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Showing 3 results for Humidity

Hossein Asakereh, Mehdi Dostkamian,
Volume 15, Issue 36 (6-2015)
Abstract

All the water vapor of atmosphere is contained in a column of the atmosphere that is capable of precipitation and it is from the ground to the final of water vapor called perceptible water. This element influenced by topography and height. The purpose of this study is survey about impact of local and spatial factors on distribution of perceptible water maximums in Iran.For this reason, pressure data, especially moisture, orbital and meridional components extracted from NCEP/NCAR and analysis. Correlation and regression methods were used in this study. In order to better survey about perceptible water gradient changes and gradient changes of maximum of perceptible water has been calculated. Results showed that among the spatial factors, height has greatest impact on the spatial distribution of the maximum of perceptible water. Unlike many scientists who believe that by increasing the latitude perceptible water reduced, this rule is less In Iran atmosphere. However, most of the gradient changes of perceptible water occurred in some parts of the Zagros highlands, West and South West. The results of cycle analysis showed that the maximums of perceptible water in Iran have short term cycles between 2 to 4 years.
Ali Hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni,
Volume 24, Issue 75 (12-2024)
Abstract

This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.


Mr Masihollah Mohammadi, Prof Behrooz Sobhani,
Volume 25, Issue 76 (3-2025)
Abstract

Relative humidity is considered to be one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. To this end, MOD05 and MOD07 products were employed to estimate total perceptible water, air temperature, and sea-level pressure Additionally, MOD35 was used for cloud verification, , resulting in the identification of 2190 cloudless images with 95% confidence level for analysis. radiosound data of Bandar Abbas ststion and synoptic stations Covering entire Hormozgan Province. were used to evaluate the results. The findings demonstrated high accuracy of the algorithms and experimental model, with acceptable R² and RMSE values between Modis product and ground data. These results align well with ground station measurements. The province's climate was determined to be semi-desert with a long warm season and a short cool period. Further analysis revealed a strong correlation between sea-level pressure and total perceptible water (TPW) with the region's topography. Maximum TPW and sea-level pressure values were recorded in coastal lowlands, while minimum values occurred in the highlands. Based on zoning maps, Hormozgan province can be divided into four regions based on relative humidity: from very dry conditions with less than 20% relative humidity in the highlands to humid areas with over 65% relative humidity along the coast.


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