An Analysis and Assessment of Kriging Interpolation Algorithm for Merging Meteorological High-Resolution Precipitation

Yang, Ming; Xu, Xueqing; Chen, Qing; Liu, Ying; Zhuyu, Gao; Chen, Yufeng; Zeng, You

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00102

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…66 . The Kriging interpolation method, which is based on spatially dependent variance, provides unbiased estimates of the target location in spacing using the known values of surrounding stations 67 . The Ordinary Kriging interpolation technique from ArcGIS is adopted in this study for patterns analysis from 50 to 5 km, which is based on data from about 24 regions.…”

Section: Methodsmentioning

confidence: 99%

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Magang,

Ojara,

Yunsheng

et al. 2024

Sci Rep

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Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

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Section: Methodsmentioning

confidence: 99%

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Magang,

Ojara,

Yunsheng

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The Ordinary Kriging interpolation method which is based on spatially dependent variance provide unbiased estimates of the target location in spacing using the known values of surrounding stations 49 . Ordinary Kriging interpolation technique is adopted in this study for patterns analysis, anomalies and trends (rainfall and temperature), which is based on from about 23 meteorological stations.…”

Section: Methodsmentioning

confidence: 99%

Future climate projection across Tanzania under CMIP6 with High-Resolution Regional Climate Model

MAGANG,

Ojara,

Yunsheng

et al. 2024

Preprint

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Climate change is one of the most pressing challenges faced by developing countries due to lower adaptive capacity, with far- reaching impacts on agriculture. Adaptation is the fundamental option to reduce the impacts. Climate change projection for future with comparison to the past is investigated by focuses on utilization of outputs of high-resolution Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment program (CORDEX-Africa) and Climate Change Knowledge Portal (CCKP) from the world Bank Group. Mid-century (2040–2060) projections of annually, monthly, and seasonal of rainfall, temperature (minimum, maximum) under two emissions scenarios. Mid-century is considered as a critical time for adaptation measures. The Shared Social-economic Pathways (SSPs): SSP2-4.5 and SSP5-8.5 under baseline period of (1991–2020) simulations. The future climate 2040–2060, departure determined with reference period 1995–2014 climate conditions. From the analysis, the result reveal that, across the country historical there’s significantly increase in temperature (minimum, maximum) trend. However, rainfall increased by + 63.2% not significantly. Projected 2040–2060 shows that, throughout the country will experience the increase of the temperature in the anomaly range from 1.4 ºC to 2.8 ºC under SSP5-8.5 emission scenario. Projected rainfall indicates that will be increased in the anomaly range from + 0.6% to + 6.3% and from + 5.2% to + 14.4% under both emissions scenarios.

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“…Traditional methods have used spatial interpolation techniques like Kriging [9] to reconstruct complete RSS maps from a subset of measured RSS values, offering a time‐saving alternative. However, conventional interpolation methods often fail to achieve a satisfactory trade‐off between accuracy and efficiency: the reconstruction accuracy is constrained by the quantity of input RSS values, especially in structurally intricate indoor settings, where a considerably larger number of RSS measurements are needed [10], resulting in increased time and computational expenses. Conversely, when the quantity of input RSS values falls short of the Kriging algorithm's requirements, the reconstruction accuracy diminishes.…”

Section: Introductionmentioning

confidence: 99%

Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments

Wu,

Ding,

Qin

et al. 2024

Electronics Letters

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With the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments.

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An Analysis and Assessment of Kriging Interpolation Algorithm for Merging Meteorological High-Resolution Precipitation

Cited by 4 publications

References 11 publications

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Future climate projection across Tanzania under CMIP6 with High-Resolution Regional Climate Model

Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments

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