Journal of Environmental Radioactivity 138 (2014) 132e136
Contents lists available at ScienceDirect
Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for 131I and 137Cs using TRMM 3B42 rainfall data R. Shrivastava a, *, S.K. Dash b, M.N. Hegde c, K.S. Pradeepkumar a, D.N. Sharma d a
Radiation Safety Systems Division, Bhabha Atomic Research Centre, Mumbai 400 085, India Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi 110 016, India Environmental Survey Laboratory, Health Physics Division, Kaiga Generating Station, Kaiga 581 400, India d Health Safety and Environment Group, Bhabha Atomic Research Centre, Mumbai 400 085, India b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 June 2014 Received in revised form 23 July 2014 Accepted 24 August 2014 Available online
The TRMM rainfall product 3B42 is compared with rain gauge observations for Kaiga, India on monthly and seasonal time scales. This comparison is carried out for the years 2004e2007 spanning four monsoon seasons. A good correlation is obtained between the two data sets however; magnitude wise, the cumulative precipitation of the satellite product on monthly and seasonal time scales is deficient by almost 33e40% as compared to the rain gauge data. The satellite product is also compared with APHRODITE's Monsoon Asia data set on the same time scales. This comparison indicates a much better agreement since both these data sets represent an average precipitation over the same area. The scavenging coefficients for 131I and 137Cs are estimated using TRMM 3B42, rain gauge and APHRODITE data. The values obtained using TRMM 3B42 rainfall data compare very well with those obtained using rain gauge and APHRODITE data. © 2014 Elsevier Ltd. All rights reserved.
Keywords: TRMM APHRODITE Scavenging coefficient
1. Introduction As the Tropical Rainfall Measuring Mission (TRMM) satellite completes more than a decade of operation, it has provided researchers throughout the world with a large volume of rainfall data for the validation of atmospheric and climate models. Due to operational difficulties over the oceans, the measurement of rainfall with rain gauges is not possible and the remotely sensed information about rainfall becomes the only source of reliable and continuous data. However, due to errors in radiance measurement, in the retrieval algorithm used in data processing and errors caused due to non uniform field of view of the sensors, the rainfall estimates obtained from the satellites should be first validated against rain gauge observations and then used to obtain estimates of rainfall where rain gauge data are not available. With a large number of meteorological satellites in orbit, it is now possible to obtain rainfall estimation at high spatial and temporal resolutions. For example, the TRMM is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan
* Corresponding author. Tel.: þ91 022 25595365; fax: þ91 022 25595313. E-mail address: [email protected] (R. Shrivastava). http://dx.doi.org/10.1016/j.jenvrad.2014.08.011 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
Aerospace Exploration Agency (JAXA) launched in 1997 with the aim of studying precipitation in the tropics. In the past there have been several studies which have compared the TRMM Multi Satellite Product 3B42 with rain gauge observations. The TRMM Precipitation Radar (PR) and 3B43 rainfall product were validated with the Global Precipitation Climatology Centre (GPCC) rain gauge data over Africa by Adeyewa and Nakamura (2003). They observed significant seasonal and regional differences and in general the 3B43 product had closest agreement with the rain gauge data. The TRMM data was analysed in various regions of the globe namely Caspian Sea, Brazil, Kwalajein in Marshall Islands, Melbourne in Australia and Houston in United States of America by Duan et al. (2012); Ferreira et al. (2012); Ji (2006); Ji and Stocker (2003). Similarly Nair et al. (2009) have analysed TRMM data over Maharashtra. Their study spanned 7 monsoon seasons from 1998 to 2004. They concluded that the satellite estimates were accurate over regions of moderate rainfall and inaccurate over regions of sharp rainfall gradient. Prakash and Gairola (2014) have validated TRMM 3B42 rainfall product over the tropical Indian Ocean using buoy data. They reported a statistically significant linear correlation from 0.4 to 0.89 between the two precipitation estimates. The satellite product can also be compared with an interpolated rainfall data like APHRODITE's (Asian
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Precipitation e Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) Monsoon Asia (Yatagai et al., 2012) data available from http://www.chikyu.ac.jp/precip/. This data is created by interpolation of the data obtained from a rain-gauge observation network. As compared with individual rain gauge observations which are scattered in space and whose density depends on the topography of a site, an interpolated rainfall data becomes a better product for comparison. Also in this case, the TRMM 3B42 and APHRODITE both are available on a 25 km grid resolution, making them compatible for comparison. In this study, the TRMM rainfall product 3B42 is compared with rain gauge observations and APHRODITE data for Kaiga, India on monthly and seasonal time scales for the years 2004e2007. At this site, four Pressurized Heavy Water Reactors (PHWR) are operated by Nuclear Power Corporation of India (NPCIL) Ltd. for generation of electricity. During the operation of the nuclear power plants, a small quantity of radioactivity is discharged into the atmosphere through the stacks and liquid effluent route. The amount of emissions depend on the power level of the reactors, number of days of reactor operation in a calendar year and the efficiency of filters and other stack exhaust systems. At this site two, 100 m tall stacks are used to discharge the pollutants to the atmosphere. These pollutants released in the atmosphere can get depleted due to dry, wet deposition and radioactive decay. Precipitation scavenging refers to the removal of pollutants by precipitation in the form of rain or snow. The physical processes responsible for scavenging are diffusion, interception, inertial impaction and growth by water vapour condensation. Both, gases and aerosols are scavenged by rain droplets. The collection efficiency describes the capture of a single
133
particle by a single hydrometeor. The scavenging coefficient is a weighted average of the collection efficiency over all hydrometeor sizes and particle sizes. The dependence of the scavenging coefficient on the particle size is because of the attachment of the particles to plume droplets and water vapour condensation. The growth by water vapour condensation depends on the solubility of the contaminant in water Slinn (1977). Hence the rate of removal of a pollutant from the plume depends on the scavenging coefficient for that particular pollutant which in turn depends on the characteristics of the pollutant and the rainfall rate. Site specific dry and wet deposition velocities for this site were estimated using the rain gauge data James et al. (2010). This paper presents a demonstration of the application of satellite derived rainfall data for the estimation of scavenging coefficients for two gamma emitting radionuclides namely 131I and 137Cs. The scavenging coefficients for 131I and 137Cs are estimated using the three data sets of precipitation namely rain gauge, APHRODITE and TRMM 3B42. Description of the site characteristics, the methodology followed and the results are presented in the subsequent sections. 2. Site description and methodology The rain gauge data used in the study is located at the Kaiga Generating Station (KGS) (14.868 N, 74.441 E). Kaiga is situated in the valley of the Kali river on the southern bank of the man made Kadra reservoir. There are minor forests and patches of agricultural land on both the banks of the river. The ghats are covered by major evergreen and semi evergreen forests with the average height of the trees being above 15 m. A small fraction of land is used for
Fig. 1. Map of Kaiga site showing the location of the two stacks (black vertical bars) and the rain gauge (black triangle).
134
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Fig. 2. Wind roses for the years 2004, 2006 and 2007.
data is a three hourly rainfall rate which is converted to cumulative rainfall for a 24 h period. The APHRODITE and rain gauge data both refer to 24 h cumulative rainfall. Following the accident at the Chernobyl Nuclear Power Plant in 1986, Jylha (1991) has suggested empirical relationships between the scavenging coefficient and rainfall rate for different radionuclides like 103Ru, 106Ru, 129mTe, 132 Te, 131I(p), 133I(p), 134Cs, 136Cs, 137Cs and 140Ba on the basis of simultaneous radioactivity and radar rainfall measurements at Helsinki, Nurmijarvi, Loviisa, Olkiluoto, Tampere, Jokionen and Pori in Finland. From their study, the scavenging coefficients for any radionuclide can be expressed in terms of rainfall rate as
agricultural purposes. The average height of the Western Ghats in the region is around 600 m. The map of the region showing the location of the stacks and the rain gauge is presented in Fig. 1. The Environmental Survey Laboratory (ESL, Kaiga) carries out meteorological measurements at Kaiga site. The meteorological instrumentation includes a Stevenson screen for measurement of temperature and relative humidity at 1.2 m, a 60 m tower for wind measurements at multiple levels, a rain gauge and a solarimeter. Based on the analysis of this data between 2004e2007 it is seen that on an annual basis, the predominant wind sectors are East (E), West (W), North North East (NNE) and North East (NE) with the average wind speed being of the order of 0.8 m s1. The annual wind roses for the years 2004, 2006 and 2007 are given in Fig. 2. The wind rose for the year 2005 is excluded due to poor data availability. In summer, temperatures reach around 40 C whereas in winter temperatures of 14 C are observed at this site. This region receives very good rainfall with the cumulative rainfall from June to September being about 3700 mm. The TRMM 3B42 rainfall product for the months of June, July, August and September (JJAS) during 2004e2007 is obtained from the TRMM website and used for comparison with the APHRODITE and rain gauge data for the same period. Only the months JuneeSeptember are chosen for analysis as the maximum precipitation for the entire country is obtained during this period. These three are compared on a monthly and seasonal time scales for the period 2004e2007. It should be noted that the TRMM 3B42 rainfall
L ¼ aRb
(1)
where L ¼ scavenging coefficient (s1). R ¼ rainfall rate (mm/hr). a, b ¼ depend on the characteristics of the pollutant like particle size distribution, solubility in water and characteristics of precipitation. They determined the values of the constants “a” and “b” based on the correlation between the observed wet deposition and predicted values of wet deposition for varying values of the constants “a” and “b” at multiple locations. The final values for the constants
TRMM Raingauge APHRODITE
1400
1000 800 600 400 200
Fig. 3. Comparison of TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
September 07
August 07
July 07
June 07
September 06
August 06
July 06
June 06
September 05
August 05
July 05
June 05
September 04
August 04
July 04
0
June 04
Cumulative rainfall (mm)
1200
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
“a” and “b” were those that produced the best agreement between the observed and predicted wet deposition. This study aims to demonstrate the application of satellite derived rainfall data in the estimation of scavenging coefficients for 131 I and 137Cs. No measurements were carried out in this study and the coefficients “a” and “b” in equation (1) were obtained from Jylha (1991). Since the rain gauge data and APHRODITE data both refer to 24 h cumulative rainfall, the rainfall rate is estimated as a constant figure by dividing the daily cumulative rainfall by 24. The TRMM rainfall product 3B42 is a three hourly rainfall rate and that was used in this study. The differences in the estimated values of scavenging coefficients are due to the varying rainfall rates used.
3500
135
TRMM Rain Gauge APHRODITE
JJAS Precipitation (mm)
3000 2500 2000 1500 1000 500 0
3. Results and discussion
2004
APHRODITE monthly rainfall (mm)
1400
TRMM monthly rainfall(mm)
1200
Y = A + B *x A = 108.15 B = 0.522 R = 0.782
1000 800 600 400 200
4. Conclusions The TRMM rainfall product 3B42 was compared with the APHRODITE and rain gauge data for the period 2004e2007 for Kaiga site. The comparison was carried out at monthly and seasonal time scales. The summary of comparison indicates that magnitude wise, the rainfall estimates by the satellite product are lower as compared to the rain gauge data. The pattern of rainfall in the different months is well captured by the satellite product. The magnitude wise comparison is found to be better with the APHRODITE data. This is primarily because both these data sets represent an average rainfall over a 625 km2 area. In short, it can be concluded that qualitatively the rainfall distribution is captured in the satellite derived data, however quantitatively there are differences with respect to the observations. The scavenging coefficients for 131I and 137Cs were estimated using the three rainfall data sets, viz. TRMM 3B42, rain gauge and APHRODITE. The values of scavenging coefficients for 131I and 137Cs obtained using TRMM 3B42
(b)
0
200
400
600
800
1000 1200 1400
Rain Gauge monthly rainfall (mm)
(c)
1400
1200
1200
Y = A + B *x A = 29.72 B = 0.7303 R = 0.7719
1000 800 600 400 200
Y = A + B *x A = 185.665 B = 0.56312 R = 0.7978
1000 800 600 400 200
0
0
2007
Finally, the average scavenging coefficients for 131I and 137Cs during the period June 2004eSeptember 2007 are estimated using the three rainfall datasets. The values obtained are shown in Table 1. The average rainfall rates in mm hr1 from the three data sets are given in brackets. In spite of the differences in rainfall rate, the average scavenging coefficients compare very well and a better match is seen in the scavenging coefficients obtained using the TRMM 3B42 and APHRODITE data. The values obtained with these two data sets differ by ~15%. Slightly higher values are obtained using the rain gauge data which is obvious due to the higher rainfall rate.
TRMM monthly rainfall (mm)
(a)
2006
Fig. 5. Cumulative rainfall using TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
The Fig. 3 represents the monthly comparison between the three data sets during June 2004eSeptember 2007. The TRMM 3B42 data has indicated a monthly cumulative rainfall estimate as compared to the rain gauge observation which differs from the rain gauge data by 33%. This is primarily due to the fact that the TRMM 3B42 represents an average over a 625 km2 (25 km 25 km) domain where as the rain gauge data is essentially a point estimate of rainfall. For a mountainous region like Kaiga, an area of 625 km2 can make a huge difference in rainfall since this a highly inhomogeneous phenomenon. On an average for the four years (i.e. 16 months of rainfall data considered for comparison) there is a 33% departure between the monthly cumulative TRMM 3B42 and rain gauge estimates. The same is 23% for APHRODITE and rain gauge data. When the TRMM 3B42 and APHRODITE data are compared, the average departure is only 11%. It is seen that the trend in the rain gauge observations is well captured by the TRMM 3B42 data i.e. maximum rainfall for a particular year is seen in July followed by August and June and finally September, however, magnitude wise it differs by ~33e40% with respect to the observation as explained earlier. The scatter plots of TRMM 3B42 versus rain gauge, APHRODITE versus rain gauge and TRMM 3B42 versus APHRODITE are shown in Fig. 4(a)e(c) respectively. It is noted that all three plots have shown a high correlation coefficient of greater than 0.75 which means that the trends in the observed rain gauge data are well captured in the satellite and APHRODITE products although the individual magnitudes vary substantially. Correlation coefficients of this order were reported by others also (Nair et al., 2009; Prakash and Gairola, 2014). The low slope value indicates that the heavy rainfall events are under estimated in the satellite product. In Fig. 5, the season total rainfall using the three data sets are compared. Here too it is noted that the TRMM 3B42 data is deficient by as much as 40% when compared with the rain gauge observations. The same shows a better match with APHRODITE data. 1400
2005
0 0
200
400
600
800
1000 1200 1400
Rain Gauge monthly rainfall (mm)
0
200
400
600
800
1000 1200 1400
APHRODITE monthly rainfall (mm)
Fig. 4. Scatter plots of TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
136
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Table 1 Average scavenging coefficients (s1) for
131
I and
References
137
Cs.
Nuclide
Rain gauge (1.157)
TRMM 3B42 (0.752)
APHRODITE (0.688)
131
8.06E-05 3.72E-05
6.77E-05 3.16E-05
5.85E-05 2.82E-05
137
I Cs
rainfall data compare very well with those obtained using rain gauge and APHRODITE data indicating the potential of application of the satellite derived rainfall product for estimation of scavenging coefficients. The similar methodology can be extended for the estimation of scavenging coefficients for other radioactive and conventional pollutants as well. Acknowledgement The authors wish to express their gratitude to Dr. R. B. Oza Head, Environmental Modelling Section of Radiation Safety Systems Division, BARC for useful suggestions and discussions. The work done by officials in NASA for the collection, archival and availability of data from TRMM and other satellites is also gratefully acknowledged. Similarly we also wish to thank officials from Kyoto University, Japan for the use of their APHRODITE data. We also thank the two anonymous reviewers for their suggestions on the previous versions of this manuscript.
Adeyewa, Z.D., Nakamura, K., 2003. Validation of TRMM radar rainfall data over major climatic regions in Africa. J. Appl. Meteorol. 42, 331e347. Duan, Z., Bastiaanssen, W.G.M., Junzhi, L., 2012. Monthly and annual validation of TRMM Multisatellite Precipitation Analysis (TMPA) products in the Caspian Sea Region for the period 1999e2003. Proc. IGARSS 2012, 3696e3699. Ferreira, M.E., Ferreira, L.G., da Silva, D.P., 2012. Annual precipitation assessment in the Brazilian savanna (2000e2010) based on TRMM satellite data at the scale of regional watersheds. Proc. IGARSS 2012, 335e337. James, J.P., Ravi, P.M., Joshi, R.M., Hegde, A.G., Sarkar, P.K., 2010. Estimation of site e specific deposition velocities and mass intrerception factor using 7Be and the prediction of deposition pattern of radionuclides at Kaiga site, India. Radiat. Prot. Dosim. 141, 248e254. Ji, Y., 2006. Validation of diurnal cycle and intra seasonal variability of TRMM satellite rainfall. Prog. Electromagn. Res. Symp. (PIERS) 2, 628e632. Ji, Y., Stocker, E., 2003. Ground validation of TRMM and AMSU microwave precipitation estimates. Proc. IGARSS 2003, 3157e3159. Jylha, K., 1991. Empirical scavenging coefficients of radioactive substances released from Chernobyl. Atmos. Environ. 25 (A), 263e270. Nair, S., Srinivasan, G., Nemani, R., 2009. Evaluation of multi e satellite TRMM derived rainfall estimates over a western state of India. J. Meteor. Soc. Jpn. 87, 927e939. Prakash, S., Gairola, R.M., 2014. Validation of TRMM e 3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array. Theor. Appl. Climatol. 115, 451e460. Slinn, W.G.N., 1977. Some approximations for the wet and dry removal of particles and gases from the atmosphere. Water Air Soil Poll. 7, 513e543. Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., Kitoh, A., 2012. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc. 93, 1401e1415.
Contents lists available at ScienceDirect
Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for 131I and 137Cs using TRMM 3B42 rainfall data R. Shrivastava a, *, S.K. Dash b, M.N. Hegde c, K.S. Pradeepkumar a, D.N. Sharma d a
Radiation Safety Systems Division, Bhabha Atomic Research Centre, Mumbai 400 085, India Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi 110 016, India Environmental Survey Laboratory, Health Physics Division, Kaiga Generating Station, Kaiga 581 400, India d Health Safety and Environment Group, Bhabha Atomic Research Centre, Mumbai 400 085, India b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 June 2014 Received in revised form 23 July 2014 Accepted 24 August 2014 Available online
The TRMM rainfall product 3B42 is compared with rain gauge observations for Kaiga, India on monthly and seasonal time scales. This comparison is carried out for the years 2004e2007 spanning four monsoon seasons. A good correlation is obtained between the two data sets however; magnitude wise, the cumulative precipitation of the satellite product on monthly and seasonal time scales is deficient by almost 33e40% as compared to the rain gauge data. The satellite product is also compared with APHRODITE's Monsoon Asia data set on the same time scales. This comparison indicates a much better agreement since both these data sets represent an average precipitation over the same area. The scavenging coefficients for 131I and 137Cs are estimated using TRMM 3B42, rain gauge and APHRODITE data. The values obtained using TRMM 3B42 rainfall data compare very well with those obtained using rain gauge and APHRODITE data. © 2014 Elsevier Ltd. All rights reserved.
Keywords: TRMM APHRODITE Scavenging coefficient
1. Introduction As the Tropical Rainfall Measuring Mission (TRMM) satellite completes more than a decade of operation, it has provided researchers throughout the world with a large volume of rainfall data for the validation of atmospheric and climate models. Due to operational difficulties over the oceans, the measurement of rainfall with rain gauges is not possible and the remotely sensed information about rainfall becomes the only source of reliable and continuous data. However, due to errors in radiance measurement, in the retrieval algorithm used in data processing and errors caused due to non uniform field of view of the sensors, the rainfall estimates obtained from the satellites should be first validated against rain gauge observations and then used to obtain estimates of rainfall where rain gauge data are not available. With a large number of meteorological satellites in orbit, it is now possible to obtain rainfall estimation at high spatial and temporal resolutions. For example, the TRMM is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan
* Corresponding author. Tel.: þ91 022 25595365; fax: þ91 022 25595313. E-mail address: [email protected] (R. Shrivastava). http://dx.doi.org/10.1016/j.jenvrad.2014.08.011 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
Aerospace Exploration Agency (JAXA) launched in 1997 with the aim of studying precipitation in the tropics. In the past there have been several studies which have compared the TRMM Multi Satellite Product 3B42 with rain gauge observations. The TRMM Precipitation Radar (PR) and 3B43 rainfall product were validated with the Global Precipitation Climatology Centre (GPCC) rain gauge data over Africa by Adeyewa and Nakamura (2003). They observed significant seasonal and regional differences and in general the 3B43 product had closest agreement with the rain gauge data. The TRMM data was analysed in various regions of the globe namely Caspian Sea, Brazil, Kwalajein in Marshall Islands, Melbourne in Australia and Houston in United States of America by Duan et al. (2012); Ferreira et al. (2012); Ji (2006); Ji and Stocker (2003). Similarly Nair et al. (2009) have analysed TRMM data over Maharashtra. Their study spanned 7 monsoon seasons from 1998 to 2004. They concluded that the satellite estimates were accurate over regions of moderate rainfall and inaccurate over regions of sharp rainfall gradient. Prakash and Gairola (2014) have validated TRMM 3B42 rainfall product over the tropical Indian Ocean using buoy data. They reported a statistically significant linear correlation from 0.4 to 0.89 between the two precipitation estimates. The satellite product can also be compared with an interpolated rainfall data like APHRODITE's (Asian
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Precipitation e Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) Monsoon Asia (Yatagai et al., 2012) data available from http://www.chikyu.ac.jp/precip/. This data is created by interpolation of the data obtained from a rain-gauge observation network. As compared with individual rain gauge observations which are scattered in space and whose density depends on the topography of a site, an interpolated rainfall data becomes a better product for comparison. Also in this case, the TRMM 3B42 and APHRODITE both are available on a 25 km grid resolution, making them compatible for comparison. In this study, the TRMM rainfall product 3B42 is compared with rain gauge observations and APHRODITE data for Kaiga, India on monthly and seasonal time scales for the years 2004e2007. At this site, four Pressurized Heavy Water Reactors (PHWR) are operated by Nuclear Power Corporation of India (NPCIL) Ltd. for generation of electricity. During the operation of the nuclear power plants, a small quantity of radioactivity is discharged into the atmosphere through the stacks and liquid effluent route. The amount of emissions depend on the power level of the reactors, number of days of reactor operation in a calendar year and the efficiency of filters and other stack exhaust systems. At this site two, 100 m tall stacks are used to discharge the pollutants to the atmosphere. These pollutants released in the atmosphere can get depleted due to dry, wet deposition and radioactive decay. Precipitation scavenging refers to the removal of pollutants by precipitation in the form of rain or snow. The physical processes responsible for scavenging are diffusion, interception, inertial impaction and growth by water vapour condensation. Both, gases and aerosols are scavenged by rain droplets. The collection efficiency describes the capture of a single
133
particle by a single hydrometeor. The scavenging coefficient is a weighted average of the collection efficiency over all hydrometeor sizes and particle sizes. The dependence of the scavenging coefficient on the particle size is because of the attachment of the particles to plume droplets and water vapour condensation. The growth by water vapour condensation depends on the solubility of the contaminant in water Slinn (1977). Hence the rate of removal of a pollutant from the plume depends on the scavenging coefficient for that particular pollutant which in turn depends on the characteristics of the pollutant and the rainfall rate. Site specific dry and wet deposition velocities for this site were estimated using the rain gauge data James et al. (2010). This paper presents a demonstration of the application of satellite derived rainfall data for the estimation of scavenging coefficients for two gamma emitting radionuclides namely 131I and 137Cs. The scavenging coefficients for 131I and 137Cs are estimated using the three data sets of precipitation namely rain gauge, APHRODITE and TRMM 3B42. Description of the site characteristics, the methodology followed and the results are presented in the subsequent sections. 2. Site description and methodology The rain gauge data used in the study is located at the Kaiga Generating Station (KGS) (14.868 N, 74.441 E). Kaiga is situated in the valley of the Kali river on the southern bank of the man made Kadra reservoir. There are minor forests and patches of agricultural land on both the banks of the river. The ghats are covered by major evergreen and semi evergreen forests with the average height of the trees being above 15 m. A small fraction of land is used for
Fig. 1. Map of Kaiga site showing the location of the two stacks (black vertical bars) and the rain gauge (black triangle).
134
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Fig. 2. Wind roses for the years 2004, 2006 and 2007.
data is a three hourly rainfall rate which is converted to cumulative rainfall for a 24 h period. The APHRODITE and rain gauge data both refer to 24 h cumulative rainfall. Following the accident at the Chernobyl Nuclear Power Plant in 1986, Jylha (1991) has suggested empirical relationships between the scavenging coefficient and rainfall rate for different radionuclides like 103Ru, 106Ru, 129mTe, 132 Te, 131I(p), 133I(p), 134Cs, 136Cs, 137Cs and 140Ba on the basis of simultaneous radioactivity and radar rainfall measurements at Helsinki, Nurmijarvi, Loviisa, Olkiluoto, Tampere, Jokionen and Pori in Finland. From their study, the scavenging coefficients for any radionuclide can be expressed in terms of rainfall rate as
agricultural purposes. The average height of the Western Ghats in the region is around 600 m. The map of the region showing the location of the stacks and the rain gauge is presented in Fig. 1. The Environmental Survey Laboratory (ESL, Kaiga) carries out meteorological measurements at Kaiga site. The meteorological instrumentation includes a Stevenson screen for measurement of temperature and relative humidity at 1.2 m, a 60 m tower for wind measurements at multiple levels, a rain gauge and a solarimeter. Based on the analysis of this data between 2004e2007 it is seen that on an annual basis, the predominant wind sectors are East (E), West (W), North North East (NNE) and North East (NE) with the average wind speed being of the order of 0.8 m s1. The annual wind roses for the years 2004, 2006 and 2007 are given in Fig. 2. The wind rose for the year 2005 is excluded due to poor data availability. In summer, temperatures reach around 40 C whereas in winter temperatures of 14 C are observed at this site. This region receives very good rainfall with the cumulative rainfall from June to September being about 3700 mm. The TRMM 3B42 rainfall product for the months of June, July, August and September (JJAS) during 2004e2007 is obtained from the TRMM website and used for comparison with the APHRODITE and rain gauge data for the same period. Only the months JuneeSeptember are chosen for analysis as the maximum precipitation for the entire country is obtained during this period. These three are compared on a monthly and seasonal time scales for the period 2004e2007. It should be noted that the TRMM 3B42 rainfall
L ¼ aRb
(1)
where L ¼ scavenging coefficient (s1). R ¼ rainfall rate (mm/hr). a, b ¼ depend on the characteristics of the pollutant like particle size distribution, solubility in water and characteristics of precipitation. They determined the values of the constants “a” and “b” based on the correlation between the observed wet deposition and predicted values of wet deposition for varying values of the constants “a” and “b” at multiple locations. The final values for the constants
TRMM Raingauge APHRODITE
1400
1000 800 600 400 200
Fig. 3. Comparison of TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
September 07
August 07
July 07
June 07
September 06
August 06
July 06
June 06
September 05
August 05
July 05
June 05
September 04
August 04
July 04
0
June 04
Cumulative rainfall (mm)
1200
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
“a” and “b” were those that produced the best agreement between the observed and predicted wet deposition. This study aims to demonstrate the application of satellite derived rainfall data in the estimation of scavenging coefficients for 131 I and 137Cs. No measurements were carried out in this study and the coefficients “a” and “b” in equation (1) were obtained from Jylha (1991). Since the rain gauge data and APHRODITE data both refer to 24 h cumulative rainfall, the rainfall rate is estimated as a constant figure by dividing the daily cumulative rainfall by 24. The TRMM rainfall product 3B42 is a three hourly rainfall rate and that was used in this study. The differences in the estimated values of scavenging coefficients are due to the varying rainfall rates used.
3500
135
TRMM Rain Gauge APHRODITE
JJAS Precipitation (mm)
3000 2500 2000 1500 1000 500 0
3. Results and discussion
2004
APHRODITE monthly rainfall (mm)
1400
TRMM monthly rainfall(mm)
1200
Y = A + B *x A = 108.15 B = 0.522 R = 0.782
1000 800 600 400 200
4. Conclusions The TRMM rainfall product 3B42 was compared with the APHRODITE and rain gauge data for the period 2004e2007 for Kaiga site. The comparison was carried out at monthly and seasonal time scales. The summary of comparison indicates that magnitude wise, the rainfall estimates by the satellite product are lower as compared to the rain gauge data. The pattern of rainfall in the different months is well captured by the satellite product. The magnitude wise comparison is found to be better with the APHRODITE data. This is primarily because both these data sets represent an average rainfall over a 625 km2 area. In short, it can be concluded that qualitatively the rainfall distribution is captured in the satellite derived data, however quantitatively there are differences with respect to the observations. The scavenging coefficients for 131I and 137Cs were estimated using the three rainfall data sets, viz. TRMM 3B42, rain gauge and APHRODITE. The values of scavenging coefficients for 131I and 137Cs obtained using TRMM 3B42
(b)
0
200
400
600
800
1000 1200 1400
Rain Gauge monthly rainfall (mm)
(c)
1400
1200
1200
Y = A + B *x A = 29.72 B = 0.7303 R = 0.7719
1000 800 600 400 200
Y = A + B *x A = 185.665 B = 0.56312 R = 0.7978
1000 800 600 400 200
0
0
2007
Finally, the average scavenging coefficients for 131I and 137Cs during the period June 2004eSeptember 2007 are estimated using the three rainfall datasets. The values obtained are shown in Table 1. The average rainfall rates in mm hr1 from the three data sets are given in brackets. In spite of the differences in rainfall rate, the average scavenging coefficients compare very well and a better match is seen in the scavenging coefficients obtained using the TRMM 3B42 and APHRODITE data. The values obtained with these two data sets differ by ~15%. Slightly higher values are obtained using the rain gauge data which is obvious due to the higher rainfall rate.
TRMM monthly rainfall (mm)
(a)
2006
Fig. 5. Cumulative rainfall using TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
The Fig. 3 represents the monthly comparison between the three data sets during June 2004eSeptember 2007. The TRMM 3B42 data has indicated a monthly cumulative rainfall estimate as compared to the rain gauge observation which differs from the rain gauge data by 33%. This is primarily due to the fact that the TRMM 3B42 represents an average over a 625 km2 (25 km 25 km) domain where as the rain gauge data is essentially a point estimate of rainfall. For a mountainous region like Kaiga, an area of 625 km2 can make a huge difference in rainfall since this a highly inhomogeneous phenomenon. On an average for the four years (i.e. 16 months of rainfall data considered for comparison) there is a 33% departure between the monthly cumulative TRMM 3B42 and rain gauge estimates. The same is 23% for APHRODITE and rain gauge data. When the TRMM 3B42 and APHRODITE data are compared, the average departure is only 11%. It is seen that the trend in the rain gauge observations is well captured by the TRMM 3B42 data i.e. maximum rainfall for a particular year is seen in July followed by August and June and finally September, however, magnitude wise it differs by ~33e40% with respect to the observation as explained earlier. The scatter plots of TRMM 3B42 versus rain gauge, APHRODITE versus rain gauge and TRMM 3B42 versus APHRODITE are shown in Fig. 4(a)e(c) respectively. It is noted that all three plots have shown a high correlation coefficient of greater than 0.75 which means that the trends in the observed rain gauge data are well captured in the satellite and APHRODITE products although the individual magnitudes vary substantially. Correlation coefficients of this order were reported by others also (Nair et al., 2009; Prakash and Gairola, 2014). The low slope value indicates that the heavy rainfall events are under estimated in the satellite product. In Fig. 5, the season total rainfall using the three data sets are compared. Here too it is noted that the TRMM 3B42 data is deficient by as much as 40% when compared with the rain gauge observations. The same shows a better match with APHRODITE data. 1400
2005
0 0
200
400
600
800
1000 1200 1400
Rain Gauge monthly rainfall (mm)
0
200
400
600
800
1000 1200 1400
APHRODITE monthly rainfall (mm)
Fig. 4. Scatter plots of TRMM, APHRODITE and rain gauge data for Kaiga site between June 2004eSeptember 2007.
136
R. Shrivastava et al. / Journal of Environmental Radioactivity 138 (2014) 132e136
Table 1 Average scavenging coefficients (s1) for
131
I and
References
137
Cs.
Nuclide
Rain gauge (1.157)
TRMM 3B42 (0.752)
APHRODITE (0.688)
131
8.06E-05 3.72E-05
6.77E-05 3.16E-05
5.85E-05 2.82E-05
137
I Cs
rainfall data compare very well with those obtained using rain gauge and APHRODITE data indicating the potential of application of the satellite derived rainfall product for estimation of scavenging coefficients. The similar methodology can be extended for the estimation of scavenging coefficients for other radioactive and conventional pollutants as well. Acknowledgement The authors wish to express their gratitude to Dr. R. B. Oza Head, Environmental Modelling Section of Radiation Safety Systems Division, BARC for useful suggestions and discussions. The work done by officials in NASA for the collection, archival and availability of data from TRMM and other satellites is also gratefully acknowledged. Similarly we also wish to thank officials from Kyoto University, Japan for the use of their APHRODITE data. We also thank the two anonymous reviewers for their suggestions on the previous versions of this manuscript.
Adeyewa, Z.D., Nakamura, K., 2003. Validation of TRMM radar rainfall data over major climatic regions in Africa. J. Appl. Meteorol. 42, 331e347. Duan, Z., Bastiaanssen, W.G.M., Junzhi, L., 2012. Monthly and annual validation of TRMM Multisatellite Precipitation Analysis (TMPA) products in the Caspian Sea Region for the period 1999e2003. Proc. IGARSS 2012, 3696e3699. Ferreira, M.E., Ferreira, L.G., da Silva, D.P., 2012. Annual precipitation assessment in the Brazilian savanna (2000e2010) based on TRMM satellite data at the scale of regional watersheds. Proc. IGARSS 2012, 335e337. James, J.P., Ravi, P.M., Joshi, R.M., Hegde, A.G., Sarkar, P.K., 2010. Estimation of site e specific deposition velocities and mass intrerception factor using 7Be and the prediction of deposition pattern of radionuclides at Kaiga site, India. Radiat. Prot. Dosim. 141, 248e254. Ji, Y., 2006. Validation of diurnal cycle and intra seasonal variability of TRMM satellite rainfall. Prog. Electromagn. Res. Symp. (PIERS) 2, 628e632. Ji, Y., Stocker, E., 2003. Ground validation of TRMM and AMSU microwave precipitation estimates. Proc. IGARSS 2003, 3157e3159. Jylha, K., 1991. Empirical scavenging coefficients of radioactive substances released from Chernobyl. Atmos. Environ. 25 (A), 263e270. Nair, S., Srinivasan, G., Nemani, R., 2009. Evaluation of multi e satellite TRMM derived rainfall estimates over a western state of India. J. Meteor. Soc. Jpn. 87, 927e939. Prakash, S., Gairola, R.M., 2014. Validation of TRMM e 3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array. Theor. Appl. Climatol. 115, 451e460. Slinn, W.G.N., 1977. Some approximations for the wet and dry removal of particles and gases from the atmosphere. Water Air Soil Poll. 7, 513e543. Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., Kitoh, A., 2012. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc. 93, 1401e1415.
