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Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF) model to provide effective forecasts of monthly extreme rainfall indices. In addition, RF feature importance is proposed in this study to identify the most and least important features for the proposed model. This study forecasts only statistically significant extreme rainfall indices over Bangladesh including consecutive dry days (CDD), the number of heavy rain days (R10mm; rainfall ≥ 10 mm), and the number of heavy rain days (R20mm; rainfall ≥ 20 mm) within 1–3 months of lead time. The proposed model uses monthly antecedent CDD, R10mm, and R20mm including atmospheric parameters and ocean–atmospheric teleconnections, namely, convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and North Atlantic Oscillation (NAO), as the inputs to the model. Results show that the proposed model yields the best performance to forecast CDD, R10mm, and R20mm with only the antecedent of these indices as input. Ocean–atmospheric teleconnections (IOD, ENSO, and NAO) are useful for CDD forecasting, and local atmospheric parameters (CAPE, RH, and TEM) are useful for R10mm and R20mm forecasting. The results suggest that adding atmospheric parameters and ocean–atmospheric teleconnections is useful to forecast extreme rainfall indices. [ABSTRACT FROM AUTHOR]

Humidity is one of the most significant climate factors influencing destination decisions and the distribution pattern of vacationers during various seasons. This variable influences the benefit of day-to-day travel industry activities and keeps up the destination competitiveness. In this paper, the univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model has been applied to conjecture month-to-month humidity for Bangladesh mainstream tourist spots up to the year 2025. Later, the influence of humidity on tourist arrival that contributes to the national economy was also assessed usi ng the Seasonal Analysis of Covariance (SANCOVA) model. Our findings indicate that the Bangladesh tourism industry is more vulnerable to seasonal variation, and this seasonality has a 72% effect on tourist arrival and a 58% effect on overall humidi ty. The findings suggest that if per unit humidity in seasonality increases, then the tourism indus try income will increase by approximately 59.463 thousand Taka (Bangladesh currency) in every season. [ABSTRACT FROM AUTHOR]

Suitable drying technique and energy efficient dryer are important concerns for drying of high moisture paddy. In this study, a small scale mixed flow dryer (MFD), with presenting systematic design approach, was developed and tested for paddy drying. Performance evaluation of the developed dryer in terms of specific energy consumption, instantaneous drying and energy efficiencies, and quality of the milled rice were investigated. A comparative performance analysis between the developed MFD and industrial Louisiana State University (LSU) dryer, which is widely used for paddy drying in Bangladesh, was also studied. In addition, drying kinetics for high moisture paddy was evaluated thorough analyzing outlet air relative humidity. Energy analysis revealed that specific electrical (in terms of primary energy) and thermal energy consumption were 0.97 and 3.17 MJ kg−1 water removed, respectively in reducing paddy moisture content (mc) from initial 25 to 12%, the final mc wet basis. Calculation indicated that 33.1% specific electrical and 21% specific thermal energy consumption can be saved by MFD in contrast to the industrial LSU dryer. The energy and drying efficiency values were found to be varied from 0.85 to 0.90 and from 0.10 to 0.17, respectively. Statistical analysis revealed that paddy samples dried by the MFD yielded 16.01% higher head rice yield than sun drying method. The energy analysis tactics presented in this paper can readily be applied in performance evaluation for heated air drying of different products. [ABSTRACT FROM AUTHOR]

The paper describes a survey that was undertaken to investigate the possible associations between damp public sector housing in London (U.K.) and the health of British Bengali tenants. The problems of establishing epidemiological evidence in this field and the accurate assessment of dampness and health are addressed, and the relative importance of housing and lifestyle factors in the prevalence of damp in the home is considered. The results show that there are significant relationships between reported and measured damp, cold and mould in the home and various aspects of reported health. It is also apparent that it is aspects of housing, in particular the provision of a good heating system rather than lifestyle that determine the degree of dampness in a home.

Understanding the impact of input variables on black‐box machine learning and deep learning models is necessary. Therefore, this study proposed SHapley Additive exPlanations (SHAP) values to address the problem of the interpretability of the output of the support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and long short‐term memory (LSTM) models to forecast climatic water balance (CWB) within 1–3 months lead‐time. The current study uses two Koppen–Geiger climate zones over Bangladesh: the humid subtropical climate with dry winter and hot summer (Cwa) and the tropical climate (Af‐Am). Monthly antecedent CWB, potential evapotranspiration (PET), convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic oscillation (NAO) are used as the inputs to the proposed model. SHAP values show that CWB (current month) for both climate zones has the maximum impact on the prediction. The effects of atmospheric variables and ocean–atmospheric teleconnections on CWB forecasts over Bangladesh are low. Forecasting results show that the SVM model shows the best performance for CWB forecasts in the Cwa and Af‐Am climate zones in terms of antecedent CWB as input. And this model effectively decreases the negative effect of increasing forecast lead‐time. Since the proposed model can predict CWB in 3‐months lead‐time, it would help policy‐makers and practitioners to reduce the drought and flood impacts in the future by adopting better preparedness plans. [ABSTRACT FROM AUTHOR]

Crop fungal diseases constitute a major cause of yield loss. The development of crop disease monitoring and forecasting tools is an important effort to aid farmers in adapting to climate variability and change. Recognizing weather as a main driver of fungal disease outbreaks, this work assesses the climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum, MoT) development in Asian wheat-producing countries. MoT was reported for the first time in Bangladesh in 2016 and could spread to other countries, provided that environmental conditions are suitable to spore development, distribution, and infection. With results from a generic infection model driven by air temperature and humidity, and motivated by the necessity to assess the potential distribution of MoT based on the response to weather drivers only, we quantify potential MoT infection events across Asia for the period 1980–2019. The results show a potential higher incidence of MoT in Bangladesh, Myanmar, and some areas of India, where the number of potential infection (NPI) events averaged up to 15 during wheat heading. Interannual trends show an increase in NPI over those three countries, which in turns show their higher interannual variability. Cold/dry conditions in countries such as Afghanistan and Pakistan appear to render them unlikely candidates for MoT establishment. The relationship between seasonal climate anomalies and NPI suggests a greater association with relative humidity than with temperature. These results could help to focus future efforts to develop management strategies where weather conditions are conducive for the establishment of MoT. [ABSTRACT FROM AUTHOR]

This paper studies the impact of climate change on the nutritional status of very young children between the ages of 0–3 years by using weather data from the last half century merged with rich information on child, mother, and household characteristics in rural coastal Bangladesh. We evaluate the health consequences of rising temperature and relative humidity and varying rainfall jointly employing alternate functional forms. Leveraging models that control for annual trends and location-specific seasonality, and that allow the impacts of temperature to vary non-parametrically while rainfall and humidity have flexible non-linear forms, we find that temperatures that exceed 25 °C (the "comfortable" benchmark) in the month of birth exert negative effects on children's nutritional status as measured by mid upper arm circumference. Humidity has a positive impact which persists when child, mother and household controls are included. We find that exposure to changing climate in utero also matters. Explanations for these results include consequences of weather fluctuations on the extent of pasture, cropland, and rainfed lands planted with rice and other crops, and on mother's age at first marriage. Our results underline that climate change has real consequences for the health of very young populations in vulnerable areas. • We study the impact of climate change over 50 years on the nutritional status of very young children. • We evaluate the consequences of temperature, relative humidity, and rainfall jointly. • Excess heat at birth has negative effects while humidity has positive effects on children's MUAC. • The impact of humidity persists when child, mother and household controls are included. • Mechanisms include changes in cropland, rainfed rice lands, and in mother's age at first marriage. [ABSTRACT FROM AUTHOR]

Because of evident climatic variations and significant contribution to national food production, Bangladesh is a climate extreme hotspot region of examination for climatic consequences for rice (Oryza Sativa) crop production. This study intends to explore the variability of climatic variables (e.g., variations in mean temperature, rainfall, relative humidity, and sunshine duration) with rice yields (e.g., Aus, Aman, and Boro rice varieties) in northwest Bangladesh. The modified Mann–Kendall test, Theil–Sen slope estimator, and multiple linear regression (MLR) modeling were used to estimate the association among these factors. Heteroskedasticity and autocorrelation constant standard error (HAC) and feasible generalized least square (FGLS) technique were adopted to measure the climate-rice crop nexus using the regional level dataset for 1976–2015. Furthermore, the spatiotemporal variation of rice yield trends with climatic variables was mapped and assessed by the coefficient of variation. The results show that observed temperature and humidity trends were beneficial for Aus and Aman yields but not Boro yields. In contrast, observed rainfall and sunshine trends were negative for all three rice seasons. The outcomes of the MLR model explained 67%, 92%, and 83% of the variability in Aus, Aman, and Boro rice yields in the study region. The model outcomes showed that humidity and rainfall have negatively affected Aus and Aman rice crops, while temperature and rainfall positively influence Boro rice yield. Regarding the climate change issues and safeguarding food safety at the regional level, the concerned authorities should provide substantial attention to improving heat and drought-tolerance high-yielding varieties against climate effects on Aus and Aman rice varieties. [ABSTRACT FROM AUTHOR]

The present study analyzed a long-term record of major floods over Bangladesh under the influence of El Niño–Southern Oscillation (ENSO). Bangladesh is highly vulnerable to floods, and ENSO has a great influence on it. El Niño (La Niña) represents the warm (cold) phase of the ENSO cycle. Southern Oscillation Index data and a long-term flood records have been identified from National Oceanic and Atmospheric Administration, Bureau of Meteorology, Australia, and historical archives, respectively. Synoptic features—precipitation, wind, geopotential height, precipitable water, relative humidity, and surface runoff data—were used to examine the influence of ENSO. Result shows that the Indian summer monsoon contributes the major flood events and largely influenced by both El Niño and La Niña events, with a slightly higher frequency for the latter. Physical mechanisms (distribution of precipitation, wind, geopotential height, precipitable water, relative humidity, and surface runoff) supported these flood events; however, they are quite distinct during El Niño and La Niña condition. Moreover, ENSO's impacts to the Bangladesh region are different to the west part of the monsoon region. Therefore, if ENSO forecast is reliable, the predictability of the monsoon-related flood events can be improved. [ABSTRACT FROM AUTHOR]

Thunderstorms are extreme localized weather phenomena that form primarily as a result of intense atmospheric convection. These are characterized by heavy rainfall, lightning and thunder. Thunderstorms occur in monsoon season over some parts of the world, and they can be found in the rain bands of many convective systems. Thunderstorms are a natural weather occurrence that results in significant damage to property and people all over the world. Lifted index, K index, total totals index, humidity index (HI), total precipitable water (TPW), convective available potential energy, deep convective index, S index, maximum temperature and rainfall parameters are investigated over Khulna region in Bangladesh during the monsoon season. We have measured all the above parameters using daily ERA5 reanalysis data for the monsoon season from 2011 to 2020. High TPW values (~ > 60 mm) and low HI values (~ < 20 K) are observed during July and August months over Khulna region. DCI values greater than 30 °C are observed which indicates highly favorable for severe convection-related thunderstorms. We have experimented ARMA model to estimate four parameters. The major motive behind this is to compare the accuracy of the ARMA model data to ERA5 data. For obtaining reliable statistical estimates of thunderstorm parameters, the ARMA model proved to be extremely useful. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) is one of the most devastating weather systems that causes enormous loss of life and property in the coastal regions of Bay of Bengal (BoB). Statistical forecasting of TC occurrence can help decision-makers and inhabitants in shoreline zones to take necessary planning and actions in advance. In this study, we have investigated the impact of El Niño–Southern Oscillation (ENSO) on the frequency of TC over the BoB by using 100 years TC and Southern Oscillation Index data. The frequency of TC is approximated through observation and Markov Chain Monte Carlo (MCMC) simulation. Two-sample Student's t test has been applied for examining the statistical significance where the results are significant at 5% level for all cyclonic disturbances. The monthly and seasonal distribution show this feature more distinctly. The total annual frequency of depressions and cyclonic storms in El Niño and La Niña conditions does not differ much, but the monthly/seasonal distribution shows high differences for certain months and seasons. The simulated frequency of TC landfall using MCMC matches well with the observation. The proposed methodology is illustrated through a case study in BoB rim countries-Bangladesh, India, Sri Lanka and Myanmar. Poisson and Bayesian regression have also been used to predict the probabilities of TC frequency over the BoB. Both the regression approaches show 10 and 32% improvement than climatology for the forecast and cross-validation skill respectively. We have also analyzed TC impact over Bangladesh as a case study. Possible links of the variation of TC activities with the largescale geographical distribution of sea surface temperature, vertical wind shear, vorticity, moisture and relative humidity are also explored. [ABSTRACT FROM AUTHOR]

The objectives of this study are to reveal the regional variation and to identify the responsible factors for making the transition of rainfall status in two consecutive days in Bangladesh at three selected regions. The covariate-dependent first-order Markov models have been used to find out how the change of rainfall status (rain and no rain) is affected by the selected factors. Among the considered factors, we found that the relative humidity, sunshine hour, maximum temperature, and cloud cover show positive association with the transition type no rain to rain for all three stations. However, the minimum temperature, maximum temperature, and sea level pressure have a negative effect on this transition type in all selected stations with the exception that the maximum temperature in Rajshahi and the minimum temperature in Rajshahi and Sylhet do not have any significant effect. On the other hand, with the exception, the maximum temperature in Rajshahi and sunshine hour in Dhaka and Sylhet, the relative humidity, maximum temperature, sunshine hour, and cloud cover show positive association with the transition type rain to rain, and the minimum temperature and sea level pressure show negative association with this transition type. The regional variations and responsible factors for changing the rainfall status also disclosed among the three selected regions over four seasons such as pre-monsoon, monsoon, post-monsoon, and winter. In all four seasons, the transition of the rainfall status experienced statistically significant positive impact for the exploratory variables relative humidity, sunshine hour, and cloud cover and negative impact for maximum temperature, minimum temperature, and sea level pressure with some exceptions. However, the humidity and cloud cover play the most important role in making the transition of the rainfall status between two consecutive days. [ABSTRACT FROM AUTHOR]

This study attempts to make comparison between different parametric regressive models for the bivariate binary data with a machine learning technique. The data on sequential occurrence of rainfall in consecutive days is considered. The outcomes are classified as rainfall in both days, rainfall in one of the consecutive days, and no rainfall in both days. The occurrence of rainfall in consecutive days is analyzed by using statistical models with covariate dependence and classification tree for the period from 1980 to 2014. We have used relative humidity, minimum temperature, maximum temperature, sea level pressure, sunshine hour, and cloud cover in the model as covariates. The binary outcome variable is defined as the occurrence or non-occurrence of rainfall. Five regions of Bangladesh are considered in this study and one station from each region is selected on the basis of two criteria: (i) contains fewer missing values and (ii) representative of the regional characteristics geographically. Several measures are used to compare the models based on Markov chain and classification tree. It is found that for yearly data, both the Markov model and classification tree performed satisfactorily. However, the seasonal data show variation of rainfall. In some seasons, both models perform equally good such as monsoon, pre-monsoon, and post-monsoon, but in the winter season, the Markov model works poorly whereas classification tree fails to work. Additionally, we also observe that the Markov model performed consistently for each season and performs better compared with the classification tree. It has been demonstrated that the covariate-dependent Markov models can be used as classifiers alternative to the classification tree. It is revealed that the predictive ability of the covariate-dependent Markov model based on Markovian assumption performs either better or equally good compared with the classification tree. The joint models also consistently showed better predictive performance compared with regressive model for whole year data as well as for several seasonal data. [ABSTRACT FROM AUTHOR]

Meteorological parameters play a significant role in affecting ambient air quality of an urban environment. As Dhaka, the capital city of Bangladesh, is one of the air pollution hotspot among the megacities in the world, however the potential meteorological influences on criteria air pollutants for this megacity are remained less studied. The objectives of this research were to examine the relationships between meteorological parameters such as daily mean temperature (°C), relative humidity (%) and rainfall (mm) and, the concentration of criteria air pollutants (SO2, CO, NOx, O3, PM2.5 and PM10) from January, 2013 to December, 2017. This study also focused on the trend analysis of the air pollutants concentration over the period. Spearman correlation was applied to illustrate the relationships between air pollutants concentration and temperature, relative humidity and rainfall. Multiple linear and non-linear regressions were compared to explore potential role of meteorological parameters on air pollutants' concentrations. Trend analysis resulted that concentration of SO2 is increasing in the air of Dhaka while others are decreasing. Most of the pollutants resulted negative correlation with atmospheric temperature and relative humidity, however, they showed variable response to seasonal variation of meteorological parameters. Regression analysis resulted that both the multiple non-linear and linear model performed similar for predicting concentrations of particulate matters but for gaseous pollutants both model performances were poor. This research is expected to contribute in improving the forecast accuracy of air pollution under variable meteorological parameters considering seasonal fluctuations. [ABSTRACT FROM AUTHOR]

Solar cell is a device that converts photons into electricity. The performance of a solar cell depends on its efficiency. The efficiency of a solar cell depends on some atmospheric variables such as light intensity, temperature, pressure, wind, humidity, precipitation and the sky condition etc. Geographical locations are the main factor for the variation of atmospheric variables. Due to atmospheric variables the solar cell efficiency is not same for all places of the World. Solar cell has been analyzed from different approach to improve its efficiency. The objective of this research is to analyze the efficiency of a silicon solar panel at three different places: Chittagong University Campus, Sandwip Island and Shibpur thana of Narshingdi district of Bangladesh. The efficiency mostly depends on the light intensity and temperature, however slightly depends on humidity. The experimental study shows that among the three places Norshingdi district is more suitable for solar power with the efficiency of 16.25% than other two places, where the efficiencies are 15.7% at Chittagong University Campus and 15.83% at Sandwip Island. [ABSTRACT FROM AUTHOR]

Background: Malaria is a major public health problem in Bangladesh, frequently occurring as epidemics since the 1990s. Many factors affect increases in malaria cases, including changes in land use, drug resistance, malaria control programs, socioeconomic issues, and climatic factors. No study has examined the relationship between malaria epidemics and climatic factors in Bangladesh. Here, we investigate the relationship between climatic parameters [rainfall, temperature, humidity, sea surface temperature (SST), El Niño-Southern Oscillation (ENSO), the normalized difference vegetation index (NDVI)], and malaria cases over the last 20 years in the malaria endemic district of Chittagong Hill Tracts (CHT). Methods and Principal Findings: Monthly malaria case data from January 1989 to December 2008, monthly rainfall, temperature, humidity sea surface temperature in the Bay of Bengal and ENSO index at the Niño Region 3 (NIÑ O3) were used. A generalized linear negative binomial regression model was developed using the number of monthly malaria cases and each of the climatic parameters. After adjusting for potential mutual confounding between climatic factors there was no evidence for any association between the number of malaria cases and temperature, rainfall and humidity. Only a low NDVI was associated with an increase in the number of malaria cases. There was no evidence of an association between malaria cases and SST in the Bay of Bengal and NIÑ O3. Conclusion and Significance: It seems counterintuitive that a low NDVI, an indicator of low vegetation greenness, is associated with increases in malaria cases, since the primary vectors in Bangladesh, such as An. dirus, are associated with forests. This relationship can be explained by the drying up of rivers and streams creating suitable breeding sites for the vector fauna. Bangladesh has very high vector species diversity and vectors suited to these habitats may be responsible for the observed results. [ABSTRACT FROM AUTHOR]

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992-2001. [ABSTRACT FROM AUTHOR]

Numerous studies on climate change and variability have revealed that these phenomena have noticeable influence on the epidemiology of dengue fever, and such relationships are complex due to the role of the vector—the Aedes mosquitoes. By undertaking a step-by-step approach, the present study examined the effects of climatic factors on vector abundance and subsequent effects on dengue cases of Dhaka city, Bangladesh. Here, we first analyzed the time-series of Stegomyia indices for Aedes mosquitoes in relation to temperature, rainfall and relative humidity for 2002–2013, and then in relation to reported dengue cases in Dhaka. These data were analyzed at three sequential stages using the generalized linear model (GLM) and generalized additive model (GAM). Results revealed strong evidence that an increase in Aedes abundance is associated with the rise in temperature, relative humidity, and rainfall during the monsoon months, that turns into subsequent increases in dengue incidence. Further we found that (i) the mean rainfall and the lag mean rainfall were significantly related to Container Index, and (ii) the Breteau Index was significantly related to the mean relative humidity and mean rainfall. The relationships of dengue cases with Stegomyia indices and with the mean relative humidity, and the lag mean rainfall were highly significant. In examining longitudinal (2001–2013) data, we found significant evidence of time lag between mean rainfall and dengue cases. [ABSTRACT FROM AUTHOR]