بررسی تطبیقی توزیع زمانی ـ مکانی تنش حرارتی به‌دست‌آمده از داده‌های بازتحلیل ERA5-Land با داده‌های مشاهداتی در ایران

ABSTRACT
This study assesses the performance of ERA5-Land reanalysis data in identifying thermal stress conditions by comparison with synoptic station observations across Iran during the period 1991 to 2020. Daily temperature, humidity, and wind speed data from both datasets were employed to calculate the Effective Temperature and Baker thermal indices. Spatial and temporal variations were examined on a seasonal basis, with further comparisons conducted for cold-dry and warm-humid climatic zones. ERA5-Land showed strong agreement with observational data in reproducing the spatiotemporal distribution of thermal indices, particularly during spring and summer, with a correlation coefficient of 0.80 (p < 0.005). During winter, both datasets indicated extensive cold stress conditions; observational data showed that 79 percent of the country experienced mild to moderate cold stress, compared with approximately 95 percent in the ERA5-Land dataset. Within the cold-dry climatic zone, the number of observed very cold days declined from approximately 100 to fewer than 10, while days classified as no thermal stress increased from about 50 to more than 150. In contrast, ERA5-Land exhibited relatively stable values, indicating approximately 80 to 100 very cold days throughout the study period. During summer, observational data indicated that more than 62 percent of Iran experienced mild to moderate heat stress, whereas ERA5-Land estimated this proportion at approximately 43 percent. In the warm-humid climatic zone, the number of observed very hot days increased from fewer than 50 in 2000 to more than 350, while ERA5-Land captured only about 150 such days. Overall, ERA5-Land effectively captures broad-scale thermal stress patterns but tends to underestimate extreme local climatic variations.
Extended Abstract
Introduction
Understanding thermal conditions is essential for promoting human health, productivity, and overall well-being, as well as for informing urban and environmental planning. These conditions, shaped by temperature, humidity, wind, and solar radiation, can produce both beneficial effects, such as facilitating economic and social activities, and adverse outcomes, including heat- and cold-related illnesses and mortality. To evaluate thermal comfort and thermal stress, numerous bioclimatic indices have been developed. Their accuracy, however, depends strongly on the quality and spatial resolution of the input data.
In recent years, reanalysis datasets such as ERA5-Land have been widely used in climate research because of their extensive spatial and temporal coverage. These datasets, generated through the integration of numerical modeling and meteorological observations, provide a consistent representation of atmospheric conditions. Nevertheless, their reliability at local scales and across diverse climatic zones requires careful validation. By contrast, observational data from synoptic stations, despite limitations such as incomplete spatial coverage and occasional missing values, generally provide more accurate measurements at the local scale. They therefore serve as an essential benchmark for evaluating the performance of reanalysis datasets.
Previous studies conducted in Iran have largely relied on observational data, demonstrating the strong influence of thermal stress on energy demand, building design, and human thermal comfort. These findings also indicate a significant increase in heat stress during recent decades, particularly in lowland regions. Nevertheless, no comprehensive study has directly compared thermal indices derived from reanalysis data with those calculated from synoptic station observations across Iran.
The present study addresses this gap by evaluating the performance of ERA5-Land data in calculating thermal indices and comparing the results with observational records across Iran’s diverse climatic conditions. This comparison is particularly relevant for applications in public health, energy management, and environmental planning.
 
Methodology
Two datasets were employed: ERA5-Land reanalysis data and synoptic meteorological observations from the Iran Meteorological Organization (IRIMO) covering the period 1991 to 2020. ERA5-Land, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), provides hourly data at a spatial resolution of 0.1 degrees. The observational dataset consisted of monthly mean air temperature, wind speed, and relative humidity records obtained from 130 synoptic stations with complete data coverage. Two thermal comfort indices, Net Effective Temperature (NET) and the Baker Index (CPI), were selected because both can be computed using variables available in reanalysis datasets. These indices were calculated on an hourly basis for both datasets using the Python programming language. ERA5-Land values were extracted from grid points nearest to the synoptic station coordinates and for specific observation times (00, 03, 06, 09, 12, 15, 18, and 21 UTC). Seasonal maps of the thermal indices were generated using Inverse Distance Weighting (IDW) interpolation in order to illustrate their spatial patterns. In addition, mean values of the indices were compared across two representative climatic regions, namely a cold-arid zone in northwestern Iran and a hot-humid zone along the southern coastal areas. The reliability of ERA5-Land was further evaluated by calculating correlation coefficients, coefficients of determination (R²), and corresponding statistical significance levels (p-values).
 
Results and Discussion
Both the observational dataset and the ERA5-Land reanalysis exhibited consistent spatial and seasonal patterns; however, ERA5-Land generally produced slightly cooler thermal values. During winter, most regions of Iran were affected by cold stress conditions, while near-comfort conditions were largely confined to the southern coastal areas. In summer, widespread heat stress was observed across central, southern, and eastern regions of the country. ERA5-Land demonstrated better performance during the warmer seasons, particularly spring and summer, showing stronger correlations with station-based observations, whereas its accuracy declined during autumn and, most notably, winter.
Spatial analyses indicated that ERA5-Land tends to underestimate topographic influences in mountainous areas, frequently reporting temperatures lower than those observed at synoptic stations. Climatic comparisons revealed that observational data more effectively captured warming trends, particularly the increase in extremely hot days in the hot-humid southern regions after 2000. By contrast, ERA5-Land substantially underestimated this increasing trend.
Statistical evaluations confirmed these findings; correlation coefficients were highest in spring and summer (r approximately 0.8), while weaker relationships were observed during autumn and winter (r approximately 0.5 to 0.7). Overall, ERA5-Land provides a reliable source for assessing thermal comfort during warmer periods; however, it requires calibration and integration with station observations for colder seasons and regions characterized by complex topography.
 
Conclusion
This study demonstrates that ERA5-Land data can reliably reproduce large-scale patterns of thermal comfort across Iran during the period 1990 to 2020, with the strongest agreement observed in spring and summer. However, ERA5-Land tends to overestimate cold stress during winter and to underestimate heat stress during summer. These discrepancies were most pronounced in mountainous areas and in the hot-humid southern coastal region. Long-term analyses further revealed that observational data more accurately capture local warming signals, such as the marked increase in extremely hot days after 2013, trends that were not adequately reflected in the ERA5-Land dataset.
In summary, ERA5-Land represents a valuable resource for identifying general patterns of thermal comfort and large-scale climatic conditions; however, it has limitations in representing localized variations and long-term warming trends. Integrating reanalysis datasets with ground-based observational data is therefore crucial for applications related to urban planning, energy management, and climate change adaptation.
 
Funding
No funding was received for this study.
 
Authors’ Contribution
All authors contributed equally to the conceptualization and writing of this article. All authors approved the final manuscript and agreed on all aspects of the work.
 
Conflict of Interest
The authors declare no conflict of interest.
 
Acknowledgments
The authors gratefully acknowledge the scientific advisors who contributed to this study.