Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010)

Sabziparvar, A A; Mousavi, Seyed Hossein Mir; Karampour, Mostafa; Doostkamian, Mehdi; Haghighi, Esmaeil; Rousta, Imán; Ólafsson, Haraldur; Sarif, Md. Omar; Gupta, R.; Moniruzzaman, Md; Hasan, Khairul; Ghasemi, Ali

doi:10.1155/2019/1612503

Cited by 8 publications

(2 citation statements)

References 45 publications

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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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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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“…Wet microbursts are downbursts that are accompanied by exceptional precipitation [13]. They are formed in thunderstorms with significant vertical growth and an environment having a mean value of the available convective potential energy and vertical gradients above the equivalent of the potential temperature [14,15]. Dry microbursts, on the other hand, are primarily formed from the negative buoyancy force due to evaporation and melting of hydrometeors [9,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Atmospheric Conditions Associated with a Storm Surge in the South-West of Iran

Abbasi¹,

Etemadi²,

Smoak³

et al. 2021

Preprint

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Severe thunderstorms are often accompanied by strong vertical air currents, temporary wind gusts, and heavy rainfall. The development of this atmospheric phenomenon over tropical shallow water zones, such as bays, can lead to intensification of atmospheric disturbances and produce a small-scale storm surge. Here, the storm surge that occurred on 19 March 2017 in the Persian Gulf coastal area has been investigated. Air temperature, precipitation, mean sea level pressure, wave height, wind direction, wind speed, geopotential height, zonal components, meridional winds, vertical velocity, relative humidity, and specific humidity obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF (and Global Forecast System (FNL) were used to implement the Weather Research and Forecasting (WRF) model. The results showed that the main cause of the storm surge was the occurrence of a supercell thunderstorm over the Persian Gulf. The formation of this destructive phenomenon resulted from a downburst under Cumulonimbus cloud and high-velocity air subsidence, after collision with the sea surface coinciding with the high tide. This caused a severe, yet temporary, gust, which in turn caused the creation of the four waves of 3.1 m height along the coast of Bandar Dayyer.

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Monitoring land cover changes and droughts using statistical analysis and multi-sensor remote sensing data

Heidari

Shamsipour

Kakroodi

et al. 2023

Environ Monit Assess

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Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010)

Cited by 8 publications

References 45 publications

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

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

Investigation of Atmospheric Conditions Associated with a Storm Surge in the South-West of Iran

Monitoring land cover changes and droughts using statistical analysis and multi-sensor remote sensing data

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