Investigation of the Spatio-Temporal Variations in Atmosphere Thickness Pattern of Iran and the Middle East with Special Focus on Precipitation in Iran

Rousta, Imán; Doostkamian, Mehdi; Taherian, Allah Morad; Haghighi, Esmaeil; Malamiri, Hamid Reza Ghafarian; Ólafsson, Haraldur

doi:10.3390/cli5040082

Cited by 26 publications

(11 citation statements)

References 29 publications

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“…In contrast, dust storm frequency decreased over time in Hail, Qaisumah, Hafar Al-Batin, Al-Qassim, and Bisha. Also, our results showed a shift in dust storms from the northeast, possibly caused by the wind between the high pressure over the Mediterranean Sea and the low pressure in Saudi Arabia [80] to the southeast. The prevailing wind direction during dust-free and dust storm days varied across different parts of the country.…”

Section: Discussionmentioning

confidence: 58%

Spatial and Temporal Variations in the Incidence of Dust Storms in Saudi Arabia Revealed from In Situ Observations

et al. 2019

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Monthly meteorological data from 27 observation stations provided by the Presidency of Meteorology and Environment (PME) of Saudi Arabia were used to analyze the spatial and temporal distribution of atmospheric dust in Saudi Arabia between 2000 and 2016. These data were used to analyze the effects of environmental forcing on the occurrence of dust storms across Saudi Arabia by considering the relationships between dust storm frequency and temperature, precipitation, and wind variables. We reveal a clear seasonality in the reported incidence of dust storms, with the highest frequency of events during the spring. Our results show significant positive relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction, and precipitation. However, we did not detect a significant relationship with temperature. Our results reveal important spatial patterns, as well as seasonal and inter-annual variations, in the occurrence of dust storms in Saudi Arabia. For instance, the eastern part of the study area experienced an increase in dust storm events over time, especially in the region near Al-Ahsa. Similarly, an increasing trend in dust storms was also observed in the west of the study area near Jeddah. However, the occurrence of dust storm events is decreasing over time in the north, in areas such as Hail and Qaisumah. Overall, the eastern part of Saudi Arabia experiences the highest number of dust storms per year (i.e., 10 to 60 events), followed by the northern region, with the south and the west having fewer dust storm events (i.e., five to 15 events per year). In addition, our results showed that the wind speeds during a dust storm are 15-20 m/s and above, while, on a non-dust day, the wind speeds are approximately 10-15 m/s or lower. Findings of this study provide insight into the relationship between environmental conditions and dust storm occurrence across Saudi Arabia, and a basis for future research into the drivers behind these observed spatio-temporal trends.

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Section: Discussionmentioning

confidence: 58%

Spatial and Temporal Variations in the Incidence of Dust Storms in Saudi Arabia Revealed from In Situ Observations

et al. 2019

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“…Land surface temperature (LST) is a principal characteristic for the evaluation of energy exchange between the land and the atmosphere [1][2][3]. This parameter is applicable to numerous sciences such as climatology, hydrology, agriculture, ecology, public health, and the environment [4][5][6][7][8][9][10][11][12][13][14][15]. One of the methods for measuring LST is to use satellite data and thermal remote sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

et al. 2018

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Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.

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“…The lowest elevation of the area is 1400 m and the highest elevation is 3800 m from the sea level. Average precipitation for the whole study area is 150 mm, while the amount comes to 60 mm in low elevation places and 230 mm in higher places [42][43][44][45][46][47]. The highest percentage of vegetation coverages are clustered in the regions where villages and gardens are located (brown color regions).…”

Section: Study Areamentioning

confidence: 99%

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

et al. 2020

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Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.

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Investigation of the Spatio-Temporal Variations in Atmosphere Thickness Pattern of Iran and the Middle East with Special Focus on Precipitation in Iran

Cited by 26 publications

References 29 publications

Spatial and Temporal Variations in the Incidence of Dust Storms in Saudi Arabia Revealed from In Situ Observations

Spatial and Temporal Variations in the Incidence of Dust Storms in Saudi Arabia Revealed from In Situ Observations

Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

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