The low resolution Automatic Picture Transmission (APT) data from the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites Advanced Very High Resolution Radiometer (AVHRR) is being received and recorded in real-time mode at ground receiving station in School of Science and Technology, Universiti Malaysia Sabah. The system is suitable for the developing and undeveloped countries in south and Southeast Asia and is said to be acceptable for engineering, agricultural, climatological and environmental applications. The system comprises a personal computer attached with a small APT receiver. The data transmission between the ground receiving station and NOAA satellites is using the electromagnetic wave. The relation for receiving and processing the electromagnetic wave in the transmission will be discussed.
Spatio-temporal datasets are a collection of datasets where data can vary in both space and time. Theoretically, such datasets can be considered as continuous and discrete. For example, specification of the function, F: Ed T Rn, where Ed denotes d-dimensional Euclidean space, T = R* ∩ {} the domain of time and Rn an n-dimensional scalar field. Examples of such data sets include time-varying simulation results, film and videos, time-varying medical datasets, geometry models with motion or deformation, meteorological measurements, and many more. It is therefore highly desirable to use visualisation to summarize meaningful information in higher dimensional spatio-temporal datasets. Our aim is to conceive an efficient visual study to facilitate scientists in identifying temporal association among complex and chaotic atom movements in ion trajectories. An application that uses a streamline for spatial motion of ion trajectories and Colour Number Coding Scheme for temporal encoding of high degree of timeline events among mobile ions is proposed. With an anthology of the visual examples, it was revealed that this application would be beneficial for scientists to visually mine any 3D spatio-temporal dataset.
Modelling observed meteorological elements can be useful. For instance, modelling rainfall has
been an interest for many researchers. In a previous research, trend surface analysis was used and
it was indicated that the residuals might spatially be correlated. When dealing with spatial data, any
modelling technique should take spatial correlation into consideration. Hence, in this project, fitting
of spatial regression models, with spatially correlated errors to the annual mean relative humidity
observed in Peninsular Malaysia, is illustrated. The data used in this study comprised of the annual
mean relative humidity for the year 2000-2004, observed at twenty principal meteorological stations
distributed throughout Peninsular Malaysia. The modelling process was done using the S-plus
Spatial Statistics Module. A total of twelve models were considered in this study and the selection
of the model was based on the p-value. It was found that a possible appropriate model for the
annual mean relative humidity should include an intercept and a term of the longitude as covariate,
together with a conditional autoregressive error structure. The significance of the coefficient of the
covariate and spatial parameter was established using the Likelihood Ratio Test. The usefulness
of the proposed model is that it could be used to estimate the annual mean relative humidity at
places where observations were not recorded and also for prediction. Some other potential models
incorporating the latitude covariate have also been proposed as viable alternatives.
The analysis of the spatial data has been carried out in many disciplines such as demography, meteorology, geology and remote sensing. The spatial data modelling is important because it recognizes the phenomenon of spatial correlation in field experiments. Three main categories of the spatial models, namely, the simultaneous autoregressive (SAR) models (Whittle, 1954), the conditional autoregressive (CAR) models (Bartlett, 1971), and the moving average (MA) models (Haining, 1978) have been studied. Whittle (1954) presented a form of bilateral autoregressive (AR) models, whereas Basu and Reinsel (1993) considered the first-order autoregressive moving average (ARMA) model of the quadrant type. Awang, N. and Mahendran Shitan (2003) presented the second-order ARMA model, and established some explicit stationary conditions for the model. When fitting the spatial models and making prediction, it is assumed that, the properties of the process would not change with sites. Properties like stationarities have to be assumed, and for this reason, it was therefore imperative that the researchers had made certain that the process was stationary. This could be achieved by providing the explicit stationarity conditions for the model. The explicit conditions, for a stationary representation of the second-order spatial unilateral ARMA model denoted as ARMA(2,1;2,1), have been established (Awang, N. and Mahendran Shitan, 2003) and in this paper, some explicit conditions are established for a stationary representation of the second-order spatial unilateral ARMA model, denoted as ARMA(2,2;2,2).
This study highlights the advantage of functional data approach in assessing and comparing the PM10 pollutant behaviour as an alternative statistical approach during and between the two extreme haze years (1997 and 2005) that have been reported in Selangor, state of Malaysia. The aim of the study was to improvise the current conventional methods used in air quality assessment so that any unforeseen implicit information can be revealed and the previous research findings can be justified. An analysis based on the daily diurnal curves in place of discrete point values was performed. The
analysis results provided evidences of the influence of the change in the climate (due to the El-Nino event), the different levels of different emission sources and meteorological conditions on the severity of the PM10 problem. By means of the cummulative exceedence index and the functional depth method, most of the monitoring stations for the year 2005 experienced the worst day of critical exceedences on the 10th of August, while for the year 1997 it occurred between 13th and 26th September inclusively at different dates among the stations.
Rainfalls data have been broadly used in researches including in hydrological and meteorological areas. Two common ways in extracting observations from hourly rainfalls data are the window-based analysis (WBA) and storm-event analysis (SEA) approach. However, the differences in the qualitative and quantitative properties of both methods are still vaguely discussed. The aim of studying these dissimilarities is to understand the effects of each approach in modelling and analysis. The qualitative difference is due to the way the two analyses define the accumulated rainfalls for observations which are referred to as rainfall and storm depths, respectively. The repetitiveness of rainfall depths provide nested structure while the storm depths are considered independent. The quantitative comparisons include their statistical and scaling properties that are linked by the self-similarity concept from simple scaling characteristics. If self-similarity concept
holds, then the rainfall or storm depths follow simple scaling and the analysis would be simplified. The rainfall depths showed clearer simple scaling characteristics compared to the storm depths. Though the storm depths do not yield self-similarity for a large range of storm duration but the characteristics of simple scaling can be observed for a reduced range of the considered duration. In general, the context of the research and the region of the time interval and duration will be an important aspects to consider in choosing which method is best to use for analyzing the data.
INTRODUCTION: Acute angle closure (AAC) without prompt treatment may lead to optic neuropathy. Environmental factor such as climate change may precipitate pupillary block, the possible mechanism of AAC.
OBJECTIVE: To determine the association of northeast monsoon and incidence of AAC in Malaysia.
MATERIALS AND METHODS: A retrospective study was conducted on AAC patients admitted to two main tertiary hospitals in Kelantan, Malaysia between January 2001 and December 2011. The cumulative number of rainy day, amount of rain, mean cloud cover and 24 hours mean humidity at the estimated day of attack were obtained from the Department of Meteorology, Malaysia.
RESULTS: A total 73 cases of AAC were admitted with mean duration of 4.1SD 2.0 days. More than half have previous history of possibility of AAC. There was higher incidence of AAC during the northeast monsoon (October to March). There was also significant correlation of number of rainy day (r=0.718, p<0.001), amount of rain (r=0.587, p<0.001), cloud cover (r=0.637, p<0.001), mean daily global radiation (r=- 0.596, P<0.001), 24 hours mean temperature (r=-0.298, p=0.015) and 24 hours mean humidity (r=0.508, p<0.001) with cumulative number of admission for AAC for 12 calendar months.
CONCLUSION: Higher incidence of AAC during northeast monsoon suggested the effect of climate as the potential risk factor. Prompt treatment to arrest pupillary block and reduction of the intraocular pressure is important to prevent potential glaucomatous damage. Public awareness of AAC and accessibility to treatment should be part of preparation to face the effect of northeast monsoon.
The study presents a baseline variability and climatology study of measured hydrodynamic, water properties and some water quality parameters of West Johor Strait, Singapore at hourly-to-seasonal scales to uncover their dependency and correlation to one or more drivers. The considered parameters include, but not limited by sea surface elevation, current magnitude and direction, solar radiation and air temperature, water temperature, salinity, chlorophyll-a and turbidity. FFT (Fast Fourier Transform) analysis is carried out for the parameters to delineate relative effect of tidal and weather drivers. The group and individual correlations between the parameters are obtained by principal component analysis (PCA) and cross-correlation (CC) technique, respectively. The CC technique also identifies the dependency and time lag between driving natural forces and dependent water property and water quality parameters. The temporal variability and climatology of the driving forces and the dependent parameters are established at the hourly, daily, fortnightly and seasonal scales.
Estimation and forecast of groundwater recharge and capacity of aquifer are essential issues in water resources investigation. In the current research, groundwater recharge, recharge coefficient and effective rainfall were determined through a case study using empirical methods applicable to the tropical zones. The related climatological data between January 2000 and December 2010 were collected in Selangor, Malaysia. The results showed that groundwater recharge was326.39 mm per year, effective precipitation was 1807.97 mm per year and recharge coefficient was 18% for the study area. In summary, the precipitation converted to recharge, surface runoff and evapotranspiration are 12, 32 and 56% of rainfall, respectively. Correlation between climatic parameters and groundwater recharge showed positive and negative relationships. The highest correlation was found between precipitation and recharge. Linear multiple regressions between
recharge and measured climatologic data proved significant relationship between recharge and rainfall and wind speed. It was also proven that the proposed model provided an accurate estimation for similar projects.
This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
This paper presents the application of TOPMODEL in the Pinang catchment of Malaysia for stream flow simulation. An attempt has been made to use remote-sensing data (ASTER DEM of 30 m resolution) as a primary input for TOPMODEL in order to simulate the stream flow pattern of this tropical catchment. A calibration period was executed based on 2007-2008 hydro-meteorological dataset which gave a satisfactory Nash-Sutcliffe model (NS) model efficiency of 0.749 and a relative volume error (RVE) of -19.2. The recession curve parameter (m) and soil transmissivity at saturation zone (To), were established as the most sensitive parameters through a sensitivity analysis processes. Hydro-meteorological datasets for the period between 2009 and 2010 were used to validate the model which resulted in satisfactory efficiencies of 0.774 (NS) and -19.84 (RVE), respectively. This study demonstrated the ability ASTER DEM acquired from remote sensing to generate the required TOPMODEL parameters for stream flow simulation which gives insights into better management of available water resources.
Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.
Renewable energy assessments for resort islands in the South China Sea were conducted that involves the collection and analysis of meteorological and topographic data. The meteorological data was used to assess the PV, wind and hydropower system potentials on the islands. Furthermore, the reconnaissance study for hydro-potentials were conducted through topographic maps in order to determine the potential sites suitable for development of run-of-river hydropower generation. The stream data was collected for 14 islands in the South China Sea with a total of 51 investigated sites. The data from this study are related to the research article "Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea" published in Energy (Khan et al., 2015) [1].
General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon.
Investigation of meteorological disasters caused by small-scale topography shows that flashover due to wind age yaw occurred quite often where col topography existed. Considering that the distribution pattern of wind profile at different locations of a col topography is not clear, this paper, with wind tunnel tests, studied the influenced of such topographic features of a col as hill slope and valley mouth width on the wind profile at different locations. The results of wind tunnel tests indicated that over-hill wind has a stronger effect on wind velocity correction coefficient than does valley wind, that compared to flat terrain wind velocity, the maximum speed-up amplitude of wind velocity at valley throat and hill summit reach 33 and 53%, respectively, apparently higher than 10% specified in Codes, that wind velocity at valley throat increases with the increase of hill slope and decreases with the increase of valley mouth width, that wind velocity in the valley basically does not go up when the slope of one hill side is smaller than 0.1 and that wind velocity at the same non-dimensional height of a 3D hill summit increases with the increase of hill height.
Lightning is a natural phenomenon that generates a high electric field during thunderstorm. It has been
reported that lightning strikes amid storms can occur around 100 times per second. The atmospheric
electric field is an imperative parameter during a thunderstorm. Therefore, monitoring the electric field
and its parameters is the best way for local lightning forecast. The electric field monitoring data can
validate the accuracy of weather prediction in a local area from meteorology department or by using
equipment specially designed to measure this electric field that exists when the phenomenon of lightning occurs. In this paper, the relationship between lightning, air humidity and temperature is discussed to understand the post lightning effect on these electric parameters. Additionally, the characteristics of the parameters are observed and analysed.
Dengue fever is one of the most dangerous vector-borne diseases. According to the World Health Organization (WHO), dengue fever is a mosquito-borne infection caused by virus serotype DEN-1, DEN-2, DEN-3 and DEN-4. In Malaysia, dengue fever cases are on the rise from 6,000 cases in 1995 to over 40,000 in 2010, and this number is still rising. In 2014, the increase of dengue fever cases was alarming. It was reported that up to the end of the year 2014, there were 108,698 notified cases, indicating an increment of 151% compared to the same period of time in 2013 with only 43,346 reported cases. Selangor was the highest contributor of dengue fever cases in 2014. The objective of this paper is to study the relationship between climatic factors namely temperature, rainfall and humidity to the prevalence of dengue fever in Subang Jaya and Sepang district, Selangor. Data on monthly average temperature, precipitation, relative humidity and dengue fever cases for each month in 2014 and 2013 were collected. Data collection was dealt with a few institutions such as Malaysian Meteorological Department, Subang Jaya and Sepang Municipal Council and health district offices. Data were analysed using SPSS (Statistical Package for the Social Sciences) Version 20. General linear model analysis was used to investigate the relationship between the climatic variables and dengue prevalence. Results and Discussion: Based on the general linear model, rainfall and humidity were found to have significant relationships to monthly dengue fever cases (p=
Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
ir quality has deteriorated in urban areas as a result of increased anthropogenic activities. Quantitative information on the influence of meteorological conditions on several pollutants in a tropical climate is still lacking. Real-time ozone (O3) and nitrogen dioxide (NO2) levels were measured nearby selected schools in Malaysia to examine the impact of meteorological factors on monitoring pollutants. The results showed the overall 10 min average concentrations of the main parameters during school holiday were 24 ppb (O3) and 33 ppb (NO2) while during school day the overall 10 min average concentrations were 26 ppb (O3) and 51 ppb (NO2). Although there are no minimum requirements for short-term exposure by MAAQG, if compared to 1 h average requirements, all concentrations were still below the suggested values. Regarding spatial distribution, a different trend in pollutant concentration among the schools was observed because of the influence of temperature (AT) and wind speed (WS). The results were verified by Pearson correlation, where significant correlations (p<0.01) were determined between air pollutants and meteorological factors, which were temperature, wind speed and relative humidity. Meanwhile, the distribution of O3 was moderately correlated with NO2. However, the results of multivariate analysis indicate that temperature and relative humidity had the most significant influence on the formation of O3. In summary, the results of this study showed that all precursors and meteorological parameters contribute to the production of O3. Hence, reducing O3 precursors, which are emitted by vehicles, is essential to lessening the exposure to O3
Determination of chlorophyll-a (Chl-a) distribution in the coastal waters is important to understand the coastal environmental conditions. This study was conducted to understand the spatial and temporal distribution of Chl-a along coastal waters of east Peninsular Malaysia and factors influencing its variability using Chl-a data derived from Aqua MODIS satellite (1 km spatial resolution) from January 2006 to December 2012. Chl-a variability was described using empirical orthogonal function (EOF) analysis. In-situ data (temperature, salinity, density and nitrate) and rainfall data from the Department of Meteorology were analyzed using spatial interpolation to determine factors influencing the distribution of Chl-a. The seasonal progressions of Chl-a showed high value during northeast monsoon along the coast. This variability was described by four modes of the EOF analysis. The first mode (72.08% of total variance) indicated seasonal cycle with high variability along the coast. Second mode (17.03% of variance) explained the northeast monsoon with high variability from river mouth to the south. Third mode (2.39% of variance) indicated variability during southwest monsoon along the coast and much higher to the south. Mode 4 (1.93% of variance) explained the inter-monsoon period observed along the northern and southern coastline. Concentration and distribution of Chl-a were related to availability of nutrient influenced by rainfall. The thermohaline front was also observed to play an important role in accumulation of phytoplankton biomass during northeast and southwest monsoon.