Inter-monsoon Seasons Research Articles

Impact of El Niño Southern Oscillation on the first inter-monsoon rainfall over Sri Lanka in the post-El Niño years

Sri Lanka has a tropical monsoon climate. The first intermonsoon (FIM) season is one of four rainfall seasons. Previous investigations have explored the concurrent ramifications of the El Niño Southern Oscillation (ENSO); however, its impact on seasonal rainfall patterns in the country in the post-El Niño years remains unexplored. This study aims to investigate anomalous FIM rainfall in Sri Lanka during the post-El Niño years. Weather station rainfall data from the Department of Meteorology for 114 stations for 1975–2019, Japanese 55-year reanalysis data, and COBE sea surface temperature data were used in this study. A significant negative correlation between rainfall at most stations and the preceding year’s November, December, and January Niño3.4 index (NDJ Niño3.4) was identified for the FIM season. All three super El Niño events and one of the two strong El Niño events agree with the above results, showing a clear negative rainfall anomaly pattern. The Indo-western Pacific Ocean Capacitor effect was identified as the cause of rainfall reduction during the FIM season in Sri Lanka and in contrast, the post-El Niño season in 1988 exhibited a positive rainfall anomaly. A rapid transition from El Niño to La Niña and the associated atmospheric conditions were identified as significant contributors to these features. The findings of this study will help overcome this research gap and improve the accuracy of seasonal forecasts for the country.

Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2.5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2.5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16–0.94) observed in the wet and inter-monsoon seasons is due to fewer number of data used in model calibration. Integration of two AOD products from the Advanced Himawari Imager (AHI) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2.5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2.5 prediction accuracy.

Temporal Variation of Water Quality in Nilwala River, Southern Province, Sri Lanka

This study intended to investigate the temporal variation of water quality of the Nilwala river in the Matara district and to determine how the changes in rainfall affect to river water quality. Water samples were collected monthly from March 2019 to October 2019 (considering the second Inter monsoon season and first inter-monsoon season) at eight locations along the main river. Water quality parameters such as chemical oxygen demand (COD), biological oxygen demand (BOD), pH, electrical conductivity, temperature, alkalinity, hardness, chloride, nitrate and phosphate were analyzed using APHA 23rd edition of standard methods for the examination of water and wastewater. Experimental results were statistically analyzed by Two-way ANOVA using Minitab 17. The pH, EC, temperature, alkalinity and nitrate concentration showed statistically significant differences among the months (p<0.001) and those showed statistically significant differences with monthly rainfall (p< 0.001). There is a statistically significant relationship between rainfall and month (p<0.001). There is a statistically significant relationship between rainfall and sampling location (p<0.001). The highest rainfall was recorded from august to November (2nd inter-monsoon season) and secondly from March to May (1st inter-monsoon season) was recorded. Conductivity, pH and temperature were decreased from March to June. This may due to 1st inter monsoon rainfall. Conductivity was increased during august-november. Flooding can increase conductivity when it washes salts and minerals from the soil into the water. Nitrate concentration in the majority of the locations was increased during march-may. Total alkalinity, pH and nitrate were increased from august to November. The temperature of river water at all locations decreased from august- November. In conclusion, the pH, EC, temperature, alkalinity and nitrate concentration showed statistically significant differences among each month. It may due to variation of rainfall. Upper part of Nilwala River had a higher rainfall than lower basin.
 Keywords: Conductivity, Nilwala River, pH, temporal variation, Water quality

Water Table Estimation in Attaragoda GN Division in Galle, Sri Lanka

The fluctuation of the groundwater table depends on geology, topography, climate and human consumption pattern. Long-term reduction of groundwater table caused by global climatic changes and overexploitation. However, short-term fluctuation is caused by climatic variations such as rainfall patterns and evaporation. Groundwater consumption is increasing at an alarming speed due to the increasement in the population, and it directly affects the reduction of the water table. Attaragoda GN division is considered a rural area in the wet zone of Sri Lanka. Most households utilize groundwater as their principal water source. There is no research conducted to assess the water table in the Attaragoda GN division. A preliminary study tried to determine the water table in Attaragoda GN division, Galle to fill this research gap. As a tropical country, the month of November is considered the second inter-monsoon season in Sri Lanka. Randomly selected 33 groundwater wells were considered as the sample size from 142/A Attaragoda GN division. The study was conducted in November 2021 from 8.00 am to 2.00 pm. Elevation was recorded using ―My elevation mobile app,‖ and the depth of the wells was measured using the measuring rope at the field in November 2021. The water table was calculated using the standard formula for all 33 sampling locations (Z=h–X; Z=depth of water table, X=elevation, h=depth measured in well). Data analysis was done using plotting the water table graph against elevation. Regarding land elevation, the water table fluctuated in the 4 m-20 m range in the Attaragoda GN area due to geographic locations. The lowest water table was recorded at a well in the highland area, and the highest was recorded at a well in the lower land. Based on the results in a plotted graph indicated that the same elevation had different groundwater table levels. Main reasons for this different water table recorded in the same elevation identified as overused groundwater for maintaining a fish farm, watering flower nurseries, and animal husbandry practices. At the same elevation, the well that recorded the low water table caused overexploitation of groundwater than the well which had a higher water table level. Water table levels can change in the same area due to their native geographic features. However, at the same elevation water table might be adjusted according to the consumption pattern of the households. 
 Keywords: Elevation, Geology, Ground water, Water table

Seasonal determination of proximate composition and essential elements in commercial fishes from Pakistan and human health risk assessment

The seasonal variability in proximate composition and essential elements demonstrates that the habitat and feeding habits of fish species play a vital role in energy transfer.
 We aimed to ascertain seasonal variability in the biochemical composition (protein, lipids, carbohydrates, ash, and moisture) and the amounts of Na, K, Ca, Mg, Mn, and Zn in the species Nemipterus japonicus, Epinephelus erythrurus, Nematalosa nasus, and Ilisha striatula inhabiting pelagic and demersal zones. We compared the nutritional profile of these fish species and their seasonal importance. The essential elements were detected by flame atomic absorption spectrometry and found in the following order: K > Na > Ca > Mg > Mn > Zn. To determine the proximate composition, we employed a number of methods: the Lowry method for protein analysis, the acid hydrolysis method for fat/lipid analysis, a formula for carbohydrates and moisture, and the incineration method for ash content.
 The spring inter-monsoon season showed the highest values for the essential elements in both pelagic and demersal species. However, the pelagic species had the highest biochemical composition levels during the southwest monsoon. The autumn intermonsoon had the lowest bio-profile for the fishes of both regimes.
 The summer season, which is not thought to be good for fish consumption, showed the highest biochemical composition levels in the pelagic fish. The nutritional profile of fish flesh can be affected by feeding habits, seasonal variation, and habitat.

Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis

Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia.

Prevalence of diseases by canine blood parasites in Companion Veterinary Clinic, Melaka, Malaysia

A disease caused by canine blood parasites (tick-borne diseases) is a condition in which dogs are bitten by tick-carrying pathogens. This study assessed the prevalence of diseases caused by canine blood parasites (Anaplasmosis, Babesiosis, and Ehrlichliosis) at the Companion Veterinary Clinic, Melaka, Malaysia, from August 2020 to July 2022. Secondary data of 103 infected dogs were obtained from medical records. Dogs at the puppy stage were more commonly diagnosed (26.21 %). Both males and females showed almost the same prevalence of 52.43% and 47.57%, respectively. Mongrel had a relatively higher prevalence compared to pedigree dogs but there was no breed predisposition. Multiple infections were more common than single infections. Canine tick-borne diseases show a higher prevalence during the intermonsoon season in Malaysia. Anorexia, inactivity, and bleeding diatheses are the most common clinical signs observed in dogs. To support the diagnosis, anaemia and thrombocytopenia were observed in the patient’s complete blood count results. The test kit was positive when antibodies or antigens were detected and parasites could also be observed under a microscope using a blood smear. The prevalence of tick-borne diseases is influenced by intrinsic (age and sex) and extrinsic factors (weather).

Phytoplankton blooms and fish kills in Lake Tanganyika related to upwelling and the limnological cycle

Characterizing the processes which cause upwelling in the East African Great Lakes enables prediction of the spatial–temporal variability of phytoplankton blooms and increases in fish abundance or incidence of fish kills. The influence of the southeast trade winds on the limnology of Lake Tanganyika is well known. Upwelling occurs to the south and downwelling to the north from May to September and subsequent upwelling and blooms at the northern end of the lake. However, the influence of the shifts in wind speed and direction after the SE trade winds, including the Northeast trade winds, and any further internal wave driven upwelling requires investigation. Here we use images from remote-sensing during a 12 year period (2003–2014) to illustrate intense phytoplankton blooms at the southeast of Lake Tanganyika in April-May, a period characterized by relaxation of the Northeast trade winds. Phytoplankton blooms progressed around the lake after a massive cyanobacteria bloom at the northern end in September 2018; recent and historical observations and local knowledge show similar patterns. Lake Tanganyika limnological cycle is revised to include four main periods: two trade winds seasons and two inter-monsoon seasons (April-May and September-October) with lake-wide up and downwelling of the thermocline associated with the monsoons, and upwelling associated with subsequent internal waves in the inter-monsoon period which are linked with increased phytoplankton blooms and sometimes also fish kills.

The Influence of Weather Patterns and the Madden‐Julian Oscillation on Extreme Precipitation Over Sri Lanka

AbstractSri Lanka is affected by extreme precipitation events every year, which cause floods, landslides and tremendous economic losses. We use the ERA5 reanalysis data set to understand the association of extreme precipitation events with 30 weather patterns, which were originally derived to represent the variability of the Indian climate during January–December 1979–2016. We find that weather patterns that are most common during the northeast monsoon (December–February) and second intermonsoon (October–November) seasons produce the highest number of extreme precipitation events. Furthermore, extreme precipitation events occurring during these two seasons are more persistent than those during the southwest monsoon (May–September) and first intermonsoon (March–April) seasons. We analyze the modulation of extreme precipitation events by the Madden‐Julian Oscillation, and find that their frequency is enhanced (suppressed) in phases 1–4 (5–8) for most weather patterns.

Seasonal Indonesian Throughflow (ITF) across southern Java determines genetic connectivity of Sardinella lemuru (Bleeker, 1835)

Sardinella lemuru populations are considered near-threatened in Bali Strait. Despite declining fish stock, documented landings of S. lemuru at the Prigi waters unexpectedly climbed during the 2nd transitional inter-monsoon season of 2019. Here we used a combination of molecular taxonomy and morphometrics-meristics to determine the genetic link between S. lemuru populations in East Java's southern seas (Prigi and Puger) and those in the Philippines. The results of both molecular and morphometric-meristic analyses suggest that extensive genetic mixing between the populations from Prigi and the Philippines is occurring. Most samples clustered with the S. lemuru populations from the Philippines in Clade-1 and Haplogroup A and B implied high genetic connectivity. The inferred patterns of haplotype network and haplogroup relative frequency indicate the dispersal, genetic homogeneity, and absence of geographical barriers between S. lemuru populations were also supported by a seasonal model of ocean circulation: during the southeast monsoon, greater Indonesian Through-Flow and South Java Current, followed by westward currents along Java's south coast. The findings of this study give much-needed evidence that S. lemuru populations disperse to a secondary fishing area (in Prigi waters), followed by high plankton abundance in 2019, as a result of ITF and local seasonal circulation. The unique sequences of Puger-2017 and the remainder of Prigi-2017 in Clade-2 with overall moderate genetic distance separated from Clade-1 because the Puger water mass are influenced by the Java Sea and Madura Strait or possibly migrated from their habitats in the western part of Indonesia or the Java Sea.

Exploring rainfall variabilities using statistical functional data analysis

Functional data analysis (FDA) has been widely applied in various scientific fields, including climatological, hydrological, environmental, and biomedical. The flexibility of the FDA in incorporating temporal elements into the statistical analysis makes the method highly demanded compared to the conventional statistical approach. This study introduces FDA methods to investigate the variations and patterns of rainfall throughout Peninsular Malaysia, which includes 16 rain gauge stations in Peninsular Malaysia from 1999 to 2019. A descriptive statistic of the functional data depicted the mean and variation of the rainfall curve over time, while the functional principal component analysis measured the temporal variability of the rainfall curve. According to the findings, the first and second principal components accounted for 87.4% of all variations. The first principal component was highly characterised by the stations over the eastern region during the northeast monsoon since the highest variability was observed from November to January. On the other hand, the stations impacted by the inter-monsoon season were best described by the second principal component. Based on the factor scores derived from the functional principal component, those rain gauge stations with comparable features were then clustered. Overall, the results showed that the rainfall pattern is strongly influenced by their geographical and topographical features and the seasonal monsoon effect.

Characteristics of Compound Climate Extremes and Impacts in Singapore, 1985–2020

Compound weather and climate extremes have amplified impacts on natural and socioeconomic systems across the world, including Singapore. To better understand the spatial and temporal characteristics of compound climate extremes, including concurrent rainfall and wind speed, as well as dry and hot conditions, we analyzed long-term observations from 11 selected meteorological stations over the period 1985–2020. The results revealed that the north and northeastern parts of Singapore were focal points for both types of compound extremes, with a higher frequency of occurrence than the southwest of the island. Concurrent rainfall and wind speed extremes were the most prominent in December and January thanks to the northeast monsoon, while dry and hot extremes were distributed mainly in the inter-monsoon season, with peaks in March and April. A notable upward trend was also detected for mild and moderate levels of both compound climate extremes over time. According to our review of the impacts, Singapore has benefited from investments in enhanced water infrastructure; water resource availability was less affected; and flash floods were not proportionally related to the severity of climate extremes. The forests in the urban landscape of Singapore also exhibit resilience to drought.

The Impact of the Surrounding Land Uses on Water Quality of Some Selected Cascade and Perennial Tanks in Anuradhapura District, Sri Lanka

The present study was conducted to evaluate the water quality of the inlet, irrigation outlet, and edges linked to Nuwarawewa reservoir, Mahakanadarawa reservoir, Hurulu wewa reservoir, Sangilikandarawa reservoir, and Nachchaduwa perennial reservoir in Anuradhapura, Sri Lanka. Water samples were taken from a designated position of each inlet, irrigation output, and edges in the second inter-monsoon, northeast monsoon, and first inter-monsoon seasons. Water quality indicators such as pH, electrical conductivity (EC), total dissolved solids (TDS), Na, K, Ca, Mg, Sodium Adsorption Ratio (SAR), NO3- -N, NH4+-N, dissolved reactive phosphorus (DRP), and heavy metals (Cd, As, Pb, and Hg) concentrations were measured in each water sample. Data was analyzed using SPSS statistica software. Considerable temporal fluctuations in water quality indicators were detected in inlets, irrigation outflows, and edges over the research period. The findings revealed that NO3- -N, NH4+-N, and DRP in Sangilikandarawa reservoir during the northeast monsoon were close but not above the FAO (1985) maximum levels guidelines for irrigation water. pH, EC, TDS, and heavy metals (Cd, As, Pb, and Hg) in all the other tanks were within permitted values according to WHO drinking water quality standards and FAO (1985) guidelines for irrigation water. This finding further demonstrated that the consequences of surrounding land use on water pollution in inlet water canals linked to Sangilikandarawa and Nuwarawewa are greater. It could be concluded that a pollution management plan needs to be implemented in order to stop further pollution.

Vampyrophrya pelagica (Chatton and Lwoff 1930), an apostome ciliate infectious on calanoid copepod from the Northeastern Arabian Sea

BackgroundZooplankton provides a dynamic habitat to various kinds of epibionts in the marine ecosystem and also forms a source of food and nutrients to various parasites and parasitoids. The study was conducted to identify the association of ciliates with calanoid copepod of Northeastern Arabian Sea. Zooplankton samples collected were analysed under stereomicroscope for any kind of ciliate attachments. The infective stage and major characteristics of the ciliates were discussed. The present observation is the first report of apostome ciliate Vampyrophrya pelagica on calanoid copepod Bestiolina similis from the open ocean waters off Tarapur, Northeastern Arabian Sea, during the early spring intermonsoon season and also a new host record for the ciliate.ResultsAnalysis of ~ 2530 copepod revealed the presence of 12 copepod specimens infested with apostome ciliate Vampyrophrya pelagica. All the host copepod, identified as Bestiolina similis, were females. Approximately 50–60 ciliates occupied the host endocuticular region. V. pelagica was represented by fully grown trophont stage characterized by the presence of a large food vacuole surrounded by a thin layer of cytoplasm with numerous lipid bodies.ConclusionThe study establishes a new host record, calanoid copepod B. similis for V. pelagica. The report is the first of its kind from the Indian waters and extends the biogeographic boundary of the apostome ciliate.

Monsoonal variations of lead (Pb) in coastal waters around Singapore

Anthropogenic lead (Pb) has been the overwhelming Pb source to the global ocean, primarily contributed from Pb gasoline and industrial emissions. However, since Pb gasoline has been phased out globally, questions about whether there was a decrease in seawater Pb concentration, or if there are other sources taking over remains unclear in Southeast Asia. Here, combining Pb concentrations in seawater from Singapore Strait in 2010–2017; trap sediment in 2018–2019; and the previously published coral reconstruction covering 1975–2010; we found that the seawater Pb concentration in Singapore Strait over past decades followed the regional gasoline emissions, and no additional major source had contributed the Pb in the seawater since ~2010. The present-day Pb in Singapore Straits' water mainly follows the monsoonal current reversals, with variable degrees of scavenging that peak in inter-monsoon season. Minor Pb sources still contribute to some local-scale variabilities, despite a decadal-scale decreasing trend of Pb in seawater.

Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data

Abstract Wind-rain interactions often lead to severe windstorm events and consequently cause damages and fatal destructions. The increase in frequency of recent windstorm events overwhelmed the nation. Thus, efforts in obtaining and recording these events are intensified with the help of current technology. This study aims to analyze the pattern of recent windstorm events by utilizing big data and GIS. In this study, the reported windstorm events in Twitter application were extracted using R-programming. Prior to analyses, the extracted data were screened to remove any outliers found. The extracted data were selected based on the credibility of its sources to ensure the accuracy and quality. These selected data were extracted from trusted users such as Meteorological Department of Malaysia (MMD), Berita Harian, Bernama and others. This study has demonstrated the possibility of Twitter data as an alternative data source in windstorm studies based on its reasonable findings. It is exhibited that there is drastic increased of windstorm events frequency in years 2018-2020, especially in the northern and west-coast regions of Peninsular. The highest frequency was recorded in April (inter-monsoon season) while the lowest is in February and December (northeast monsoon). The increase of frequency in several locations in the Peninsular is very alarming especially in the Klang Valley since this region is highly populated and serves as Malaysia’s important economic zones. Hence, risk control should be considered in this region to reduce the negative impacts as suggested in SDG11 and SDG13.

Evaluation of Long-Term Trends of Rainfall in Perak, Malaysia

This study aimed to examine the spatiotemporal seasonal and annual trends of rainfall indices in Perak, Malaysia, during the last 35 years, as any seasonal or spatial variability in rainfall may influence the regional hydrological cycle and water resources. Mann–Kendall and Sequential Mann–Kendall (SMK) tests were used to assess seasonal and annual trends. Precipitation concentration index was used to estimate variations in rainfall concentration, and Theil–Sen’s slope estimator was used to determine the spatial variability of rainfall. It was found that most of the rainfall indices are showing decreasing trends, and it was most prominent for the southwest monsoon season with a decreasing rate of 2.20 mm/year. The long-term trends for seasonal rainfall showed that rainfall declined by 0.29 mm/year during the southwest monsoon. In contrast, the northeast and the inter-monsoon seasons showed slight increases. Rainfall decreased gradually from 1994 to 2008, and the trend became more pronounced in 2008. On a spatial basis, rainfall trends have shifted from the western regions (i.e., −19 mm/year) to the southeastern regions (i.e., 10 mm/year). Overall, slightly decreasing trends in rainfall were observed in Perak Malaysia.

Seasonal variation of fluxes of planktic foraminiferal tests collected by a time-series sediment trap in the central Bay of Bengal during three different years

Sediment trap samples from the southwestern Bay of Bengal were obtained at a water depth of about 900 m at sampling intervals of 17–27 days during three time periods (November 1988 to October 1989, December 1990 to October 1991, and January 1993 to October 1993). The planktic foraminifer abundances and assemblages of the samples were analyzed, and the factors governing the assemblages and their potential as indicators of climate and ocean processes in the sedimentary record were examined. We identified 28 species of planktic foraminifers in the samples, of which seven species accounted for more than 80% of each assemblage. The highest fluxes of planktic foraminifer tests were generally observed during the summer and winter monsoon seasons, and the lowest fluxes were observed during the intermonsoon seasons, especially from March to May. Periods of enhanced primary production during monsoons were accompanied by a sharp increase in the flux of Globigerina bulloides, whereas nutrient depletion in the surface ocean in spring led to subsurface small size plankton production and low planktic foraminifer test fluxes. The seasonality of foraminiferal assemblages and fluxes was controlled mainly by seasonal surface and subsurface primary production. The transition of abundant species from subsurface dwellers to surface ones throughout the monsoon season can be attributed to initial subsurface primary and secondary production being succeeded by enhanced surface production. The foraminiferal flux pattern differed among the three observation periods, especially in summer, because of interannual variation in the regional physical forces that induce nutrient replenishment at the surface. However, the consistent features of the flux pattern, with peaks during the monsoon seasons and minimal values in spring, suggest that our results reveal the essential, long-term flux pattern of planktic foraminifers in the southwestern Bay of Bengal.

Lightning Climatology and Human Vulnerability to Lightning Hazards in a School Community: A Case Study in Sri Lanka using LIS Data from TRMM Satellite

This study reported lightning climatology and human vulnerability to lightning in a 20 km × 20 km high-density school area in Colombo city in Sri Lanka from 1998 to 2014 using Lightning Imaging Sensor (LIS) flash data of NASA’s Tropical Rainfall Measuring Mission (TRMM). An average annual flash density recorded over the study area was 9.43 flashes km-2 year-1. A maximum of 49% lightning flashes happened during the first inter-monsoon season. There were only 4% lightning flashes that occurred during 06.00-12.00 LT and during 18.00-24.00 LT, it was 67%, whereas 94% of lightning flashes within a day had occurred after 14.00 LT. It is recommended that, without having proper lightning hazard preventive measures, schools in the study area should avoid or minimalize scheduling their outdoor activities in high lightning risk months of April and November. Especially, after-school outdoor activities should be planned with proper safety measures during the aforementioned months as per the diurnal analysis. Moreover, May to September and December to February were the months with the least lightning risk levels. It is recommended to follow the proposed five-level lightning safety guideline which includes, schedule outdoor activities by considering the variation of lightning activities, follow the 30-30 rule whenever required, avoid staying at the most hazardous locations which are vulnerable to lightning accidents, crouching action if required and providing first-aid whenever necessary. Not only for the Sri Lankan context but also the study is crucial and highly applicable for all schools and other institutes especially in other tropical countries.

Surface pCO2 variability in two contrasting basins of North Indian Ocean using satellite data

The spatial and temporal variability of the partial pressure of carbon dioxide (pCO2) over the northern tropical Indian Ocean was estimated based on satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll concentration (Chl-a). The distribution of sea surface pCO2 is controlled by various physical processes, such as mixing, entrainment, upwelling, and advection, and by associated biological processes, such as phytoplankton blooms. The results showed lower surface water pCO2 over the Bay of Bengal (BoB) (< 300 μatm) as compared to the Arabian Sea (AS), which could be attributed to low salinity during the northeast monsoon season (December–January) in the BoB. High pCO2 values (> 500 μatm) were found near the Somali coast during the southwest monsoon season (June–August) caused by intense coastal upwelling. The pCO2 over BoB (400–450 μatm) was more than that of the AS in the fall inter-monsoon season (September–November) as a response to the relatively warmer SST over the BoB. The highest spatiotemporal variability of surface pCO2 was observed in the western AS near to the coast and over the northern BoB as a response to the higher variability in biological productivity and salinity, respectively, over these regions. The effect of tropical cyclones on pCO2 variability over the BoB was also assessed in this study. The sea surface pCO2 values were found to be much smaller along the cyclone track after the passage of cyclones because of the strong SST drop and the sudden phytoplankton blooms caused by the entrainment and vertical mixing of subsurface cold and nutrient-rich water with surface water. The cyclone-induced surface cooling and the associated decrease in surface water pCO2 were found to be higher after the passage of the Madi cyclone compared to the Hudhud cyclone.