Space-Time Variability of Summer Temperature Field over Bangladesh during 1948-2007

Bhowmik, Avit Kumar; Cabral, Pedro

doi:10.1007/978-3-642-39649-6_9

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…This study showed that these surfaces are not satisfactorily accurate and precise, thus they do not provide enough detail and should be used cautiously in comprehensive research. In contrast, the regional temperature surfaces, as computed by Bhowmik (2012Bhowmik ( , 2013 and Bhowmik and Cabral (2013) are satisfactorily accurate and precise and can be used in further research. 376 Bhowmik and Costa…”

Section: Discussionmentioning

confidence: 99%

“…Thus, PRCPTOT and TXx values were obtained for 8–32 and 10–34 data locations, respectively, for the 1948–2007 period. Detailed formulation of the indices and quality control procedures can be found in Zhang and Yang (), Bhowmik (, ) and Bhowmik and Cabral ().…”

Section: Methodsmentioning

confidence: 99%

“…Spatial variability analyses on the climate of Bangladesh has become more relevant in recent decades (Karmakar and Shrestha, ; Mia, ; Klein Tank et al , ; Shahid, 2009, 2010; Cai et al , ) even though the number of studies in this region is still small. Only 34 meteorological stations currently report daily precipitation and temperature data over this country of 147 570 km 2 extent (DMICCDMP, ), thus distinguishing it as a data‐scarce region.…”

Section: Study Area and Climate Data Scarcitymentioning

confidence: 99%

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Representativeness impacts on accuracy and precision of climate spatial interpolation in data-scarce regions

Bhowmik

Costa

2014

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Data scarcity is a major scientific challenge for accuracy and precision of the spatial interpolation of climatic fields, especially in climate-stressed developing countries. Methodologies have been suggested for coping with data scarcity but data have rarely been checked for their representativeness of corresponding climatic fields. This study proved that satisfactory accuracy and precision can be ensured in spatial interpolation if data are satisfactorily representative of corresponding climatic fields despite their scarcity. The influence of number and representativeness of climate data on accuracy and precision of their spatial interpolation has been investigated and compared. Two precipitation and temperature indices were computed for a long time series in Bangladesh, which is a data-scarce region. The representativeness was quantified by dispersion in the data and the accuracy and precision of spatial interpolation were computed by four commonly used error statistics derived through cross-validation. The precipitation data showed very little and sometimes null representativeness whereas the temperature data showed very high representativeness of the corresponding fields. Consequently, precipitation data denoted scarcity but the temperature data denoted sufficiency regarding the required number of data for ensuring satisfactory accuracy and precision for spatial interpolation. It was also found that with the available data, accurate and precise precipitation surfaces can be produced only for representative synoptic spatial scales whereas such temperature surfaces can be generated for the regional scale of Bangladesh. It is highly recommended that the rain-gauge network of Bangladesh be increased or redistributed for computing representative regional precipitation surfaces.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Study Area and Climate Data Scarcitymentioning

confidence: 99%

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Representativeness impacts on accuracy and precision of climate spatial interpolation in data-scarce regions

Bhowmik

Costa

2014

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Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management

Akash

Sarkar

Bindajam

et al. 2023

Environ Sci Pollut Res

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G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia

Zewdie

Wubit²,

Ayele³

2018

Turkish Journal of Forecasting

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Among many indicators of climate change, the temperature is a key indicator to take remedial action for world global warming. This finding provides application of space-time models for temperature data, which is selected in three meteorology stations (Mekelle, Adigrat and Adwa) of Northern Ethiopia. The objectives of this research are to see the space-time variations of temperature and to find better forecasting model. The steps for building this model starting from order selection of space and autoregressive order, parameters estimation, a diagnostic check of errors and finally forecasting for the long term. The preliminary model is identified by VAR (vector autoregressive) model and tentatively selects the order by using MIC (minimum information criteria) and uses the autoregressive order for the model and fixes the spatial effect, model parameters are estimated using the least square method. Weighted matrix computed by using queen contiguity criteria. It is found that the model STAR(1,1) and GSTAR(1,1) are two options, finally the best-fitted model is GSTAR(1,1) which has high forecasting performance and smallest RMSEF. The outcome of the forecast indicated that in northern Ethiopia, the weather conditions especially temperature of future is increasing trend in dry seasons in all 3 stations in similar fashion but more consistent and has less variation across the region, and less consistent and high variation within the region and the researcher found that spatial effect has high impact on prediction of models.

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Space-Time Variability of Summer Temperature Field over Bangladesh during 1948-2007

Cited by 3 publications

References 35 publications

Representativeness impacts on accuracy and precision of climate spatial interpolation in data-scarce regions

Representativeness impacts on accuracy and precision of climate spatial interpolation in data-scarce regions

Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management

G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia

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