KnE Engineering | International Conference on Basic Sciences and Its Applications (ICBSA-2018) | pages: 259–265

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1. Introduction

Rain is a problem in telecommunication system. Raindrops cause significant attenuation of electromagnetic wave for the frequencies above 5GHz [1-4]. Attenuation is the function of rain rate and the operating frequency in which higher rain rates lead to the high attenuation. Therefore, accurate rain rate prediction is necessary for modeling the specific attenuation of rain.

International Telecommunication Union-Radiocommunication Sector (ITU-R) provides mathematical model to predict the rain rate. The ITU-R uses 40-year ECMWF re-analysis (ERA40) data as input. ERA40 data have low spatial and temporal resolutions, namely, 1.125 x 1.125 and 6 hours, respectively. In this work, the use of ERA40 data was replaced with Global Satellite Mapping of Precipitation (GSMaP) data with better spatial and temporal resolutions. Replacing ERA40 data with Tropical Rainfall Measuring Mission (TRMM) PR 3A25 and TMPA 3B43 have been conducted for Malaysia [2]. The result shows that the use of TRMM data as input of ITU-R P.837 model provide better estimation of rain rate. In Indonesia, replacing ERA40 data with TRMM data have been also conducted particularly using TRMM 3B42 and 3B43 data [5].

2. Methods

The main data used in this work are GSMaP reanalysis gauge data. GSMaP reanalysis gauge data contain rainfall data for tropical and subtropical areas (60 N - 60 S and 180 E - 180 W). Ten years GSMaP data were used in this work. The data have high spatial and temporal resolutions, i.e., 0.1 x 0.1 and 1 hour, respectively. GSMaP data are developed from several satellite observations that carry microwave radiometers with specified sensors. These sensors are in low earth orbit. The characteristics of microwave radiometers used in GSMaP data are given in Table 1 [6] and in GSMaP Data Format Description [7].

Table 1

Characteristics of satellites involved in GSMaP data.


Satellite Altitude (km) Sensor Frequncies (GHz)
TRMM 402 TMI 10,19,21,37,85
AQUA 705 AMSR-E 7,10,19,24,37,89
DMSP-F11 803 SSM/I 19,37,85
DMSP-F13 803 SSM/I 19,37,85

To calculate 1-min integrated rain rate, ITU-R P.837-6 required Mt and P 0 , which are respectively mean yearly rain accumulaltion (mm) and average probability to have rain. In this work, those parameters are extracted from GSMaP data and compared with the TRMM and rain gauge data. The following equation is the way to calculate 1-min rain rate available in the Annex 1 of the ITU-R P.837-6 [8], as in Azlan et al. [2], which is given by:

P(R)1=P0exp1.09R1+bR1+cR
P0=Pr6h1exp0.0079Mt(1β)Pr6h
b=Mt21797P0
c=26.03b

Nevertheless, P r6h and β are no longer needed, because P 0 was simply calculated by [2]:

P0=NRNT

where NR is the number of rainy pixel and NT is the number of total pixel. To validate the results, 7 years (2003, 2004, 2005, 2008, 2009, 2010, 2011) Optical Rain Gauge (ORG) data from Kototabang, West Sumatera, Indonesia (0.20 S, 100.32 E; 864 m above sea level) were used. Those data have availability more than 80% [1]. In addition to ORG data, the DBSG3 data also used to validate 1-min rain rate. DBSG3 data contain radiowave propagation measurement data that have been submitted to and accepted by ITU-R Study Groups 3. DBSG3 data are used by ITU-R for testing related prediction methods contained in the P-series of ITU-R Recommendation – Radiowave Propagation such as ITU-R P.837-6 which is used in this work.

The accuracy of the rain rate of each model is examined using root mean square error (RMSE). The smaller the value of RMSE, the higher the accuracy of the method. RMSE-1 denotes the accuracy between GSMaP data and the ORG, RMSE-2 denotes the accuracy between TRMM and the ORG, and the RMSE-3 denotes the accuracy between ITU-R and the ORG.

3. Results

Fig. 1 shows the comparison of 1-min rain rate derived using yearly Mt and P 0 from GSMaP, TRMM data, ITU-R model and ORG at Kototabang. It can be observed that the ITU-R provides better 1-min rain rate especially for percentage of time > 0.01%. On the other hand, for percentage of time 0.01% GSMaP provides better 1-min rain rate, while ITU-R with default input underestimates the rain rate.

fig-1.jpg
Figure 1
Comparison of 1-min rain rate estimated using yearly Mt and P 0 of TRMM and GSMaP along with the result of ITU-R model with default input and rain gauge data at Kototabang.
fig-2.jpg
Figure 2
Same as Fig.1 but for average Mt and P 0 .

Fig. 2 shows the comparison of 1-min rain rate derived from average Mt and P 0 from GSMaP data (10 years), TRMM data (17 years), ITU-R, and the ORG (7 years) at Kototabang. It was found that 1-min rain rate obtained from GSMaP data show the best performance for percentage of time 0.01%. For percentage of time > 0.01%, ITU-R shows better performance in calculating the rain rate. However, for the entire data, GSMaP provides the best performance in calculating the rain rate, indicated by smaller RMSE (Fig. 2).

fig-3.jpg
Figure 3
Same as Fig. 2 but the comparison with DBSG3 data for Bandung.

Fig. 3 shows 1-min rain rate estimated by using average Mt and P 0 from GSMaP (blue line) and TRMM (green line), ITU-R (red line), and the DBSG3 (cross) data for Bandung. Bandung #1, Bandung #2 and Bandung #3 are data from 1992 to 1994, the rest are data with unknown year period. It is found that the accuracy of model for all data vary from year to year. For Bandung#3, one-minute rain rate estimated by ITU-R model with GSMaP input is in good agreement with DBSG3. However, for Bandung#2, one-minute rain rate estimated by ITU-R model with default input is in good agreement with DBSG3.

In general, for GSMaP data the best accuracy is obtained for percentage of time 0.01%, while for percentage of time > 0.01% default ITU-R model shows the best rain rate estimation. This condition may be due to the original data of ITU-R to estimate rain rate. The ITU-R data mostly come from middle and high latitudes such as Europe, North America and Japan which are dominated by light rain (high percentage of time) [9].

4. Conclusions

In general, the use of yearly and average Mt and P 0 derived from GSMaP data as input of ITU-R model show better performance to calculate 1-min rain rate than the original ITU-R model, indicated by a smaller RMSE. However, the accuracy of 1-min rain rate estimates from yearly data varies from year to year, as observed from the result which is validated using the DBSG3 data. An overall, the use of GSMaP data to calculate 1-min rain rate shows the best performance for percentage of time 0.01%, while the ITU-R model provides the best result for the percentage of time > 0.01%.

Acknowledgments

This study was supported by the 2017 and 2018 International Joint Collaboration and Scientific Publication grant from the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (Contract No. 02/UN.16.1.17/PP.KLN/LPPM/2017 and 050/SP2H/LT/DRPM/2018). The authors thank to Japan Aerospace Exploration Agency and Goddard Space Flight Center (JAXA) and National Aeronautics and Space Administration (NASA) for providing the TRMM, and GSMaP data. The authors also thank to Dr. Toyoshi Shimomai for providing Optical Rain Gauge (ORG) data at Kototabang.

References

1 

Marzuki et al.(2016). Cumulative Distributions of Rainfall Rate over Sumatra, Progress in Electromagnetics Research M. 49, 1-8 [10.2528/PIERM16043007].

2 

Azlan et al.(2013). 1-Minute Integrated Rain Rate Statistics Estimated From Tropical Rainfall Measuring Mission Data, IEEE Antennas and Wireless Propagation Letters, 12, 132 – 135 [10.1109/LAWP.2013.224310].

3 

Marzuki et al.(2009). Diurnal Variation of Rain Attenuation Obtained From Measurement of Raindrop Size Distribution in Equatorial Indonesia, IEEE Transactions on Antennas and Propagation, 57, 1191-1196 [10.1109/TAP.2009.2015812]

4 

Saleem,M.A. et al.(2008).Attenuation Analysis for Optical Wireless Link Measurements under Moderate Continental Fog Conditions at Milan and Graz, in 68 th Vehicular Technology Conference,Proc.IEEE 2008, 1-5 [10.1109/VETECF.2008.58]

5 

Meylani, L. and Marzuki.(2017). Pemanfaatan Data Tropical Rainfall Measuring Mission (TRMM) Sebagai Input Model ITU-R untuk Mengestimasi Intensitas Curah Hujan di Indonesia, Prosiding Seminar Nasional Fisika Universitas Andalas.

6 

Ushio et al.(2009). A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data, Journal of the Meteorological Society of Japan, 87A, 137-151 [10.2151/jmsj.87A.137].

7 

Japan Aerospace Exploration Agency(JAXA).(2010). GSMaP Data Format Description, [http://sharaku.eorc.jaxa.jp/GSMaP_crest/gdac/doc/gsmap_dataformat.pdf]

8 

Radiowave Propagation Series.(2012). Characteristics of Precipitation for Propagation Modeling, Recommendation ITU-R P.837-6.

9 

Blarzino, G. et al.(2009). Development of A New Global Rainfall Rate Model Based on ERA40, TRMM, GPCC and GPCP Products., Proc. 3rd EuCAP, 671 – 675.

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ISSN: 2518-6841