Rain fade slope predicted from rain rate data

Kamp, Max van de

doi:10.1029/2006rs003480

Cited by 2 publications

(9 citation statements)

References 15 publications

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“…The attenuation time series can be simulated based on the measured rain‐rate data by the following equation [ Van de Kamp , ]:

A (t) = \int_{0}^{h_{R} / sin Θ} γ ((),, t, l) normald l (normaldB)

where, h R is rain height in kilometers [ ITU‐R P . 839‐3 , ], Θ is link elevation angle in degree, l is coordinate along the slant path in kilometers, γ ( t , l ) is the specific attenuation in dB/km.…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…839‐3 , ], Θ is link elevation angle in degree, l is coordinate along the slant path in kilometers, γ ( t , l ) is the specific attenuation in dB/km. Equation () can be changed to

A (t) = \frac{1}{cos Θ} \int_{0}^{h_{R} / tan Θ} γ ((),, t, s) normald s (normaldB)

where, s is the horizontal projection coordinate of location l [see Van de Kamp , ]. γ ( t , s ) can be calculated by (41) [see ITU‐R P .…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…In (41), R ( t , s ), which is dependent on the measured rain rate R 1 = R ( t ,0) at s = 0, is derived by Van de Kamp []. The probability distribution of instantaneous rain rate R 2 = R ( t , s ) conditional to R 1 is lognormal [ Van de Kamp , ]

p (R_{2} | R_{1}) = \frac{1}{\sqrt{2 π} σ_{2} (t) R_{2}} e^{- {ln}^{2} (R_{2} / m_{2} (t)) / 2 {([], σ_{2} ((), t))}^{2}}

where, m 2 ( t ) is the median value of R 2 conditional to R 1 , σ 2 ( t ) is the standard deviation of ln R 2 conditional to R 1 . m 2 ( t ) and σ 2 ( t ) are given by Van de Kamp [] as following equations:

m_{2} (t) = m_{R}^{1 - e^{- B_{s} s}} R_{1}^{e^{- B_{s} s}}

σ_{2} (t) = σ_{ln R} \sqrt{1 - e^{- 2 B_{s} s}}

…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…In (45), B s describes the spatial dynamics of rain rate, for example B s can be given as (46) [ Van de Kamp , ]

B_{s} = 7.76 \times 10^{- 4} m^{‐ 1}

…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…Strictly speaking, m R in (44) is instantaneous (unconditional) spatial median value of rain rate at any coordinate s , and σ ln R in (45) is instantaneous standard deviation of ln R ( s ), where R ( s ) is rain rate at any coordinate s . Van de Kamp [] shows that m R can be replaced by long‐term median value of rain rate, and σ ln R can be replaced by long‐term standard deviation of ln R ( t ). Therefore, m R and σ ln R can be decided by measured rain rate time series.…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

See 4 more Smart Citations

A practical MGA‐ARIMA model for forecasting real‐time dynamic rain‐induced attenuation

et al. 2013

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[1] The novel and practical modified genetic algorithm (MGA)-autoregressive integrated moving average (ARIMA) model for forecasting real-time dynamic rain-induced attenuation has been established by combining genetic algorithm ideas with the ARIMA model. It is proved that due to the introduction of MGA into the ARIMA(1,1,7) model, the MGA-ARIMA model has the potential to be conveniently applied in every country or area by creating a parameter database used by the ARIMA (1,1,7) model. The parameter database is given in this paper based on attenuation data measured in Xi'an, China. The methods to create the parameter databases in other countries or areas are offered, too. Based on the experimental results, the MGA-ARIMA model has been proved practical for forecasting dynamic rain-induced attenuation in real time. The novel model given in this paper is significant for developing adaptive fade mitigation technologies at millimeter wave bands.

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“…The attenuation time series can be simulated based on the measured rain‐rate data by the following equation [ Van de Kamp , ]:

A (t) = \int_{0}^{h_{R} / sin Θ} γ ((),, t, l) normald l (normaldB)

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…839‐3 , ], Θ is link elevation angle in degree, l is coordinate along the slant path in kilometers, γ ( t , l ) is the specific attenuation in dB/km. Equation () can be changed to

A (t) = \frac{1}{cos Θ} \int_{0}^{h_{R} / tan Θ} γ ((),, t, s) normald s (normaldB)

where, s is the horizontal projection coordinate of location l [see Van de Kamp , ]. γ ( t , s ) can be calculated by (41) [see ITU‐R P .…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

p (R_{2} | R_{1}) = \frac{1}{\sqrt{2 π} σ_{2} (t) R_{2}} e^{- {ln}^{2} (R_{2} / m_{2} (t)) / 2 {([], σ_{2} ((), t))}^{2}}

m_{2} (t) = m_{R}^{1 - e^{- B_{s} s}} R_{1}^{e^{- B_{s} s}}

σ_{2} (t) = σ_{ln R} \sqrt{1 - e^{- 2 B_{s} s}}

…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

“…In (45), B s describes the spatial dynamics of rain rate, for example B s can be given as (46) [ Van de Kamp , ]

B_{s} = 7.76 \times 10^{- 4} m^{‐ 1}

…”

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

Section: More Parameters and Methods To Build Parameter Databases In mentioning

confidence: 99%

See 3 more Smart Citations

A practical MGA‐ARIMA model for forecasting real‐time dynamic rain‐induced attenuation

et al. 2013

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show abstract

Analysis of precipitation characteristics over Southern peninsular Malaysia for satellite propagation application

Jong

Lam

D’Amico

et al. 2016

2016 URSI Asia-Pacific Radio Science Conference (URSI AP-RASC)

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This contribution investigates precipitation characteristics in Southern peninsular Malaysia, by exploiting local meteorological measurements. Several peculiar characteristics of precipitation are derived from one year of tipping bucket rain gauge in Universiti Teknologi Malaysia (UTM), Johor Bahru. Stratiform and convective types of precipitation are investigated based on monthly distribution, and several rain rate distributions are compared for few locations; moreover, we present the statistics of seasonal and diurnal variations of rainfall rate as well as rainfall intensity. Furthermore, combined effects of both, seasonal and diurnal variations of rain rate are also presented for better representation of local precipitation characteristics. In addition, the measured rain attenuation statistics show that higher attenuation is most likely to occur during the afternoon hours from 12 pm to 6 pm. These results provide a significant information to service provider who needs to guarantee the Quality of Service (QoS) of the link service during a typical month or day by implements an appropriate PIMTs during severe rain period

show abstract

Rain fade slope predicted from rain rate data

Cited by 2 publications

References 15 publications

A practical MGA‐ARIMA model for forecasting real‐time dynamic rain‐induced attenuation

A practical MGA‐ARIMA model for forecasting real‐time dynamic rain‐induced attenuation

Analysis of precipitation characteristics over Southern peninsular Malaysia for satellite propagation application

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