An Improvement to Precipitation Inversion Model Using Oblique Earth–Space Link Based on the Melting Layer Attenuation

Xian, Minghao; Liu, Xichuan; Gao, Taichang

doi:10.1109/tgrs.2020.3027465

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…A variant to this scheme is to retain the simplified model in Figure 1 and fictitiously modify (increase) the LL height to take into account the impact of the ML [26,36], while keeping the same values for a and b valid for the LL. Further analytical ad hoc models have been developed by other authors, e.g., in [8,40]. Additional details on these topics are given in Section 5.4.2.…”

Section: Specific Attenuation Model For the MLmentioning

confidence: 99%

“…Then, the specific rain attenuation k (in dB/km) in the LL can be obtained by resorting to the tropospheric model in Figure 1, and the instantaneous RR value is eventually achieved by inverting the power-law in (5). Alternatively, the effect of the ML can be taken into account, by means of improved precipitation inversion models, such as that in [8], which is based on the three-layer model in Figure 4 or that in [40].…”

Section: Cots-receiver-based Approach-ku Bandmentioning

confidence: 99%

“…In addition [5], other authors [1,40] have investigated the use of an appropriately trained neural network to identify rainy periods, geared to analyze, recognize and classify the statistical behavior of the received signal. This category of techniques appears promising even though, at the moment, it does not seem to clearly demonstrate that it is definitely more advantageous than the other more traditional methods, particularly with regard to the accuracy achievable in identifying the instants of start and stop of a rain event.…”

Section: Identification Of the Rainy Periodsmentioning

confidence: 99%

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Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey

Giannetti

Reggiannini

2021

Sensors

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Recent years have witnessed a growing interest in techniques and systems for rainfall surveillance on regional scale, with increasingly stringent requirements in terms of the following: (i) accuracy of rainfall rate measurements, (ii) adequate density of sensors over the territory, (iii) space-time continuity and completeness of data and (iv) capability to elaborate rainfall maps in near real time. The devices deployed to monitor the precipitation fields are traditionally networks of rain gauges distributed throughout the territory, along with weather radars and satellite remote sensors operating in the optical or infrared band, none of which, however, are suitable for full compliance to all of the requirements cited above. More recently, a different approach to rain rate estimation techniques has been proposed and investigated, based on the measurement of the attenuation induced by rain on signals of pre-existing radio networks either in terrestrial links, e.g., the backhaul connections in cellular networks, or in satellite-to-earth links and, among the latter, notably those between geostationary broadcast satellites and domestic subscriber terminals in the Ku and Ka bands. Knowledge of the above rain-induced attenuation permits the retrieval of the corresponding rain intensity provided that a number of meteorological and geometric parameters are known and ultimately permits estimating the rain rate locally at the receiver site. In this survey paper, we specifically focus on such a type of “opportunistic” systems for rain field monitoring, which appear very promising in view of the wide diffusion over the territory of low-cost domestic terminals for the reception of satellite signals, prospectively allowing for a considerable geographical capillarity in the distribution of sensors, at least in more densely populated areas. The purpose of the paper is to present a broad albeit synthetic overview of the numerous issues inherent in the above rain monitoring approach, along with a number of solutions and algorithms proposed in the literature in recent years, and ultimately to provide an exhaustive account of the current state of the art. Initially, the main relevant aspects of the satellite link are reviewed, including those related to satellite dynamics, frequency bands, signal formats, propagation channel and radio link geometry, all of which have a role in rainfall rate estimation algorithms. We discuss the impact of all these factors on rain estimation accuracy while also highlighting the substantial differences inherent in this approach in comparison with traditional rain monitoring techniques. We also review the basic formulas relating rain rate intensity to a variation of the received signal level or of the signal-to-noise ratio. Furthermore, we present a comprehensive literature survey of the main research issues for the aforementioned scenario and provide a brief outline of the algorithms proposed for their solution, highlighting their points of strength and weakness. The paper includes an extensive list of bibliographic references from which the material presented herein was taken.

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Section: Specific Attenuation Model For the MLmentioning

confidence: 99%

Section: Cots-receiver-based Approach-ku Bandmentioning

confidence: 99%

Section: Identification Of the Rainy Periodsmentioning

confidence: 99%

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Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey

Giannetti

Reggiannini

2021

Sensors

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“…They first extracted the features from the received signals and then trained an LSTM network to detect whether the period is rainy or not. Artificial neural networks (ANN) have also been used to recognize the rainy and non-rainy periods by [ 10 , 19 ] and convolutional neural networks (CNN) by [ 20 ]. The classification of rainy and non-rainy periods has been performed using Markov switching models [ 21 ] and by analyzing the signal in the frequency domain by applying Fourier transformations on a rolling window [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Gianoglio

Alyosef

Colli³

et al. 2023

Sensors

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In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers.

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Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data

Zhao

Liu

Xian

et al. 2021

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

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The large-scale monitoring of rainfall is of great significance in the research of meteorology, hydrology, and atmospheric measurement science. In recent years, with the quick development of communication satellite constellation, the use of Earth-space link (ESL) to measure rainfall in the atmosphere is expected to be a potential approach for the largescale monitoring of global rainfall. In this paper, to verify the long-term performance of rainfall measurement using ESL, the data of an ESL at the Ku band and a Thies Laser Precipitation Monitor (LPM) in Nanjing were collected, the rainfall inversion model using ESL was optimized according the height of 0 ℃layer from to the radiosonde data of 10 years, and the inversion results in the different types of rainfall were discussed. The results show that the rainfall inversed by the optimized ESL model are in good agreement with the rainfall measured by LPM (correlative coefficient is 0.985), the relative errors of rain intensity inversed by ESL in light rain, moderate rain, heavy rain, and extreme rain are 20.00%, 15.17%, 8.93%, and 8.99% respectively. The average relative errors (RE) of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59% respectively.

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An Improvement to Precipitation Inversion Model Using Oblique Earth–Space Link Based on the Melting Layer Attenuation

Cited by 4 publications

References 40 publications

Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey

Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data

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