The Impact of Spatial–Temporal Averaging on the Dynamic–Statistical Properties of Rain Fields

Yang, Guangguang; Ndzi, David; Grémont, Boris; Paulson, Kevin; Filip, Misha; Al-Hassani, Abdul-Hadi

doi:10.1109/tap.2019.2930137

Cited by 5 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The synthesis of rain fields requires detailed understanding of four key characteristics of rain; the probability of rain occurrence, first order statistics and, space and time correlation function of rain [32]- [34]. It has been demonstrated that point rainfall rate at one location for any combination of spatial and temporal integration length is well modeled as a lognormal process with a mixed probability density function (PDF) [41]:…”

Section: Space-time Modeling Of Point Rain Ratementioning

confidence: 99%

“…where P 0 denotes the probability of rain occurrence at a point when it is raining (R > 0), and {µ, σ } are the lognormal parameters required to describe the distribution of rainfall rate, where µ and σ are the mean and standard deviation of R, respectively. Previous study in [34] showed that the statistical parameters {P 0 , µ, σ } depend on the location x and the integration volume. P 0 is equivalent to the long term probability of rain over a period T i.e P 0 = T rainy /T.…”

Section: Space-time Modeling Of Point Rain Ratementioning

confidence: 99%

“…To assess the impact of space-time averaging on rain intermittence, the theory presented in [34] and the mixed rain distribution in Eq. (3) can be used to obtain P 0 at different space-time scales.…”

Section: Integration and Downscaling Of Rain Characteristics A Pmentioning

confidence: 99%

“…In particular, the authors proposed a statistical model in [34] based on the assessment of the impact of varying spatial and temporal integration lengths on key characteristics of point rainfall rate and their dependencies on the integration volumes. However, the proposed model is only valid for cases where either the spatial or temporal integration length is constant (C), whilst the other is changing, denoted as: {L, T = C} or {L = C, T }.…”

Section: Introductionmentioning

confidence: 99%

“…However, the proposed model is only valid for cases where either the spatial or temporal integration length is constant (C), whilst the other is changing, denoted as: {L, T = C} or {L = C, T }. As an extension of the work in [34], this paper aims to fulfill two objectives: 1) assess the impact of varying 3D spatial-temporal integration lengths on rain; and 2) develop an appropriate model to estimate the rain characteristics at changing space-time scales, particularly at smaller ones. These objectives, when fulfilled, will enable the estimation of point rainfall rate at any given scale together with the prediction of rain-induced attenuation for wide area networks with multiple links of varying lengths.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Space-Time Modeling of Rainfall Rate for Satellite Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

A new comprehensive space-time model for the characterization of point rainfall rate is presented. A detailed assessment of four key rain characteristics (probability of rain/no rain condition, first and second order lognormal statistics and, space and time correlation functions) with consideration of the impact of varying spatial-temporal integration lengths are discussed. A set of empirical equations have been developed and the results show that they provide estimates of probability of rain/no rain with root mean square errors of less than 1.3 in space and 0.04 in time. They provide good estimates of the parameters at any space-time scales, particularly at higher resolutions that are of great importance to the design and planning of networks operating at frequencies above 10 GHz. In particular, the authors have created databases of rain characteristic parameters spanning North West Europe from which rain rate at any location of interest at different space-time scales can be conveniently obtained. These have been validated by comparing the rain rate exceedance distribution, R 0.01 , from the model estimates at different space-time scales across the British Isles with values calculated from measured data. It has been found that the proposed model gives highly accurate estimates of R 0.01 for the continental area with error percentages (E) generally less than 2.5 but the error percentage increases at the edges of the radar scans and in the oceanic area due to low data availability. INDEX TERMS Rainfall rate, rain characteristics, radio-wave propagation, space-time model, satellite.

show abstract

Section: Space-time Modeling Of Point Rain Ratementioning

confidence: 99%

Section: Space-time Modeling Of Point Rain Ratementioning

confidence: 99%

Section: Integration and Downscaling Of Rain Characteristics A Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Space-Time Modeling of Rainfall Rate for Satellite Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Characterization of Rainfall Rate Distribution for Satellite Networks

Yang,

Song,

Ndzi

et al. 2023

2023 Photonics &Amp; Electromagnetics Research Symposium (PIERS)

View full text Add to dashboard Cite

Space–Time Channel Model for Rain-Affected Communication Networks

Yang

Grémont

Yang

et al. 2019

IEEE Trans. Antennas Propagat.

Self Cite

View full text Add to dashboard Cite

This paper presents a new comprehensive spacetime model of rainfall rate and its associated rainfield synthesizer called GRAM (Grid Rain Attenuation Model). A great deal of care is placed on explicitly including the rain/no rain condition so that the planning and simulation of high frequency radio wave communication systems can be achieved with full consideration of rain intermittency effects. We also look in some details at the modification of first and second order statistical descriptions of the rain precipitation rate upon application of space and time integration to point rainfall rate.

show abstract

The Impact of Spatial–Temporal Averaging on the Dynamic–Statistical Properties of Rain Fields

Abstract: A-H. (2019). The impact of spatial-temporal averaging on the dynamic-statistical properties of rain fields. IEEE Transactions on Antennas and Propagation, 67(10), [AP1610-153].

Cited by 5 publications

References 41 publications

Space-Time Modeling of Rainfall Rate for Satellite Networks

Space-Time Modeling of Rainfall Rate for Satellite Networks

Characterization of Rainfall Rate Distribution for Satellite Networks

Space–Time Channel Model for Rain-Affected Communication Networks

Contact Info

Product

Resources

About