Abstract
Increasing weather variability and corresponding increased threat to the sustainability of the system and to the food security of any nation raises the importance of weather analysis in a range of studies. Meteorological data, hence, is used as a key component while develo** a weather-based risk assessment and impact assessment models. However, despite of the availability of global meteorological data in real time and several state-of the art dynamic prediction system, such models demand downscaling of these datasets to the regions of interest. The present scientific fraternity has been able to provide a range of datasets at needed spatial resolution, which are generated through interpolation, weather generation methods, satellite-based remote sensing methods, and others. Each of the datasets has their own advantages and limitations. They are not universal, because of which their robustness and reproducibility varies with location. Therefore, the present study is basically evaluation of the freely available data sources (Grid IMD, NASA POWER and MarkSim) to know which one fits best to the study area. Statistical techniques such as error statistics, correlation analysis, anomaly, and percent deviation have been used for weather dataset at three timescales (daily, weekly, and monthly). Results for maximum and minimum temperature indicated that NASA POWER datasets are more reliable than IMD data for Ranichauri (at all the three timescales) and Roorkee (only at daily and weekly timescale), unlike Udham Singh Nagar for which IMD gives better results for daily data; and MarkSim at weekly and monthly scale. It was also observed that for Udham Singh Nagar and Roorkee, MarkSim results are found to be better for RCP 2.6 as well as RCP 4.5 at higher timescales. Better performance of Tmax under RCP 4.5 indicates that the emission activities have increased in the districts, which can be attributed directly to the increased industrial establishments in the region.
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Data availability
The datasets used in this study are available upon request.
Code availability
The software used was Gretl and R Studio and scripts used in R Studio are can be shared on request.
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Acknowledgements
The authors acknowledge the India Meteorological Department (IMD) for providing the Gridded data.
Funding
We also acknowledge the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.
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ASN designed the study. PS and MS collected the data, performed the analysis and drafted the manuscript. ASN provided the guidance throughout the study. All authors have read and approved the manuscript.
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Setiya, P., Singh, M. & Nain, A.S. Evaluating the performance of Grid IMD, NASA POWER, and MarkSim timeseries weather dataset for Uttarakhand Climatic Condition. Theor Appl Climatol 155, 2657–2668 (2024). https://doi.org/10.1007/s00704-023-04787-5
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DOI: https://doi.org/10.1007/s00704-023-04787-5