scholarly journals Using Markov Analysis to Study the Impact of Temperature in Bangladesh

2019 ◽  
Vol 6 (2) ◽  
pp. 69-76
Author(s):  
Janardan Mahanta ◽  
Soumen Kishor Nath ◽  
Md. Haronur Rashid
Keyword(s):  
Historical Data ◽  
Monsoon Season ◽  
Winter Season ◽  
Surface Air Temperature ◽  
Markov Analysis ◽  
Average Temperature ◽  
Chain Analysis ◽  
The Impact ◽  
Stationary Probabilities

In this paper has been studied the temperature trend in Bangladesh. Long-term changes of surface air temperature over Bangladesh have been studied using the available historical data collected by the Bangladesh meteorological Department (BMD). Daily temperature data is collected from BMD in Dhaka and Chittagong. Then month have been divided according to season and their descriptive statistics are computed. Maximum average temperature in pre-monsoon season and minimum average temperature in winter season have been shown in the paper. This study also reveals that temperature has increased over the time. Markov chain analysis has been applied for these data so as to find the stationary probability. After 26 and 13 days stationary probabilities in Dhaka and Chittagong stations respectively have observed.

scholarly journals Biodiversity of butterflies in endosulfan-affected areas of Kerala, India

2020 ◽  
Vol 81 (1) ◽  
Author(s):  
K. N. Raghavendra ◽  
Kumar Arvind ◽  
G. K. Anushree ◽  
Tony Grace
Keyword(s):  
Monsoon Season ◽  
Winter Season ◽  
Wiener Index ◽  
Long Term Effects ◽  
Affected Area ◽  
Butterfly Assemblages ◽  
Abundance And Diversity ◽  
The One ◽  
One Way Anova

Abstract Background Butterflies are considered as bio-indicators of a healthy and diversified ecosystem. Endosulfan was sprayed indiscriminately in large plantations of Kasaragod district, Kerala which had caused serious threats to the ecosystem. In this study, we surveyed the butterflies for their abundance and diversity in three differentially endosulfan-affected areas viz., Enmakaje—highly affected area, Periye—moderately affected area, Padanakkad—unaffected area, carried out between the end of the monsoon season and the start of the winter season, lasting approximately 100 days. Seven variables viz., butterfly abundance (N), species richness (S), Simpson’s reciprocal index (D), the Shannon–Wiener index (H′), the exponential of the Shannon–Wiener index (expH′), Pielou’s evenness (J) and species evenness (D/S), related to species diversity were estimated, followed by the one-way ANOVA (F = 25.01, p < 0.001) and the Kruskal-Wallis test (H = 22.59, p < 0.001). Results A population of three different butterfly assemblages comprised of 2300 butterflies which represented 61 species were encountered. Our results showed that Enmakaje displayed significantly lower butterfly diversity and abundance, compared to the other two communities. Conclusion So far, this is the first study concerning the effect of endosulfan on the biodiversity of butterfly in the affected areas of Kasaragod, Kerala, India. This study may present an indirect assessment of the persisting effects of endosulfan in the affected areas, suggesting its long-term effects on the ecosystem.

scholarly journals Satellite-based study of physico-optical properties of aerosols over a westernmost location of Brahmaputra valley

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Jhuma Biswas 1
Keyword(s):  
Atmospheric Aerosols ◽  
Monsoon Season ◽  
Cloud Condensation Nuclei ◽  
Winter Season ◽  
Earth Observing System ◽  
Brahmaputra Valley ◽  
Increasing Trend ◽  
Long Term Trend ◽  
Term Analysis

This study examines the long term trend of the radiatively active atmospheric aerosols which can influence the Earth’s energy budget directly by scattering and absorbing radiation and indirectly by acting as cloud condensation nuclei. MODIS sensor on board the NASA Earth Observing System Terra and Aqua satellite based Aerosol Optical Depth (AOD) data are used for long term analysis of aerosols over Bongaigaon, Assam for the period August, 2002 to March, 2017. Highest AOD values are observed in pre-monsoon (March-May) season due to long range transportation as well as intense biomass burning activities especially as a part of Jhum cultivation. In general, AOD values are low in post-monsoon (October-November) season which may be due to wash out of aerosols by rain in the preceding months without enough replacement. The monthly AOD values vary from its highest value 0.949 in April, 2016 to its lowest value 0.107 in November, 2002 for the study period. From the comparison of MODIS Terra and Aqua AOD at 550 nm, it is clearly seen that generally Terra AOD at 10:30 hr is higher than the Aqua AOD at 13:30hr. A slowly increasing trend of both Aqua and Terra AOD at 550 nm is observed over the study location. The observed Ångström exponent value varies from its minimum value in monsoon season to its maximum value in winter season. With increasing AOD values, horizontal visibility decreases over Bongaigaon.

scholarly journals An Integrated Procedure to Determine a Reference Station Network for Evaluating and Adjusting Urban Bias in Surface Air Temperature Data

2015 ◽  
Vol 54 (6) ◽  
pp. 1248-1266 ◽  
Author(s):  
Guoyu Ren ◽  
Jiao Li ◽  
Yuyu Ren ◽  
Ziying Chu ◽  
Aiying Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Historical Data ◽  
Spatial Scales ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Reference Station ◽  
Reference Network ◽  
Station Network

AbstractTrends in surface air temperature (SAT) are a critical indicator for climate change at varied spatial scales. Because of urbanization effects, however, the current SAT records of many urban stations can hardly meet the demands of the studies. Evaluation and adjustment of the urbanization effects on the SAT trends are needed, which requires an objective selection of reference (rural) stations. Based on the station history information from all meteorological stations with long-term records in mainland China, an integrated procedure for determining the reference SAT stations has been developed and is applied in forming a network of reference SAT stations. Historical data from the network are used to assess the urbanization effects on the long-term SAT trends of the stations of the national Reference Climate Network and Basic Meteorological Network (RCN+BMN or national stations), which had been used most frequently in studies of regional climate change throughout the country. This paper describes in detail the integrated procedure and the assessment results of urbanization effects on the SAT trends of the national stations applying the data from the reference station network determined using the procedure. The results showed a highly significant urbanization effect of 0.074°C (10 yr)−1 and urbanization contribution of 24.9% for the national stations of mainland China during the time period 1961–2004, which compared well to results that were reported in previous studies by the authors using the predecessor of the present reference network and the reference stations selected but when applying other methods. The authors are thus confident that the SAT data from the updated China reference station network as reported in this paper best represented the baseline SAT trends nationwide and could be used for evaluating and adjusting the urban biases in the historical data series of the SAT from different observational networks.

Trend and Variability Analysis of Sunshine Duration in Divisional Headquarters of Bangladesh

2021 ◽  
Vol 12 (1-2) ◽  
pp. 127-133
Author(s):  
MA Farukh ◽  
MA Islam ◽  
L Akter ◽  
R Khatun
Keyword(s):  
General Circulation ◽  
Sunshine Duration ◽  
Monsoon Season ◽  
Winter Season ◽  
Circulation Model ◽  
North East ◽  
Before And After ◽  
Post Monsoon Season ◽  
Sunshine Hour

In this study, Sunshine duration data of eight divisional headquarters of Bangladesh Meteorological Stations (Dhaka, Rangpur, Rajshahi, Mymensingh, Sylhet, Barishal, Khulna and Chattagram) were analyzed to evaluate the long-term changes and trends. The data used are the BMD data spanning from 1980 to 2010. The annual sunshine duration has decreased by the month of (June –September) from 1980 to 2010. Seasonal changes in sunshine duration were also analyzed where the maximum decline was found in rainy-monsoon season (June – September), the winter season (December – February), then the post-monsoon season (October – November) and the minimum in the pre-monsoon season (March – May). Analysis of observed data before and after 2000 represents the sunshine durations have decreasing trends in all divisional headquarters of Bangladesh except Chattagram station during the month of (January – December). General Circulation Model (GCM) defined that the maximum sunshine hour was decline north-east and south-west in all divisional headquarters of Bangladesh during the month of (June – September) and the minimum sunshine hour was decline in eastern part of the country during the month of (March – May). Environ. Sci. & Natural Resources, 12(1&2): 127-133, 2019

A Significant Bias of Tmax and Tmin Average Temperature and Its Trend

2019 ◽  
Vol 58 (10) ◽  
pp. 2235-2246 ◽  
Author(s):  
Yulian Liu ◽  
Guoyu Ren ◽  
Hengyuan Kang ◽  
Xiubao Sun
Keyword(s):  
Arid Region ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Positive Trend ◽  
Systematic Bias ◽  
Average Temperature ◽  
Mean Temperature ◽  
Daily Data ◽  
Autumn And Winter

AbstractThe systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China.

scholarly journals Assessment of long-term changes in the surface air temperature from the High Arctic archipelago Franz Joseph Land from 1929 to the present (2017)

2021 ◽  
Vol 11 (1) ◽  
pp. 114-133
Author(s):  
Boris Ivanov ◽  
Tatiana Karandasheva ◽  
Valery Demin ◽  
Anastasiia Revina ◽  
Pavel Sviashchennikov ◽  
...  
Keyword(s):  
Temperature Increase ◽  
Maximum Rate ◽  
Winter Season ◽  
Surface Air Temperature ◽  
High Arctic ◽  
Data Series ◽  
Regression Equations ◽  
Air Temperatures ◽  
Long Term Changes

Electronic archives of data from standard meteorological observations (mean daily/monthly surface air temperatures - SAT) at the meteorological stations at Bukhta Tikhaya (Hooker Island, 1929-1960) and Krenkel Observatory (Hayes Island, 1957-2017) on Franz Josef Land (FJL) are presented. Parallel data series of SAT made in 1958 and 1959 on both meteorological stations were analyzed. Linear regression equations used for extrapolation of observational data representative for Krenkel Observatory for the period 1929-1957 are also presented. The assessment of long-term changes in SAT on FJL was carried out based on the analysis of the obtained series (1929-2017). The main conclusions that follow from our study are: (1) The total warming in the FJL archipelago was 1.6-1.8°C (0.2°C/decade) for the entire available period of instrumental observations (1929-2017); (2) The highest rates of warming were recorded in March-April and amounted to 0.6°C/decade; (3) A particular strong warming has been observed since the 1990s. The annual temperature increased by 6.3°C (2.2°C/decade) for the period 1990-2017 and 5.2°C (2.9°C/decade) for the period 2000-2017; (4) For the period 1990-2017 the maximum rate of warming occurred between October to February with 4.4°C/decade; (5) For the period 2000-2017 the maximum rate of warming occurred between January to April and from November to December with 5.6°C/decade; (6) The dominant seasons of the year are winter (November-April), spring (May), summer (June-September) and autumn (October); (7) Over the entire observation period the largest temperature increase was observed in the winter season. During the period of modern warming (1990-2017), the largest temperature increase was observed in winter and autumn.

scholarly journals Trend Analysis of Rainfall in Prakasam District of Andhra Pradesh State in India

2020 ◽  
pp. 103-110
Author(s):  
M. Satya Swarupa Rani ◽  
R. Asha ◽  
G. M. V. Prasadarao
Keyword(s):  
Trend Analysis ◽  
Crop Production ◽  
Monsoon Season ◽  
Winter Season ◽  
Vital Role ◽  
Precipitation Trend ◽  
Large Variability ◽  
Cropping Patterns ◽  
Climate Analysis

Globally, precipitation trend analysis in different space and time has great impact on crop-planning activities. To get accurate unbiased results a long-term climate analysis of a particular area required in large variability in both spatially, temporally. For sustainable crop production long term weather analysis act as vital role in alternation of existing cropping patterns. This study aimed at analysing the trend of rainfall events in Prakasam district of Andhra state of India the data consists of annual precipitation time series from 1991-2019. Initially study concerns with analysis of data base using descriptive statistics, later trend change was detected by using non parametric tests. The results indicate an increased trend in June and monsoon season, with a decreased trend in July and winter season at 5% level of significance.

Variability and Trend Analysis of the Rainfall of the Past 119 (1901-2019) Years using Statistical Techniques: A Case Study of Uttar Dinajpur, India

2021 ◽  
Vol 7 (2) ◽  
pp. 49-61
Author(s):  
Ajit Sarkar ◽  
Sunil Saha ◽  
Debabrata Sarkar ◽  
Prolay Mondal
Keyword(s):  
Monsoon Season ◽  
Winter Season ◽  
Monthly Rainfall ◽  
Statistical Techniques ◽  
Study Region ◽  
Maximum Rainfall ◽  
Sen’S Slope ◽  
Average Monthly Rainfall ◽  
June July ◽  
The Impact

The present study aims to identify and measure the impact of climate change on rainfall patterns in the Uttar Dinajpur district of West Bengal. The hydro-meteorological time series rainfall data was collected from the IMD and CHRS data portals and subsequently analysed using various statistical methods. Agriculture in this district is the main economic activity, but the rainfall propensity is very unpredictable and sporadic that has a significant impact on agriculture. The rainfall results (1901-2019) were examined and assessed using statistical techniques for Mann-Kendall’s Z-statistic and Sen’s slope estimators. From the estimation, it is understood that the pre-monsoon, monsoon, and winter seasons have positive trends in rainfall, whereas the post-monsoon rainfall shows a negative trend and both Mann-Kendall and Sen’s slope projections depict the same. Likewise, January, February, April, May, June, July, August, and December reflect upward positive change, while a downward trend (decline trend) was recorded in March, September, and October. The winter Kharif crops are more impacted by this negative or decreasing pattern of seasonal rainfall than other crops. The maximum average monthly rainfall in July (892.1 mm) and January showed the lowest average monthly rainfall of 63.3 mm. The results revealed that during the monsoon season the maximum rainfall (75.2%) occurred and the coefficient of variance value is 20.4%. In the winter season, the minimal rainfall (2.87%) with a coefficient of variance (CV) is 72.9%. The rainfall forecast using SMOreg and linear regression methods has been calculated. This research contributes greatly to adopting different strategies by the planners, researchers, numerous government institutions, and NGOs for the overall development of the study area. This study may also be effective in the management of water resources in the study region.

scholarly journals Daylight Saving Time Transitions: Impact on Total Mortality

2020 ◽  
Vol 17 (5) ◽  
pp. 1611 ◽  
Author(s):  
Michael Poteser ◽  
Hanns Moshammer
Keyword(s):  
Optimal Solution ◽  
Total Mortality ◽  
Working Hours ◽  
Daylight Saving Time ◽  
Daylight Saving ◽  
Average Temperature ◽  
Health Related ◽  
The Impact ◽  
Daily Total

In Europe and many countries worldwide, a half-yearly changing time scheme has been adopted with the aim of optimizing the use of natural daylight during working hours and saving energy. Because the expected net economic benefit was not achieved, the discussion about the optimal solution has been reopened with a shifted focus on social and health related consequences. We set out to produce evidence for this discussion and analysed the impact of daylight saving time on total mortality of a general population in a time series study on daily total mortality for the years 1970–2018 in the city of Vienna, Austria. Daily deaths were modelled by Poisson regression controlling for seasonal and long-term trend, same-day and 14-day average temperature, humidity, and day of week. During the week after the spring transition a significant increase in daily total mortality of about 3% per day was observed. This was not the case during the week after the fall transition. The increase in daily mortality as observed in the week after spring DST-transition is most likely causally linked to the change in time scheme.

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