Determination of daily relative humidity estimation patterns in various climates and months in Iran

Mahboobeh Farzandi () and Nafiseh Seyyed Nezhad Golkhatmi
Additional contact information
Mahboobeh Farzandi: Ferdowsi University of Mashhad
Nafiseh Seyyed Nezhad Golkhatmi: University of Tehran

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 2, No 52, 3965-3987

Abstract: Abstract The conventional method of estimating daily relative humidity (DRH) involves computing the average relative humidity (RH) at three Greenwich Times (3:00, 9:00, and 15:00). The hourly RH (HRH) curve on a typical day is an asymmetrical curve and depends on climate and month. The traditional equations for estimating the DRH are not accurate. This paper aims to propose patterns that can help reduce errors in estimations DRH, taking into account the month and climate. To determine the different climates in Iran, we employed the Partitioning method to cluster the 149 stations. Specifically, we utilized nine climate variables and three climate indices to partition Iran into three clusters. Then, we conducted further studies to assess the homogeneity and discrepancy among these clusters. To select sample stations, we employed a systematic sampling design. Quality control measures conducted on the HRH data revealed that around 15% of data from coastal, 27% from desert, and 16.5% from mountainous stations were classified as polluted and had to be eliminated. The area under the curve of HRH represented DRH, which was estimated using the Simpson approximation method. Various patterns of linear and nonlinear regression were fitted to the sample data from every cluster, during separate analyses for each month as well as in a general form. We performed statistical tests and diagnostic tests of residuals to identify the best patterns for predicting actual DRH. These suggested patterns were then compared to traditional patterns using the Root Mean Square Error (RMSE) criteria. Our evaluation showed that the proposed models were more accurate in predicting DRH.

Keywords: Daily relative humidity; Simpson approximation; Climate clustering; Systematic sampling; Regression analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-023-04055-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:2:d:10.1007_s10668-023-04055-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-023-04055-6

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().