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<p>Continuous monitoring of upper atmospheric temperatures is vital for advancing weather forecasting, climate research, and space weather applications, yet traditional methods like radiosondes and satellites face cost, resolution, and coverage limitations. This study pioneers the use of cosmic ray muons as a transformative, cost-effective alternative, leveraging a decade-long dataset (2002–2012) from the KACST detector in Riyadh, Saudi Arabia (Rc = 14.4 GV). Analyzing pressure-corrected ground-level muon flux together with radiosonde-derived temperatures at 18 pressure levels (50–900&#xa0;hPa), we confirm a dual temperature effect: in the upper troposphere (250–350&#xa0;hPa), reduced air density enhances muon production (positive effect, <i>R</i> = 0.85 at 250&#xa0;hPa), while in the lower troposphere (700–900&#xa0;hPa), atmospheric expansion increases muon decay, reducing flux (negative effect, <i>R</i> = 0.30 at 800&#xa0;hPa). An Artificial Neural Network (ANN) model outperforms regression, achieving an RMSE of 2.12&#xa0;K (vs. 3.11&#xa0;K), a 31.82% improvement. Seasonal analysis reveals the strongest average correlations in Spring (mean <i>R</i> = 0.51 across levels, peak <i>R</i> = 0.86 at 450&#xa0;hPa), while Winter exhibits the weakest (mean <i>R</i> = 0.35). Despite an anomaly at 150 hPa (R = 0.05), the model excels in the troposphere, offering a pathway toward cost-effective, ground-based, near-continuous tropospheric temperature estimation when combined with independent height information (e.g. from numerical weather prediction models or reanalysis).</p>

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Probing upper atmospheric temperatures with cosmic ray muons in Riyadh: preliminary findings

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Abdullrahman Maghrabi,
  • Maohammed Almutairi,
  • Abdulah Al-Dosari,
  • Mohammed Altlasi,
  • Abdulah Alsherhri

摘要

Continuous monitoring of upper atmospheric temperatures is vital for advancing weather forecasting, climate research, and space weather applications, yet traditional methods like radiosondes and satellites face cost, resolution, and coverage limitations. This study pioneers the use of cosmic ray muons as a transformative, cost-effective alternative, leveraging a decade-long dataset (2002–2012) from the KACST detector in Riyadh, Saudi Arabia (Rc = 14.4 GV). Analyzing pressure-corrected ground-level muon flux together with radiosonde-derived temperatures at 18 pressure levels (50–900 hPa), we confirm a dual temperature effect: in the upper troposphere (250–350 hPa), reduced air density enhances muon production (positive effect, R = 0.85 at 250 hPa), while in the lower troposphere (700–900 hPa), atmospheric expansion increases muon decay, reducing flux (negative effect, R = 0.30 at 800 hPa). An Artificial Neural Network (ANN) model outperforms regression, achieving an RMSE of 2.12 K (vs. 3.11 K), a 31.82% improvement. Seasonal analysis reveals the strongest average correlations in Spring (mean R = 0.51 across levels, peak R = 0.86 at 450 hPa), while Winter exhibits the weakest (mean R = 0.35). Despite an anomaly at 150 hPa (R = 0.05), the model excels in the troposphere, offering a pathway toward cost-effective, ground-based, near-continuous tropospheric temperature estimation when combined with independent height information (e.g. from numerical weather prediction models or reanalysis).