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<p>Reanalysis datasets help address the scarcity of daily meteorological station data; this study evaluates and corrects biases in NASA’s POWER MERRA-2 climate reanalysis products in the central Algerian steppe and Zahrez watershed (2010–2022) using data from 16 weather stations. The parameters include 2 m height data (mean temperature <i>T</i>, minimum temperature <i>T</i><sub>min</sub>, maximum temperature <i>T</i><sub>max</sub>, relative humidity <i>H</i>, precipitation <i>P</i>), and 10 m wind speed <i>W</i><sub>s</sub>. The bias correction techniques used are the standard deviation method, which aligns model outputs for temperature, humidity, and wind speed with observations. For precipitation, quantile mapping with a 1% percentile threshold improves sensitivity to extreme events. The main results at two levels of the study reveal strong agreement for the majority of variables (<i>R</i><sup>2</sup> ≥ 0.81), except for wind speed and precipitation. The bias correction for <i>W</i><sub>s</sub> at individual stations reduced systematic errors (NMBE) along the time series from 18.76 to 6.11%, while the NRMSE error for precipitation was reduced from 15.25 to 4.53% at the regional level and from 0.93 to 0.25% in the Zahrez basin. These findings demonstrate that NASA POWER data are effective in compensating for missing local data or completing incomplete datasets. However, adjustments are still needed to correct biases in wind speed. Correcting these biases provides a more accurate representation of environmental conditions, which is essential for various applications, including environmental health studies such as soil erosion monitoring. This contributes to reducing environmental risks and fostering efficient management grounded in sustainable solutions.</p>

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Toward more efficiency of some MERRA-2 reanalysis products in the central Algerian steppe: Zahrez watershed case

  • 期刊论文

  • 发表日期：

    2025年4月9日

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  • Khoudiri Fatima,
  • Benchettouh Ahmed,
  • Farouk Benaceur

摘要

Reanalysis datasets help address the scarcity of daily meteorological station data; this study evaluates and corrects biases in NASA’s POWER MERRA-2 climate reanalysis products in the central Algerian steppe and Zahrez watershed (2010–2022) using data from 16 weather stations. The parameters include 2 m height data (mean temperature T, minimum temperature Tmin, maximum temperature Tmax, relative humidity H, precipitation P), and 10 m wind speed Ws. The bias correction techniques used are the standard deviation method, which aligns model outputs for temperature, humidity, and wind speed with observations. For precipitation, quantile mapping with a 1% percentile threshold improves sensitivity to extreme events. The main results at two levels of the study reveal strong agreement for the majority of variables (R2 ≥ 0.81), except for wind speed and precipitation. The bias correction for Ws at individual stations reduced systematic errors (NMBE) along the time series from 18.76 to 6.11%, while the NRMSE error for precipitation was reduced from 15.25 to 4.53% at the regional level and from 0.93 to 0.25% in the Zahrez basin. These findings demonstrate that NASA POWER data are effective in compensating for missing local data or completing incomplete datasets. However, adjustments are still needed to correct biases in wind speed. Correcting these biases provides a more accurate representation of environmental conditions, which is essential for various applications, including environmental health studies such as soil erosion monitoring. This contributes to reducing environmental risks and fostering efficient management grounded in sustainable solutions.