Missing Data Imputations for Upper Air Temperature at 24 Standard Pressure Levels over Pakistan Collected from Aqua Satellite

Saleem, Muhammad; Ahmed, Sajid Rashid

doi:10.4236/jdaip.2016.43012

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…Conversely, when the percentage of missing data exceeds 5 % or spans over a month, the regression model is applied. Regression models, whether utilizing a single closest neighbor station [77] or multiple nearby stations [78][79][80], have demonstrated effectiveness in estimating daily weather observations [78,[81][82][83], and the imputation processes are applied to both T max and T min .…”

Section: Spatiotemporal Ground-observed Air Temperature Gap-fillingmentioning

confidence: 99%

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

Chongtaku,

Taparugssanagorn,

Miyazaki

et al. 2024

Preprint

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In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r&gt;0.90) and peri-urban (HWM, HWA, r&gt;0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

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Section: Spatiotemporal Ground-observed Air Temperature Gap-fillingmentioning

confidence: 99%

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

Chongtaku,

Taparugssanagorn,

Miyazaki

et al. 2024

Preprint

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“…Regression models, whether utilizing a single closest neighbor station [73] or multiple nearby stations [74][75][76], have proven effective in estimating daily weather observations [74,[77][78][79]. The imputation processes, which are applied to both T max and T min , are represented by…”

Section: Data Gap-fillingmentioning

confidence: 99%

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

Chongtaku,

Taparugssanagorn,

Miyazaki

et al. 2024

Applied Sciences

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In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.

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“…One of the major issues with long-term weather data is the presence of missing data values (Afrifa-Yamoah et al, 2020). The missing data may occur due to inadequate sample, measurement mistakes, or data collection flaws (Saleem and Ahmed, 2016). Addressing missing data in a time series is a critical challenge for research.…”

Section: Introductionmentioning

confidence: 99%

Comparison of missing data imputation methods using weather data

Nida

2023

PAKJAS

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Researchers and data analysts commonly experience with missing data in their field of studies. It is necessary to handle missing data properly to obtain better and more reliable outcomes of any research. The objective of this research is to evaluate different imputation techniques for handling missing observations in weather data. For this purpose weather data of daily rainfall maximum temperature (Tmax) and minimum temperature (Tmin) of 23 stations of Pakistan were taken from Pakistan Metrological department from 1981 to 2020. There are total 14610 observation of each variable and each variable have different number of missing observations at different stations. For the estimation of missing observation at each station mean imputation k nearest neighbors (KNN) imputation predictive mean matching (PMM) imputation and sample imputation techniques are used. Among different imputation techniques the technique which provides minimum root mean square error (RMSE) is selected and used to estimate the missing observations at each station. The result shows that the selection of imputation method is different for each station and mainly depends upon the size of missing observation in the data. For rainfall data the KNN technique is the most appropriate while for Tmax and Tmin the mean imputation technique is recommended for the estimation of missing observation in the data as compared to other methods.

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Missing Data Imputations for Upper Air Temperature at 24 Standard Pressure Levels over Pakistan Collected from Aqua Satellite

Cited by 6 publications

References 23 publications

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

Comparison of missing data imputation methods using weather data

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