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Showing 3 results for Humidity

Mr Masihollah Mohammadi, Prof Behrooz Sobhani,
Volume 0, Issue 0 (3-1921)
Abstract

Relative humidity is considered as 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. In this regard, the products (MOD05 and MOD07) were used to for estimating the total perceptible water, air temperature and sea- level pressure. Also the product (MOD35) was used for cloud testing, which by performing this test, 2190 cloudless images with 95% confidence for processing was identified. To evaluate the results, radio sound data of Bandar Abbas and synoptic stations in all over the Hormozgan were used. The results showed high accuracy of the used algorithms and experimental model so that R2 and RMSE values of the recorded layers of the sensor and ground data were acceptable. They are in good agreement with ground station measurements. The results showed that the climate of the province is semi-desert with a long warm season and a short cool one. With a closer look, it was found that sea-level pressure and total perceptible water (TPW) in this province are highly correlated with the topography of the region, so that, maximum total perceptible water and sea level pressure were recorded in coastal lowland areas and minimum in the highlands of the province. According to zoning maps, Hormozgan province can be divided into four parts due to relative humidity: from very dry climate with less than 20% relative humidity which is recorded at the highlands to humid areas with more than 65% relative humidity at the coastal area.
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 (2-2025)
Abstract

Studying the effect of climatic variables on vegetation indices (Case study: Orange orchards in Hassan Abad, Darab County)
 
Abstract
Climatic variables are the most significant factors affecting vegetation changes. Nowadays, the satellite imagery is widely used to investigate the effect of fluctuations in climatic variables on vegetation changes. This research aims to investigate the effect of climatic variables of precipitation, temperature, and humidity on changes in vegetation indices of orange orchards in Hassan Abad, Darab County using satellite data. Hence, observational data, including orange tree phenology data and meteorological data on the agricultural weather station have been collected for over 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on the territorial data and 1:25000 maps of the Iran National Cartographic Center. These images were used to calculate the remote sensing vegetation indices including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). 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 the NDVI and EVI dependent variables. To determine the significance of each of the independent variables in predicting the dependent variables, the artificial neural network method was used. Findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity and maximum humidity with values (0.39, 0.3, 0.13, 0.1 and 0.06) had the greatest effect on EVI, respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients (0.2, 0.28, 0.22, 0.11 and 0.17), respectively. Finally, ARMAX regression method was used to increase the explanatory power of the model. Results showed that this method could increase the explanatory power of the model and reduce the forecasting error.

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