcourt recorded about 3000mm of rainfall. In 2021, 

the general rainfall amount for the locations was low. 

Figure 3 (a), presented the maximum temperature for the study area. The maximum temperature ranged between 

30ºC to 32.5ºC. The maximum temperature for Enugu has been on the rise since 2012 and peaked at the year 2018 

with a mean maximum temperature of 32.5ºC. Maximum temperature in Enugu also represents the highest in the 

region. The reason for this is that the area is further hinterland compared to other states of the region. It is also 

common to see less water bodies around this part of the region. Consequently, the maximum temperature of this 

area appears to be the highest in the continuum.  On the other hand, the Maximum temperature of Calabar revealed 

that it is the lowest in the study region and ranged between 30ºC. The other locations within the region simply falls 

within these two extremes described above (figure 3a). For the minimum temperature, it is conspicuous that the 

mean temperature ranges between 20.5ºC and 24ºC (Figure 3b). Temperature for Owerri appears to be lower than 

that of other locations within the region. It is possible that the topography and the spatial extent of development in 

the region partly explains these minimum temperature characteristics.  Other location just simply ranged minimum 

temperature between 21ºC and 24ºC accordingly. However, in Enugu the year 2015 had a remarkably low 

minimum temperature (21ºC), same can be said of Owerri in 2019. 

  
Figure 3: Temperature characteristics in Eastern Nigeria (2012-2021) 

Wind characteristics is one weather element that is very essential for the operation of the aviation industry. The 

characteristics of which is very important to measure from time to time and the direction and buoyance determined. 

This will help determine the magnitude of the wind and adequate precautions taken. For the wind direction of the 

region showed that it is mostly south west winds in direction (Figure 4). 

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8 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 4: Wind direction in south eastern Nigeria (2012-2021) 

The speed of the wind is presented in figure 4. The wind appeared to have more speed in the Enugu axis of the 

region with an average of 7 Knot to 8 knots. This region is closer to the northern part of the country and the grass 

land vegetation is more rampant in this area, and so the buoyance of the wind in this region is expected to be 

higher. 

 

 
Figure 5: Wind Speed in south eastern Nigeria (2012-2021) 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

W
in

d
 S

p
ee

d
 in

 K
n

o
t

Years 

Portharcourt Enugu Owerri Calabar Uyo

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9 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

Surprisingly, the wind speed in Calabar was lower than that of other regions even though it is a coastal town 

surrounded with a lot of water bodies. However, in this study our main aim is not to reveal with area has more 

wind speed or not, but to determine if there are departures from the norm and how such departures from the norms 

affects the aviation industry in the region.  

The visibility characteristics of the study area is displayed in figure 6. Port Harcourt appears to be having better 

visibility than the other areas in the study area. Enugu is very poor with annual visibility that range from 5km to 

10km. Calabar also presented a very weak visibility although it seems to have improved since 2019. The main 

reasons for the behavior of visibility in the study area is that to the north section of the region, there is the 

interference of the highlands and the intercontinental airmass which comes in from the north bearing dust and 

hazardous weather. To the Calabar section, the highlands of the Cameroons plays serious roles in the modification 

of the weather and visibility inclusive.    

 
Figure 6: Visibility characteristics in Eastern Nigeria (2012-2021) 

3.2 The changes in the sudden and slow weather events of south eastern Nigeria (2012-2021) 

The annual extreme weather events between 2012 and 2021 is presented in Table 1. Rainfall data revealed a slight 

variation in occurrences of rainfall amongst the cities sampled. Port Harcourt, Calabar and Uyo recorded higher 

rainfall in 2012 with 28, 25 and 26 occurrences. This significant variation from Enugu (17 times) and Owerri (12 

times) is attributable to the sub equatorial climate in the South South region of Nigeria.  While the case of Enugu 

is adduced to the character of the hinterland climate, that of Owerri present evidence for critical investigation. 

There is evidence of lack of uniformity in rainfall occurrences and increase in 2016, but the most recent increase 

is recorded in Port Harcourt with 39 to times in 2016. What is very conspicuous is that Port Harcourt, Calabar and 

Uyo had high decadal mean rainfall with 227, 215 and 180 respectively. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

V
is

si
b

ili
ty

 in
 K

m

Years 

Portharcourt Enugu Owerri Calabar Uyo

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10 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 

 

 

Table 1: Annual extreme weather (sudden) events in south-east Nigeria 2012-2021  

Rainfall 

Locations  2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Decadal  Mean  

Port 

Harcourt 28 34 12 29 39 14 12 19 23 17 227 22.7 

Enugu 17 19 11 16 18 14 9 15 12 10 141 14.1 

Owerri 12 11 13 4 1 16 15 16 12 4 104 10.4 

Calabar 25 23 22 14 22 15 18 31 11 34 215 21.5 

Uyo 26 21 17 8 15 19 14 17 25 18 180 18 

Cloud Oktas 

Port 

Harcourt 6 4 1 2 3 5 6 7 1 5 40 4 

Enugu 4 2 5 1 5 6 1 3 5 5 37 3.7 

Owerri 1 2 3 2 3 6 2 1 4 5 29 2.9 

Calabar 3 2 1 1 1 2 3 2 1 3 19 1.9 

Uyo 2 1 2 3 4 1 4 5 4 2 28 2.8 

Visibility 

Port 

Harcourt 18.0 21.0 23.0 15.0 33.0 15.0 14.0 14.0 3.0 14.0 170 17.0 

Enugu 45 39 38 47 32 28 34 32 8 22 325 32.5 

Owerri 23 23 14 25 11 13 15 21 4 22 171 17.1 

Calabar 3 1 5 3 6 7 2 6 1 12 46 4.6 

Uyo 2 13 1 3 4 5 6 3 1 7 45 4.5 

The obvious implication is that more flight delays caused by obscured weather conditions would be recorded in 

those three cities with spiraling effect in Enugu and Owerri. Table 1 also present annual and decadal cloud cover 

between 2012 and 2021. Remarkably, only Port Harcourt recorded 7 oktas in 2019 which is not full cloud and not 

sufficient to obscure visibility. There is prevalence of 1 oktas and mean decadal cloud cover of less than 3.0 given 

that the city with the highest decadal cloud cover is Uyo with 2.8 oktas. It can be generalized from the foregoing 

that airline operation in the decade under investigation was not significantly disrupted by poor visibility and cloud 

cover. 

In Table 2, the extreme monthly weather events for 2012-2021 is shown. Port Harcourt recorded a remarkable 

exponential progression in rainfall occurrences from June to September with 52 and June 12 times respectively. 

The case of Port Harcourt is similar to Uyo and Calabar. However, all the sampled cities recorded a blip between 

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11 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

November and March. This is characteristic of the two seasons in Nigeria which are largely determined by the 

tropical continental and tropical maritime air masses. Only Port Harcourt recorded a full cloud in June with 8 and 

August with 9. 

 

 

Table 2:  Monthly extreme weather (sudden) events in south-east Nigeria 2012-2021 

Rainfall 

Month  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Port Harcourt 2 3 4 23 15 49 41 52 31 6 1 0 

Enugu 4 7 9 12 9 26 23 26 19 5 1 0 

Owerri 0 2 4 6 7 20 18 21 18 6 2 0 

Calabar 5 7 9 11 9 41 33 38 31 23 5 3 

Uyo 2 4 5 7 4 29 33 35 31 22 5 3 

Cloud OKtas 

 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Port Harcourt 1 2 3 3 2 8 6 9 3 2 1 0 

Enugu 0 1 3 1 6 6 6 5 6 3 0 0 

Owerri 1 2 3 3 4 5 2 4 1 4 0 0 

Calabar 0 2 1 3 1 2 3 2 2 3 0 0 

Uyo 0 1 2 3 4 1 4 5 4 4 0 0 

Visibility 

 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Port Harcourt 18 21 2 1 3 14 13 17 15 14 31 21 

Enugu 45 19 14 11 14 12 10 32 34 38 52 44 

Owerri 23 3 4 1 2 15 18 14 13 21 35 22 

Calabar 1 0 1 0 4 1 1 1 0 3 19 15 

Uyo 1 2 1 3 1 4 1 3 2 3 9 15 

The implication of having a full cloud is that the sky was obscured in the two months which could cause disruption 

in flight operations. Enugu, Owerri, Calabar and Uyo recorded zero cloud cover in November and December which 

is highly supportive of flight operations. 

3.3 Effects of sudden-slow weather events on airports operations in South Eastern Nigeria (2012-2021)  

Figure 7 presents effects of sudden slow weather events on airports operations. Flight cancellation and delay are 

the dominant reactionary measures to slow weather events with 700 and 800 cases respectively. The data in figure 

7 also demonstrated diversion of flight as a measure to surmount slow weather events. But this will have very little 

effect on passengers’ satisfaction. 

 

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12 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Figure 7: Effects of sudden-slow weather events on airports operations in South Eastern Nigeria (2012-

2021) 

3. 4: Sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) 

Table 3 present sudden weather events with more impacts on flight operations. The table reflects that cities in the 

sub equatorial climate recorded more occurrences of rainfall cumulatively. This is linked in extant literature to the 

tropical maritime air mass that causes rainy climate. But Owerri, though in the South East, recorded 104 rainfall 

occurrences, which is less than Enugu that is characteristic of hinterland climate. The case of Owerri can be 

adduced to the weather dynamics associated with the elevation of the state. 

Table 3: Sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) 

Locations  Rainfall Cloud Visibility 

Port Harcourt 227 40 170 

Enugu 141 37 325 

Owerri 104 29 171 

Calabar 215 19 46 

Uyo 180 28 45 

Furthermore, the sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) 

reflected in table 4.3 showed that Port Harcourt, Uyo and Calabar had more rainfall events that have affected 

aviation operations in the region. But the numbers of events in Owerri and Enugu is sufficient to disrupt flight 

operations. The figure show that visibility is not in sync with data on rainfall events. While Enugu and Owerri had 

less occurrences of rainfall, the two cities had more occurrences of when visibility disrupted flight operations, thus 

evident that poor visibility in the two cities is not a function of rainfall. Other climatic events could be responsible 

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13 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

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for poor visibility. All the cities had cases of cloud effects on flight operations but Port Harcourt, Uyo and Owerri 

had more effects on cloud, this is because only the three cities had more oktas. 

3.4: Passengers’ perception of the effects of sudden or slow weather events on aviation (2012-2021) 

The perception of the passengers regarding the effects sudden slow weather events on the aviation industry was 

also considered and these passengers comprised of 72.3% of males and 27.2% females (see Table 4). Table 4 

shows that more males (72.5%) boarded flight than females (27.7%) within the period under investigation. 19.8% 

of the passengers are less than 40 years of age while 36.2% are between 20 to 30 years. This is consistent with 

more volume of air travels for the economically active population in Nigeria that utilizes air travel for work, 

education, healthcare care, tourism, recreation and business. 

Table 4: Demographic characteristics of the passengers 

Sex  Frequency Percentages (%) 

Male  289 72.3 

Female  111 27.7 

Total  400 100 

Age  Frequency Percentages (%) 

20-30 145 36.2 

31-40 176 44 

>40 79 19.8 

Total  400 100 

Education status Frequency Percentages (%) 

Basic 1-9 27 6.8 

SSCE 104 26 

Tertiary education  269 67.2 

Total  400 100 

Frequency of air travel  Frequency Percentages (%) 

Weekly 156 39 

Every fortnight  121 30.3 

Monthly 89 22.2 

> Monthly 34 8.5 

Total  400 100 

 

Data also showed that 67.2% of the passengers have tertiary education and 26% have SSCE. Only 6.8% possess 

basic education which is also consistent with studies that air travel is elitist. Frequency of air travel is recorded 

more in fortnight with 30.3% and weekly with 39%. This shows a high volume of the utility of air traffic in Nigeria. 

                       

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14 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Table 5 indicates that a good number of the passengers are aware of the effects of weather on aviation. High level 

of awareness can be adduced to high literacy rate of passengers. However, 5.3% of the passengers are skeptical.  

                       Table 5: Effects of sudden or slow weather events on aviation  

 Awareness of effects of weather on aviation 

 Option Frequency Percentage 

1                    Yes                          379                94.7 

2                    No                          00                 00 

3               Undecided                           21                 5.3 

 Weather element that has affected your flight 

 Option Frequency Percentage 

1 Temperature                          00                   00 

2 Rainfall                        103                 25.8 

3 Cloudiness                          82                 20.5 

4 Visibility                         192                 48 

5 Wind                          00                 00 

6 Pressure                          23                 5.7 

 Total                        400                 100 

 Perceived effects of sudden slow weather events on aviation operations 

 Option Frequency Percentage 

1 Flight delays                              193                   48.3 

2 Flight cancellations                              134                   33.5 

3 Flight diversion                                55                   13.8 

4 Near crash                                18                     4.5 

 Total                         400               100 

 Perceived economic loss from sudden-slow weather events 

 Option Frequency Percentage 

1 Financial loss                         178               44.5 

2 Appointment 

cancelations   

                        143               35.7 

3 Loss of opportunities                            79               19.8 

 Total                          400               100 

In Table 5, passengers affirmed that visibility, rainfall, and cloudiness are more impactful on flight operations with 

48%, 25.8% and 20.5% respectively. Though they did not see temperature and wind as impactful. 5.7% of the 

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15 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

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passengers recognized pressure as a cause of air travel disruption. It is also clearly revealed in Table 5 that the 

main effects of sudden slow weather events on flights and aviation operation in the study area are flight delays 

(48.3%) and flight cancellations (33.5%). Other challenges of sudden slow weather events are flight diversion 

(13.8%) and near crash (4.5%). Overall, there appears to be a significant effect of sudden slow weather events on 

aviation operations in the south eastern part of Nigeria.  The economic losses from sudden slow weather events 

are multidimensional. Passengers recognized financial losses, cancellation of appointments and loss of 

opportunities as major offshoots of sudden slow weather events. However, financial losses is more prevalent with 

44.5%.  

Table 6A: ANOVA table showing the spatial difference in the effects of sudden weather 

events (rainfall) on aviation in the south east of Nigeria 

ANOVA 
Rainfall   

 Sum of Squares Df Mean Square F Sig. 

Between Groups 8761.000 4 2190.250 12.684 .000 

Within Groups 102747.500 595 172.685   

Total 111508.500 599    
 

Table 6B: Duncan statistics showing the spatial difference in the effects of sudden weather 

events (rainfall) on aviation in the south east of Nigeria 

Rainfall 
Duncan 

Identifiers N 

Subset for alpha = 0.05 

1 2 3 4 

Owerri 120 8.6667    

Enugu 120 11.7500 11.7500   

Uyo 120  15.0000 15.0000  

Calabar 120   17.9167 17.9167 

Port Harcourt 120    18.9167 

Sig.  .070 .056 .086 .556 

Means for groups in homogeneous subsets are displayed. 

a. Uses Harmonic Mean Sample Size = 120.000. 
 

Analysis of variance of the spatial variation in the effects of sudden weather events in rainfall for sampled cities 

in Table 4A (Port Harcourt, Uyo, Enugu, Owerri and Calabar) is significant at p<0.05 level, F 12.684, sig 0.000. 

Since the significant level is below 0.05(p value). It indicates that there is a statistically significant difference 

amongst the five states. Duncan statistics in Table 4B was further computed to decipher where the variation in 

rainfall is conspicuous. The analysis revealed that Owerri (M= 8.667), Sig 0.070) is significantly varied from Uyo 

(M= 15.000 Sig 0.86) and varied from Port Harcourt (M= 18.9167, Sig 0.556). 

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16 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

Table 6C: ANOVA table showing the spatial difference in the effects of sudden weather 

events (Cloud-Oktas) on aviation in the south east of Nigeria 

ANOVA 
Cloud_Oktas 

 Sum of Squares df Mean Square F Sig. 

Between Groups 227.667 4 56.917 13.651 .000 

Within Groups 2480.833 595 4.169   

Total 2708.500 599    
 

Table 6D: Duncan statistics showing the spatial difference in the effects of sudden weather 

events (cloud-Oktas) on aviation in the south east of Nigeria 

Cloud-Oktas 

Duncan 

Identifiers N 

Subset for alpha = 0.05 

1 2 3 

Calabar 120 1.5833   

Uyo 120  2.3333  

Owerri 120  2.4167  

Enugu 120   3.0833 

Port Harcourt 120   3.3333 

Sig.  1.000 .752 .343 

Means for groups in homogeneous subsets are displayed. 

a. Uses Harmonic Mean Sample Size = 120.000. 
 

Analysis of variance of the spatial variation in the effects of sudden weather events in cloud -oktas for sampled 

cities (Port Harcourt, Uyo, Enugu, Owerri and Calabar) is presented in Table 4C and it is significant at p<0.05 

level, F 13.651 sig 0.000. Since the significant level is below 0.05(p value). It indicates that there is a statistically 

significant difference amongst the five states. Duncan statistics was further computed to decipher where the 

variation in cloud oktas is conspicuous. Table 4D revealed that Calabar (M= 1.5833), Sig 1.000) is significantly 

varied from Uyo (M= 2.333 Sig 0.752) and varied from Port Harcourt (M= 3.333, Sig 0.343) 

Table 6E: ANOVA table showing the spatial difference in the effects of sudden weather 

events (Visibility) on aviation in the south east of Nigeria 

ANOVA 
Visibility   

 Sum of Squares df Mean Square F Sig. 

Between Groups 44414.333 4 11103.583 125.345 .000 

Within Groups 52707.500 595 88.584   

Total 97121.833 599    
 

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17 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

Table 6F: Duncan Statistics showing the spatial difference in the effects of sudden weather 

events (Visibility) on aviation in the south east of Nigeria 

Visibility 
Duncana 

Identifiers N 

Subset for alpha = 0.05 

1 2 3 

Uyo 120 3.7500   

Calabar 120 3.8333   

Port Harcourt 120  14.1667  

Owerri 120  14.2500  

Enugu 120   27.0833 

Sig.  .945 .945 1.000 

Means for groups in homogeneous subsets are displayed. 

a. Uses Harmonic Mean Sample Size = 120.000. 
 

Analysis of variance of the spatial variation in the effects of sudden weather events in visibility for sampled cities 

(Port Harcourt, Uyo, Enugu, Owerri and Calabar) is presented in Table 4E and it is significant at p<0.05 level, F 

125.345 sig 0.000. Since the significant level is below 0.05 (p value). It indicates that there is a statistically 

significant difference amongst the five states. Duncan statistics was further computed to decipher where the 

variation in visibility is conspicuous. Table 4F revealed that Uyo (M= 3.7500, sig 0.945) is significantly varied 

from Port Harcourt (M= 14.1667 Sig 0.45) and varied from Enugu (M= 27.0883, Sig 1.000) 

4: DISCUSSION OF RESULTS 

The critical characteristics of sudden and slow weather events over the years in South Eastern Nigeria were 

evaluated. The character of weather and climate showed changes in frequency, intensity, blip, spatial coverage, 

number of occurrences and timing. Extremity of rainfall and cloud cover in Port Harcourt and Uyo can be linked 

to their geographic location and dominance of the south west trade wind. The implication is that the tropical 

maritime gives more potency for cloud and rainfall to reduce visibility, and affect flight operations given the 

ubiquity of thunderstorms. Enugu had higher wind speed, but its closeness to the northern part of the country 

would mean dominance of the tropical continental air-mass. Rainfall, temperature, cloud cover, wind and cloud 

cover showed remarkable variation annually and monthly.  Rainfall showed lack of consistency in most of the 

years, but 2013 had a similar pattern, temperature ranged between 30 ⁰C to 35⁰C with Enugu having the highest 

temperature maximum, and this is consistent with the dynamics of the various climate belts in Nigeria. These 

findings are consistent with those of Samson & Sumi, (2019). This study has identified the slow and sudden 

weather events characteristics in the South East. What was very conspicuous is the spatiality of weather 

characteristics and similarities amongst Port Harcourt, Uyo and Calabar. They showed near similarities in rainfall, 

cloudiness and visibility which is consistent with the character of the sub equatorial climate. However, this finding 

is not in tandem with the finding of Ogunrinde et al., (2023). The weather characteristics of Enugu appeared 

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18 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

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different and similar to that of the tropical hinterland climate, and have higher wind speed. However, all the 

sampled cities demonstrated that cloudiness is not very impactful on visibility. The study observed changes in 

weather characteristics in the decade under investigation, one deviation from the norm is that Calabar which is a 

coastal town has lower wind speed than other cities, and recorded the most recent increase occurrences in rainfall 

of 34 evets in 2016. However, studies have shown that most of the disruption, delays and cancellations of flight 

are avoidable if human errors and negligence are tackled (Borsky & Unterberger, 2019).  

The changes in the sudden and slow weather and climate events in the south eastern Nigeria is consistent with the 

environmental changes. But empirical evidences have shown that climate change is one of the defining 

contemporary issues that have been recognized as a major driver of change in the period under investigation. All 

the cities sampled showed remarkable differences in changes in precipitation, temperature, cloud cover and 

visibility. The wind in Enugu which shares the characteristics of the tropical hinterland climate had more speed 

than other cities but with marked variation across the years. Port Harcourt recorded remarkable shift with double 

maxima of high rainfall events in 2013 (34 occurrences) and 2016 (39 occurrences). Only 12 and 12 rainfall events 

recorded in port Harcourt in 2014 and 2018 are the only similarities, but the difference between rainfall events of 

12 which is the lowest and that of 2016 (39 events which is the highest is significant. The case of Owerri where 

rainfall was recorded just ounces in 2016, 4 times 2015 and 2021 represent concrete evidence of climate. The only 

city that had one level of consistency in rainfall is Uyo, but there is abrupt reduction in 2018 with only 8 events. 

These findings are in line with those of Eludoyin & Akinbode (2009), although Ugochukwu, Emmanuel, & Lekia, 

(2023) reporter otherwise. Adverse weather and climate events are external factors that determines the operations 

of airports operations globally, but there is perceived over generalization of the climatic parameters that causes 

more disruptions in various climate regions. This study showed concrete evidence of slow and sudden weather 

events and potential effects on flight operations in the south east, but there is spatial variation among the cities 

investigated. As also reported by Uchechukwu et al. (2018). The effects of the sudden and slow events are 

multidimensional, one the one hand, it increases the cost of operations, and create a negative public perception for 

airlines that do not have the required expertise to be proactive, and minimize the effects on passengers. On the 

other hands, sudden weather events cause delays, flight cancelation and additional cost for travelers, it can also 

lead to fatalities. In most cases, delays occur when there is no accurate forecast of imminent sudden weather events, 

and when communication with passengers is poor. It has been established in the literature that weather events are 

not always controllable (Oriola, 2014), however, the onus is in airlines to minimize cost and losses of productive 

hours for passengers. Occurrences of heavy rainfall and strong wind are sudden, and occur at specific time of the 

day, but causes shift in disruption in logistics planning with economic cost for operators and passengers, but data 

on amount of departure delays in various airline companies is not availability for research (Balogun & Odjugo, 

2020). The constancy of the effects of rainfall and cloud cover on visibility and airline operations in the decade 

under review is attributable to the dynamics in the sub equatorial and hinterland climate. Similar observation has 

been made by Borsky & Unterberger, (2019), they however, averred that being acquainted with the weather and 

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19 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

climate characteristics of the locale is key to ameliorating the sudden and slow weather events effects of aviation 

operations. 

Passengers are largely aware of the effects of weather events on air travels. Over the years, financial losses, flight 

delays and cancelation and loss of opportunities have been recorded by passengers. But there is still no policy to 

compensate for poor service. Evidently, flight travels are still largely patronized by the elites as there seem to be 

a correlation between literacy and the utility of air transport. Only few passengers with basic education utilizes air 

transport in the sampled cities and they are obviously the respondents that are not aware of the effects of weather 

events on air travels (Adefolalu, 2007). Annual and decadal rainfall revealed steady increase from June to August 

and slight decrease from October to March. There is positive statistical relationship and variation in weather 

characteristics which also manifest in flight delays, cancellation and diversion. This is consistent with Mande 

(2019) where he reported that 71% of air travel disruption in Nigeria are due to the poor weather conditions, with 

the inclusion of human errors, aging aircraft and deficiency in safety management system. This case of full cloud 

in Port Harcourt also aligns with Mande (2019) where he reported flight disruption in the city with reduced 

horizontal visibility to between 200 to 800 for several days leading to spiraling flight disruption across the country. 

5. CONCLUSION AND RECOMMENDATIONS 

It is obvious as captured in several studies in the literature that knowledge on weather phenomena is very important 

for flight operations while others revealed that adverse weather conditions may cause delays, diversions, accidents 

and even outright cancellations of journeys, causing loss of time, revenue and even lives. In addition, available 

reports maintained that in aviation, weather has remained the important parameter in determining safety, regularity 

and efficiency of aviation operations. The plethora of studies in the literature that interrogate the effects of weather 

parameters on aviation have obviously failed to differentiate between slow and sudden weather events. Thus, there 

is perceived wrongful generalization and errors in responses to weather related problems in the aviation industry. 

This study has established the difference between slow and sudden weather events and disparity in weather 

characteristics amongst cities in the south east. It revealed that there are variations in cloudiness, rainfall and 

visibility amongst the cities and given the interconnectedness of aviation infrastructure and operations with 

weather parameters, disruption in one city leads to chain disruption in other cities resulting in flight disruptions. 

Noting that the economic cost of flight cancelation, diversion and delays are not quantified and duly compensated, 

the need to device various methods to address the peculiarities of problems in various cities are necessary. On the 

premise of the above, the following recommendations are made: 

1. improvement in the expertise of aviation staff through training and retraining to keep pace with effects of 

weather variability and change in airline operations. 

2. migrating from analog data collection methods to automated instruments to engender real time and accurate 

data collection for planning due to the dynamic nature of atmospheric parameters. 

3. enactment and enforcement of stringent regulations on airline companies to engender global best practices. 

4. replacement of aging aircraft and weather monitoring facilities. 

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|  https://topjournals.org/index.php/AJSET 

5. improvement in maintenance culture. 

6. synergy between all airport services providers is needed to activate proactive planning, anticipate climate 

change and design appropriate resilience and adaptation plan.as well measures to tackle flight disruption, 

damage of airport infrastructure given the progression of rainfall and changing wind.  

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Diamond, S. (2017). In search of the primitive: A critique of civilization. Taylor & Francis. 

Dönmez, K. (2023). Aircraft sequencing under the uncertainty of the runway occupancy times of arrivals during 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 5; September-October 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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