Modified rain attenuation prediction method considering the effect of wind direction

Mello, Luiz da Silva; Pontes, M.S.; Fagundes, Igor Freitas; Almeida, M. P.; Andrade, F. J. A.

doi:10.1590/s2179-10742014000200012

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Rainfall evaluation against data from nearby gauges was found to be possible for 116 links from a network of 213 CMLs. Previously, da Silva Mello et al (2002) studied the attenuation along ML due to rainfall for São Paulo. They used six links (7-43 km) with frequencies between 15 and 18 GHz.…”

Section: Introductionmentioning

confidence: 99%

Rainfall retrieval with commercial microwave links in São Paulo, Brazil

et al. 2018

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Abstract. In the last decade there has been a growing interest from the hydrometeorological community regarding rainfall estimation from commercial microwave link (CML) networks. Path-averaged rainfall intensities can be retrieved from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities for the retrieval of rainfall rates at high spatiotemporal resolutions very close to the Earth's surface. Rainfall measurements at high spatiotemporal resolutions are highly valued in urban hydrology, for instance, given the large impact that flash floods exert on society. Flash floods are triggered by intense rainfall events that develop over short time scales. Here, we present one of the first evaluations of this measurement technique for a humid subtropical climate. Rainfall estimation for subtropical climates is highly relevant, as many countries with few surface rainfall observations are located in such areas. The test bed of the current study is the Brazilian city of São Paulo. The open-source algorithm RAINLINK was applied to retrieve rainfall intensities from (power) attenuation measurements. The performance of RAINLINK estimates was evaluated for 145 of the 213 CMLs in the São Paulo metropolitan area for which we received data, for 81 days between October 2014 and January 2015. We evaluated the retrieved rainfall intensities and accumulations from CMLs against those from a dense automatic gauge network. Results were found to be promising and encouraging when it comes to capturing the city-average rainfall dynamics. Mixed results were obtained for individual CML estimates, which may be related to erroneous metadata.

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Section: Introductionmentioning

confidence: 99%

Rainfall retrieval with commercial microwave links in São Paulo, Brazil

et al. 2018

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“…In contrast, there are no examples exploring the correlation between precipitation and other climatic variables, especially atmospheric pressure in the literature is never considered for this type of analysis, but rather is often correlated with teleconnective indices [27]. However, a good significance was observed in the correlation between daily precipitation and atmospheric pressure, so this was on average the most important variable for the model in the analysis.Regarding the prevailing wind direction, in some cases,it was used as a wellcorrelated variable for precipitation prediction [28,29], exactly the opposite of what was found in this analysis, where wind direction was observed to be of marginal importance compared to the other variables used.Finally, relative humidity is most often part of analyses that combine climate and pollution [30], although in this case, it shows a good correlation for precipitation prediction.From the point of view of model architecture, MLP-type artificial neural networks have been increasingly used for predictive purposes over the past 20 years, and they have also found quite interesting applications in rainfall forecasting [31]. Normally, however, predictive climate analyses of this type analyse monthly scales, whereas in this case the daily scale was evaluated.…”

Section: Discussionmentioning

confidence: 69%

Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

Gentilucci

Pambianchi

2022

Climate

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The reconstruction of daily precipitation data is a much-debated topic of great practical use, especially when weather stations have missing data. Missing data are particularly numerous if rain gauges are poorly maintained by their owner institutions and if they are located in inaccessible areas.In this context, an attempt was made to assess the possibility of reconstructing daily rainfall data from other climatic variables other than the rainfall itself, namely atmospheric pressure, relative humidity and prevailing wind direction.The pilot area for the study was identified in Central Italy, especially on the Adriatic side, and 119 weather stations were considered.The parameters of atmospheric pressure, humidity and prevailing wind direction were reconstructed at all weather stations on a daily basis by means of various models, in order to obtain almost continuous values rain gauge by rain gauge. The results obtained using neural networks to reconstruct daily precipitation revealed a lack of correlation for the prevailing wind direction, while correlation is significant for humidity and atmospheric pressure, although they explain only 10–20% of the total precipitation variance. At the same time, it was verified by binary logistic regression that it is certainly easier to understand when it will or will not rain without determining the amount. In this case, in fact, the model achieves an accuracy of about 80 percent in identifying rainy and non-rainy days from the aforementioned climatic parameters. In addition, the modelling was also verified on all rain gauges at the same time and this showed reliability comparable to an arithmetic average of the individual models, thus showing that the neural network model fails to prepare a model that performs better from learning even in the case of many thousands of data (over 400,000). This shows that the relationships between precipitation, relative humidity and atmospheric pressure are predominantly local in nature without being able to give rise to broader generalisations.

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“…The proper scaling mechanism regarding these parameters is yet to be devised. The ITU-R model is the most significant model that has been moderated for tropical regions by many researchers [29,[142][143][144][145][146]. However, these models do not support the scaling properties.…”

Section: Scaling Improvementmentioning

confidence: 99%

A Survey of Rain Fade Models for Earth–Space Telecommunication Links—Taxonomy, Methods, and Comparative Study

2021

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Satellite communication is a promising transmission technique to implement 5G and beyond networks. Attenuation due to rain begins at a frequency of 10 GHz in temperate regions. However, some research indicates that such attenuation effects start from 5–7 GHz, especially in tropical regions. Therefore, modeling rain attenuation is significant for propagating electromagnetic waves to achieve the required quality of service. In this survey, different slant link rain attenuation prediction models have been examined, classified, and analyzed, and various features like improvements, drawbacks, and particular aspects of these models have been tabulated. This survey provides various techniques for obtaining input data sets, including rain height, efficient trajectory length measurement techniques, and rainfall rate conversion procedures. No survey of the Earth–space link models for rain attenuation is available to the best of our knowledge. In this study, 23 rain attenuation models have been investigated. For easy readability and conciseness, the details of each model have not been included. The comparative analysis will assist in propagation modeling and planning the link budget of slant links.

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Modified rain attenuation prediction method considering the effect of wind direction

Cited by 11 publications

References 19 publications

Rainfall retrieval with commercial microwave links in São Paulo, Brazil

Rainfall retrieval with commercial microwave links in São Paulo, Brazil

Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

A Survey of Rain Fade Models for Earth–Space Telecommunication Links—Taxonomy, Methods, and Comparative Study

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