Analysis of Tropospheric Scintillation Intensity on Earth to Space in Malaysia

Singh, Mandeep Singh Jit; Hassan, Syed Idris Syed; Ain, Mohd Fadzil; Igarashi, K.; Tanaka, Kenji; Iida, M.

doi:10.3844/ajassp.2006.2029.2032

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…According to the literature [39], the free-space attenuation A F of the OELs in Jiangning district is between 205.77 and 206.16 dB. We suppose the V has a sinusoidal variation from 244 to 246 dB which simulates the effect of other atmospheric factors [46,47]. Based on Equation 4, the true SNR is obtained.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation

et al. 2020

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High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.

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Section: Simulation Experimentsmentioning

confidence: 99%

Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation

et al. 2020

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“…In order to clarify the operation of the double-Kalman-based algorithm outlined above, let us first consider the application example in Fig. 9, whose upper part shows a sequence of raw s0 / ( ) E N kT (light blue line) produced by a SmartLNB, along with 26 the outputs of the two trackers (black line:…”

Section: Numerical Resultsmentioning

confidence: 99%

“…This effect is significant for frequencies above 10 GHz and grows with frequency. In Ku band, fluctuations are within ±0.5 dB and the period of scintillation fades varies from 1 to 10 s. Accordingly, the spectral width of the fluctuations in the Ku band is about 0.1 Hz[26]. These fluctuations are thus much faster than the rain events and even faster if compared to the long-term effects mentioned here below, and as such they can be smoothed out by a KF (Sect.…”

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confidence: 78%

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The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals

Giannetti

Moretti

Reggiannini

et al. 2019

IEEE Aerosp. Electron. Syst. Mag.

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In this paper we present results from the NEFOCAST project, funded by the Tuscany Region, aiming at detecting and estimating rainfall fields from the opportunistic use of the rain-induced excess attenuation incurred in the downlink channel by a commercial DVB satellite signal. The attenuation is estimated by reverse-engineering the effects of the various propagation phenomena affecting the received signal, among which, in first place, the perturbations factors affecting geostationary orbits, such as the gravitational attraction from the moon and the sun and the inhomogeneity in Earth mass distribution and, secondly, the small-scale irregularities in the atmospheric refractive index, causing rapid fluctuations in signal amplitude. The latter impairments, in particular, even if periodically counteracted by correction maneuvers, may give rise to significant departures of the actual satellite position from the nominal orbit. A further problem to deal with is the daily and seasonal random fluctuation of the rain height and altitude/size of the associated melting layer. All of the above issues lead to non-negligible random deviations from the dry nominal downlink attenuation, that can be misinterpreted as rain events. In this paper we show how to counteract these issues by employing two differentially-configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, so that the rain events can be reliably detected and the relevant rainfall rate estimated.

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“…Moreover, the liquid water in cloud also affects the Ku band signal [31]. The scintillation caused by turbulence makes the signal amplitude fluctuate rapidly and irregularly [32]. In addition to the effects from above sources, non-atmospheric factors also have an influence on earth-space link such as free-space propagation and orbit perturbations; but these effects are generally constant or regular [12].…”

Section: A Rain Attenuation Of Earth-space Linkmentioning

confidence: 99%

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Xian

Liu

Yin

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most present studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: (1) identification of rain and no-rain periods; and (2) determination of attenuation baseline. To solve these problems, this paper adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) neural network is selected for the determination of attenuation baseline since it has a good ability to solve time series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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Analysis of Tropospheric Scintillation Intensity on Earth to Space in Malaysia

Cited by 17 publications

References 6 publications

Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation

Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation

The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

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