Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Kumar, Lakshmi Sutha; Lee, Yee Hui; Yeo, Jun Xiang; Ong, Jin

doi:10.2528/pierb11040402

Cited by 37 publications

(29 citation statements)

References 25 publications

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“…The radar only provides the radar reflectivity (Z) and we need to convert it to rainfall rate (R) by using an equation which is commonly known as the Z-R relation. The equation relates the measured Z and the estimated R and can be derived traditionally from long-term observations of DSD [6,7]. Finally, knowledge of the DSD is also important in cloud physics [8,9], soil erosion study [10], and harvesting energy from raindrop [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Marzuki¹,

Hashiguchi²,

Shimomai³

et al. 2016

PIER M

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Abstract-Micro Rain Radar (MRR) is a vertical pointing microwave profiler to measure hydrometeors and related parameters in high resolution. However, it is known that the MRR suffers from certain limitations due to several factors. This paper evaluates the performance of the MRR installed at Kototabang, west Sumatra, Indonesia (0.20 • S, 100.32 • E, 864 m above sea level). The DSD and rainfall rate from the MRR standard processing method had been evaluated by using collocated measurements of MRR, Parsivel disdrometer and Optical Rain Gauge (ORG) during 2014. Furthermore, 1.3 GHz wind profiler (BLR) observation was used to examine the vertical profiles of radar reflectivity and Doppler velocity. It was found that there were noticeable differences between the MRR and Parsivel in the small and large size ends of the DSD. At small sized drop (< 1 mm), the DSD spectra of MRR was higher than that obtained by the Parsivel otherwise it was smaller for large sized drop (> 2 mm). Underestimation of large sized drops in the MRR could be responsible for the underestimation of surface rainfall rate and daily rainfall. The source of differences in the DSD seems the measurement shortcomings such as attenuation correction and vertical wind effects, particularly during heavy rain. The shortcomings were observed from the comparison of mean Doppler velocity profiles between the MRR and the BLR. While the melting layer height of the two instruments was the same, the mean Doppler velocities of MRR shown downward increasing (DI) pattern through all rainfall intensities. On the other hand, for the BLR, the DI was only observed for heavy rain (> 10 mm/h), while downward decreasing was observed for light rain (< 5 mm/h). Similar pattern was also observed for the vertical profile of radar reflectivity. Thus, some corrections are needed for heavy rain, nevertheless, the MRR installed at Kototabang can be utilized for light rain. Comparisons indicated that the mean Doppler velocity and the DSD for the light rain as well as Z-R relation were in reasonable agreement with the reference of BLR, Parsivel and previous studies using the MRR.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Marzuki¹,

Hashiguchi²,

Shimomai³

et al. 2016

PIER M

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“…A neuron with R inputs is shown in Figure 2. The individual inputs p 1 , p 2 ,……, p g are each Weighted by corresponding elements W 1 , 1 W 1,2 ,….W 1,R of the weight matrix W. The neuron has a bias b, which is summed with the weight inputs to form the net input n: n =W 1,1 P 1 +W 1,2 P 2 +... +W 1,R P R +b (3) This expression can be written in matrix form as: n =Wp+b (4) Where the matrix W is the single neuron case has only one row. Now the neuron output can be written as:…”

Section: Multiple-input Neuronmentioning

confidence: 99%

“…Prediction [1] [2]involves using some variables or fields in the dataset to predict unknown or future values of other variables of interest. Classification [3] refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify unseen data object into one of several predefined classes. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Prediction Using Data Mining Techniques - A Survey

Rani¹,

Govardhan

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…In order to analyze the raindrop size distributions, the rain rate ranges were divided to adequately cover the four rain types in the [29] have presented alternative rainfall classifications of convective and stratiform types for Singapore.) In order to account for errors due to "dead times" in the distrometer, all entries with total raindrops (summed over all 20 bins) below 10 were eliminated prior to data analysis.…”

Section: Measurement and Rain Parametersmentioning

confidence: 99%

Raindrop Size Distribution Modeling for Radio Link Design Along the Eastern Coast of South Africa

Afullo¹

2011

PIER B

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Abstract-A study of the raindrop size distribution along the eastern coast of South Africa (Durban) is presented. The Biweight kernel estimator based on distometer measurement is used to determine the best estimate of the measured raindrop size probability distribution function (pdf). The best kernel estimator, which results in the lowest integral square error (ISE), is used to measure the closeness of the estimated lognormal and gamma pdf of raindrop size to the measured raindrop size distribution. It is established that the optimised lognormal pdf slightly outperforms the optimised gamma pdf in terms of the mean ISE and the RMSE values, with mean ISE values of 0.026 for lognormal and 0.04 for gamma distributions, respectively, and corresponding mean RMSE values of 0.073 and 0.081, respectively. The method-of-moments gamma and lognormal distributions are observed to be worse estimators of the measured pdf than the two optimized distributions. The N (D) distributions using the optimised lognormal and gamma distributions for the region are compared with those for different tropical regions, namely, India, Singapore, Nigeria, Indonesia, and Brazil. While the Indian lognormal N (D) model gives the highest peak for low raindrop sizes for all rain rates, Durban's gamma and lognormal models exhibit the widest raindrop size spread over all rain rates ranging from 1-120 mm/h. Finally, the specific attenuation due to rain using the Durban models are compared against the ITU-R models and actual measurements over a 19.5 GHz LOS link; the results indicate a need for further work involving both distrometer and radio link measurements for rain rates exceeding 30 mm/h in the eastern coast of South Africa.

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Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Cited by 37 publications

References 25 publications

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Rainfall Prediction Using Data Mining Techniques - A Survey

Raindrop Size Distribution Modeling for Radio Link Design Along the Eastern Coast of South Africa

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