Spatial rainfall pattern using conventional weather radar in the middle of Indochina peninsular 1 Satomura
Nattapon Mahavik ,Takehiko and Somchai 1Climate Physics Laboratory, Graduate School of Science, Kyoto University 2Thai Meteorological Department Email:
[email protected] 1*
2 Baimuang
Introduction
Radar calibration and validation
Weather radar is an efficient tool for using to study spatial and temporal pattern of rainfall. Z-R relationship between radar reflectivity and gauge rainfall can be achieved by using the high frequency of observation time of radar and rain gauge observation. There are a few studies in Indochina peninsular employing the weather radar to study rainfall pattern because of lacking of good observational data both radar and rain gauge. Therefore, We have two objectives in this study. Firstly, we try to find an suitable method of calibration for our radar rainfall by using gauge rainfall to have the quantity of radar rainfall as near as gauge rainfall. Secondly, we apply EOF to the radar rainfall to see a spatial rainfall pattern of the study area.
The radar reflectivity is converted by conventional Z-R relation, B=200 and β=1.6, to derive a radar rainfall. It is accumulated to be daily radar rainfall for corresponding to gauge position. The radar rainfall shows a strong underestimate compared with gauge rainfall as shown in fig.3 (a,b,c). Conversion factor, slope and y-intercepted axis, between radar rainfall and gauge rainfall is calculated to calibrate the radar rainfall as close as possible to observation rainfall. The results of calibrated radar rainfall are shown in the fig.3(d,e,f) , which clearly show an agreement of radar and gauge rainfall. Therefore, we use the calibrated results in EOF analysis.
Data
The time variation of rainfall intensity of both radar and gauge before calibration are shown in the fig. 4(a,b,c) of July, August and September 2009 respectively. The radar and gauge rainfall are spatially daily averaged. The comparison results show that the radar rainfall always underestimate of gauge rainfall as shown in fig. 4(a,b,c). In contrast, the radar rainfall after calibration with gauge rainfall are clearly improved the radar rainfall rate, which is close to gauge rainfall as shown in fig. 4(d,e,f).
The weather radar is located in Vientiane, Lao PDR (102°34'14.2", 17º58'15.9") at 168 m MSL using Cband frequency belonged to Department of Meteorology and hydrology (DMH). There are two observation ranges, which are at 120 km as short observation and 400km as long observation as shown in Fig 1. A volume scan of radar observation consists of these two observations, which are used to create a CAPPI (Constant Altitude Plan Position Indicator) at 3 km after removing a beam blockage. The rain gauge of Thai Meteorological Department (TMD) has 146 gauges within 200 km of radar observation radius in Thailand territory as shown in Fig.2b.Time period are in July-September 2009 and 2010. (A)
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Fig.1 Shows range-height diagram Numbers on each curve are elevation Fig.2A Indochina peninsular map and Fig.2B radar radius and gauges angles in degrees.
The accumulation of spatially daily averaged radar and gauge rainfall are compared as shown in fig5(a-c), which are before calibration by gauge rainfall. The spatially accumulated radar rainfall show a strong underestimate of gauge rainfall. In the fig 5(df), the radar rainfall after calibration by gauge rainfall shows the improvement of radar rainfall.
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Fig.3(a,b,c) are the comparison of radar rainfall before calibration and gauge rainfall of July, August and September 2009 respectively. Fig3.(d,e,f) are as same as fig3.(a,b,c) but for after calibration gauge
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Fig.4(a,b,c) are the comparison of daily averaged radar rainfall before calibration and gauge rainfall with after calibration, Fig.4(d,e,f), of July, August and September 2009 respectively. gauge
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Fig.5(a,b,c) are the comparison of monthly accumulation of daily averaged radar rainfall before calibration and gauge rainfall with after calibration fig.5(a,b,c) of July, August and September 2009 respectively.
Conclusion
EOF Spatial pattern and Time-varying amplitudes (A)
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Fig.6(a,b,c) EOF1,2,3 of 2009. Fig.6(d,e,f) EOF1,2,3 of 2010. Fig.6(g,h,i) EOF1,2,3 of combination of 2009 and 2010.The thin and thick lines in the fig.6 are elevation contour at 500 and 1000 meter respectively
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Fig.7(a,b,c) Time series of EOF1,2,3 of 2009. Fig.7(d,e,f) time series of EOF1,2,3 of 2010. Fig.7(g,h,i) time series of EOF1,2,3 of combination of 2009 and 2010.
The influence of terrain into the spatial rainfall pattern is investigated. The radar rainfall of two years are used, which are July to September of 2009 and 2010. In here, we convert by applying a cube-root to the original radar rainfall. Then, EOF analysis is applied to the radar data to extract an EOF mode explaining the spatial rainfall patterns. There are three modes of EOF that show the high variances. The fig.6 (a-c), fig.6(d-f) and fig.6(g-i) are EOF1,EOF2 and EOF3 of spatial pattern in 2009, 2010 and combination of 2009 and 2010 respectively. EOF1 shows 30% of variance of totally spatial rainfall in large area, which means their contributions of rainfall variability to the spatial rainfall pattern may caused by typhoon or monsoon active period. In contrast, EOF2 and EOF3 show a see-saw pattern of spatial rainfall. In here we still cannot clearly see the relationship between terrain and rainfall pattern. The time-varying amplitudes are shown as in fig7 (a-i).
- Radar rainfall converted by conventional Z-R shows the underestimate of gauge rainfall about 1.7 to 2 times. - Conversion factor method is the suitable method for our radar data, which calibrates radar rainfall to be equal with gauge rainfall as close as possible. - EOF1 shows the spatial rainfall pattern of large scale of meteorological phenomena. - EOF2 and EOF3 can be used to explain a local variability of spatial rainfall - The terrain effect to spatial rainfall pattern is still not clear in all EOF modes.
Acknowledgements The authors thank to MEXT, IMPACT project and GCOE-ARS for providing a financial support. The rain gauge data is provided by TMD. The radar data is provided by DMH. All figures are drawn by using NCL from NCAR, GMT and R software.
