Comparison of regression-based and combined versions of Inverse Distance Weighted methods for spatial interpolation of daily mean temperature data

Kayıkçı, Emine Tanır; Kazancı, S. Zengin

doi:10.1007/s12517-016-2723-0

Cited by 12 publications

(11 citation statements)

References 26 publications

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“…At present, the methods for obtaining meteorological data for a region based on the spatial interpolation of discrete meteorological stations mainly include the inverse distance weighting (IDW) [ 64 ], Kriging [ 49 ], Parameter-Elevation Regressions on Independent Slopes Model (PRISM), trend surface analysis (TSA), and thin plate smoothing spline (TPS) [ 65 ] methods. Among them, Kriging and IDW are the most widely used in practical applications.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990–2019

Liu

et al. 2023

IJERPH

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Heatwaves occur frequently in summer, severely harming the natural environment and human society. While a few long-term spatiotemporal heatwave studies have been conducted in China at the grid scale, their shortcomings involve their discrete distribution and poor spatiotemporal continuity. We used daily data from 691 meteorological stations to obtain torridity index (TI) and heatwave index (HWI) datasets (0.01°) in order to evaluate the spatiotemporal distribution of heatwaves in the Chinese mainland for the period of 1990–2019. The results were as follows: (1) The TI values rose but with fluctuations, with the largest increase occurring in North China in July. The areas with hazard levels of medium and above accounted for 22.16% of the total, mainly in the eastern and southern provinces of China, South Tibet, East and South Xinjiang, and Chongqing. (2) The study areas were divided into four categories according to the spatiotemporal distribution of hazards. The “high hazard and rapidly increasing” and “low hazard and continually increasing” areas accounted for 8.71% and 41.33% of the total, respectively. (3) The “ten furnaces” at the top of the provincial capitals were Zhengzhou, Nanchang, Wuhan, Changsha, Shijiazhuang, Nanjing, Hangzhou, Haikou, Chongqing, and Hefei. While the urbanization level and population aging in the developed areas were further increased, the continuously increasing heatwave hazard should be fully considered.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990–2019

Liu

et al. 2023

IJERPH

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“…The precipitation data in this study were spatially interpolated by the GIDS, which has been proven to be more realistic than the common kriging interpolation and inverse distance weighted interpolation because it can take terrain, latitude and longitude into account through multiple regression analysis (Nalder and Wei, 1998). The formula is as follows (Kayıkc and Kazancı, 2016):

P goodbreak= ([], \sum_{i = 1}^{N} \frac{P_{i} + (λ_{p} goodbreak- λ_{i}) \times C_{x} + (φ_{p} goodbreak- φ_{i}) \times C_{Y} + (h_{p} goodbreak- h_{i}) \times C_{e}}{d_{i}^{2}}) / ([], \sum_{i = 1}^{N} \frac{1}{d_{i}^{2}}),

wherein P is expected to be interpolated precipitation in mm at point p with longitude λ p , latitude φ p and altitude h p ; P i is the observed mean precipitation at station i at longitude λ i , latitude φ i and altitude h i ; d i is the distance from the interpolation point p to measurement station i , N is the number of total measured stations; and C x , C y and C e is regression coefficients for X , Y and elevation, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Gradient Inverse Distance Squaredmentioning

confidence: 99%

Quantifying spatiotemporal variations and driving factors of the energy budget in the Loess Plateau

Gou

Liang

Yan

et al. 2022

Intl Journal of Climatology

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The land surface energy exchange indirectly describes the energy forcing effect of solar radiation on the atmospheric system. Exploring the exchange process is of great significance to understand the formation and change of weather and climate. Based on the ERA5 reanalysis data and a process‐based land surface model (the Ecosystem‐Atmosphere Simulation Scheme), this study analysed the spatiotemporal variations and influencing factors in the energy budget in the Loess Plateau (LP). The results showed that from 1990 to 2017, the average annual surface net radiation (Rn) and latent heat (LE) in the LP showed a decreasing trend. The Rn and LE presented an increased spatial pattern from northwest to southeast. On a monthly scale, the Grain for Green (GFG) project amplified the negative effect in the period of November to February and September, but diminished the negative effect in other months. Climate change contributed more to energy exchange than land cover change during the study period. Our results provide useful information for developing adaptive strategies for the region to adapt to global climate change.

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“…where T p is the estimated temperature at interpolation points; E d is digital elevation. Numbers of researches (Pan et al, 2004;Kayıkçı and Kazancı, 2016;Ma et al, 2020) were shown that a combination of temperature lapse rate and IDW technique for considering the elevation effects are needed for high-resolution daily temperature estimation.…”

Section: Freezing and Thawing Indicesmentioning

confidence: 99%

“…This study was used combined IDW and temperature lapse rate method (Kayıkçı and Kazancı, 2016) to evaluate the influence of elevation on the temperature and calculation of FI and TI. The spatial analysis showed that FI and TI were strongly correlated with altitude.…”

Section: Application and Uncertainty Of Freezing And Thawing Indicesmentioning

confidence: 99%

Spatiotemporal Variations of Freezing and Thawing Indices During the Past Four Decades in Tibet

Liao

Fan

et al. 2021

Front. Ecol. Evol.

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Freezing and thawing indices (FI and TI) are commonly used as indicators for climate change assessment and permafrost extent estimation in cold regions. In this study, based on the meteorological daily data (1978–2017) among 34 meteorological stations in Tibet, the temperature in space has been interpolated and FI and TI have been calculated. Finally, spatiotemporal variations have been analyzed and the permafrost area has been estimated. The results showed the mean annual of FI and TI in Tibet are 1241.36 and 1290.22°C⋅day, respectively. A significant downward trend in freezing index (FI) and an upward trend in thawing index (TI) have been reported in the time series, in against, analyzing the spatial distribution showed there is an increasing trend from southeast to northwest for FI while TI was decreased gradually in the same region in Tibet. This research indicates that altitude has a significant influence on the change of FI and TI. With the increase of altitude, FI decreased and TI increased more significantly. The permafrost area was estimated at about 0.59 × 106 km2 in Tibet.

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Comparison of regression-based and combined versions of Inverse Distance Weighted methods for spatial interpolation of daily mean temperature data

Cited by 12 publications

References 26 publications

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990–2019

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990–2019

Quantifying spatiotemporal variations and driving factors of the energy budget in the Loess Plateau

Spatiotemporal Variations of Freezing and Thawing Indices During the Past Four Decades in Tibet

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