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<p>The present study was conducted to assess the accuracy of interpolated climate data generated by the India Meteorological Department for Punjab. The temperature data was bias-corrected with three and rainfall with six techniques. The 21-year (2000–2020) data was divided into two periods; i.e. 2000–2010 data was used for computing correction factors, and the remaining period data was used for validation by comparing with the ground station actual datasets. In temperature parameters, the CF<sub>x</sub> (change factor daily basis) technique outperformed the SSBC<sub>x</sub> (simple seasonal bias correction) and BC<sub>x</sub> (bias correction daily basis) and showed excellent estimates similar to observed data. In the case of rainfall parameters, the CDF (cumulative distribution function) plots and KS (Kolmogorov–Smirnov) tests revealed no significant distribution differences across all locations before and after bias correction, except QMLC<sub>x</sub> (quantile mapping linear correction) and QMPC<sub>x</sub> (quantile mapping second-order polynomial correction) which added biases after bias correction. Amongst the six techniques evaluated for rainfall, the QM<sub>x</sub> (basic quantile mapping) method reduces biases compared to raw rainfall data. We, therefore, recommend the direct usage of IMD raw gridded data for rainfall and bias-corrected data for temperature for climate change impact analysis for use in agricultural planning.</p>

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Evaluating the robustness of IMD gridded data of temperature and rainfall with/without statistical bias correction techniques

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Agatambidi Bala Krishna,
  • Prabhjyot-Kaur,
  • Samanpreet Kaur,
  • Sandeep Singh Sandhu

摘要

The present study was conducted to assess the accuracy of interpolated climate data generated by the India Meteorological Department for Punjab. The temperature data was bias-corrected with three and rainfall with six techniques. The 21-year (2000–2020) data was divided into two periods; i.e. 2000–2010 data was used for computing correction factors, and the remaining period data was used for validation by comparing with the ground station actual datasets. In temperature parameters, the CFx (change factor daily basis) technique outperformed the SSBCx (simple seasonal bias correction) and BCx (bias correction daily basis) and showed excellent estimates similar to observed data. In the case of rainfall parameters, the CDF (cumulative distribution function) plots and KS (Kolmogorov–Smirnov) tests revealed no significant distribution differences across all locations before and after bias correction, except QMLCx (quantile mapping linear correction) and QMPCx (quantile mapping second-order polynomial correction) which added biases after bias correction. Amongst the six techniques evaluated for rainfall, the QMx (basic quantile mapping) method reduces biases compared to raw rainfall data. We, therefore, recommend the direct usage of IMD raw gridded data for rainfall and bias-corrected data for temperature for climate change impact analysis for use in agricultural planning.